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diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index 84a76ba..2dee77b 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -10,7 +10,10 @@ eye-tracking data. It is a community-driven effort to provide a versatile set
 of tools to work with the rich data (gaze, eye states, IMU, events, etc.)
 provided by Neon.
 
-PyNeon is licensed under the MIT License.
+PyNeon works with the cloud-processed data from Pupil Cloud instead of
+"native" data from the Companion app. To read data in the "native" format, please
+see ``pl-neon-recording`` https://github.com/pupil-labs/pl-neon-recording/
+(which also inspired PyNeon).
 
 Here we provide tutorials and API reference to help you get started with PyNeon.
 
@@ -33,10 +36,11 @@ PyNeon works with the "Timeseries Data" or "Timeseries Data + Scene Video" forma
 as exported from Pupil Clouds. The data could be from a single recording or from a 
 project with multiple recordings.
 
-Credits
+License
 =======
 
-PyNeon was inspired by https://github.com/pupil-labs/pl-neon-recording/
+.. literalinclude:: ../LICENSE
+   :language: none
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/_sources/reference/index.rst.txt b/docs/_sources/reference/index.rst.txt
index 81148be..5fb5250 100644
--- a/docs/_sources/reference/index.rst.txt
+++ b/docs/_sources/reference/index.rst.txt
@@ -4,7 +4,7 @@ PyNeon API
 ==========
 
 .. toctree::
-    :maxdepth: 3
+    :maxdepth: 2
 
     dataset
     recording
diff --git a/docs/_sources/tutorials/index.rst.txt b/docs/_sources/tutorials/index.rst.txt
index e74a60e..c5e9781 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -9,7 +9,6 @@ started with PyNeon.
 .. toctree::
     :maxdepth: 1
 
-    load_recording
-    load_dataset
+    read_recording
     resample_and_concat
     export_to_bids
\ No newline at end of file
diff --git a/docs/_sources/tutorials/load_dataset.ipynb.txt b/docs/_sources/tutorials/load_dataset.ipynb.txt
deleted file mode 100644
index 6f2a872..0000000
--- a/docs/_sources/tutorials/load_dataset.ipynb.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load a Neon dataset (Pupil Cloud project)\n",
-    "In this tutorial, we will show how to load a Neon dataset downloaded from [Pupil Cloud](https://docs.pupil-labs.com/neon/pupil-cloud/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/tutorials/load_recording.ipynb.txt b/docs/_sources/tutorials/load_recording.ipynb.txt
deleted file mode 100644
index 90e463a..0000000
--- a/docs/_sources/tutorials/load_recording.ipynb.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load a Neon recording\n",
-    "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/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/tutorials/read_recording.ipynb.txt b/docs/_sources/tutorials/read_recording.ipynb.txt
new file mode 100644
index 0000000..d1c18c5
--- /dev/null
+++ b/docs/_sources/tutorials/read_recording.ipynb.txt
@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading a Neon dataset/recording\n",
+    "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/).\n",
+    "\n",
+    "## Reading sample data\n",
+    "We will use a sample recording produced by the NCC Lab called `OfficeWalk`. It's a project with 2 recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import numpy as np\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from pyneon import get_sample_data, NeonDataset, NeonRecording\n",
+    "\n",
+    "sample_dir = get_sample_data(\"OfficeWalk\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `OfficeWalk` data has the following structure:\n",
+    "\n",
+    "```plaintext\n",
+    "OfficeWalk\n",
+    "├── Timeseries Data\n",
+    "│   ├── walk1-e116e606\n",
+    "│   │   ├── info.json\n",
+    "│   │   ├── gaze.csv\n",
+    "│   │   └── ....\n",
+    "│   ├── walk2-93b8c234\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",
+    "```\n",
+    "\n",
+    "The `Timeseries Data` folder contains what PyNeon calls a `NeonDataset`. It contains multiple recordings, each with its own `info.json` file and data files. These recordings can either be loaded individually as `NeonRecording`s or as a wholist `NeonDataset`.\n",
+    "\n",
+    "If loading a `NeonDataset`, specify the path to the `Timeseries Data` folder to create a `NeonDataset` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NeonDataset | 2 recordings\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_dir = sample_dir / \"Timeseries Data\"\n",
+    "dataset = NeonDataset(dataset_dir)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NeonDataset has a `recordings` attribute that contains a list of `NeonRecording` objects. These `NeonRecording` objects can be accessed by their index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pyneon.recording.NeonRecording'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "first_recording = dataset[0]\n",
+    "print(type(first_recording))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, one can directly load a single `NeonRecording` by specifying the path to the recording's folder:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pyneon.recording.NeonRecording'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "recording_dir = dataset_dir / \"walk1-e116e606\"\n",
+    "recording = NeonRecording(recording_dir)\n",
+    "print(type(recording))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing data and metadata of a NeonRecording\n",
+    "An overview of basic metadata and contents of a `NeonRecording` can be obtained by printing the object. An initiated `NeonRecording` locates data files in the recording directory but does not load them until requested to be memory efficient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n",
+      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
+      "Wearer name: Qian\n",
+      "Recording start time: 2024-08-30 17:37:01.527000\n",
+      "Recording duration: 98.213 s\n",
+      "                  exist              filename                                                                                                           path\n",
+      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
+      "blinks             True            blinks.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
+      "events             True            events.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
+      "fixations          True         fixations.csv         C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
+      "gaze               True              gaze.csv              C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
+      "imu                True               imu.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
+      "labels             True            labels.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
+      "saccades           True          saccades.csv          C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
+      "world_timestamps   True  world_timestamps.csv  C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
+      "scene_video       False                  None                                                                                                           None\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(recording)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "recording._gaze size before accessing `gaze`: 16\n",
+      "recording.gaze is of type: <class 'pyneon.data.NeonGaze'>\n",
+      "recording._gaze size after accessing `gaze`: 48\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"recording._gaze size before accessing `gaze`: {sys.getsizeof(recording._gaze)}\")\n",
+    "\n",
+    "gaze = recording.gaze\n",
+    "print(f\"recording.gaze is of type: {type(gaze)}\")\n",
+    "print(f\"recording._gaze size after accessing `gaze`: {sys.getsizeof(recording._gaze)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the timeseries data in the gaze stream as a pandas DataFrame by accessing the `data` attribute of the gaze stream. The columns of the DataFrame include `timestamp [ns]` and channel data columns. During loading, PyNeon strips the redundant `section id` and `recording id` columns and adds a more human-readable `time [s]` column to represent the time of each sample in seconds relative to the start of the data stream."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        timestamp [ns]  gaze x [px]  gaze y [px]  worn  fixation id  blink id  \\\n",
+      "0  1725032224852161732     1067.486      620.856  True            1      <NA>   \n",
+      "1  1725032224857165732     1066.920      617.117  True            1      <NA>   \n",
+      "2  1725032224862161732     1072.699      615.780  True            1      <NA>   \n",
+      "3  1725032224867161732     1067.447      617.062  True            1      <NA>   \n",
+      "4  1725032224872161732     1071.564      613.158  True            1      <NA>   \n",
+      "\n",
+      "   azimuth [deg]  elevation [deg]  time [s]  \n",
+      "0      16.213030        -0.748998  0.000000  \n",
+      "1      16.176285        -0.511733  0.005004  \n",
+      "2      16.546413        -0.426618  0.010000  \n",
+      "3      16.210049        -0.508251  0.015000  \n",
+      "4      16.473521        -0.260388  0.020000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gaze.data.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyNeon also automatically sets the column datatype to appropriate types, such as `Int64` for timestamps, `Int32` for event IDs, and `float64` for float data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestamp [ns]       Int64\n",
+      "gaze x [px]        float64\n",
+      "gaze y [px]        float64\n",
+      "worn                  bool\n",
+      "fixation id          Int32\n",
+      "blink id             Int32\n",
+      "azimuth [deg]      float64\n",
+      "elevation [deg]    float64\n",
+      "time [s]           float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gaze.data.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing data timeseries and sampling gaps\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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp [ns]', ylabel='gyro x [deg/s]'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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",
+    "saccades = 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",
+    "\n",
+    "# Visualize the 2nd saccade\n",
+    "saccade = saccades.data.iloc[1]\n",
+    "ax.axvspan(\n",
+    "    saccade[\"start timestamp [ns]\"], saccade[\"end timestamp [ns]\"], color=\"lightgray\"\n",
+    ")\n",
+    "ax.text(\n",
+    "    (saccade[\"start timestamp [ns]\"] + saccade[\"end timestamp [ns]\"]) / 2,\n",
+    "    1050,\n",
+    "    \"Saccade\",\n",
+    "    horizontalalignment=\"center\",\n",
+    ")\n",
+    "\n",
+    "# Visualize gaze x and pupil diameter left\n",
+    "sns.scatterplot(\n",
+    "    ax=ax,\n",
+    "    data=gaze.data.head(100),\n",
+    "    x=\"timestamp [ns]\",\n",
+    "    y=\"gaze x [px]\",\n",
+    "    color=gaze_color,\n",
+    ")\n",
+    "sns.scatterplot(\n",
+    "    ax=ax2,\n",
+    "    data=imu.data.head(60),\n",
+    "    x=\"timestamp [ns]\",\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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gaze: nominal sampling frequency = 200, effective sampling frequency = 197.8078038925275\n",
+      "IMU: nominal sampling frequency = 110, effective sampling frequency = 115.35532450871617\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n",
+    "    f\"effective sampling frequency = {gaze.sampling_freq_effective}\"\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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing gaze heatmap\n",
+    "Finally, we will show how to plot a heatmap of the gaze/fixation data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Scene camera y [px]')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_edges = np.linspace(0, 1600, 160)\n",
+    "y_edges = np.linspace(0, 1200, 120)\n",
+    "\n",
+    "gaze_x = gaze.data[\"gaze x [px]\"]\n",
+    "gaze_y = gaze.data[\"gaze y [px]\"]\n",
+    "gaze_heatmap, _, _ = np.histogram2d(gaze_x, gaze_y, bins=(x_edges, y_edges))\n",
+    "gaze_heatmap = gaussian_filter(gaze_heatmap, sigma=3)\n",
+    "\n",
+    "fixations = recording.fixations\n",
+    "fix_x = fixations.data[\"fixation x [px]\"]\n",
+    "fix_y = fixations.data[\"fixation y [px]\"]\n",
+    "\n",
+    "# Plot a heatmap and scatter plot fixations on top\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "ax.imshow(gaze_heatmap.T, cmap=\"inferno\", extent=[0, 1600, 0, 1200], origin=\"lower\")\n",
+    "ax.scatter(fix_x, fix_y, color=\"white\", s=10, alpha=0.3)\n",
+    "ax.set_aspect(\"equal\", \"box\")\n",
+    "ax.set_xlabel(\"Scene camera x [px]\")\n",
+    "ax.set_ylabel(\"Scene camera y [px]\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyneon",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_sources/tutorials/resample_and_concat.ipynb.txt b/docs/_sources/tutorials/resample_and_concat.ipynb.txt
index 375b925..bbee9ff 100644
--- a/docs/_sources/tutorials/resample_and_concat.ipynb.txt
+++ b/docs/_sources/tutorials/resample_and_concat.ipynb.txt
@@ -6,11 +6,312 @@
    "source": [
     "# Resample data and concatenate channels\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we show how to resample different continuous data streams to be evenly sampled. We firstly import the necessary libraries and define our recording as well as output directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from pyneon import get_sample_data, NeonRecording\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "recording_dir = get_sample_data(\"OfficeWalk\") / \"Timeseries Data\" / \"walk1-e116e606\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will save local instances of the unprocessed data for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recording = NeonRecording(recording_dir)\n",
+    "raw_gaze_data = recording.gaze.data\n",
+    "raw_eye_states_data = recording.eye_states.data\n",
+    "raw_imu_data = recording.imu.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For imu, the effective rate is actually higher.\n",
+    "We can further explore this behaviour by plotting the distances between subsequent datapoints as a histogram. Dataframes can be operated upon with regular pandas logic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.010090909090909091, 100, 'IMU nominal sampling rate')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the data points\n",
+    "gaze_times = raw_gaze_data[\"time [s]\"].values\n",
+    "eye_state_times = raw_eye_states_data[\"time [s]\"].values\n",
+    "imu_times = raw_imu_data[\"time [s]\"].values\n",
+    "\n",
+    "# Calculate the distances between subsequent data points\n",
+    "gaze_diffs = np.diff(gaze_times)\n",
+    "eye_state_diffs = np.diff(eye_state_times)\n",
+    "imu_diffs = np.diff(imu_times)\n",
+    "\n",
+    "# Plot the differences as a histogram\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "plt.hist(imu_diffs, bins=20, alpha=0.5, label=\"IMU\")\n",
+    "plt.hist(eye_state_diffs, bins=20, alpha=0.5, label=\"Eye states\")\n",
+    "plt.hist(gaze_diffs, bins=10, alpha=0.8, label=\"Gaze\")\n",
+    "# create a logaritmic scale on the y-axis\n",
+    "plt.yscale(\"log\")\n",
+    "plt.legend()\n",
+    "plt.title(\"Histogram of time differences between subsequent data points\")\n",
+    "plt.xlabel(\"Time difference [s]\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "\n",
+    "# draw a vertical line at the nominal sampling rate, add text\n",
+    "plt.axvline(\n",
+    "    1 / recording.gaze.sampling_freq_nominal, color=\"k\", linestyle=\"dashed\", linewidth=2\n",
+    ")\n",
+    "plt.text(\n",
+    "    1 / recording.gaze.sampling_freq_nominal,\n",
+    "    1,\n",
+    "    \"Gaze nominal sampling rate\",\n",
+    "    rotation=90,\n",
+    "    verticalalignment=\"bottom\",\n",
+    "    horizontalalignment=\"right\",\n",
+    ")\n",
+    "plt.axvline(\n",
+    "    1 / recording.imu.sampling_freq_nominal, color=\"k\", linestyle=\"dashed\", linewidth=2\n",
+    ")\n",
+    "plt.text(\n",
+    "    1 / recording.imu.sampling_freq_nominal + 0.001,\n",
+    "    100,\n",
+    "    \"IMU nominal sampling rate\",\n",
+    "    rotation=90,\n",
+    "    verticalalignment=\"bottom\",\n",
+    "    horizontalalignment=\"left\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we concat the channels with a sampling frequency of 100 Hz. While this downsamples both imu and gaze, it will put them on a common reference frame. Under the hood, continuous values are linearly interpolated, whereas boolean values follow the nearest neighbour."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Concatenating streams:\n",
+      "\tGaze\n",
+      "\t3D eye states\n",
+      "\tIMU\n",
+      "Using customized sampling rate: 100 Hz ([])\n",
+      "Using latest start timestamp: 1725032224878547732 (['imu'])\n",
+      "Using earliest last timestamp: 1725032319533909732 (['imu'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "concat_df = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"], sampling_freq=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We show an exemplary sampling of eye, imu and concatenated data below. It can be seen that imu data has subsequent missing values which can in turn be interpolated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x241c7cf7680>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_time = 5\n",
+    "end_time = 5.2\n",
+    "\n",
+    "raw_gaze_interval = raw_gaze_data[\n",
+    "    (raw_gaze_data[\"time [s]\"] >= start_time) & (raw_gaze_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "raw_eye_states_interval = raw_eye_states_data[\n",
+    "    (raw_eye_states_data[\"time [s]\"] > start_time)\n",
+    "    & (raw_eye_states_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "raw_imu_interval = raw_imu_data[\n",
+    "    (raw_imu_data[\"time [s]\"] >= start_time) & (raw_imu_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "concat_interval = concat_df[\n",
+    "    (concat_df[\"time [s]\"] >= start_time) & (concat_df[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "\n",
+    "# plot all data in the same scatter plot\n",
+    "plt.figure(figsize=(16, 2))\n",
+    "plt.scatter(\n",
+    "    raw_gaze_interval[\"time [s]\"],\n",
+    "    np.zeros_like(raw_gaze_interval[\"time [s]\"]) + 0.5,\n",
+    "    label=\"Raw gaze data\",\n",
+    "    color=\"red\",\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    raw_imu_interval[\"time [s]\"],\n",
+    "    np.zeros_like(raw_imu_interval[\"time [s]\"]),\n",
+    "    label=\"Raw eye states data\",\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    concat_interval[\"time [s]\"],\n",
+    "    np.zeros_like(concat_interval[\"time [s]\"]) - 1,\n",
+    "    label=\"Concatenated data\",\n",
+    "    color=\"green\",\n",
+    ")\n",
+    "# set x-ticks with higher frequency and add gridlines\n",
+    "plt.xticks(np.arange(start_time, end_time, 0.01), labels=None)\n",
+    "# remove x labels\n",
+    "# remove y-ticks\n",
+    "plt.yticks([])\n",
+    "plt.grid()\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A linear interpolation allows us to estimate missing values. In the end, the concatenated dataframe combines all continuous data into one central location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x241c7d07680>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot imu data and interpolated data in same plot\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(16, 3))\n",
+    "plt.scatter(\n",
+    "    raw_imu_interval[\"time [s]\"],\n",
+    "    raw_imu_interval[\"acceleration x [g]\"],\n",
+    "    label=\"Raw imu data\",\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    concat_interval[\"time [s]\"],\n",
+    "    concat_interval[\"acceleration x [g]\"],\n",
+    "    label=\"Interpolated imu data\",\n",
+    "    color=\"green\",\n",
+    "    linestyle=\"dashed\",\n",
+    ")\n",
+    "plt.legend()"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "pyneon",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/genindex.html b/docs/genindex.html
index 689178e..21468ec 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -323,7 +323,8 @@
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
- <a href="#C"><strong>C</strong></a>
+ <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
  | <a href="#D"><strong>D</strong></a>
  | <a href="#E"><strong>E</strong></a>
  | <a href="#F"><strong>F</strong></a>
@@ -338,13 +339,23 @@ <h1 id="index">Index</h1>
  | <a href="#T"><strong>T</strong></a>
  
 </div>
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/recording.html#pyneon.NeonRecording.blinks">blinks (pyneon.NeonRecording property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference/preprocess.html#pyneon.preprocess.concat_channels">concat_channels() (in module pyneon.preprocess)</a>
+      <li><a href="reference/recording.html#pyneon.NeonRecording.concat_events">concat_events() (pyneon.NeonRecording method)</a>
+</li>
+      <li><a href="reference/preprocess.html#pyneon.preprocess.concat_streams">concat_streams() (in module pyneon.preprocess)</a>
 
       <ul>
-        <li><a href="reference/recording.html#pyneon.NeonRecording.concat_channels">(pyneon.NeonRecording method)</a>
+        <li><a href="reference/recording.html#pyneon.NeonRecording.concat_streams">(pyneon.NeonRecording method)</a>
 </li>
       </ul></li>
   </ul></td>
@@ -357,19 +368,23 @@ <h2 id="C">C</h2>
 <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.data">data (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.data">data (pyneon.data.NeonStream attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="reference/dataset.html#pyneon.NeonDataset.dataset_dir">dataset_dir (pyneon.NeonDataset attribute)</a>
 </li>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.duration">duration (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.duration">duration (pyneon.data.NeonStream attribute)</a>
 </li>
   </ul></td>
 </tr></table>
 
 <h2 id="E">E</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/recording.html#pyneon.NeonRecording.events">events (pyneon.NeonRecording property)</a>
+</li>
+  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="reference/recording.html#pyneon.NeonRecording.eye_states">eye_states (pyneon.NeonRecording property)</a>
 </li>
@@ -379,7 +394,11 @@ <h2 id="E">E</h2>
 <h2 id="F">F</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.first_ts">first_ts (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.first_ts">first_ts (pyneon.data.NeonStream attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/recording.html#pyneon.NeonRecording.fixations">fixations (pyneon.NeonRecording property)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -407,7 +426,7 @@ <h2 id="I">I</h2>
 <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.last_ts">last_ts (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.last_ts">last_ts (pyneon.data.NeonStream attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -436,21 +455,31 @@ <h2 id="M">M</h2>
 <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/data.html#pyneon.data.NeonBlinks">NeonBlinks (class in pyneon.data)</a>
+</li>
       <li><a href="reference/data.html#pyneon.data.NeonData">NeonData (class in pyneon.data)</a>
 </li>
       <li><a href="reference/dataset.html#pyneon.NeonDataset">NeonDataset (class in pyneon)</a>
+</li>
+      <li><a href="reference/data.html#pyneon.data.NeonEV">NeonEV (class in pyneon.data)</a>
+</li>
+      <li><a href="reference/data.html#pyneon.data.NeonEvents">NeonEvents (class in pyneon.data)</a>
 </li>
       <li><a href="reference/data.html#pyneon.data.NeonEyeStates">NeonEyeStates (class in pyneon.data)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/data.html#pyneon.data.NeonFixations">NeonFixations (class in pyneon.data)</a>
+</li>
       <li><a href="reference/data.html#pyneon.data.NeonGaze">NeonGaze (class in pyneon.data)</a>
 </li>
       <li><a href="reference/data.html#pyneon.data.NeonIMU">NeonIMU (class in pyneon.data)</a>
 </li>
       <li><a href="reference/recording.html#pyneon.NeonRecording">NeonRecording (class in pyneon)</a>
 </li>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal">NeonSignal (class in pyneon.data)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonSaccades">NeonSaccades (class in pyneon.data)</a>
+</li>
+      <li><a href="reference/data.html#pyneon.data.NeonStream">NeonStream (class in pyneon.data)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -498,7 +527,7 @@ <h2 id="R">R</h2>
       <li><a href="reference/preprocess.html#pyneon.preprocess.resample">resample() (in module pyneon.preprocess)</a>
 
       <ul>
-        <li><a href="reference/data.html#pyneon.data.NeonSignal.resample">(pyneon.data.NeonSignal method)</a>
+        <li><a href="reference/data.html#pyneon.data.NeonStream.resample">(pyneon.data.NeonStream method)</a>
 </li>
       </ul></li>
   </ul></td>
@@ -507,12 +536,14 @@ <h2 id="R">R</h2>
 <h2 id="S">S</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.sampling_freq_effective">sampling_freq_effective (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/recording.html#pyneon.NeonRecording.saccades">saccades (pyneon.NeonRecording property)</a>
 </li>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.sampling_freq_nominal">sampling_freq_nominal (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.sampling_freq_effective">sampling_freq_effective (pyneon.data.NeonStream attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.sampling_freq_nominal">sampling_freq_nominal (pyneon.data.NeonStream attribute)</a>
+</li>
       <li><a href="reference/dataset.html#pyneon.NeonDataset.sections">sections (pyneon.NeonDataset attribute)</a>
 </li>
       <li><a href="reference/recording.html#pyneon.NeonRecording.start_datetime">start_datetime (pyneon.NeonRecording attribute)</a>
@@ -523,9 +554,9 @@ <h2 id="S">S</h2>
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.times">times (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.times">times (pyneon.data.NeonStream attribute)</a>
 </li>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.timestamps">timestamps (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.timestamps">timestamps (pyneon.data.NeonStream attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -533,7 +564,7 @@ <h2 id="T">T</h2>
 </li>
       <li><a href="reference/recording.html#pyneon.NeonRecording.to_motion_bids">to_motion_bids() (pyneon.NeonRecording method)</a>
 </li>
-      <li><a href="reference/data.html#pyneon.data.NeonSignal.ts">ts (pyneon.data.NeonSignal attribute)</a>
+      <li><a href="reference/data.html#pyneon.data.NeonStream.ts">ts (pyneon.data.NeonStream attribute)</a>
 </li>
   </ul></td>
 </tr></table>
diff --git a/docs/index.html b/docs/index.html
index c11a36f..e412222 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -329,7 +329,10 @@
 eye-tracking data. It is a community-driven effort to provide a versatile set
 of tools to work with the rich data (gaze, eye states, IMU, events, etc.)
 provided by Neon.</p>
-<p>PyNeon is licensed under the MIT License.</p>
+<p>PyNeon works with the cloud-processed data from Pupil Cloud instead of
+“native” data from the Companion app. To read data in the “native” format, please
+see <code class="docutils literal notranslate"><span class="pre">pl-neon-recording</span></code> <a class="github reference external" href="https://github.com/pupil-labs/pl-neon-recording/">pupil-labs/pl-neon-recording</a>
+(which also inspired PyNeon).</p>
 <p>Here we provide tutorials and API reference to help you get started with PyNeon.</p>
 </section>
 <section id="installation">
@@ -347,9 +350,31 @@ <h1>Data format<a class="headerlink" href="#data-format" title="Link to this hea
 as exported from Pupil Clouds. The data could be from a single recording or from a
 project with multiple recordings.</p>
 </section>
-<section id="credits">
-<h1>Credits<a class="headerlink" href="#credits" title="Link to this heading">#</a></h1>
-<p>PyNeon was inspired by <a class="github reference external" href="https://github.com/pupil-labs/pl-neon-recording/">pupil-labs/pl-neon-recording</a></p>
+<section id="license">
+<h1>License<a class="headerlink" href="#license" title="Link to this heading">#</a></h1>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>MIT License
+
+Copyright (c) 2024 PyNeon developers
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the &quot;Software&quot;), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre></div>
+</div>
 <div class="toctree-wrapper compound">
 </div>
 </section>
diff --git a/docs/objects.inv b/docs/objects.inv
index d359c1e..6fefc60 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/reference/data.html b/docs/reference/data.html
index 7e035a2..e3f3ac0 100644
--- a/docs/reference/data.html
+++ b/docs/reference/data.html
@@ -379,20 +379,20 @@
 </dd></dl>
 
 <dl class="py class">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonSignal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonSignal" title="Link to this definition">#</a></dt>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonStream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonStream" title="Link to this definition">#</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonData" title="pyneon.data.NeonData"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonData</span></code></a></p>
-<p>Base for Neon continuous signals (gaze, eye states, IMU).
+<p>Base for Neon continuous data (gaze, eye states, IMU).
 It must contain a <code class="docutils literal notranslate"><span class="pre">timestamp</span> <span class="pre">[ns]</span></code> column.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>file</strong> (<a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pathlib.Path</span></code></a>) – Path to the CSV file containing the signal data.</p>
+<dd class="field-odd"><p><strong>file</strong> (<a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pathlib.Path</span></code></a>) – Path to the CSV file containing the stream data.</p>
 </dd>
 </dl>
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.data">
-<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.data" title="Link to this definition">#</a></dt>
-<dd><p>DataFrame containing the signal data.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.data">
+<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#pyneon.data.NeonStream.data" title="Link to this definition">#</a></dt>
+<dd><p>DataFrame containing the stream data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)">pandas.DataFrame</a></p>
@@ -401,9 +401,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.timestamps">
-<span class="sig-name descname"><span class="pre">timestamps</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.timestamps" title="Link to this definition">#</a></dt>
-<dd><p>Timestamps of the signal.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.timestamps">
+<span class="sig-name descname"><span class="pre">timestamps</span></span><a class="headerlink" href="#pyneon.data.NeonStream.timestamps" title="Link to this definition">#</a></dt>
+<dd><p>Timestamps of the stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>np.ndarray</p>
@@ -412,8 +412,8 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.ts">
-<span class="sig-name descname"><span class="pre">ts</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.ts" title="Link to this definition">#</a></dt>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.ts">
+<span class="sig-name descname"><span class="pre">ts</span></span><a class="headerlink" href="#pyneon.data.NeonStream.ts" title="Link to this definition">#</a></dt>
 <dd><p>Alias for timestamps.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
@@ -423,9 +423,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.first_ts">
-<span class="sig-name descname"><span class="pre">first_ts</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.first_ts" title="Link to this definition">#</a></dt>
-<dd><p>First timestamp of the signal.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.first_ts">
+<span class="sig-name descname"><span class="pre">first_ts</span></span><a class="headerlink" href="#pyneon.data.NeonStream.first_ts" title="Link to this definition">#</a></dt>
+<dd><p>First timestamp of the stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
@@ -434,9 +434,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.last_ts">
-<span class="sig-name descname"><span class="pre">last_ts</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.last_ts" title="Link to this definition">#</a></dt>
-<dd><p>Last timestamp of the signal.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.last_ts">
+<span class="sig-name descname"><span class="pre">last_ts</span></span><a class="headerlink" href="#pyneon.data.NeonStream.last_ts" title="Link to this definition">#</a></dt>
+<dd><p>Last timestamp of the stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
@@ -445,9 +445,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.times">
-<span class="sig-name descname"><span class="pre">times</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.times" title="Link to this definition">#</a></dt>
-<dd><p>Timestamps converted to seconds relative to signal start.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.times">
+<span class="sig-name descname"><span class="pre">times</span></span><a class="headerlink" href="#pyneon.data.NeonStream.times" title="Link to this definition">#</a></dt>
+<dd><p>Timestamps converted to seconds relative to stream start.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>np.ndarray</p>
@@ -456,9 +456,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.duration">
-<span class="sig-name descname"><span class="pre">duration</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.duration" title="Link to this definition">#</a></dt>
-<dd><p>Duration of the signal in seconds.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.duration">
+<span class="sig-name descname"><span class="pre">duration</span></span><a class="headerlink" href="#pyneon.data.NeonStream.duration" title="Link to this definition">#</a></dt>
+<dd><p>Duration of the stream in seconds.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
@@ -467,9 +467,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.sampling_freq_effective">
-<span class="sig-name descname"><span class="pre">sampling_freq_effective</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.sampling_freq_effective" title="Link to this definition">#</a></dt>
-<dd><p>Effective sampling frequency of the signal
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.sampling_freq_effective">
+<span class="sig-name descname"><span class="pre">sampling_freq_effective</span></span><a class="headerlink" href="#pyneon.data.NeonStream.sampling_freq_effective" title="Link to this definition">#</a></dt>
+<dd><p>Effective sampling frequency of the stream
 (number of time points divided by duration).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
@@ -479,9 +479,9 @@
 </dd></dl>
 
 <dl class="py attribute">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.sampling_freq_nominal">
-<span class="sig-name descname"><span class="pre">sampling_freq_nominal</span></span><a class="headerlink" href="#pyneon.data.NeonSignal.sampling_freq_nominal" title="Link to this definition">#</a></dt>
-<dd><p>Nominal sampling frequency of the signal as specified by Pupil Labs
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.sampling_freq_nominal">
+<span class="sig-name descname"><span class="pre">sampling_freq_nominal</span></span><a class="headerlink" href="#pyneon.data.NeonStream.sampling_freq_nominal" title="Link to this definition">#</a></dt>
+<dd><p>Nominal sampling frequency of the stream as specified by Pupil Labs
 (<a class="reference external" href="https://pupil-labs.com/products/neon/specs">https://pupil-labs.com/products/neon/specs</a>).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
@@ -491,20 +491,20 @@
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="pyneon.data.NeonSignal.resample">
-<span class="sig-name descname"><span class="pre">resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_ts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">ndarray</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.data.NeonSignal.resample" title="Link to this definition">#</a></dt>
-<dd><p>Resample the signal to a new set of timestamps.</p>
+<dt class="sig sig-object py" id="pyneon.data.NeonStream.resample">
+<span class="sig-name descname"><span class="pre">resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_ts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">ndarray</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.data.NeonStream.resample" title="Link to this definition">#</a></dt>
+<dd><p>Resample the stream to a new set of timestamps.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>new_ts</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – New timestamps to resample the signal to. If <code class="docutils literal notranslate"><span class="pre">None</span></code>,
-the signal is resampled to its nominal sampling frequency according to
+<li><p><strong>new_ts</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – New timestamps to resample the stream to. If <code class="docutils literal notranslate"><span class="pre">None</span></code>,
+the stream is resampled to its nominal sampling frequency according to
 <a class="reference external" href="https://pupil-labs.com/products/neon/specs">https://pupil-labs.com/products/neon/specs</a>.</p></li>
 <li><p><strong>float_kind</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Kind of interpolation applied on columns of float type,
 by default “linear”. For details see <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d" title="(in SciPy v1.14.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">scipy.interpolate.interp1d</span></code></a>.</p></li>
 <li><p><strong>other_kind</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Kind of interpolation applied on columns of other types,
 by default “nearest”.</p></li>
-<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Replace signal data with resampled data if <code class="docutils literal notranslate"><span class="pre">True</span></code>,
+<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Replace stream data with resampled data if <code class="docutils literal notranslate"><span class="pre">True</span></code>,
 by default <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
@@ -522,24 +522,55 @@
 <dl class="py class">
 <dt class="sig sig-object py" id="pyneon.data.NeonGaze">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonGaze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonGaze" title="Link to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonSignal" title="pyneon.data.NeonSignal"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonSignal</span></code></a></p>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonStream" title="pyneon.data.NeonStream"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonStream</span></code></a></p>
 <p>Gaze data.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="pyneon.data.NeonEyeStates">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonEyeStates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonEyeStates" title="Link to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonSignal" title="pyneon.data.NeonSignal"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonSignal</span></code></a></p>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonStream" title="pyneon.data.NeonStream"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonStream</span></code></a></p>
 <p>3D eye states data.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="pyneon.data.NeonIMU">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonIMU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonIMU" title="Link to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonSignal" title="pyneon.data.NeonSignal"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonSignal</span></code></a></p>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonStream" title="pyneon.data.NeonStream"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonStream</span></code></a></p>
 <p>IMU data.</p>
 </dd></dl>
 
+<dl class="py class">
+<dt class="sig sig-object py" id="pyneon.data.NeonEV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonEV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonEV" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonData" title="pyneon.data.NeonData"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonData</span></code></a></p>
+<p>Base for Neon event data (blinks, fixations, saccades, messages).</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="pyneon.data.NeonBlinks">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonBlinks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonBlinks" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonEV" title="pyneon.data.NeonEV"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonEV</span></code></a></p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="pyneon.data.NeonFixations">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonFixations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonFixations" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonEV" title="pyneon.data.NeonEV"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonEV</span></code></a></p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="pyneon.data.NeonSaccades">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonSaccades</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonSaccades" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonEV" title="pyneon.data.NeonEV"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonEV</span></code></a></p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="pyneon.data.NeonEvents">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyneon.data.</span></span><span class="sig-name descname"><span class="pre">NeonEvents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.data.NeonEvents" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyneon.data.NeonEV" title="pyneon.data.NeonEV"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeonEV</span></code></a></p>
+</dd></dl>
+
 </section>
 
 
@@ -589,22 +620,27 @@
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonData"><code class="docutils literal notranslate"><span class="pre">NeonData</span></code></a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal"><code class="docutils literal notranslate"><span class="pre">NeonSignal</span></code></a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.data"><code class="docutils literal notranslate"><span class="pre">NeonSignal.data</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.timestamps"><code class="docutils literal notranslate"><span class="pre">NeonSignal.timestamps</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.ts"><code class="docutils literal notranslate"><span class="pre">NeonSignal.ts</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.first_ts"><code class="docutils literal notranslate"><span class="pre">NeonSignal.first_ts</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.last_ts"><code class="docutils literal notranslate"><span class="pre">NeonSignal.last_ts</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.times"><code class="docutils literal notranslate"><span class="pre">NeonSignal.times</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.duration"><code class="docutils literal notranslate"><span class="pre">NeonSignal.duration</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.sampling_freq_effective"><code class="docutils literal notranslate"><span class="pre">NeonSignal.sampling_freq_effective</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.sampling_freq_nominal"><code class="docutils literal notranslate"><span class="pre">NeonSignal.sampling_freq_nominal</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSignal.resample"><code class="docutils literal notranslate"><span class="pre">NeonSignal.resample()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream"><code class="docutils literal notranslate"><span class="pre">NeonStream</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.data"><code class="docutils literal notranslate"><span class="pre">NeonStream.data</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.timestamps"><code class="docutils literal notranslate"><span class="pre">NeonStream.timestamps</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.ts"><code class="docutils literal notranslate"><span class="pre">NeonStream.ts</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.first_ts"><code class="docutils literal notranslate"><span class="pre">NeonStream.first_ts</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.last_ts"><code class="docutils literal notranslate"><span class="pre">NeonStream.last_ts</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.times"><code class="docutils literal notranslate"><span class="pre">NeonStream.times</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.duration"><code class="docutils literal notranslate"><span class="pre">NeonStream.duration</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.sampling_freq_effective"><code class="docutils literal notranslate"><span class="pre">NeonStream.sampling_freq_effective</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.sampling_freq_nominal"><code class="docutils literal notranslate"><span class="pre">NeonStream.sampling_freq_nominal</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonStream.resample"><code class="docutils literal notranslate"><span class="pre">NeonStream.resample()</span></code></a></li>
 </ul>
 </li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonGaze"><code class="docutils literal notranslate"><span class="pre">NeonGaze</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonEyeStates"><code class="docutils literal notranslate"><span class="pre">NeonEyeStates</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonIMU"><code class="docutils literal notranslate"><span class="pre">NeonIMU</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonEV"><code class="docutils literal notranslate"><span class="pre">NeonEV</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonBlinks"><code class="docutils literal notranslate"><span class="pre">NeonBlinks</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonFixations"><code class="docutils literal notranslate"><span class="pre">NeonFixations</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonSaccades"><code class="docutils literal notranslate"><span class="pre">NeonSaccades</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.data.NeonEvents"><code class="docutils literal notranslate"><span class="pre">NeonEvents</span></code></a></li>
 </ul>
   </nav></div>
 
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 5711511..b870634 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -370,55 +370,29 @@
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="dataset.html">NeonDataset class</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="dataset.html#pyneon.NeonDataset"><code class="docutils literal notranslate"><span class="pre">NeonDataset</span></code></a><ul>
-<li class="toctree-l3"><a class="reference internal" href="dataset.html#pyneon.NeonDataset.dataset_dir"><code class="docutils literal notranslate"><span class="pre">NeonDataset.dataset_dir</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="dataset.html#pyneon.NeonDataset.recordings"><code class="docutils literal notranslate"><span class="pre">NeonDataset.recordings</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="dataset.html#pyneon.NeonDataset.sections"><code class="docutils literal notranslate"><span class="pre">NeonDataset.sections</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="dataset.html#pyneon.NeonDataset.load_enrichment"><code class="docutils literal notranslate"><span class="pre">NeonDataset.load_enrichment()</span></code></a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="dataset.html#pyneon.NeonDataset"><code class="docutils literal notranslate"><span class="pre">NeonDataset</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="recording.html">NeonRecording class</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="recording.html#pyneon.NeonRecording"><code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code></a><ul>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.recording_id"><code class="docutils literal notranslate"><span class="pre">NeonRecording.recording_id</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.recording_dir"><code class="docutils literal notranslate"><span class="pre">NeonRecording.recording_dir</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.info"><code class="docutils literal notranslate"><span class="pre">NeonRecording.info</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.start_datetime"><code class="docutils literal notranslate"><span class="pre">NeonRecording.start_datetime</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.contents"><code class="docutils literal notranslate"><span class="pre">NeonRecording.contents</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.gaze"><code class="docutils literal notranslate"><span class="pre">NeonRecording.gaze</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.imu"><code class="docutils literal notranslate"><span class="pre">NeonRecording.imu</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.eye_states"><code class="docutils literal notranslate"><span class="pre">NeonRecording.eye_states</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.concat_channels"><code class="docutils literal notranslate"><span class="pre">NeonRecording.concat_channels()</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.to_motion_bids"><code class="docutils literal notranslate"><span class="pre">NeonRecording.to_motion_bids()</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="recording.html#pyneon.NeonRecording.to_eye_bids"><code class="docutils literal notranslate"><span class="pre">NeonRecording.to_eye_bids()</span></code></a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="recording.html#pyneon.NeonRecording"><code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="data.html">Classes for individual data types</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonData"><code class="docutils literal notranslate"><span class="pre">NeonData</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonSignal"><code class="docutils literal notranslate"><span class="pre">NeonSignal</span></code></a><ul>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.data"><code class="docutils literal notranslate"><span class="pre">NeonSignal.data</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.timestamps"><code class="docutils literal notranslate"><span class="pre">NeonSignal.timestamps</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.ts"><code class="docutils literal notranslate"><span class="pre">NeonSignal.ts</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.first_ts"><code class="docutils literal notranslate"><span class="pre">NeonSignal.first_ts</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.last_ts"><code class="docutils literal notranslate"><span class="pre">NeonSignal.last_ts</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.times"><code class="docutils literal notranslate"><span class="pre">NeonSignal.times</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.duration"><code class="docutils literal notranslate"><span class="pre">NeonSignal.duration</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.sampling_freq_effective"><code class="docutils literal notranslate"><span class="pre">NeonSignal.sampling_freq_effective</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.sampling_freq_nominal"><code class="docutils literal notranslate"><span class="pre">NeonSignal.sampling_freq_nominal</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="data.html#pyneon.data.NeonSignal.resample"><code class="docutils literal notranslate"><span class="pre">NeonSignal.resample()</span></code></a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonStream"><code class="docutils literal notranslate"><span class="pre">NeonStream</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonGaze"><code class="docutils literal notranslate"><span class="pre">NeonGaze</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonEyeStates"><code class="docutils literal notranslate"><span class="pre">NeonEyeStates</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonIMU"><code class="docutils literal notranslate"><span class="pre">NeonIMU</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonEV"><code class="docutils literal notranslate"><span class="pre">NeonEV</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonBlinks"><code class="docutils literal notranslate"><span class="pre">NeonBlinks</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonFixations"><code class="docutils literal notranslate"><span class="pre">NeonFixations</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonSaccades"><code class="docutils literal notranslate"><span class="pre">NeonSaccades</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html#pyneon.data.NeonEvents"><code class="docutils literal notranslate"><span class="pre">NeonEvents</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="preprocess.html">Preprocessing module</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="preprocess.html#pyneon.preprocess.resample"><code class="docutils literal notranslate"><span class="pre">resample()</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="preprocess.html#pyneon.preprocess.concat_channels"><code class="docutils literal notranslate"><span class="pre">concat_channels()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="preprocess.html#pyneon.preprocess.concat_streams"><code class="docutils literal notranslate"><span class="pre">concat_streams()</span></code></a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/reference/preprocess.html b/docs/reference/preprocess.html
index 093cc8c..1f63a96 100644
--- a/docs/reference/preprocess.html
+++ b/docs/reference/preprocess.html
@@ -372,12 +372,12 @@
 <dl class="py function">
 <dt class="sig sig-object py" id="pyneon.preprocess.resample">
 <span class="sig-prename descclassname"><span class="pre">pyneon.preprocess.</span></span><span class="sig-name descname"><span class="pre">resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_ts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><span class="pre">ndarray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.preprocess.resample" title="Link to this definition">#</a></dt>
-<dd><p>Resample the signal to a new set of timestamps.</p>
+<dd><p>Resample the stream to a new set of timestamps.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>new_ts</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – New timestamps to resample the signal to. If <code class="docutils literal notranslate"><span class="pre">None</span></code>,
-the signal is resampled to its nominal sampling frequency according to
+<li><p><strong>new_ts</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – New timestamps to resample the stream to. If <code class="docutils literal notranslate"><span class="pre">None</span></code>,
+the stream is resampled to its nominal sampling frequency according to
 <a class="reference external" href="https://pupil-labs.com/products/neon/specs">https://pupil-labs.com/products/neon/specs</a>.</p></li>
 <li><p><strong>old_data</strong> (<em>pd.DataFrame</em>) – Data to resample. Must contain a monotonically increasing
 <code class="docutils literal notranslate"><span class="pre">timestamp</span> <span class="pre">[ns]</span></code> column.</p></li>
@@ -397,31 +397,32 @@
 </dd></dl>
 
 <dl class="py function">
-<dt class="sig sig-object py" id="pyneon.preprocess.concat_channels">
-<span class="sig-prename descclassname"><span class="pre">pyneon.preprocess.</span></span><span class="sig-name descname"><span class="pre">concat_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="recording.html#pyneon.NeonRecording" title="pyneon.NeonRecording"><span class="pre">NeonRecording</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_freq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.preprocess.concat_channels" title="Link to this definition">#</a></dt>
-<dd><p>Concatenate data from different channels under common timestamps.
-Since the signals may have different timestamps and sampling frequencies,
-resampling of all signals to a set of common timestamps is performed.
-The latest start timestamp and earliest last timestamp of the selected channels
+<dt class="sig sig-object py" id="pyneon.preprocess.concat_streams">
+<span class="sig-prename descclassname"><span class="pre">pyneon.preprocess.</span></span><span class="sig-name descname"><span class="pre">concat_streams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="recording.html#pyneon.NeonRecording" title="pyneon.NeonRecording"><span class="pre">NeonRecording</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_freq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.preprocess.concat_streams" title="Link to this definition">#</a></dt>
+<dd><p>Concatenate data from different streams under common timestamps.
+Since the streams may have different timestamps and sampling frequencies,
+resampling of all streams to a set of common timestamps is performed.
+The latest start timestamp and earliest last timestamp of the selected streams
 are used to define the common timestamps.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>rec</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">NeonRecording</span></code>) – NeonRecording object containing the signals to concatenate.</p></li>
-<li><p><strong>ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – List of channel names to concatenate. Channel names must be in
+<li><p><strong>rec</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">NeonRecording</span></code>) – NeonRecording object containing the streams to concatenate.</p></li>
+<li><p><strong>stream_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Stream names to concatenate. If “all”, then all streams will be used.
+If a list, items must be in
 <code class="docutils literal notranslate"><span class="pre">{&quot;gaze&quot;,</span> <span class="pre">&quot;imu&quot;,</span> <span class="pre">&quot;eye_states&quot;,</span> <span class="pre">&quot;3d_eye_states&quot;}</span></code>.</p></li>
-<li><p><strong>sampling_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Sampling frequency to resample the signals to.
-If numeric, the signals will be resampled to this frequency.
+<li><p><strong>sampling_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Sampling frequency to resample the streams to.
+If numeric, the streams will be resampled to this frequency.
 If <code class="docutils literal notranslate"><span class="pre">&quot;min&quot;</span></code>, the lowest nominal sampling frequency
-of the selected channels will be used.
+of the selected streams will be used.
 If <code class="docutils literal notranslate"><span class="pre">&quot;max&quot;</span></code>, the highest nominal sampling frequency will be used.
 Defaults to <code class="docutils literal notranslate"><span class="pre">&quot;min&quot;</span></code>.</p></li>
 <li><p><strong>resamp_float_kind</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Kind of interpolation applied on columns of float type,
 Defaults to <code class="docutils literal notranslate"><span class="pre">&quot;linear&quot;</span></code>. For details see <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d" title="(in SciPy v1.14.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">scipy.interpolate.interp1d</span></code></a>.</p></li>
 <li><p><strong>resamp_other_kind</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Kind of interpolation applied on columns of other types.
 Defaults to <code class="docutils literal notranslate"><span class="pre">&quot;nearest&quot;</span></code>.</p></li>
-<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Replace selected signal data with resampled data during concatenation
-if <code class="docutils literal notranslate"><span class="pre">True</span></code>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Replace selected stream data with resampled data during concatenation
+if``True``. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -473,7 +474,7 @@
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.preprocess.resample"><code class="docutils literal notranslate"><span class="pre">resample()</span></code></a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.preprocess.concat_channels"><code class="docutils literal notranslate"><span class="pre">concat_channels()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.preprocess.concat_streams"><code class="docutils literal notranslate"><span class="pre">concat_streams()</span></code></a></li>
 </ul>
   </nav></div>
 
diff --git a/docs/reference/recording.html b/docs/reference/recording.html
index cdc4cf2..892efdc 100644
--- a/docs/reference/recording.html
+++ b/docs/reference/recording.html
@@ -392,8 +392,8 @@ <h1>NeonRecording class<a class="headerlink" href="#neonrecording-class" title="
 └── &lt;scene_video&gt;.mp4 (if present)
 </pre></div>
 </div>
-<p>Channels, events, (and scene video) will be located but not loaded until
-accessed as attributes such as <code class="docutils literal notranslate"><span class="pre">gaze</span></code>, <code class="docutils literal notranslate"><span class="pre">imu</span></code>, and <code class="docutils literal notranslate"><span class="pre">eye_states</span></code>.</p>
+<p>Streams, events, (and scene video) will be located but not loaded until
+accessed as properties such as <code class="docutils literal notranslate"><span class="pre">gaze</span></code>, <code class="docutils literal notranslate"><span class="pre">imu</span></code>, and <code class="docutils literal notranslate"><span class="pre">eye_states</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>recording_dir</strong> (str or <a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pathlib.Path</span></code></a>) – Path to the directory containing the recording.</p>
@@ -437,7 +437,7 @@ <h1>NeonRecording class<a class="headerlink" href="#neonrecording-class" title="
 <dt class="sig sig-object py" id="pyneon.NeonRecording.start_datetime">
 <span class="sig-name descname"><span class="pre">start_datetime</span></span><a class="headerlink" href="#pyneon.NeonRecording.start_datetime" title="Link to this definition">#</a></dt>
 <dd><p>Start time of the recording as in <code class="docutils literal notranslate"><span class="pre">info.json</span></code>.
-May not match the start time of each data channel.</p>
+May not match the start time of each data stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">datetime.datetime</span></code></a></p>
@@ -448,7 +448,7 @@ <h1>NeonRecording class<a class="headerlink" href="#neonrecording-class" title="
 <dl class="py attribute">
 <dt class="sig sig-object py" id="pyneon.NeonRecording.contents">
 <span class="sig-name descname"><span class="pre">contents</span></span><a class="headerlink" href="#pyneon.NeonRecording.contents" title="Link to this definition">#</a></dt>
-<dd><p>Contents of the recording directory. Each index is a channel or event name
+<dd><p>Contents of the recording directory. Each index is a stream or event name
 (e.g. <code class="docutils literal notranslate"><span class="pre">gaze</span></code> or <code class="docutils literal notranslate"><span class="pre">imu</span></code>) and columns are <code class="docutils literal notranslate"><span class="pre">exist</span></code> (bool),
 <code class="docutils literal notranslate"><span class="pre">filename</span></code> (str), and <code class="docutils literal notranslate"><span class="pre">path</span></code> (Path).</p>
 <dl class="field-list simple">
@@ -460,46 +460,72 @@ <h1>NeonRecording class<a class="headerlink" href="#neonrecording-class" title="
 
 <dl class="py property">
 <dt class="sig sig-object py" id="pyneon.NeonRecording.gaze">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">gaze</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonGaze" title="pyneon.data.NeonGaze"><span class="pre">NeonGaze</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.gaze" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">gaze</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonGaze" title="pyneon.data.NeonGaze"><span class="pre">NeonGaze</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.gaze" title="Link to this definition">#</a></dt>
 <dd><p>Returns a NeonGaze object or None if no gaze data is found.</p>
 </dd></dl>
 
 <dl class="py property">
 <dt class="sig sig-object py" id="pyneon.NeonRecording.imu">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">imu</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonIMU" title="pyneon.data.NeonIMU"><span class="pre">NeonIMU</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.imu" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">imu</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonIMU" title="pyneon.data.NeonIMU"><span class="pre">NeonIMU</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.imu" title="Link to this definition">#</a></dt>
 <dd><p>Returns a NeonIMU object or None if no IMU data is found.</p>
 </dd></dl>
 
 <dl class="py property">
 <dt class="sig sig-object py" id="pyneon.NeonRecording.eye_states">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">eye_states</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonEyeStates" title="pyneon.data.NeonEyeStates"><span class="pre">NeonEyeStates</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.eye_states" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">eye_states</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonEyeStates" title="pyneon.data.NeonEyeStates"><span class="pre">NeonEyeStates</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.eye_states" title="Link to this definition">#</a></dt>
 <dd><p>Returns a NeonEyeStates object or None if no eye states data is found.</p>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="pyneon.NeonRecording.blinks">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">blinks</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonBlinks" title="pyneon.data.NeonBlinks"><span class="pre">NeonBlinks</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.blinks" title="Link to this definition">#</a></dt>
+<dd><p>Returns a NeonBlinks object or None if no blinks data is found.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="pyneon.NeonRecording.fixations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fixations</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonFixations" title="pyneon.data.NeonFixations"><span class="pre">NeonFixations</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.fixations" title="Link to this definition">#</a></dt>
+<dd><p>Returns a NeonFixations object or None if no fixations data is found.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="pyneon.NeonRecording.saccades">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">saccades</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonSaccades" title="pyneon.data.NeonSaccades"><span class="pre">NeonSaccades</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.saccades" title="Link to this definition">#</a></dt>
+<dd><p>Returns a NeonSaccades object or None if no saccades data is found.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="pyneon.NeonRecording.events">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">events</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="data.html#pyneon.data.NeonEvents" title="pyneon.data.NeonEvents"><span class="pre">NeonEvents</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#pyneon.NeonRecording.events" title="Link to this definition">#</a></dt>
+<dd><p>Returns a NeonEvents object or None if no events data is found.</p>
+</dd></dl>
+
 <dl class="py method">
-<dt class="sig sig-object py" id="pyneon.NeonRecording.concat_channels">
-<span class="sig-name descname"><span class="pre">concat_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_freq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.NeonRecording.concat_channels" title="Link to this definition">#</a></dt>
-<dd><p>Concatenate data from different channels under common timestamps.
-Since the signals may have different timestamps and sampling frequencies,
-resampling of all signals to a set of common timestamps is performed.
-The latest start timestamp and earliest last timestamp of the selected channels
+<dt class="sig sig-object py" id="pyneon.NeonRecording.concat_streams">
+<span class="sig-name descname"><span class="pre">concat_streams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_freq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_float_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resamp_other_kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.NeonRecording.concat_streams" title="Link to this definition">#</a></dt>
+<dd><p>Concatenate data from different streams under common timestamps.
+Since the streams may have different timestamps and sampling frequencies,
+resampling of all streams to a set of common timestamps is performed.
+The latest start timestamp and earliest last timestamp of the selected sreams
 are used to define the common timestamps.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – List of channel names to concatenate. Channel names must be in
+<li><p><strong>stream_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Stream names to concatenate. If “all”, then all streams will be used.
+If a list, items must be in
 <code class="docutils literal notranslate"><span class="pre">{&quot;gaze&quot;,</span> <span class="pre">&quot;imu&quot;,</span> <span class="pre">&quot;eye_states&quot;,</span> <span class="pre">&quot;3d_eye_states&quot;}</span></code>.</p></li>
-<li><p><strong>sampling_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Sampling frequency to resample the signals to.
-If numeric, the signals will be resampled to this frequency.
+<li><p><strong>sampling_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Sampling frequency to resample the streams to.
+If numeric, the streams will be resampled to this frequency.
 If <code class="docutils literal notranslate"><span class="pre">&quot;min&quot;</span></code>, the lowest nominal sampling frequency
-of the selected channels will be used.
+of the selected streams will be used.
 If <code class="docutils literal notranslate"><span class="pre">&quot;max&quot;</span></code>, the highest nominal sampling frequency will be used.
 Defaults to <code class="docutils literal notranslate"><span class="pre">&quot;min&quot;</span></code>.</p></li>
 <li><p><strong>resamp_float_kind</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Kind of interpolation applied on columns of float type,
 Defaults to <code class="docutils literal notranslate"><span class="pre">&quot;linear&quot;</span></code>. For details see <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d" title="(in SciPy v1.14.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">scipy.interpolate.interp1d</span></code></a>.</p></li>
 <li><p><strong>resamp_other_kind</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Kind of interpolation applied on columns of other types.
 Defaults to <code class="docutils literal notranslate"><span class="pre">&quot;nearest&quot;</span></code>.</p></li>
-<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Replace selected signal data with resampled data during concatenation            if <code class="docutils literal notranslate"><span class="pre">True</span></code>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Replace selected stream data with resampled data during concatenation
+if``True``. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -511,6 +537,24 @@ <h1>NeonRecording class<a class="headerlink" href="#neonrecording-class" title="
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="pyneon.NeonRecording.concat_events">
+<span class="sig-name descname"><span class="pre">concat_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><span class="pre">DataFrame</span></a></span></span><a class="headerlink" href="#pyneon.NeonRecording.concat_events" title="Link to this definition">#</a></dt>
+<dd><p>Concatenate types of events and return a DataFrame with all events.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>event_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – List of event names to concatenate. Event names must be in
+<code class="docutils literal notranslate"><span class="pre">{&quot;blinks&quot;,</span> <span class="pre">&quot;fixations&quot;,</span> <span class="pre">&quot;saccades&quot;,</span> <span class="pre">&quot;events&quot;}</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>concat_events</strong> – Concatenated events.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="pyneon.NeonRecording.to_motion_bids">
 <span class="sig-name descname"><span class="pre">to_motion_bids</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><span class="pre">Path</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'sub-XX_task-YY_tracksys-NeonIMU'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyneon.NeonRecording.to_motion_bids" title="Link to this definition">#</a></dt>
@@ -626,7 +670,12 @@ <h1>NeonRecording class<a class="headerlink" href="#neonrecording-class" title="
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.gaze"><code class="docutils literal notranslate"><span class="pre">NeonRecording.gaze</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.imu"><code class="docutils literal notranslate"><span class="pre">NeonRecording.imu</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.eye_states"><code class="docutils literal notranslate"><span class="pre">NeonRecording.eye_states</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.concat_channels"><code class="docutils literal notranslate"><span class="pre">NeonRecording.concat_channels()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.blinks"><code class="docutils literal notranslate"><span class="pre">NeonRecording.blinks</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.fixations"><code class="docutils literal notranslate"><span class="pre">NeonRecording.fixations</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.saccades"><code class="docutils literal notranslate"><span class="pre">NeonRecording.saccades</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.events"><code class="docutils literal notranslate"><span class="pre">NeonRecording.events</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.concat_streams"><code class="docutils literal notranslate"><span class="pre">NeonRecording.concat_streams()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.concat_events"><code class="docutils literal notranslate"><span class="pre">NeonRecording.concat_events()</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.to_motion_bids"><code class="docutils literal notranslate"><span class="pre">NeonRecording.to_motion_bids()</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyneon.NeonRecording.to_eye_bids"><code class="docutils literal notranslate"><span class="pre">NeonRecording.to_eye_bids()</span></code></a></li>
 </ul>
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 28b9b1c..00ffeda 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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diff --git a/docs/tutorials/load_dataset.ipynb b/docs/tutorials/load_dataset.ipynb
deleted file mode 100644
index 6f2a872..0000000
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@@ -1,30 +0,0 @@
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deleted file mode 100644
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diff --git a/docs/tutorials/load_recording.ipynb b/docs/tutorials/load_recording.ipynb
deleted file mode 100644
index 90e463a..0000000
--- a/docs/tutorials/load_recording.ipynb
+++ /dev/null
@@ -1,26 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load a Neon recording\n",
-    "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/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/tutorials/read_recording.html b/docs/tutorials/read_recording.html
new file mode 100644
index 0000000..4fc0484
--- /dev/null
+++ b/docs/tutorials/read_recording.html
@@ -0,0 +1,844 @@
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+  <div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">Reading a Neon dataset/recording</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="export_to_bids.html">Export Neon data recording to BIDS formats</a></li>
+</ul>
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+    <li class="breadcrumb-item active" aria-current="page">Reading a...</li>
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+      
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+                <article class="bd-article">
+                  
+  <section id="Reading-a-Neon-dataset/recording">
+<h1>Reading a Neon dataset/recording<a class="headerlink" href="#Reading-a-Neon-dataset/recording" title="Link to this heading">#</a></h1>
+<p>In this tutorial, we will show how to load a single Neon recording downloaded from <a class="reference external" href="https://docs.pupil-labs.com/neon/pupil-cloud/">Pupil Cloud</a>.</p>
+<section id="Reading-sample-data">
+<h2>Reading sample data<a class="headerlink" href="#Reading-sample-data" title="Link to this heading">#</a></h2>
+<p>We will use a sample recording produced by the NCC Lab called <code class="docutils literal notranslate"><span class="pre">OfficeWalk</span></code>. It’s a project with 2 recordings and multiple enrichments and can be downloaded with the <code class="docutils literal notranslate"><span class="pre">get_sample_data()</span></code> function:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
+<span class="kn">from</span> <span class="nn">pyneon</span> <span class="kn">import</span> <span class="n">get_sample_data</span><span class="p">,</span> <span class="n">NeonDataset</span><span class="p">,</span> <span class="n">NeonRecording</span>
+
+<span class="n">sample_dir</span> <span class="o">=</span> <span class="n">get_sample_data</span><span class="p">(</span><span class="s2">&quot;OfficeWalk&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">OfficeWalk</span></code> data has the following structure:</p>
+<div class="highlight-plaintext notranslate"><div class="highlight"><pre><span></span>OfficeWalk
+├── Timeseries Data
+│   ├── walk1-e116e606
+│   │   ├── info.json
+│   │   ├── gaze.csv
+│   │   └── ....
+│   ├── walk2-93b8c234
+│   │   ├── info.json
+│   │   ├── gaze.csv
+│   │   └── ....
+|   ├── enrichment_info.txt
+|   └── sections.csv
+├── OfficeWalk_FACE-MAPPER_FaceMap
+├── OfficeWalk_MARKER-MAPPER_TagMap_csv
+└── OfficeWalk_STATIC-IMAGE-MAPPER_ManualMap_csv
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">Timeseries</span> <span class="pre">Data</span></code> folder contains what PyNeon calls a <code class="docutils literal notranslate"><span class="pre">NeonDataset</span></code>. It contains multiple recordings, each with its own <code class="docutils literal notranslate"><span class="pre">info.json</span></code> file and data files. These recordings can either be loaded individually as <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code>s or as a wholist <code class="docutils literal notranslate"><span class="pre">NeonDataset</span></code>.</p>
+<p>If loading a <code class="docutils literal notranslate"><span class="pre">NeonDataset</span></code>, specify the path to the <code class="docutils literal notranslate"><span class="pre">Timeseries</span> <span class="pre">Data</span></code> folder to create a <code class="docutils literal notranslate"><span class="pre">NeonDataset</span></code> object:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset_dir</span> <span class="o">=</span> <span class="n">sample_dir</span> <span class="o">/</span> <span class="s2">&quot;Timeseries Data&quot;</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">NeonDataset</span><span class="p">(</span><span class="n">dataset_dir</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+NeonDataset | 2 recordings
+</pre></div></div>
+</div>
+<p>NeonDataset has a <code class="docutils literal notranslate"><span class="pre">recordings</span></code> attribute that contains a list of <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code> objects. These <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code> objects can be accessed by their index.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">first_recording</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">first_recording</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;class &#39;pyneon.recording.NeonRecording&#39;&gt;
+</pre></div></div>
+</div>
+<p>Alternatively, one can directly load a single <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code> by specifying the path to the recording’s folder:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">recording_dir</span> <span class="o">=</span> <span class="n">dataset_dir</span> <span class="o">/</span> <span class="s2">&quot;walk1-e116e606&quot;</span>
+<span class="n">recording</span> <span class="o">=</span> <span class="n">NeonRecording</span><span class="p">(</span><span class="n">recording_dir</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">recording</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;class &#39;pyneon.recording.NeonRecording&#39;&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Accessing-data-and-metadata-of-a-NeonRecording">
+<h2>Accessing data and metadata of a NeonRecording<a class="headerlink" href="#Accessing-data-and-metadata-of-a-NeonRecording" title="Link to this heading">#</a></h2>
+<p>An overview of basic metadata and contents of a <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code> can be obtained by printing the object. An initiated <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code> locates data files in the recording directory but does not load them until requested to be memory efficient.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">recording</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8
+Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561
+Wearer name: Qian
+Recording start time: 2024-08-30 17:37:01.527000
+Recording duration: 98.213 s
+                  exist              filename                                                                                                           path
+3d_eye_states      True     3d_eye_states.csv     C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\3d_eye_states.csv
+blinks             True            blinks.csv            C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\blinks.csv
+events             True            events.csv            C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\events.csv
+fixations          True         fixations.csv         C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\fixations.csv
+gaze               True              gaze.csv              C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\gaze.csv
+imu                True               imu.csv               C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\imu.csv
+labels             True            labels.csv            C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\labels.csv
+saccades           True          saccades.csv          C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\saccades.csv
+world_timestamps   True  world_timestamps.csv  C:\Users\qian.chu\Documents\GitHub\pyneon\data\OfficeWalk\Timeseries Data\walk1-e116e606\world_timestamps.csv
+scene_video       False                  None                                                                                                           None
+
+</pre></div></div>
+</div>
+<p>Individual data streams can be accessed as properties of the <code class="docutils literal notranslate"><span class="pre">NeonRecording</span></code> object. For example, the gaze data can be accessed as <code class="docutils literal notranslate"><span class="pre">recording.gaze</span></code>, and upon accessing, the tabular data is loaded into memory.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;recording._gaze size before accessing `gaze`: </span><span class="si">{</span><span class="n">sys</span><span class="o">.</span><span class="n">getsizeof</span><span class="p">(</span><span class="n">recording</span><span class="o">.</span><span class="n">_gaze</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="n">gaze</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">gaze</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;recording.gaze is of type: </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">gaze</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;recording._gaze size after accessing `gaze`: </span><span class="si">{</span><span class="n">sys</span><span class="o">.</span><span class="n">getsizeof</span><span class="p">(</span><span class="n">recording</span><span class="o">.</span><span class="n">_gaze</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+recording._gaze size before accessing `gaze`: 16
+recording.gaze is of type: &lt;class &#39;pyneon.data.NeonGaze&#39;&gt;
+recording._gaze size after accessing `gaze`: 48
+</pre></div></div>
+</div>
+<p>We can access the timeseries data in the gaze stream as a pandas DataFrame by accessing the <code class="docutils literal notranslate"><span class="pre">data</span></code> attribute of the gaze stream. The columns of the DataFrame include <code class="docutils literal notranslate"><span class="pre">timestamp</span> <span class="pre">[ns]</span></code> and channel data columns. During loading, PyNeon strips the redundant <code class="docutils literal notranslate"><span class="pre">section</span> <span class="pre">id</span></code> and <code class="docutils literal notranslate"><span class="pre">recording</span> <span class="pre">id</span></code> columns and adds a more human-readable <code class="docutils literal notranslate"><span class="pre">time</span> <span class="pre">[s]</span></code> column to represent the time of each sample in seconds relative to the start of the data stream.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">gaze</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+        timestamp [ns]  gaze x [px]  gaze y [px]  worn  fixation id  blink id  \
+0  1725032224852161732     1067.486      620.856  True            1      &lt;NA&gt;
+1  1725032224857165732     1066.920      617.117  True            1      &lt;NA&gt;
+2  1725032224862161732     1072.699      615.780  True            1      &lt;NA&gt;
+3  1725032224867161732     1067.447      617.062  True            1      &lt;NA&gt;
+4  1725032224872161732     1071.564      613.158  True            1      &lt;NA&gt;
+
+   azimuth [deg]  elevation [deg]  time [s]
+0      16.213030        -0.748998  0.000000
+1      16.176285        -0.511733  0.005004
+2      16.546413        -0.426618  0.010000
+3      16.210049        -0.508251  0.015000
+4      16.473521        -0.260388  0.020000
+</pre></div></div>
+</div>
+<p>PyNeon also automatically sets the column datatype to appropriate types, such as <code class="docutils literal notranslate"><span class="pre">Int64</span></code> for timestamps, <code class="docutils literal notranslate"><span class="pre">Int32</span></code> for event IDs, and <code class="docutils literal notranslate"><span class="pre">float64</span></code> for float data.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">gaze</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dtypes</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+timestamp [ns]       Int64
+gaze x [px]        float64
+gaze y [px]        float64
+worn                  bool
+fixation id          Int32
+blink id             Int32
+azimuth [deg]      float64
+elevation [deg]    float64
+time [s]           float64
+dtype: object
+</pre></div></div>
+</div>
+</section>
+<section id="Visualizing-data-timeseries-and-sampling-gaps">
+<h2>Visualizing data timeseries and sampling gaps<a class="headerlink" href="#Visualizing-data-timeseries-and-sampling-gaps" title="Link to this heading">#</a></h2>
+<p>Up to this point, PyNeon simply reads and re-organizes the raw .csv files. Let’s plot some samples from the <code class="docutils literal notranslate"><span class="pre">gaze</span></code> and <code class="docutils literal notranslate"><span class="pre">eye_states</span></code> streams and a saccade from the <code class="docutils literal notranslate"><span class="pre">saccades</span></code> events.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+<span class="n">gaze_color</span> <span class="o">=</span> <span class="s2">&quot;royalblue&quot;</span>
+<span class="n">gyro_color</span> <span class="o">=</span> <span class="s2">&quot;darkorange&quot;</span>
+
+<span class="n">imu</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">imu</span>
+<span class="n">saccades</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">saccades</span>
+
+<span class="c1"># Create a figure</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="n">gaze_color</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="n">gyro_color</span><span class="p">)</span>
+
+<span class="c1"># Visualize the 2nd saccade</span>
+<span class="n">saccade</span> <span class="o">=</span> <span class="n">saccades</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axvspan</span><span class="p">(</span>
+    <span class="n">saccade</span><span class="p">[</span><span class="s2">&quot;start timestamp [ns]&quot;</span><span class="p">],</span> <span class="n">saccade</span><span class="p">[</span><span class="s2">&quot;end timestamp [ns]&quot;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;lightgray&quot;</span>
+<span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+    <span class="p">(</span><span class="n">saccade</span><span class="p">[</span><span class="s2">&quot;start timestamp [ns]&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">saccade</span><span class="p">[</span><span class="s2">&quot;end timestamp [ns]&quot;</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="mi">1050</span><span class="p">,</span>
+    <span class="s2">&quot;Saccade&quot;</span><span class="p">,</span>
+    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Visualize gaze x and pupil diameter left</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">gaze</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;timestamp [ns]&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;gaze x [px]&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="n">gaze_color</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">imu</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">60</span><span class="p">),</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;timestamp [ns]&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;gyro x [deg/s]&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="n">gyro_color</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;timestamp [ns]&#39;, ylabel=&#39;gyro x [deg/s]&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_read_recording_17_1.png" src="../_images/tutorials_read_recording_17_1.png" />
+</div>
+</div>
+<p>It’s apparent that at the beginning of the recording, there are some missing data points in both the <code class="docutils literal notranslate"><span class="pre">gaze</span></code> and <code class="docutils literal notranslate"><span class="pre">imu</span></code> 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.</p>
+<p>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.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Gaze: nominal sampling frequency = </span><span class="si">{</span><span class="n">gaze</span><span class="o">.</span><span class="n">sampling_freq_nominal</span><span class="si">}</span><span class="s2">, &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;effective sampling frequency = </span><span class="si">{</span><span class="n">gaze</span><span class="o">.</span><span class="n">sampling_freq_effective</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;IMU: nominal sampling frequency = </span><span class="si">{</span><span class="n">recording</span><span class="o">.</span><span class="n">imu</span><span class="o">.</span><span class="n">sampling_freq_nominal</span><span class="si">}</span><span class="s2">, &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;effective sampling frequency = </span><span class="si">{</span><span class="n">recording</span><span class="o">.</span><span class="n">imu</span><span class="o">.</span><span class="n">sampling_freq_effective</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Gaze: nominal sampling frequency = 200, effective sampling frequency = 197.8078038925275
+IMU: nominal sampling frequency = 110, effective sampling frequency = 115.35532450871617
+</pre></div></div>
+</div>
+</section>
+<section id="Visualizing-gaze-heatmap">
+<h2>Visualizing gaze heatmap<a class="headerlink" href="#Visualizing-gaze-heatmap" title="Link to this heading">#</a></h2>
+<p>Finally, we will show how to plot a heatmap of the gaze/fixation data.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1600</span><span class="p">,</span> <span class="mi">160</span><span class="p">)</span>
+<span class="n">y_edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1200</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
+
+<span class="n">gaze_x</span> <span class="o">=</span> <span class="n">gaze</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;gaze x [px]&quot;</span><span class="p">]</span>
+<span class="n">gaze_y</span> <span class="o">=</span> <span class="n">gaze</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;gaze y [px]&quot;</span><span class="p">]</span>
+<span class="n">gaze_heatmap</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram2d</span><span class="p">(</span><span class="n">gaze_x</span><span class="p">,</span> <span class="n">gaze_y</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="p">(</span><span class="n">x_edges</span><span class="p">,</span> <span class="n">y_edges</span><span class="p">))</span>
+<span class="n">gaze_heatmap</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">gaze_heatmap</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+
+<span class="n">fixations</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">fixations</span>
+<span class="n">fix_x</span> <span class="o">=</span> <span class="n">fixations</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;fixation x [px]&quot;</span><span class="p">]</span>
+<span class="n">fix_y</span> <span class="o">=</span> <span class="n">fixations</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;fixation y [px]&quot;</span><span class="p">]</span>
+
+<span class="c1"># Plot a heatmap and scatter plot fixations on top</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">gaze_heatmap</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;inferno&quot;</span><span class="p">,</span> <span class="n">extent</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1600</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1200</span><span class="p">],</span> <span class="n">origin</span><span class="o">=</span><span class="s2">&quot;lower&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">fix_x</span><span class="p">,</span> <span class="n">fix_y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s2">&quot;equal&quot;</span><span class="p">,</span> <span class="s2">&quot;box&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Scene camera x [px]&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Scene camera y [px]&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;Scene camera y [px]&#39;)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
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+<img alt="../_images/tutorials_read_recording_21_1.png" src="../_images/tutorials_read_recording_21_1.png" />
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#Reading-sample-data">Reading sample data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#Accessing-data-and-metadata-of-a-NeonRecording">Accessing data and metadata of a NeonRecording</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#Visualizing-data-timeseries-and-sampling-gaps">Visualizing data timeseries and sampling gaps</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#Visualizing-gaze-heatmap">Visualizing gaze heatmap</a></li>
+</ul>
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diff --git a/docs/tutorials/read_recording.ipynb b/docs/tutorials/read_recording.ipynb
new file mode 100644
index 0000000..d1c18c5
--- /dev/null
+++ b/docs/tutorials/read_recording.ipynb
@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading a Neon dataset/recording\n",
+    "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/).\n",
+    "\n",
+    "## Reading sample data\n",
+    "We will use a sample recording produced by the NCC Lab called `OfficeWalk`. It's a project with 2 recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import numpy as np\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from pyneon import get_sample_data, NeonDataset, NeonRecording\n",
+    "\n",
+    "sample_dir = get_sample_data(\"OfficeWalk\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `OfficeWalk` data has the following structure:\n",
+    "\n",
+    "```plaintext\n",
+    "OfficeWalk\n",
+    "├── Timeseries Data\n",
+    "│   ├── walk1-e116e606\n",
+    "│   │   ├── info.json\n",
+    "│   │   ├── gaze.csv\n",
+    "│   │   └── ....\n",
+    "│   ├── walk2-93b8c234\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",
+    "```\n",
+    "\n",
+    "The `Timeseries Data` folder contains what PyNeon calls a `NeonDataset`. It contains multiple recordings, each with its own `info.json` file and data files. These recordings can either be loaded individually as `NeonRecording`s or as a wholist `NeonDataset`.\n",
+    "\n",
+    "If loading a `NeonDataset`, specify the path to the `Timeseries Data` folder to create a `NeonDataset` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NeonDataset | 2 recordings\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_dir = sample_dir / \"Timeseries Data\"\n",
+    "dataset = NeonDataset(dataset_dir)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NeonDataset has a `recordings` attribute that contains a list of `NeonRecording` objects. These `NeonRecording` objects can be accessed by their index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pyneon.recording.NeonRecording'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "first_recording = dataset[0]\n",
+    "print(type(first_recording))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, one can directly load a single `NeonRecording` by specifying the path to the recording's folder:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pyneon.recording.NeonRecording'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "recording_dir = dataset_dir / \"walk1-e116e606\"\n",
+    "recording = NeonRecording(recording_dir)\n",
+    "print(type(recording))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing data and metadata of a NeonRecording\n",
+    "An overview of basic metadata and contents of a `NeonRecording` can be obtained by printing the object. An initiated `NeonRecording` locates data files in the recording directory but does not load them until requested to be memory efficient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n",
+      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
+      "Wearer name: Qian\n",
+      "Recording start time: 2024-08-30 17:37:01.527000\n",
+      "Recording duration: 98.213 s\n",
+      "                  exist              filename                                                                                                           path\n",
+      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
+      "blinks             True            blinks.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
+      "events             True            events.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
+      "fixations          True         fixations.csv         C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
+      "gaze               True              gaze.csv              C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
+      "imu                True               imu.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
+      "labels             True            labels.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
+      "saccades           True          saccades.csv          C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
+      "world_timestamps   True  world_timestamps.csv  C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
+      "scene_video       False                  None                                                                                                           None\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(recording)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "recording._gaze size before accessing `gaze`: 16\n",
+      "recording.gaze is of type: <class 'pyneon.data.NeonGaze'>\n",
+      "recording._gaze size after accessing `gaze`: 48\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"recording._gaze size before accessing `gaze`: {sys.getsizeof(recording._gaze)}\")\n",
+    "\n",
+    "gaze = recording.gaze\n",
+    "print(f\"recording.gaze is of type: {type(gaze)}\")\n",
+    "print(f\"recording._gaze size after accessing `gaze`: {sys.getsizeof(recording._gaze)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the timeseries data in the gaze stream as a pandas DataFrame by accessing the `data` attribute of the gaze stream. The columns of the DataFrame include `timestamp [ns]` and channel data columns. During loading, PyNeon strips the redundant `section id` and `recording id` columns and adds a more human-readable `time [s]` column to represent the time of each sample in seconds relative to the start of the data stream."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        timestamp [ns]  gaze x [px]  gaze y [px]  worn  fixation id  blink id  \\\n",
+      "0  1725032224852161732     1067.486      620.856  True            1      <NA>   \n",
+      "1  1725032224857165732     1066.920      617.117  True            1      <NA>   \n",
+      "2  1725032224862161732     1072.699      615.780  True            1      <NA>   \n",
+      "3  1725032224867161732     1067.447      617.062  True            1      <NA>   \n",
+      "4  1725032224872161732     1071.564      613.158  True            1      <NA>   \n",
+      "\n",
+      "   azimuth [deg]  elevation [deg]  time [s]  \n",
+      "0      16.213030        -0.748998  0.000000  \n",
+      "1      16.176285        -0.511733  0.005004  \n",
+      "2      16.546413        -0.426618  0.010000  \n",
+      "3      16.210049        -0.508251  0.015000  \n",
+      "4      16.473521        -0.260388  0.020000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gaze.data.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyNeon also automatically sets the column datatype to appropriate types, such as `Int64` for timestamps, `Int32` for event IDs, and `float64` for float data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestamp [ns]       Int64\n",
+      "gaze x [px]        float64\n",
+      "gaze y [px]        float64\n",
+      "worn                  bool\n",
+      "fixation id          Int32\n",
+      "blink id             Int32\n",
+      "azimuth [deg]      float64\n",
+      "elevation [deg]    float64\n",
+      "time [s]           float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gaze.data.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing data timeseries and sampling gaps\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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp [ns]', ylabel='gyro x [deg/s]'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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",
+    "saccades = 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",
+    "\n",
+    "# Visualize the 2nd saccade\n",
+    "saccade = saccades.data.iloc[1]\n",
+    "ax.axvspan(\n",
+    "    saccade[\"start timestamp [ns]\"], saccade[\"end timestamp [ns]\"], color=\"lightgray\"\n",
+    ")\n",
+    "ax.text(\n",
+    "    (saccade[\"start timestamp [ns]\"] + saccade[\"end timestamp [ns]\"]) / 2,\n",
+    "    1050,\n",
+    "    \"Saccade\",\n",
+    "    horizontalalignment=\"center\",\n",
+    ")\n",
+    "\n",
+    "# Visualize gaze x and pupil diameter left\n",
+    "sns.scatterplot(\n",
+    "    ax=ax,\n",
+    "    data=gaze.data.head(100),\n",
+    "    x=\"timestamp [ns]\",\n",
+    "    y=\"gaze x [px]\",\n",
+    "    color=gaze_color,\n",
+    ")\n",
+    "sns.scatterplot(\n",
+    "    ax=ax2,\n",
+    "    data=imu.data.head(60),\n",
+    "    x=\"timestamp [ns]\",\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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gaze: nominal sampling frequency = 200, effective sampling frequency = 197.8078038925275\n",
+      "IMU: nominal sampling frequency = 110, effective sampling frequency = 115.35532450871617\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n",
+    "    f\"effective sampling frequency = {gaze.sampling_freq_effective}\"\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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing gaze heatmap\n",
+    "Finally, we will show how to plot a heatmap of the gaze/fixation data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Scene camera y [px]')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_edges = np.linspace(0, 1600, 160)\n",
+    "y_edges = np.linspace(0, 1200, 120)\n",
+    "\n",
+    "gaze_x = gaze.data[\"gaze x [px]\"]\n",
+    "gaze_y = gaze.data[\"gaze y [px]\"]\n",
+    "gaze_heatmap, _, _ = np.histogram2d(gaze_x, gaze_y, bins=(x_edges, y_edges))\n",
+    "gaze_heatmap = gaussian_filter(gaze_heatmap, sigma=3)\n",
+    "\n",
+    "fixations = recording.fixations\n",
+    "fix_x = fixations.data[\"fixation x [px]\"]\n",
+    "fix_y = fixations.data[\"fixation y [px]\"]\n",
+    "\n",
+    "# Plot a heatmap and scatter plot fixations on top\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "ax.imshow(gaze_heatmap.T, cmap=\"inferno\", extent=[0, 1600, 0, 1200], origin=\"lower\")\n",
+    "ax.scatter(fix_x, fix_y, color=\"white\", s=10, alpha=0.3)\n",
+    "ax.set_aspect(\"equal\", \"box\")\n",
+    "ax.set_xlabel(\"Scene camera x [px]\")\n",
+    "ax.set_ylabel(\"Scene camera y [px]\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyneon",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/resample_and_concat.html b/docs/tutorials/resample_and_concat.html
index 6822018..8437efb 100644
--- a/docs/tutorials/resample_and_concat.html
+++ b/docs/tutorials/resample_and_concat.html
@@ -29,6 +29,7 @@
 <link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />
 
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
+    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
   
   <!-- Pre-loaded scripts that we'll load fully later -->
   <link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=dfe6caa3a7d634c4db9b" />
@@ -45,7 +46,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Export Neon data recording to BIDS formats" href="export_to_bids.html" />
-    <link rel="prev" title="Load a Neon dataset (Pupil Cloud project)" href="load_dataset.html" />
+    <link rel="prev" title="Reading a Neon dataset/recording" href="read_recording.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -309,8 +310,7 @@
      aria-label="Section Navigation">
   <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
   <div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="load_recording.html">Load a Neon recording</a></li>
-<li class="toctree-l1"><a class="reference internal" href="load_dataset.html">Load a Neon dataset (Pupil Cloud project)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="read_recording.html">Reading a Neon dataset/recording</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">Resample data and concatenate channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="export_to_bids.html">Export Neon data recording to BIDS formats</a></li>
 </ul>
@@ -372,6 +372,235 @@
                   
   <section id="Resample-data-and-concatenate-channels">
 <h1>Resample data and concatenate channels<a class="headerlink" href="#Resample-data-and-concatenate-channels" title="Link to this heading">#</a></h1>
+<p>Here, we show how to resample different continuous data streams to be evenly sampled. We firstly import the necessary libraries and define our recording as well as output directory</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyneon</span> <span class="kn">import</span> <span class="n">get_sample_data</span><span class="p">,</span> <span class="n">NeonRecording</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+<span class="n">recording_dir</span> <span class="o">=</span> <span class="n">get_sample_data</span><span class="p">(</span><span class="s2">&quot;OfficeWalk&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;Timeseries Data&quot;</span> <span class="o">/</span> <span class="s2">&quot;walk1-e116e606&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Next, we will save local instances of the unprocessed data for comparison</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">recording</span> <span class="o">=</span> <span class="n">NeonRecording</span><span class="p">(</span><span class="n">recording_dir</span><span class="p">)</span>
+<span class="n">raw_gaze_data</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">gaze</span><span class="o">.</span><span class="n">data</span>
+<span class="n">raw_eye_states_data</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">eye_states</span><span class="o">.</span><span class="n">data</span>
+<span class="n">raw_imu_data</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">imu</span><span class="o">.</span><span class="n">data</span>
+</pre></div>
+</div>
+</div>
+<p>For imu, the effective rate is actually higher. We can further explore this behaviour by plotting the distances between subsequent datapoints as a histogram. Dataframes can be operated upon with regular pandas logic</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get the data points</span>
+<span class="n">gaze_times</span> <span class="o">=</span> <span class="n">raw_gaze_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+<span class="n">eye_state_times</span> <span class="o">=</span> <span class="n">raw_eye_states_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+<span class="n">imu_times</span> <span class="o">=</span> <span class="n">raw_imu_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+<span class="c1"># Calculate the distances between subsequent data points</span>
+<span class="n">gaze_diffs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">gaze_times</span><span class="p">)</span>
+<span class="n">eye_state_diffs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">eye_state_times</span><span class="p">)</span>
+<span class="n">imu_diffs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">imu_times</span><span class="p">)</span>
+
+<span class="c1"># Plot the differences as a histogram</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">imu_diffs</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;IMU&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">eye_state_diffs</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Eye states&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">gaze_diffs</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Gaze&quot;</span><span class="p">)</span>
+<span class="c1"># create a logaritmic scale on the y-axis</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Histogram of time differences between subsequent data points&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Time difference [s]&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Count&quot;</span><span class="p">)</span>
+
+<span class="c1"># draw a vertical line at the nominal sampling rate, add text</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span>
+    <span class="mi">1</span> <span class="o">/</span> <span class="n">recording</span><span class="o">.</span><span class="n">gaze</span><span class="o">.</span><span class="n">sampling_freq_nominal</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+    <span class="mi">1</span> <span class="o">/</span> <span class="n">recording</span><span class="o">.</span><span class="n">gaze</span><span class="o">.</span><span class="n">sampling_freq_nominal</span><span class="p">,</span>
+    <span class="mi">1</span><span class="p">,</span>
+    <span class="s2">&quot;Gaze nominal sampling rate&quot;</span><span class="p">,</span>
+    <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
+    <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
+    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span>
+    <span class="mi">1</span> <span class="o">/</span> <span class="n">recording</span><span class="o">.</span><span class="n">imu</span><span class="o">.</span><span class="n">sampling_freq_nominal</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+    <span class="mi">1</span> <span class="o">/</span> <span class="n">recording</span><span class="o">.</span><span class="n">imu</span><span class="o">.</span><span class="n">sampling_freq_nominal</span> <span class="o">+</span> <span class="mf">0.001</span><span class="p">,</span>
+    <span class="mi">100</span><span class="p">,</span>
+    <span class="s2">&quot;IMU nominal sampling rate&quot;</span><span class="p">,</span>
+    <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
+    <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span>
+    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0.010090909090909091, 100, &#39;IMU nominal sampling rate&#39;)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_resample_and_concat_6_1.png" src="../_images/tutorials_resample_and_concat_6_1.png" />
+</div>
+</div>
+<p>Now, we concat the channels with a sampling frequency of 100 Hz. While this downsamples both imu and gaze, it will put them on a common reference frame. Under the hood, continuous values are linearly interpolated, whereas boolean values follow the nearest neighbour.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">concat_df</span> <span class="o">=</span> <span class="n">recording</span><span class="o">.</span><span class="n">concat_streams</span><span class="p">([</span><span class="s2">&quot;gaze&quot;</span><span class="p">,</span> <span class="s2">&quot;eye_states&quot;</span><span class="p">,</span> <span class="s2">&quot;imu&quot;</span><span class="p">],</span> <span class="n">sampling_freq</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Concatenating streams:
+        Gaze
+        3D eye states
+        IMU
+Using customized sampling rate: 100 Hz ([])
+Using latest start timestamp: 1725032224878547732 ([&#39;imu&#39;])
+Using earliest last timestamp: 1725032319533909732 ([&#39;imu&#39;])
+</pre></div></div>
+</div>
+<p>We show an exemplary sampling of eye, imu and concatenated data below. It can be seen that imu data has subsequent missing values which can in turn be interpolated</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">end_time</span> <span class="o">=</span> <span class="mf">5.2</span>
+
+<span class="n">raw_gaze_interval</span> <span class="o">=</span> <span class="n">raw_gaze_data</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">raw_gaze_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">start_time</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">raw_gaze_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">end_time</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="n">raw_eye_states_interval</span> <span class="o">=</span> <span class="n">raw_eye_states_data</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">raw_eye_states_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">start_time</span><span class="p">)</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">raw_eye_states_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">end_time</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="n">raw_imu_interval</span> <span class="o">=</span> <span class="n">raw_imu_data</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">raw_imu_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">start_time</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">raw_imu_data</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">end_time</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="n">concat_interval</span> <span class="o">=</span> <span class="n">concat_df</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">concat_df</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">start_time</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">concat_df</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">end_time</span><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># plot all data in the same scatter plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">raw_gaze_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">],</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">raw_gaze_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Raw gaze data&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">raw_imu_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">],</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">raw_imu_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">]),</span>
+    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Raw eye states data&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">concat_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">],</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">concat_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Concatenated data&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># set x-ticks with higher frequency and add gridlines</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="c1"># remove x labels</span>
+<span class="c1"># remove y-ticks</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x241c7cf7680&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_resample_and_concat_10_1.png" src="../_images/tutorials_resample_and_concat_10_1.png" />
+</div>
+</div>
+<p>A linear interpolation allows us to estimate missing values. In the end, the concatenated dataframe combines all continuous data into one central location</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plot imu data and interpolated data in same plot</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">raw_imu_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">],</span>
+    <span class="n">raw_imu_interval</span><span class="p">[</span><span class="s2">&quot;acceleration x [g]&quot;</span><span class="p">],</span>
+    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Raw imu data&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">concat_interval</span><span class="p">[</span><span class="s2">&quot;time [s]&quot;</span><span class="p">],</span>
+    <span class="n">concat_interval</span><span class="p">[</span><span class="s2">&quot;acceleration x [g]&quot;</span><span class="p">],</span>
+    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Interpolated imu data&quot;</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span>
+    <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x241c7d07680&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_resample_and_concat_12_1.png" src="../_images/tutorials_resample_and_concat_12_1.png" />
+</div>
+</div>
 </section>
 
 
@@ -385,12 +614,12 @@ <h1>Resample data and concatenate channels<a class="headerlink" href="#Resample-
                   
 <div class="prev-next-area">
     <a class="left-prev"
-       href="load_dataset.html"
+       href="read_recording.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Load a Neon dataset (Pupil Cloud project)</p>
+        <p class="prev-next-title">Reading a Neon dataset/recording</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/docs/tutorials/resample_and_concat.ipynb b/docs/tutorials/resample_and_concat.ipynb
index 375b925..bbee9ff 100644
--- a/docs/tutorials/resample_and_concat.ipynb
+++ b/docs/tutorials/resample_and_concat.ipynb
@@ -6,11 +6,312 @@
    "source": [
     "# Resample data and concatenate channels\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we show how to resample different continuous data streams to be evenly sampled. We firstly import the necessary libraries and define our recording as well as output directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from pyneon import get_sample_data, NeonRecording\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "recording_dir = get_sample_data(\"OfficeWalk\") / \"Timeseries Data\" / \"walk1-e116e606\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will save local instances of the unprocessed data for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recording = NeonRecording(recording_dir)\n",
+    "raw_gaze_data = recording.gaze.data\n",
+    "raw_eye_states_data = recording.eye_states.data\n",
+    "raw_imu_data = recording.imu.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For imu, the effective rate is actually higher.\n",
+    "We can further explore this behaviour by plotting the distances between subsequent datapoints as a histogram. Dataframes can be operated upon with regular pandas logic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.010090909090909091, 100, 'IMU nominal sampling rate')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the data points\n",
+    "gaze_times = raw_gaze_data[\"time [s]\"].values\n",
+    "eye_state_times = raw_eye_states_data[\"time [s]\"].values\n",
+    "imu_times = raw_imu_data[\"time [s]\"].values\n",
+    "\n",
+    "# Calculate the distances between subsequent data points\n",
+    "gaze_diffs = np.diff(gaze_times)\n",
+    "eye_state_diffs = np.diff(eye_state_times)\n",
+    "imu_diffs = np.diff(imu_times)\n",
+    "\n",
+    "# Plot the differences as a histogram\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "plt.hist(imu_diffs, bins=20, alpha=0.5, label=\"IMU\")\n",
+    "plt.hist(eye_state_diffs, bins=20, alpha=0.5, label=\"Eye states\")\n",
+    "plt.hist(gaze_diffs, bins=10, alpha=0.8, label=\"Gaze\")\n",
+    "# create a logaritmic scale on the y-axis\n",
+    "plt.yscale(\"log\")\n",
+    "plt.legend()\n",
+    "plt.title(\"Histogram of time differences between subsequent data points\")\n",
+    "plt.xlabel(\"Time difference [s]\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "\n",
+    "# draw a vertical line at the nominal sampling rate, add text\n",
+    "plt.axvline(\n",
+    "    1 / recording.gaze.sampling_freq_nominal, color=\"k\", linestyle=\"dashed\", linewidth=2\n",
+    ")\n",
+    "plt.text(\n",
+    "    1 / recording.gaze.sampling_freq_nominal,\n",
+    "    1,\n",
+    "    \"Gaze nominal sampling rate\",\n",
+    "    rotation=90,\n",
+    "    verticalalignment=\"bottom\",\n",
+    "    horizontalalignment=\"right\",\n",
+    ")\n",
+    "plt.axvline(\n",
+    "    1 / recording.imu.sampling_freq_nominal, color=\"k\", linestyle=\"dashed\", linewidth=2\n",
+    ")\n",
+    "plt.text(\n",
+    "    1 / recording.imu.sampling_freq_nominal + 0.001,\n",
+    "    100,\n",
+    "    \"IMU nominal sampling rate\",\n",
+    "    rotation=90,\n",
+    "    verticalalignment=\"bottom\",\n",
+    "    horizontalalignment=\"left\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we concat the channels with a sampling frequency of 100 Hz. While this downsamples both imu and gaze, it will put them on a common reference frame. Under the hood, continuous values are linearly interpolated, whereas boolean values follow the nearest neighbour."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Concatenating streams:\n",
+      "\tGaze\n",
+      "\t3D eye states\n",
+      "\tIMU\n",
+      "Using customized sampling rate: 100 Hz ([])\n",
+      "Using latest start timestamp: 1725032224878547732 (['imu'])\n",
+      "Using earliest last timestamp: 1725032319533909732 (['imu'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "concat_df = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"], sampling_freq=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We show an exemplary sampling of eye, imu and concatenated data below. It can be seen that imu data has subsequent missing values which can in turn be interpolated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x241c7cf7680>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start_time = 5\n",
+    "end_time = 5.2\n",
+    "\n",
+    "raw_gaze_interval = raw_gaze_data[\n",
+    "    (raw_gaze_data[\"time [s]\"] >= start_time) & (raw_gaze_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "raw_eye_states_interval = raw_eye_states_data[\n",
+    "    (raw_eye_states_data[\"time [s]\"] > start_time)\n",
+    "    & (raw_eye_states_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "raw_imu_interval = raw_imu_data[\n",
+    "    (raw_imu_data[\"time [s]\"] >= start_time) & (raw_imu_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "concat_interval = concat_df[\n",
+    "    (concat_df[\"time [s]\"] >= start_time) & (concat_df[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "\n",
+    "# plot all data in the same scatter plot\n",
+    "plt.figure(figsize=(16, 2))\n",
+    "plt.scatter(\n",
+    "    raw_gaze_interval[\"time [s]\"],\n",
+    "    np.zeros_like(raw_gaze_interval[\"time [s]\"]) + 0.5,\n",
+    "    label=\"Raw gaze data\",\n",
+    "    color=\"red\",\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    raw_imu_interval[\"time [s]\"],\n",
+    "    np.zeros_like(raw_imu_interval[\"time [s]\"]),\n",
+    "    label=\"Raw eye states data\",\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    concat_interval[\"time [s]\"],\n",
+    "    np.zeros_like(concat_interval[\"time [s]\"]) - 1,\n",
+    "    label=\"Concatenated data\",\n",
+    "    color=\"green\",\n",
+    ")\n",
+    "# set x-ticks with higher frequency and add gridlines\n",
+    "plt.xticks(np.arange(start_time, end_time, 0.01), labels=None)\n",
+    "# remove x labels\n",
+    "# remove y-ticks\n",
+    "plt.yticks([])\n",
+    "plt.grid()\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A linear interpolation allows us to estimate missing values. In the end, the concatenated dataframe combines all continuous data into one central location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x241c7d07680>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot imu data and interpolated data in same plot\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(16, 3))\n",
+    "plt.scatter(\n",
+    "    raw_imu_interval[\"time [s]\"],\n",
+    "    raw_imu_interval[\"acceleration x [g]\"],\n",
+    "    label=\"Raw imu data\",\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    concat_interval[\"time [s]\"],\n",
+    "    concat_interval[\"acceleration x [g]\"],\n",
+    "    label=\"Interpolated imu data\",\n",
+    "    color=\"green\",\n",
+    "    linestyle=\"dashed\",\n",
+    ")\n",
+    "plt.legend()"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "pyneon",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/pyneon/data.py b/pyneon/data.py
index 55d60ed..9bc21d0 100644
--- a/pyneon/data.py
+++ b/pyneon/data.py
@@ -17,12 +17,16 @@ def __init__(self, file: Path):
             data = pd.read_csv(file)
         else:  # TODO: Implement reading native data formats
             pass
-        # Assert that all section id are the same
-        if data["section id"].nunique() > 1:
-            raise ValueError(f"{file.name} contains multiple section IDs")
+
         if data["recording id"].nunique() > 1:
             raise ValueError(f"{file.name} contains multiple recording IDs")
-        self.data = data.drop(columns=["section id", "recording id"])
+        self.data = data.drop(columns=["recording id"])
+
+        # Every data file except events.csv has a section id column
+        if "section id" in self.data.columns:
+            if data["section id"].nunique() > 1:
+                raise ValueError(f"{file.name} contains multiple section IDs")
+            self.data.drop(columns=["section id"], inplace=True)
 
     def __len__(self) -> int:
         return self.data.shape[0]
@@ -206,21 +210,89 @@ def __init__(self, file: Path):
         )
 
 
-# class NeonEvents(NeonData):
-#     def __init__(self, file):
-#         super().__init__(file)
+class NeonEV(NeonData):
+    """
+    Base for Neon event data (blinks, fixations, saccades, messages).
+    """
 
+    def __init__(self, file):
+        super().__init__(file)
 
-# class NeonBlinks(NeonEvents):
-#     def __init__(self, file):
-#         super().__init__(file)
+    def __getitem__(self, index) -> pd.Series:
+        """Get an event timeseries by index."""
+        return self.data.iloc[index]
 
+    # def _get_attributes(self):
+    #     """
+    #     Get attributes given self.data DataFrame.
+    #     """
+    #     # If there is a timestamp [ns] column, rename it to start timestamp [ns]
+    #     if "timestamp [ns]" in self.data.columns:
+    #         self.data.rename(
+    #             columns={"timestamp [ns]": "start timestamp [ns]"}, inplace=True
+    #         )
+    #         self.data["end timestamp [ns]"] = self.data["start timestamp [ns]"]
+    #     self.data.sort_values(by=["start timestamp [ns]"], inplace=True)
+    #     self.start_timestamps = self.data["start timestamp [ns]"].to_numpy()
+    #     self.start_ts = self.start_timestamps
+    #     self.end_timestamps = self.data["end timestamp [ns]"].to_numpy()
+    #     self.end_ts = self.end_timestamps
 
-# class NeonFixations(NeonData):
-#     def __init__(self, file):
-#         super().__init__(file)
+
+class NeonBlinks(NeonEV):
+    def __init__(self, file):
+        super().__init__(file)
+        self.data = self.data.astype(
+            {
+                "blink id": "Int32",
+                "start timestamp [ns]": "Int64",
+                "end timestamp [ns]": "Int64",
+                "duration [ms]": "Int64",
+            }
+        )
 
 
-# class NeonLabels(NeonData):
-#     def __init__(self, file):
-#         super().__init__(file)
+class NeonFixations(NeonEV):
+    def __init__(self, file):
+        super().__init__(file)
+        self.data = self.data.astype(
+            {
+                "fixation id": "Int32",
+                "start timestamp [ns]": "Int64",
+                "end timestamp [ns]": "Int64",
+                "duration [ms]": "Int64",
+                "fixation x [px]": float,
+                "fixation y [px]": float,
+                "azimuth [deg]": float,
+                "elevation [deg]": float,
+            }
+        )
+
+
+class NeonSaccades(NeonEV):
+    def __init__(self, file):
+        super().__init__(file)
+        self.data = self.data.astype(
+            {
+                "saccade id": "Int32",
+                "start timestamp [ns]": "Int64",
+                "end timestamp [ns]": "Int64",
+                "duration [ms]": "Int64",
+                "amplitude [px]": float,
+                "amplitude [deg]": float,
+                "mean velocity [px/s]": float,
+                "peak velocity [px/s]": float,
+            }
+        )
+
+
+class NeonEvents(NeonEV):
+    def __init__(self, file):
+        super().__init__(file)
+        self.data = self.data.astype(
+            {
+                "timestamp [ns]": "Int64",
+                "name": str,
+                "type": str,
+            }
+        )
diff --git a/pyneon/preprocess/__init__.py b/pyneon/preprocess/__init__.py
index 87af556..a4937db 100644
--- a/pyneon/preprocess/__init__.py
+++ b/pyneon/preprocess/__init__.py
@@ -1,3 +1,3 @@
-from .preprocess import resample, concat_streams
+from .preprocess import resample, concat_streams, concat_events
 
 __all__ = ["resample", "concat_streams"]
diff --git a/pyneon/preprocess/preprocess.py b/pyneon/preprocess/preprocess.py
index f92a5ec..a5842a4 100644
--- a/pyneon/preprocess/preprocess.py
+++ b/pyneon/preprocess/preprocess.py
@@ -62,12 +62,12 @@ def resample(
     return resamp_data
 
 
-_VALID_STREAMS = ["3d_eye_states", "eye_states", "gaze", "imu"]
+_VALID_STREAMS = {"3d_eye_states", "eye_states", "gaze", "imu"}
 
 
 def concat_streams(
     rec: "NeonRecording",
-    stream_names: list[str],
+    stream_names: Union[str, list[str]] = "all",
     sampling_freq: Union[float, int, str] = "min",
     resamp_float_kind: str = "linear",
     resamp_other_kind: str = "nearest",
@@ -84,8 +84,9 @@ def concat_streams(
     ----------
     rec : :class:`NeonRecording`
         NeonRecording object containing the streams to concatenate.
-    stream_names : list of str
-        List of stream names to concatenate. Stream names must be in
+    stream_names : str or list of str
+        Stream names to concatenate. If "all", then all streams will be used.
+        If a list, items must be in
         ``{"gaze", "imu", "eye_states", "3d_eye_states"}``.
     sampling_freq : float or int or str, optional
         Sampling frequency to resample the streams to.
@@ -109,13 +110,21 @@ def concat_streams(
     concat_data : :class:`pandas.DataFrame`
         Concatenated data.
     """
+    if isinstance(stream_names, str):
+        if stream_names == "all":
+            stream_names = list(_VALID_STREAMS).remove("eye_states")
+        else:
+            raise ValueError(
+                "Invalid stream_names, must be 'all' or a list of stream names."
+            )
+
     if len(stream_names) <= 1:
         raise ValueError("Must provide at least two streams to concatenate.")
 
     stream_names = [ch.lower() for ch in stream_names]
     # Check if all streams are valid
     if not all([ch in _VALID_STREAMS for ch in stream_names]):
-        raise ValueError(f"Invalid stream name, can only be {_VALID_STREAMS}")
+        raise ValueError(f"Invalid stream name, can only one of {_VALID_STREAMS}")
 
     ch_info = pd.DataFrame(columns=["stream", "name", "sf", "first_ts", "last_ts"])
     print("Concatenating streams:")
@@ -226,3 +235,45 @@ def concat_streams(
         )
         assert concat_data.shape[0] == resamp_df.shape[0]
     return concat_data
+
+
+VALID_EVENTS = {"blinks", "fixations", "saccades", "events"}
+
+
+def concat_events(
+    rec: "NeonRecording",
+    event_names: Union[str, list[str]],
+) -> pd.DataFrame:
+    """
+    Concatenate events from different streams under common timestamps.
+    The latest start timestamp and earliest last timestamp of the selected events
+    are used to define the common timestamps.
+
+    Parameters
+    ----------
+    rec : :class:`NeonRecording`
+        NeonRecording object containing the events to concatenate.
+    event_names : list of str
+        List of event names to concatenate. Event names must be in
+        ``{"blinks", "fixations", "saccades", "events"}``.
+
+    Returns
+    -------
+    concat_events : :class:`pandas.DataFrame`
+        Concatenated events.
+    """
+    if isinstance(event_names, str):
+        if event_names == "all":
+            event_names = list(VALID_EVENTS)
+        else:
+            raise ValueError(
+                "Invalid event_names, must be 'all' or a list of event names."
+            )
+
+    if len(event_names) <= 1:
+        raise ValueError("Must provide at least two events to concatenate.")
+
+    event_names = [ev.lower() for ev in event_names]
+    # Check if all events are valid
+    if not all([ev in VALID_EVENTS for ev in event_names]):
+        raise ValueError(f"Invalid event name, can only be {VALID_EVENTS}")
diff --git a/pyneon/recording.py b/pyneon/recording.py
index 8de3d6c..28cc62d 100644
--- a/pyneon/recording.py
+++ b/pyneon/recording.py
@@ -3,9 +3,18 @@
 import pandas as pd
 import json
 from datetime import datetime
-
-from .data import NeonGaze, NeonIMU, NeonEyeStates
-from .preprocess import concat_streams
+import warnings
+
+from .data import (
+    NeonGaze,
+    NeonIMU,
+    NeonEyeStates,
+    NeonBlinks,
+    NeonFixations,
+    NeonSaccades,
+    NeonEvents,
+)
+from .preprocess import concat_streams, concat_events
 from .io import export_motion_bids, exports_eye_bids
 
 
@@ -83,12 +92,13 @@ def __init__(self, recording_dir: Union[str, Path]):
         self.recording_id = self.info["recording_id"]
         self.recording_dir = recording_dir
 
+        self._gaze = None
         self._eye_states = None
+        self._imu = None
         self._blinks = None
-        self._events = None
         self._fixations = None
-        self._gaze = None
-        self._imu = None
+        self._saccades = None
+        self._events = None
 
         self._get_contents()
 
@@ -140,7 +150,7 @@ def _get_contents(self):
         self.contents = contents
 
     @property
-    def gaze(self) -> NeonGaze:
+    def gaze(self) -> Union[NeonGaze, None]:
         """
         Returns a NeonGaze object or None if no gaze data is found.
         """
@@ -149,13 +159,11 @@ def gaze(self) -> NeonGaze:
                 gaze_file = self.contents.loc["gaze", "path"]
                 self._gaze = NeonGaze(gaze_file)
             else:
-                raise UserWarning(
-                    "Gaze data not loaded because no recording was found."
-                )
+                warnings.warn("Gaze data not loaded because no recording was found.")
         return self._gaze
 
     @property
-    def imu(self) -> NeonIMU:
+    def imu(self) -> Union[NeonIMU, None]:
         """
         Returns a NeonIMU object or None if no IMU data is found.
         """
@@ -164,11 +172,11 @@ def imu(self) -> NeonIMU:
                 imu_file = self.contents.loc["imu", "path"]
                 self._imu = NeonIMU(imu_file)
             else:
-                raise UserWarning("IMU data not loaded because no recording was found.")
+                warnings.warn("IMU data not loaded because no recording was found.")
         return self._imu
 
     @property
-    def eye_states(self) -> NeonEyeStates:
+    def eye_states(self) -> Union[NeonEyeStates, None]:
         """
         Returns a NeonEyeStates object or None if no eye states data is found.
         """
@@ -177,14 +185,70 @@ def eye_states(self) -> NeonEyeStates:
                 eye_states_file = self.contents.loc["3d_eye_states", "path"]
                 self._eye_states = NeonEyeStates(eye_states_file)
             else:
-                raise UserWarning(
+                warnings.warn(
                     "3D eye states data not loaded because no recording was found."
                 )
         return self._eye_states
 
+    @property
+    def blinks(self) -> Union[NeonBlinks, None]:
+        """
+        Returns a NeonBlinks object or None if no blinks data is found.
+        """
+        if self._blinks is None:
+            if self.contents.loc["blinks", "exist"]:
+                blinks_file = self.contents.loc["blinks", "path"]
+                self._blinks = NeonBlinks(blinks_file)
+            else:
+                warnings.warn("Blinks data not loaded because no recording was found.")
+        return self._blinks
+
+    @property
+    def fixations(self) -> Union[NeonFixations, None]:
+        """
+        Returns a NeonFixations object or None if no fixations data is found.
+        """
+        if self._fixations is None:
+            if self.contents.loc["fixations", "exist"]:
+                fixations_file = self.contents.loc["fixations", "path"]
+                self._fixations = NeonFixations(fixations_file)
+            else:
+                warnings.warn(
+                    "Fixations data not loaded because no recording was found."
+                )
+        return self._fixations
+
+    @property
+    def saccades(self) -> Union[NeonSaccades, None]:
+        """
+        Returns a NeonSaccades object or None if no saccades data is found.
+        """
+        if self._saccades is None:
+            if self.contents.loc["saccades", "exist"]:
+                saccades_file = self.contents.loc["saccades", "path"]
+                self._saccades = NeonSaccades(saccades_file)
+            else:
+                warnings.warn(
+                    "Saccades data not loaded because no recording was found."
+                )
+        return self._saccades
+
+    @property
+    def events(self) -> Union[NeonEvents, None]:
+        """
+        Returns a NeonEvents object or None if no events data is found.
+        """
+        if self._events is None:
+            if self.contents.loc["events", "exist"]:
+                events_file = self.contents.loc["events", "path"]
+                self._events = NeonEvents(events_file)
+            else:
+                warnings.warn("Events data not loaded because no recording was found.")
+        return self._events
+
     def concat_streams(
         self,
-        stream_names: list[str],
+        stream_names: Union[str, list[str]],
         sampling_freq: Union[float, int, str] = "min",
         resamp_float_kind: str = "linear",
         resamp_other_kind: str = "nearest",
@@ -199,8 +263,9 @@ def concat_streams(
 
         Parameters
         ----------
-        stream_names : list of str
-            List of stream names to concatenate. Stream names must be in
+        stream_names : str or list of str
+            Stream names to concatenate. If "all", then all streams will be used.
+            If a list, items must be in
             ``{"gaze", "imu", "eye_states", "3d_eye_states"}``.
         sampling_freq : float or int or str, optional
             Sampling frequency to resample the streams to.
@@ -233,6 +298,23 @@ def concat_streams(
             inplace,
         )
 
+    def concat_events(self, event_names: list[str]) -> pd.DataFrame:
+        """
+        Concatenate types of events and return a DataFrame with all events.
+
+        Parameters
+        ----------
+        event_names : list of str
+            List of event names to concatenate. Event names must be in
+            ``{"blinks", "fixations", "saccades", "events"}``.
+
+        Returns
+        -------
+        concat_events : :class:`pandas.DataFrame`
+            Concatenated events.
+        """
+        return concat_events(self, event_names)
+
     def to_motion_bids(
         self,
         output_dir: Union[str, Path],
diff --git a/source/tutorials/index.rst b/source/tutorials/index.rst
index e74a60e..c5e9781 100644
--- a/source/tutorials/index.rst
+++ b/source/tutorials/index.rst
@@ -9,7 +9,6 @@ started with PyNeon.
 .. toctree::
     :maxdepth: 1
 
-    load_recording
-    load_dataset
+    read_recording
     resample_and_concat
     export_to_bids
\ No newline at end of file
diff --git a/source/tutorials/load_dataset.ipynb b/source/tutorials/load_dataset.ipynb
deleted file mode 100644
index 3d349c1..0000000
--- a/source/tutorials/load_dataset.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load a Neon dataset (Pupil Cloud project)\n",
-    "In this tutorial, we will show how to load a Neon dataset downloaded from [Pupil Cloud](https://docs.pupil-labs.com/neon/pupil-cloud/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pyneon import get_sample_data\n",
-    "\n",
-    "# all paths are relative to the location of the notebook\n",
-    "dataset_path = get_sample_data('OfficeWalk') / 'Timeseries Data'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now create a NeonDataset object with the corresponding dataset_path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pyneon import NeonDataset\n",
-    "\n",
-    "# create a dataset object\n",
-    "dataset = NeonDataset(dataset_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see which recordings are included within this dataset by calling dataset.recordings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "Recording ID: 93b8c234-6bbe-4764-a745-e7aafb7e1709\n",
-      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
-      "Wearer name: Qian\n",
-      "Recording start time: 2024-08-30 17:39:23.736000\n",
-      "Recording duration: 57.32 s\n",
-      "                  exist              filename                                                                                                                     path\n",
-      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\3d_eye_states.csv\n",
-      "blinks             True            blinks.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\blinks.csv\n",
-      "events             True            events.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\events.csv\n",
-      "fixations          True         fixations.csv         C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\fixations.csv\n",
-      "gaze               True              gaze.csv              C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\gaze.csv\n",
-      "imu                True               imu.csv               C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\imu.csv\n",
-      "labels             True            labels.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\labels.csv\n",
-      "saccades           True          saccades.csv          C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\saccades.csv\n",
-      "world_timestamps   True  world_timestamps.csv  C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\world_timestamps.csv\n",
-      "scene_video       False                  None                                                                                                                     None\n",
-      ", \n",
-      "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n",
-      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
-      "Wearer name: Qian\n",
-      "Recording start time: 2024-08-30 17:37:01.527000\n",
-      "Recording duration: 98.213 s\n",
-      "                  exist              filename                                                                                                                     path\n",
-      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
-      "blinks             True            blinks.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
-      "events             True            events.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
-      "fixations          True         fixations.csv         C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
-      "gaze               True              gaze.csv              C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
-      "imu                True               imu.csv               C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
-      "labels             True            labels.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
-      "saccades           True          saccades.csv          C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
-      "world_timestamps   True  world_timestamps.csv  C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
-      "scene_video       False                  None                                                                                                                     None\n",
-      "]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(dataset.recordings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can access a single recording by calling the dataset with an integer argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Recording ID: 93b8c234-6bbe-4764-a745-e7aafb7e1709\n",
-      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
-      "Wearer name: Qian\n",
-      "Recording start time: 2024-08-30 17:39:23.736000\n",
-      "Recording duration: 57.32 s\n",
-      "                  exist              filename                                                                                                                     path\n",
-      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\3d_eye_states.csv\n",
-      "blinks             True            blinks.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\blinks.csv\n",
-      "events             True            events.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\events.csv\n",
-      "fixations          True         fixations.csv         C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\fixations.csv\n",
-      "gaze               True              gaze.csv              C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\gaze.csv\n",
-      "imu                True               imu.csv               C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\imu.csv\n",
-      "labels             True            labels.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\labels.csv\n",
-      "saccades           True          saccades.csv          C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\saccades.csv\n",
-      "world_timestamps   True  world_timestamps.csv  C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\\world_timestamps.csv\n",
-      "scene_video       False                  None                                                                                                                     None\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "Recording_1 = dataset.recordings[0]\n",
-    "Recording_2 = dataset.recordings[1]\n",
-    "\n",
-    "print(Recording_1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "On the single recording level, data can be operated like specified in the load_recording.ipynb tutorial"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pyneon",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/source/tutorials/load_recording.ipynb b/source/tutorials/load_recording.ipynb
deleted file mode 100644
index af3c5c1..0000000
--- a/source/tutorials/load_recording.ipynb
+++ /dev/null
@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load a Neon recording\n",
-    "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/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To download sample code, execute __pyneon/sample_data.py__ or call ``get_sample_data(NAME)``"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pyneon import get_sample_data\n",
-    "\n",
-    "# all paths are relative to the location of the notebook\n",
-    "dataset_path = get_sample_data('OfficeWalk')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "``get_sample_data`` yields the relative path of the dataset. We can define the path to one particular recording manually. Alternatively, we can also define a path directly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "recording_path = dataset_path / 'Timeseries Data' /'walk1-e116e606'\n",
-    "\n",
-    "# recording_path = r'..\\..\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can load one recording from the dataset. Printing it provides us with extensive information about the recording including metadata and a summary of underlying objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n",
-      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
-      "Wearer name: Qian\n",
-      "Recording start time: 2024-08-30 17:37:01.527000\n",
-      "Recording duration: 98.213 s\n",
-      "                  exist              filename                                                                                                                     path\n",
-      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
-      "blinks             True            blinks.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
-      "events             True            events.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
-      "fixations          True         fixations.csv         C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
-      "gaze               True              gaze.csv              C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
-      "imu                True               imu.csv               C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
-      "labels             True            labels.csv            C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
-      "saccades           True          saccades.csv          C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
-      "world_timestamps   True  world_timestamps.csv  C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
-      "scene_video       False                  None                                                                                                                     None\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pyneon import NeonRecording\n",
-    "\n",
-    "recording = NeonRecording(recording_path)\n",
-    "\n",
-    "print(recording)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each recording file has an associated object. Calling its .data attribute yields a pandas dataframe with all the included data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<pyneon.data.NeonGaze object at 0x0000024D1A2D0230>\n",
-      "            timestamp [ns]  gaze x [px]  ...  elevation [deg]   time [s]\n",
-      "0      1725032224852161732     1067.486  ...        -0.748998   0.000000\n",
-      "1      1725032224857165732     1066.920  ...        -0.511733   0.005004\n",
-      "2      1725032224862161732     1072.699  ...        -0.426618   0.010000\n",
-      "3      1725032224867161732     1067.447  ...        -0.508251   0.015000\n",
-      "4      1725032224872161732     1071.564  ...        -0.260388   0.020000\n",
-      "...                    ...          ...  ...              ...        ...\n",
-      "18764  1725032319717194732      800.364  ...       -11.091931  94.865033\n",
-      "18765  1725032319722198732      799.722  ...       -10.470156  94.870037\n",
-      "18766  1725032319727194732      799.901  ...       -10.832782  94.875033\n",
-      "18767  1725032319732194732      796.982  ...       -10.903787  94.880033\n",
-      "18768  1725032319737194732      797.285  ...       -10.923870  94.885033\n",
-      "\n",
-      "[18769 rows x 9 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(recording.gaze)\n",
-    "print(recording.gaze.data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us see what else we can do with the data. One consideration is that realistic devices might drop data points due to a variety of reasons. We can compare the nominal and effective sampling rates for two continuous data streams: .gaze and .imu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gaze: nominal sampling rate = 200, effective sampling rate = 197.8078038925275\n",
-      "IMU: nominal sampling rate = 110, effective sampling rate = 115.35532450871617\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Gaze: nominal sampling rate = {recording.gaze.sampling_freq_nominal}, effective sampling rate = {recording.gaze.sampling_freq_effective}')\n",
-    "print(f'IMU: nominal sampling rate = {recording.imu.sampling_freq_nominal}, effective sampling rate = {recording.imu.sampling_freq_effective}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the effective sampling rate in the Gaze recording is lower than the nominal. For imu, the effective rate is actually higher.\n",
-    "We can further explore this behaviour by plotting the distances between subsequent datapoints as a histogram. Dataframes can be operated upon with regular pandas logic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.010090909090909091, 100, 'IMU nominal sampling rate')"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Get the data points\n",
-    "gaze_times = recording.gaze.data['time [s]'].values\n",
-    "imu_times = recording.imu.data['time [s]'].values\n",
-    "\n",
-    "# Calculate the distances between subsequent data points\n",
-    "gaze_diffs = np.diff(gaze_times)\n",
-    "imu_diffs = np.diff(imu_times)\n",
-    "\n",
-    "# Plot the differences as a histogram\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "plt.hist(imu_diffs, bins=20, alpha=0.5, label='IMU')\n",
-    "plt.hist(gaze_diffs, bins=10, alpha=0.8, label='Gaze')\n",
-    "# create a logaritmic scale on the y-axis\n",
-    "plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.title('Histogram of time differences between subsequent data points')\n",
-    "plt.xlabel('Time difference [s]')\n",
-    "plt.ylabel('Count')\n",
-    "\n",
-    "# draw a vertical line at the nominal sampling rate, add text\n",
-    "plt.axvline(1/recording.gaze.sampling_freq_nominal, color='k', linestyle='dashed', linewidth=2)\n",
-    "plt.text(1/recording.gaze.sampling_freq_nominal, 1, 'Gaze nominal sampling rate', rotation=90, verticalalignment='bottom', horizontalalignment='right')\n",
-    "plt.axvline(1/recording.imu.sampling_freq_nominal, color='k', linestyle='dashed', linewidth=2)\n",
-    "plt.text(1/recording.imu.sampling_freq_nominal+0.001, 100, 'IMU nominal sampling rate', rotation=90, verticalalignment='bottom', horizontalalignment='left')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can clearly see that the Gaze data only has a low number of erroneous higher time differences. In contrast, the time differences for imu data are distributed all over the place, so __be cautious when using it__. See also the resample_and_concat tutorial where this problem is addressed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course, we can already do preliminary analysis of data on this single recording basis. For example, we might be interested in the distribution of fixations in time and space. For this, we will read fixation data and subsequently create both a histogram for durations as well as a scatterplot for the x- and y-coordinates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'NeonRecording' object has no attribute 'fixations'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[60], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mrecording\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfixations\u001b[49m\u001b[38;5;241m.\u001b[39mdata)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#durations = recording.fixations.data['duration [s]'].values\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#xs = recording.fixations.data['start [s]'].values\u001b[39;00m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'NeonRecording' object has no attribute 'fixations'"
-     ]
-    }
-   ],
-   "source": [
-    "print(recording.fixations.data)\n",
-    "\n",
-    "#durations = recording.fixations.data['duration [s]'].values\n",
-    "#xs = recording.fixations.data['start [s]'].values"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pyneon",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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diff --git a/source/tutorials/read_recording.ipynb b/source/tutorials/read_recording.ipynb
new file mode 100644
index 0000000..d1c18c5
--- /dev/null
+++ b/source/tutorials/read_recording.ipynb
@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading a Neon dataset/recording\n",
+    "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/).\n",
+    "\n",
+    "## Reading sample data\n",
+    "We will use a sample recording produced by the NCC Lab called `OfficeWalk`. It's a project with 2 recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import numpy as np\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from pyneon import get_sample_data, NeonDataset, NeonRecording\n",
+    "\n",
+    "sample_dir = get_sample_data(\"OfficeWalk\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `OfficeWalk` data has the following structure:\n",
+    "\n",
+    "```plaintext\n",
+    "OfficeWalk\n",
+    "├── Timeseries Data\n",
+    "│   ├── walk1-e116e606\n",
+    "│   │   ├── info.json\n",
+    "│   │   ├── gaze.csv\n",
+    "│   │   └── ....\n",
+    "│   ├── walk2-93b8c234\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",
+    "```\n",
+    "\n",
+    "The `Timeseries Data` folder contains what PyNeon calls a `NeonDataset`. It contains multiple recordings, each with its own `info.json` file and data files. These recordings can either be loaded individually as `NeonRecording`s or as a wholist `NeonDataset`.\n",
+    "\n",
+    "If loading a `NeonDataset`, specify the path to the `Timeseries Data` folder to create a `NeonDataset` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NeonDataset | 2 recordings\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_dir = sample_dir / \"Timeseries Data\"\n",
+    "dataset = NeonDataset(dataset_dir)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NeonDataset has a `recordings` attribute that contains a list of `NeonRecording` objects. These `NeonRecording` objects can be accessed by their index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pyneon.recording.NeonRecording'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "first_recording = dataset[0]\n",
+    "print(type(first_recording))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, one can directly load a single `NeonRecording` by specifying the path to the recording's folder:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pyneon.recording.NeonRecording'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "recording_dir = dataset_dir / \"walk1-e116e606\"\n",
+    "recording = NeonRecording(recording_dir)\n",
+    "print(type(recording))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing data and metadata of a NeonRecording\n",
+    "An overview of basic metadata and contents of a `NeonRecording` can be obtained by printing the object. An initiated `NeonRecording` locates data files in the recording directory but does not load them until requested to be memory efficient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n",
+      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
+      "Wearer name: Qian\n",
+      "Recording start time: 2024-08-30 17:37:01.527000\n",
+      "Recording duration: 98.213 s\n",
+      "                  exist              filename                                                                                                           path\n",
+      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
+      "blinks             True            blinks.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
+      "events             True            events.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
+      "fixations          True         fixations.csv         C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
+      "gaze               True              gaze.csv              C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
+      "imu                True               imu.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
+      "labels             True            labels.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
+      "saccades           True          saccades.csv          C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
+      "world_timestamps   True  world_timestamps.csv  C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
+      "scene_video       False                  None                                                                                                           None\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(recording)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "recording._gaze size before accessing `gaze`: 16\n",
+      "recording.gaze is of type: <class 'pyneon.data.NeonGaze'>\n",
+      "recording._gaze size after accessing `gaze`: 48\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"recording._gaze size before accessing `gaze`: {sys.getsizeof(recording._gaze)}\")\n",
+    "\n",
+    "gaze = recording.gaze\n",
+    "print(f\"recording.gaze is of type: {type(gaze)}\")\n",
+    "print(f\"recording._gaze size after accessing `gaze`: {sys.getsizeof(recording._gaze)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the timeseries data in the gaze stream as a pandas DataFrame by accessing the `data` attribute of the gaze stream. The columns of the DataFrame include `timestamp [ns]` and channel data columns. During loading, PyNeon strips the redundant `section id` and `recording id` columns and adds a more human-readable `time [s]` column to represent the time of each sample in seconds relative to the start of the data stream."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        timestamp [ns]  gaze x [px]  gaze y [px]  worn  fixation id  blink id  \\\n",
+      "0  1725032224852161732     1067.486      620.856  True            1      <NA>   \n",
+      "1  1725032224857165732     1066.920      617.117  True            1      <NA>   \n",
+      "2  1725032224862161732     1072.699      615.780  True            1      <NA>   \n",
+      "3  1725032224867161732     1067.447      617.062  True            1      <NA>   \n",
+      "4  1725032224872161732     1071.564      613.158  True            1      <NA>   \n",
+      "\n",
+      "   azimuth [deg]  elevation [deg]  time [s]  \n",
+      "0      16.213030        -0.748998  0.000000  \n",
+      "1      16.176285        -0.511733  0.005004  \n",
+      "2      16.546413        -0.426618  0.010000  \n",
+      "3      16.210049        -0.508251  0.015000  \n",
+      "4      16.473521        -0.260388  0.020000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gaze.data.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyNeon also automatically sets the column datatype to appropriate types, such as `Int64` for timestamps, `Int32` for event IDs, and `float64` for float data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestamp [ns]       Int64\n",
+      "gaze x [px]        float64\n",
+      "gaze y [px]        float64\n",
+      "worn                  bool\n",
+      "fixation id          Int32\n",
+      "blink id             Int32\n",
+      "azimuth [deg]      float64\n",
+      "elevation [deg]    float64\n",
+      "time [s]           float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gaze.data.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing data timeseries and sampling gaps\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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp [ns]', ylabel='gyro x [deg/s]'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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",
+    "saccades = 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",
+    "\n",
+    "# Visualize the 2nd saccade\n",
+    "saccade = saccades.data.iloc[1]\n",
+    "ax.axvspan(\n",
+    "    saccade[\"start timestamp [ns]\"], saccade[\"end timestamp [ns]\"], color=\"lightgray\"\n",
+    ")\n",
+    "ax.text(\n",
+    "    (saccade[\"start timestamp [ns]\"] + saccade[\"end timestamp [ns]\"]) / 2,\n",
+    "    1050,\n",
+    "    \"Saccade\",\n",
+    "    horizontalalignment=\"center\",\n",
+    ")\n",
+    "\n",
+    "# Visualize gaze x and pupil diameter left\n",
+    "sns.scatterplot(\n",
+    "    ax=ax,\n",
+    "    data=gaze.data.head(100),\n",
+    "    x=\"timestamp [ns]\",\n",
+    "    y=\"gaze x [px]\",\n",
+    "    color=gaze_color,\n",
+    ")\n",
+    "sns.scatterplot(\n",
+    "    ax=ax2,\n",
+    "    data=imu.data.head(60),\n",
+    "    x=\"timestamp [ns]\",\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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gaze: nominal sampling frequency = 200, effective sampling frequency = 197.8078038925275\n",
+      "IMU: nominal sampling frequency = 110, effective sampling frequency = 115.35532450871617\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n",
+    "    f\"effective sampling frequency = {gaze.sampling_freq_effective}\"\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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing gaze heatmap\n",
+    "Finally, we will show how to plot a heatmap of the gaze/fixation data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Scene camera y [px]')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_edges = np.linspace(0, 1600, 160)\n",
+    "y_edges = np.linspace(0, 1200, 120)\n",
+    "\n",
+    "gaze_x = gaze.data[\"gaze x [px]\"]\n",
+    "gaze_y = gaze.data[\"gaze y [px]\"]\n",
+    "gaze_heatmap, _, _ = np.histogram2d(gaze_x, gaze_y, bins=(x_edges, y_edges))\n",
+    "gaze_heatmap = gaussian_filter(gaze_heatmap, sigma=3)\n",
+    "\n",
+    "fixations = recording.fixations\n",
+    "fix_x = fixations.data[\"fixation x [px]\"]\n",
+    "fix_y = fixations.data[\"fixation y [px]\"]\n",
+    "\n",
+    "# Plot a heatmap and scatter plot fixations on top\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "ax.imshow(gaze_heatmap.T, cmap=\"inferno\", extent=[0, 1600, 0, 1200], origin=\"lower\")\n",
+    "ax.scatter(fix_x, fix_y, color=\"white\", s=10, alpha=0.3)\n",
+    "ax.set_aspect(\"equal\", \"box\")\n",
+    "ax.set_xlabel(\"Scene camera x [px]\")\n",
+    "ax.set_ylabel(\"Scene camera y [px]\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyneon",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/source/tutorials/resample_and_concat.ipynb b/source/tutorials/resample_and_concat.ipynb
index 893f08c..bbee9ff 100644
--- a/source/tutorials/resample_and_concat.ipynb
+++ b/source/tutorials/resample_and_concat.ipynb
@@ -16,31 +16,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jan-gabriel.hartel\\Documents\\GitHub\\PyNeon\\data\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from pathlib import Path\n",
-    "from pyneon import NeonRecording, get_sample_data\n",
-    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "from pyneon import get_sample_data, NeonRecording\n",
     "\n",
-    "# all paths are relative to the location of the notebook\n",
-    "dataset_path = get_sample_data('OfficeWalk')\n",
-    "recording_dir = dataset_path / 'Timeseries Data' /'walk1-e116e606'\n",
-    "\n",
-    "test_output_dir = dataset_path\n",
-    "test_output_dir.mkdir(exist_ok=True)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "\n",
-    "\n"
+    "recording_dir = get_sample_data(\"OfficeWalk\") / \"Timeseries Data\" / \"walk1-e116e606\""
    ]
   },
   {
@@ -52,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,6 +48,88 @@
     "raw_imu_data = recording.imu.data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For imu, the effective rate is actually higher.\n",
+    "We can further explore this behaviour by plotting the distances between subsequent datapoints as a histogram. Dataframes can be operated upon with regular pandas logic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.010090909090909091, 100, 'IMU nominal sampling rate')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the data points\n",
+    "gaze_times = raw_gaze_data[\"time [s]\"].values\n",
+    "eye_state_times = raw_eye_states_data[\"time [s]\"].values\n",
+    "imu_times = raw_imu_data[\"time [s]\"].values\n",
+    "\n",
+    "# Calculate the distances between subsequent data points\n",
+    "gaze_diffs = np.diff(gaze_times)\n",
+    "eye_state_diffs = np.diff(eye_state_times)\n",
+    "imu_diffs = np.diff(imu_times)\n",
+    "\n",
+    "# Plot the differences as a histogram\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "plt.hist(imu_diffs, bins=20, alpha=0.5, label=\"IMU\")\n",
+    "plt.hist(eye_state_diffs, bins=20, alpha=0.5, label=\"Eye states\")\n",
+    "plt.hist(gaze_diffs, bins=10, alpha=0.8, label=\"Gaze\")\n",
+    "# create a logaritmic scale on the y-axis\n",
+    "plt.yscale(\"log\")\n",
+    "plt.legend()\n",
+    "plt.title(\"Histogram of time differences between subsequent data points\")\n",
+    "plt.xlabel(\"Time difference [s]\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "\n",
+    "# draw a vertical line at the nominal sampling rate, add text\n",
+    "plt.axvline(\n",
+    "    1 / recording.gaze.sampling_freq_nominal, color=\"k\", linestyle=\"dashed\", linewidth=2\n",
+    ")\n",
+    "plt.text(\n",
+    "    1 / recording.gaze.sampling_freq_nominal,\n",
+    "    1,\n",
+    "    \"Gaze nominal sampling rate\",\n",
+    "    rotation=90,\n",
+    "    verticalalignment=\"bottom\",\n",
+    "    horizontalalignment=\"right\",\n",
+    ")\n",
+    "plt.axvline(\n",
+    "    1 / recording.imu.sampling_freq_nominal, color=\"k\", linestyle=\"dashed\", linewidth=2\n",
+    ")\n",
+    "plt.text(\n",
+    "    1 / recording.imu.sampling_freq_nominal + 0.001,\n",
+    "    100,\n",
+    "    \"IMU nominal sampling rate\",\n",
+    "    rotation=90,\n",
+    "    verticalalignment=\"bottom\",\n",
+    "    horizontalalignment=\"left\",\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -71,14 +139,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Concatenating channels:\n",
+      "Concatenating streams:\n",
       "\tGaze\n",
       "\t3D eye states\n",
       "\tIMU\n",
@@ -89,10 +157,7 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "concat_df = recording.concat_channels([\"gaze\", \"eye_states\", 'imu'], sampling_freq=100)\n",
-    "concat_df.to_csv(test_output_dir / \"concat_gaze_eye_states.csv\", index=False)"
+    "concat_df = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"], sampling_freq=100)"
    ]
   },
   {
@@ -104,22 +169,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24c38dc8c80>"
+       "<matplotlib.legend.Legend at 0x241c7cf7680>"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x200 with 1 Axes>"
       ]
@@ -129,28 +194,50 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
     "start_time = 5\n",
     "end_time = 5.2\n",
     "\n",
-    "raw_gaze_interval = raw_gaze_data[(raw_gaze_data['time [s]'] >= start_time) & (raw_gaze_data['time [s]'] <= end_time)]\n",
-    "raw_eye_states_interval = raw_eye_states_data[(raw_eye_states_data['time [s]'] > start_time) & (raw_eye_states_data['time [s]'] <= end_time)]\n",
-    "raw_imu_interval = raw_imu_data[(raw_imu_data['time [s]'] >= start_time) & (raw_imu_data['time [s]'] <= end_time)]\n",
-    "concat_interval = concat_df[(concat_df['time [s]'] >= start_time) & (concat_df['time [s]'] <= end_time)]\n",
+    "raw_gaze_interval = raw_gaze_data[\n",
+    "    (raw_gaze_data[\"time [s]\"] >= start_time) & (raw_gaze_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "raw_eye_states_interval = raw_eye_states_data[\n",
+    "    (raw_eye_states_data[\"time [s]\"] > start_time)\n",
+    "    & (raw_eye_states_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "raw_imu_interval = raw_imu_data[\n",
+    "    (raw_imu_data[\"time [s]\"] >= start_time) & (raw_imu_data[\"time [s]\"] <= end_time)\n",
+    "]\n",
+    "concat_interval = concat_df[\n",
+    "    (concat_df[\"time [s]\"] >= start_time) & (concat_df[\"time [s]\"] <= end_time)\n",
+    "]\n",
     "\n",
     "# plot all data in the same scatter plot\n",
     "plt.figure(figsize=(16, 2))\n",
-    "plt.scatter(raw_gaze_interval['time [s]'], np.zeros_like(raw_gaze_interval['time [s]'])+0.5, label='Raw gaze data', color='red')\n",
-    "plt.scatter(raw_imu_interval['time [s]'], np.zeros_like(raw_imu_interval['time [s]']), label='Raw eye states data', color='blue')\n",
-    "plt.scatter(concat_interval['time [s]'], np.zeros_like(concat_interval['time [s]'])-1, label='Concatenated data', color='green')\n",
+    "plt.scatter(\n",
+    "    raw_gaze_interval[\"time [s]\"],\n",
+    "    np.zeros_like(raw_gaze_interval[\"time [s]\"]) + 0.5,\n",
+    "    label=\"Raw gaze data\",\n",
+    "    color=\"red\",\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    raw_imu_interval[\"time [s]\"],\n",
+    "    np.zeros_like(raw_imu_interval[\"time [s]\"]),\n",
+    "    label=\"Raw eye states data\",\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "plt.scatter(\n",
+    "    concat_interval[\"time [s]\"],\n",
+    "    np.zeros_like(concat_interval[\"time [s]\"]) - 1,\n",
+    "    label=\"Concatenated data\",\n",
+    "    color=\"green\",\n",
+    ")\n",
     "# set x-ticks with higher frequency and add gridlines\n",
     "plt.xticks(np.arange(start_time, end_time, 0.01), labels=None)\n",
     "# remove x labels\n",
     "# remove y-ticks\n",
     "plt.yticks([])\n",
     "plt.grid()\n",
-    "plt.legend()\n"
+    "plt.legend()"
    ]
   },
   {
@@ -162,22 +249,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24c38495e50>"
+       "<matplotlib.legend.Legend at 0x241c7d07680>"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x300 with 1 Axes>"
       ]
@@ -191,9 +278,20 @@
     "\n",
     "\n",
     "plt.figure(figsize=(16, 3))\n",
-    "plt.scatter(raw_imu_interval['time [s]'], raw_imu_interval['acceleration x [g]'], label='Raw imu data', color='blue')\n",
-    "plt.plot(concat_interval['time [s]'], concat_interval['acceleration x [g]'], label='Interpolated imu data', color='green', linestyle='dashed')\n",
-    "plt.legend()\n"
+    "plt.scatter(\n",
+    "    raw_imu_interval[\"time [s]\"],\n",
+    "    raw_imu_interval[\"acceleration x [g]\"],\n",
+    "    label=\"Raw imu data\",\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    concat_interval[\"time [s]\"],\n",
+    "    concat_interval[\"acceleration x [g]\"],\n",
+    "    label=\"Interpolated imu data\",\n",
+    "    color=\"green\",\n",
+    "    linestyle=\"dashed\",\n",
+    ")\n",
+    "plt.legend()"
    ]
   }
  ],
@@ -213,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/tests/test_concat_channels.py b/tests/test_concat_channels.py
index fd5850e..a0f04e2 100644
--- a/tests/test_concat_channels.py
+++ b/tests/test_concat_channels.py
@@ -5,11 +5,11 @@
 from pyneon import get_sample_data
 
 # all paths are relative to the location of the notebook
-dataset_path = get_sample_data('OfficeWalk')
-recording_dir = dataset_path / 'Timeseries Data' /'walk1-e116e606'
+dataset_path = get_sample_data("OfficeWalk")
+recording_dir = dataset_path / "Timeseries Data" / "walk1-e116e606"
 
 
-test_output_dir = 'data/outputs'
+test_output_dir = "data/outputs"
 test_output_dir.mkdir(exist_ok=True)
 
 recording = NeonRecording(recording_dir)
@@ -17,7 +17,7 @@
 raw_eye_states_data = recording.eye_states.data
 raw_imu_data = recording.imu.data
 
-concat_df = recording.concat_channels(["gaze", "eye_states"])
+concat_df = recording.concat_streams(["gaze", "eye_states"])
 concat_df.to_csv(test_output_dir / "concat_gaze_eye_states.csv", index=False)
 fig, ax = plt.subplots()
 ax.plot(
@@ -35,7 +35,7 @@
 ax.legend()
 fig.savefig(test_output_dir / "concat_gaze_eye_states.png")
 
-concat_df = recording.concat_channels(["gaze", "eye_states", "IMU"], sampling_freq=1000)
+concat_df = recording.concat_streams(["gaze", "eye_states", "IMU"], sampling_freq=1000)
 concat_df.to_csv(test_output_dir / "concat_gaze_eye_states_imu.csv", index=False)
 fig, ax = plt.subplots()
 ax.plot(