From 2f2e985bc9c190577e4d8afee9e4b7e1c11c26b3 Mon Sep 17 00:00:00 2001 From: qian-chu Date: Fri, 22 Nov 2024 11:56:29 +0100 Subject: [PATCH] tutorial update --- pyneon/stream.py | 2 +- source/tutorials/read_recording.ipynb | 336 +++++++++++++++++++------- 2 files changed, 249 insertions(+), 89 deletions(-) diff --git a/pyneon/stream.py b/pyneon/stream.py index 90eccc0..4a6be07 100644 --- a/pyneon/stream.py +++ b/pyneon/stream.py @@ -82,7 +82,7 @@ def is_uniformly_sampled(self) -> bool: return np.allclose(self.ts_diff, self.ts_diff[0]) def time_to_ts(self, time: Union[Number, np.ndarray]) -> np.ndarray: - """Convert time(s) in seconds to timestamp(s) in nanoseconds.""" + """Convert relative time(s) in seconds to closest timestamp(s) in nanoseconds.""" time = np.array([time]) return np.array([self.ts[np.absolute(self.times - t).argmin()] for t in time]) diff --git a/source/tutorials/read_recording.ipynb b/source/tutorials/read_recording.ipynb index 9568f42..2cbdeab 100644 --- a/source/tutorials/read_recording.ipynb +++ b/source/tutorials/read_recording.ipynb @@ -5,21 +5,31 @@ "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", + "In this tutorial, we will show how to load a single Neon recording downloaded from [Pupil Cloud](https://docs.pupil-labs.com/neon/pupil-cloud/) and give an overview of the data structure.\n", "\n", "## Reading sample data\n", - "We will use a sample recording produced by the NCC Lab called `OfficeWalk`. It's a project with 2 recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function. It returns a `Pathlib.Path` object to the downloaded & unzipped directory." + "We will use a sample recording produced by the NCC Lab, called `OfficeWalk`. This project (collection of recordings on Pupil Cloud) contains two recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) object pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "D:\\GitHub\\pyneon\\data\\OfficeWalk\n" + ] + } + ], "source": [ "from pyneon import get_sample_data, NeonDataset, NeonRecording\n", "\n", - "sample_dir = get_sample_data(\"OfficeWalk\")" + "# Download sample data (if not existing) and return the path\n", + "sample_dir = get_sample_data(\"OfficeWalk\")\n", + "print(sample_dir)" ] }, { @@ -28,7 +38,7 @@ "source": [ "The `OfficeWalk` data has the following structure:\n", "\n", - "```plaintext\n", + "```text\n", "OfficeWalk\n", "├── Timeseries Data\n", "│ ├── walk1-e116e606\n", @@ -46,9 +56,14 @@ "└── 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 a `NeonRecording` as a wholist `NeonDataset`.\n", - "\n", - "If loading a `NeonDataset`, specify the path to the `Timeseries Data` folder to create a `NeonDataset` object:" + "The `Timeseries Data` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load a `NeonDataset`, specify the path to the `Timeseries Data` folder:" ] }, { @@ -74,7 +89,7 @@ "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." + "NeonDataset has a `recordings` attribute containing a list of `NeonRecording` objects. You can access individual recordings by index:" ] }, { @@ -86,20 +101,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n", + "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\n" ] } ], "source": [ "first_recording = dataset[0]\n", - "print(type(first_recording))" + "print(type(first_recording))\n", + "print(first_recording.recording_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Equivalently, one can directly load a single `NeonRecording` by specifying the path to the recording's folder." + "Alternatively, you can directly load a single `NeonRecording` by specifying the recording's folder path:" ] }, { @@ -111,14 +128,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n", + "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\n" ] } ], "source": [ "recording_dir = dataset_dir / \"walk1-e116e606\"\n", "recording = NeonRecording(recording_dir)\n", - "print(type(recording))" + "print(type(recording))\n", + "print(recording.recording_dir)" ] }, { @@ -126,7 +145,7 @@ "metadata": {}, "source": [ "## 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." + "You can quickly get an overview of the metadata and contents of a `NeonRecording` by printing the object. The basic metadata (e.g., recording and wearer ID, recording start time and duration) and the path to available data will be displayed. At this point, the data is simply located from the recording's folder path, but it is not yet loaded into memory." ] }, { @@ -168,7 +187,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As seen in the output, this recording contains every file other than the scene video. This is because we downloaded the \"Timeseries Data\" instead of \"Timeseries Data + Scene Video\" from Pupil Cloud. For more information on how to process video files, see the [video tutorial](video.ipynb).\n", + "As seen in the output, this recording includes all data files except the scene video and its metadata because we downloaded only the \"Timeseries Data\" instead of \" \"Timeseries Data + Scene Video\" from Pupil Cloud. For processing video, refer to the [Neon video tutorial](video.ipynb).\n", "\n", "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data like the video, it will simply return `None` and a warning message." ] @@ -182,33 +201,61 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "None\n" + "recording.gaze is \n", + "recording.fixations is \n", + "recording.video is None\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "D:\\GitHub\\pyneon\\pyneon\\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.\n", + "D:\\GitHub\\pyneon\\pyneon\\recording.py:271: UserWarning: Scene video not loaded because not all video-related files (video, scene_camera.json, world_timestamps.csv) are found.\n", " warnings.warn(\n" ] } ], "source": [ + "# Gaze and fixation data are available\n", "gaze = recording.gaze\n", - "print(gaze)\n", + "print(f\"recording.gaze is {gaze}\")\n", + "fixations = recording.fixations\n", + "print(f\"recording.fixations is {fixations}\")\n", + "\n", + "# Video is not available\n", "video = recording.video\n", - "print(video)" + "print(f\"recording.video is {video}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can access the timeseries data in the gaze stream as a [`pandas.DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) by accessing the `data` attribute of the gaze stream. The DataFrame has the datetime of each data point as its index. The raw UTC `timestamp [ns]` is available as a column along with data from channnels like `gaze x [px]`.\n", + "PyNeon reads tabular CSV file into specialized classes (e.g., gaze.csv to `NeonGaze`) which all have a `data` attribute that holds the tabular data as a `pandas.DataFrame` [(reference)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html). Depending on the nature of the data, such classes could be of `NeonStream` or `NeonEV` super classes. `NeonStream` contains (semi)-continuous data streams, while `NeonEV` (dubbed so to avoid confusion with the `NeonEvent` subclass that holds data from `events.csv`) contains sparse event data.\n", + "\n", + "The class inheritance relationship is as follows:\n", "\n", - "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." + "```text\n", + "NeonTabular\n", + "├── NeonStream\n", + "│ ├── NeonGaze\n", + "│ ├── NeonEyeStates\n", + "│ └── NeonIMU\n", + "└── NeonEV\n", + " ├── NeonBlinks\n", + " ├── NeonSaccades\n", + " ├── NeonFixations\n", + " └── NeonEvents\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data as dataframes\n", + "\n", + "The essence of `NeonTabular` is the `data` attribute—a `pandas.DataFrame`. This is a common data structure in Python for handling tabular data. For example, you can print the first 5 rows of the gaze data by calling `gaze.data.head()`, and inspect the data type of each column by calling `gaze.data.dtypes`." ] }, { @@ -222,11 +269,11 @@ "text": [ " gaze x [px] gaze y [px] worn fixation id blink id \\\n", "timestamp [ns] \n", - "1725032224852161732 1067.486 620.856 True 1 \n", - "1725032224857165732 1066.920 617.117 True 1 \n", - "1725032224862161732 1072.699 615.780 True 1 \n", - "1725032224867161732 1067.447 617.062 True 1 \n", - "1725032224872161732 1071.564 613.158 True 1 \n", + "1725032224852161732 1067.486 620.856 1 1 \n", + "1725032224857165732 1066.920 617.117 1 1 \n", + "1725032224862161732 1072.699 615.780 1 1 \n", + "1725032224867161732 1067.447 617.062 1 1 \n", + "1725032224872161732 1071.564 613.158 1 1 \n", "\n", " azimuth [deg] elevation [deg] \n", "timestamp [ns] \n", @@ -234,43 +281,195 @@ "1725032224857165732 16.176285 -0.511733 \n", "1725032224862161732 16.546413 -0.426618 \n", "1725032224867161732 16.210049 -0.508251 \n", - "1725032224872161732 16.473521 -0.260388 \n" + "1725032224872161732 16.473521 -0.260388 \n", + "gaze x [px] float64\n", + "gaze y [px] float64\n", + "worn Int32\n", + "fixation id Int32\n", + "blink id Int32\n", + "azimuth [deg] float64\n", + "elevation [deg] float64\n", + "dtype: object\n" ] } ], "source": [ - "print(gaze.data.head())" + "print(gaze.data.head())\n", + "print(gaze.data.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fixation id end timestamp [ns] duration [ms] \\\n", + "start timestamp [ns] \n", + "1725032224852161732 1 1725032225007283732 155 \n", + "1725032225027282732 2 1725032225282527732 255 \n", + "1725032225347526732 3 1725032225617770732 270 \n", + "1725032225667907732 4 1725032225798022732 130 \n", + "1725032225833015732 5 1725032225958137732 125 \n", + "\n", + " fixation x [px] fixation y [px] azimuth [deg] \\\n", + "start timestamp [ns] \n", + "1725032224852161732 1069.932 614.843 16.369094 \n", + "1725032225027282732 906.439 538.107 5.878844 \n", + "1725032225347526732 694.843 533.982 -7.781338 \n", + "1725032225667907732 572.983 488.800 -15.679003 \n", + "1725032225833015732 601.861 491.125 -13.813521 \n", + "\n", + " elevation [deg] \n", + "start timestamp [ns] \n", + "1725032224852161732 -0.367312 \n", + "1725032225027282732 4.561914 \n", + "1725032225347526732 4.819739 \n", + "1725032225667907732 7.636408 \n", + "1725032225833015732 7.512433 \n", + "fixation id Int32\n", + "end timestamp [ns] Int64\n", + "duration [ms] Int64\n", + "fixation x [px] float64\n", + "fixation y [px] float64\n", + "azimuth [deg] float64\n", + "elevation [deg] float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "print(fixations.data.head())\n", + "print(fixations.data.dtypes)" ] }, { "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." + "PyNeon performs the following preprocessing when reading the CSV files:\n", + "1. Removes the redundant `section id` and `recording id` columns that are present in the raw CSVs.\n", + "2. Sets the `timestamp [ns]` (or `start timestamp [ns]` for most event files) column as the DataFrame index.\n", + "3. Automatically assigns appropriate data types to columns. For instance, `Int64` type is assigned to timestamps, `Int32` to event IDs (blink/fixation/saccade ID), and `float64` to float data (e.g. gaze location, pupil size)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` returns the gaze x-coordinate column." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "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", - "dtype: object\n" + "First row of gaze data:\n", + "gaze x [px] 1067.486\n", + "gaze y [px] 620.856\n", + "worn 1.0\n", + "fixation id 1.0\n", + "blink id \n", + "azimuth [deg] 16.21303\n", + "elevation [deg] -0.748998\n", + "Name: 1725032224852161732, dtype: Float64\n", + "\n", + "All gaze x values:\n", + "timestamp [ns]\n", + "1725032224852161732 1067.486\n", + "1725032224857165732 1066.920\n", + "1725032224862161732 1072.699\n", + "1725032224867161732 1067.447\n", + "1725032224872161732 1071.564\n", + " ... \n", + "1725032319717194732 800.364\n", + "1725032319722198732 799.722\n", + "1725032319727194732 799.901\n", + "1725032319732194732 796.982\n", + "1725032319737194732 797.285\n", + "Name: gaze x [px], Length: 18769, dtype: float64\n" ] } ], "source": [ - "print(gaze.data.dtypes)" + "print(f\"First row of gaze data:\\n{gaze.data.iloc[0]}\\n\")\n", + "print(f\"All gaze x values:\\n{gaze.data['gaze x [px]']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful attributes and methods for NeonStream and NeonEV\n", + "On top of analyzing `data` with `pandas.DataFrame` attributes and methods, you may also use attributes and methods of the `NeonStream` and `NeonEV` instances containing the `data` to facilitate Neon-specific data analysis. For example, `NeonStream` class has a `ts` property that allows quick access of all timestamps in the data as a `numpy.ndarray` [(reference)](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html).\n", + "\n", + "Useful as they are, UTC timestamps in nanoseconds are usually too large for human comprehension. Often we would want to simply know what is the relative time for each data point since the stream start (which is different from the recording start). In PyNeon, this is referred to as `times` and is in seconds. You can access it as a `numpy.ndarray` by calling the `times` property.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1725032224852161732 1725032224857165732 1725032224862161732 ...\n", + " 1725032319727194732 1725032319732194732 1725032319737194732]\n", + "[0.0000000e+00 5.0040000e-03 1.0000000e-02 ... 9.4875033e+01 9.4880033e+01\n", + " 9.4885033e+01]\n" + ] + } + ], + "source": [ + "print(gaze.ts)\n", + "print(gaze.times)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Timestamps (UTC, in ns) and relative time (relative to the stream start, in s) are thus the two units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes two arguments: `start` and `end`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "94.885033\n", + "9.999289\n" + ] + } + ], + "source": [ + "# Last data time of the original gaze data\n", + "print(gaze.times[-1])\n", + "\n", + "# Crop the gaze data to the first 10 seconds\n", + "gaze_cropped = gaze.crop(0, 10, by=\"time\") # Crop by time\n", + "print(gaze_cropped.times[-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many other attributes and methods available for `NeonStream` and `NeonEV` classes. For a full list, refer to the [API reference](https://ncc-brain.github.io/PyNeon/reference/data.html). We will also cover some of them in the following tutorials (e.g., [interpolation and concatenation of streams](interpolate_and_concat.ipynb))." ] }, { @@ -308,7 +507,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m 17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m saccades\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m 21\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 22\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m 23\u001b[0m (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mto_numpy() \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m 24\u001b[0m \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m 25\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 26\u001b[0m horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 27\u001b[0m )\n\u001b[0;32m 29\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n", + "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m 17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m fixations\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m 21\u001b[0m (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m 22\u001b[0m \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m 23\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 24\u001b[0m horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 25\u001b[0m )\n\u001b[0;32m 27\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1087\u001b[0m, in \u001b[0;36mAxes.axvspan\u001b[1;34m(self, xmin, xmax, ymin, ymax, **kwargs)\u001b[0m\n\u001b[0;32m 1085\u001b[0m \u001b[38;5;66;03m# Strip units away.\u001b[39;00m\n\u001b[0;32m 1086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_no_units([ymin, ymax], [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m-> 1087\u001b[0m (xmin, xmax), \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1089\u001b[0m p \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle((xmin, ymin), xmax \u001b[38;5;241m-\u001b[39m xmin, ymax \u001b[38;5;241m-\u001b[39m ymin, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1090\u001b[0m p\u001b[38;5;241m.\u001b[39mset_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_xaxis_transform(which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2585\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m 2583\u001b[0m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m 2584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2585\u001b[0m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2586\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 2587\u001b[0m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m 2588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axis.py:1756\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 1754\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m 1755\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1756\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1758\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n", @@ -337,7 +536,7 @@ "gyro_color = \"darkorange\"\n", "\n", "imu = recording.imu\n", - "saccades = recording.saccades\n", + "fixations = recording.saccades\n", "\n", "# Create a figure\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", @@ -346,7 +545,7 @@ "ax2.yaxis.label.set_color(gyro_color)\n", "\n", "# Visualize the 2nd saccade\n", - "saccade = saccades.data.iloc[1]\n", + "saccade = fixations.data.iloc[1]\n", "print(saccade)\n", "ax.axvspan(saccade.index.values, saccade[\"end timestamp [ns]\"], color=\"lightgray\")\n", "ax.text(\n", @@ -384,18 +583,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "print(\n", " f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n", @@ -417,39 +607,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\pyneon\\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "recording.plot_distribution()" ] @@ -464,7 +624,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "pyneon", "language": "python", "name": "python3" },