# -*- coding: utf-8 -*-
import json
import os
import pickle
import urllib
import pandas as pd
from sklearn import datasets as sklearn_datasets
[docs]
def data(dataset="bio_eventrelated_100hz"):
"""**NeuroKit Datasets**
NeuroKit includes datasets that can be used for testing. These datasets are not downloaded
automatically with the package (to avoid increasing its weight), but can be downloaded via the
``nk.data()`` function (note that an internet connection is necessary). See the examples below.
**Signals**: The following signals (that will return an array) are available:
* **ecg_1000hz**: Returns a vector containing ECG signal (``sampling_rate=1000``).
* **ecg_3000hz**: Returns a vector containing ECG signal (``sampling_rate=3000``).
* **rsp_1000hz**: Returns a vector containing RSP signal (``sampling_rate=1000``).
* **eeg_150hz**: Returns a vector containing EEG signal (``sampling_rate=150``).
* **eog_100hz**: Returns a vector containing vEOG signal (``sampling_rate=100``).
**DataFrames**: The following datasets (that will return a ``pd.DataFrame``) are available:
* **iris**: Convenient access to the Iris dataset in a DataFrame, exactly how it is in R.
* **eogs_200hz**: Returns a DataFrame with ``hEOG``, ``vEOG``.
* Single subject
* Visual and horizontal electrooculagraphy
* ``sampling_rate=200``
* **bio_resting_5min_100hz**: Returns a DataFrame with ``ECG``, ``PPG``, ``RSP``.
* Single subject
* Resting-state of 5 min (pre-cropped, with some ECG noise towards the end)
* ``sampling_rate=100``
* **bio_resting_8min_100hz**: Returns a DataFrame with ``ECG``, ``RSP``, ``EDA``,
``PhotoSensor``.
* Single subject
* Resting-state of 8 min when the photosensor is low (need to crop the data)
* ``sampling_rate=100``
* **bio_resting_8min_200hz**: Returns a dictionary with four subjects (``S01``, ``S02``,
``S03``, ``S04``).
* Resting-state recordings
* 8 min (``sampling_rate=200``)
* Each subject is DataFrame with ``ECG``, ``RSP`, ``PhotoSensor``, ``Participant``
* **bio_eventrelated_100hz**: Returns a DataFrame with ``ECG``, ``EDA``, ``Photosensor``,
``RSP``.
* Single subject
* Event-related recording of a participant watching 4 images for 3 seconds (the condition
order was: ``["Negative", "Neutral", "Neutral", "Negative"]``)
* ``sampling_rate=100``
* **eeg_1min_200hz**: Returns an MNE raw object containing 1 min of EEG
data (from the MNE-sample dataset).
Parameters
----------
dataset : str
The name of the dataset.
Returns
-------
DataFrame
The data.
Examples
---------
**Single signals and vectors**
.. ipython:: python
import neurokit2 as nk
ecg = nk.data(dataset="ecg_1000hz")
@savefig p_datasets1.png scale=100%
nk.signal_plot(ecg[0:10000], sampling_rate=1000)
@suppress
plt.close()
.. ipython:: python
rsp = nk.data(dataset="rsp_1000hz")
@savefig p_datasets2.png scale=100%
nk.signal_plot(rsp[0:20000], sampling_rate=1000)
@suppress
plt.close()
.. ipython:: python
eeg = nk.data("eeg_150hz")
@savefig p_data3.png scale=100%
nk.signal_plot(eeg, sampling_rate=150)
@suppress
plt.close()
.. ipython:: python
eog = nk.data("eog_100hz")
@savefig p_data4.png scale=100%
nk.signal_plot(eog[0:2000], sampling_rate=100)
@suppress
plt.close()
**DataFrames**
.. ipython:: python
data = nk.data("iris")
data.head()
.. ipython:: python
data = nk.data(dataset="eogs_200hz")
@savefig p_datasets5.png scale=100%
nk.signal_plot(data[0:4000], standardize=True, sampling_rate=200)
@suppress
plt.close()
.. ipython:: python
data = nk.data(dataset="bio_resting_5min_100hz")
@savefig p_datasets6.png scale=100%
nk.standardize(data).plot()
@suppress
plt.close()
.. ipython:: python
data = nk.data(dataset="bio_resting_8min_100hz")
@savefig p_datasets7.png scale=100%
nk.standardize(data).plot()
@suppress
plt.close()
.. ipython:: python
data = nk.data("bio_resting_8min_200hz")
data.keys()
data["S01"].head()
.. ipython:: python
data = nk.data("bio_eventrelated_100hz")
@savefig p_data8.png scale=100%
nk.standardize(data).plot()
@suppress
plt.close()
.. ipython:: python
raw = nk.data("eeg_1min_200hz")
@savefig p_data9.png scale=100%
nk.signal_plot(raw.get_data()[0:3, 0:2000], sampling_rate=200)
@suppress
plt.close()
"""
# TODO: one could further improve this function with like
# selectors 'ecg=True, eda=True, restingstate=True' that would
# find the most appropriate dataset
dataset = dataset.lower()
path = "https://raw.githubusercontent.com/neuropsychology/NeuroKit/master/data/"
# Signals as vectors =======================
if dataset in ["eeg", "eeg_150hz", "eeg.txt"]:
return pd.read_csv(path + "eeg.txt").values[:, 0]
if dataset in ["rsp", "rsp_1000hz", "rsp_1000hz.txt"]:
return pd.read_csv(path + "rsp_1000hz.txt", header=None).values[:, 0]
if dataset in ["ecg", "ecg_1000hz", "ecg_1000hz.csv"]:
return pd.read_csv(path + "ecg_1000hz.csv")["ECG"].values
if dataset in ["ecg_3000hz", "ecg_3000hz.csv"]:
return pd.read_csv(path + "ecg_1000hz.csv")["ECG"].values
if dataset in ["eog", "veog", "eog_100hz", "eog_100hz.csv"]:
return pd.read_csv(path + "eog_100hz.csv")["vEOG"].values
# Dataframes ===============================
if dataset == "iris":
info = sklearn_datasets.load_iris()
data = pd.DataFrame(
info.data, columns=["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"]
)
data["Species"] = info.target_names[info.target]
return data
if dataset in ["eogs", "eogs_200hz", "eog_200hz", "eog_200hz.csv"]:
return pd.read_csv(path + "eog_200hz.csv")
# Add extension
if dataset in ["bio_resting_8min_200hz"]:
dataset += ".json"
# Specific case for json file
if dataset.endswith(".json"):
if "https" not in dataset:
data = pd.read_json(path + dataset, orient="index")
else:
data = pd.read_json(dataset, orient="index")
df = {}
for participant, row in data.iterrows():
for _, data_string in row.items():
data_list = json.loads(data_string)
data_pd = pd.DataFrame(data_list)
df[participant] = data_pd
return df
# TODO: Add more EEG (fif and edf datasets)
if dataset in ["eeg_1min_200hz"]:
return pickle.load(
urllib.request.urlopen(
"https://github.com/neuropsychology/NeuroKit/blob/dev/data/eeg_1min_200hz.pickle?raw=true"
)
)
# General case
file, ext = os.path.splitext(dataset) # pylint: disable=unused-variable
if ext == "":
df = pd.read_csv(path + dataset + ".csv")
else:
if "https" not in dataset:
df = pd.read_csv(path + dataset)
else:
df = pd.read_csv(dataset)
return df
