Source code for pyhgf
# Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>
import pkgutil
from importlib.metadata import version
from io import BytesIO
from typing import Union
import numpy as np
import pandas as pd
__version__ = version("pyhgf")
[docs]
def load_data(dataset: str) -> Union[tuple[np.ndarray, ...], np.ndarray]:
"""Load dataset for continuous or binary HGF.
Parameters
----------
dataset : str
The type of data to load. Can be `"continous"` or `"binary"`.
Returns
-------
data : np.ndarray
The data (a 1d timeseries).
Notes
-----
The continuous time series is the standard USD-CHF conversion rates over time used
in the Matlab examples.
The binary dataset is from Iglesias et al. (2013) [#] (see the full dataset
`here <https://www.research-collection.ethz.ch/handle/20.500.11850/454711)>`_. The
binary set consist of one vector *u*, the observations, and one vector *y*, the
decisions.
References
----------
.. [#] Iglesias, S., Kasper, L., Harrison, S. J., Manka, R., Mathys, C., & Stephan,
K. E. (2021). Cholinergic and dopaminergic effects on prediction error and
uncertainty responses during sensory associative learning. In NeuroImage (Vol.
226, p. 117590). Elsevier BV. https://doi.org/10.1016/j.neuroimage.2020.117590
"""
if dataset == "continuous":
data = pd.read_csv(
BytesIO(pkgutil.get_data(__name__, "data/usdchf.txt")), # type: ignore
names=["x"],
).x.to_numpy()
elif dataset == "binary":
u = pd.read_csv(
BytesIO(
pkgutil.get_data(__name__, "data/binary_input.txt") # type: ignore
),
names=["x"],
).x.to_numpy()
y = pd.read_csv(
BytesIO(
pkgutil.get_data(__name__, "data/binary_response.txt") # type: ignore
),
names=["x"],
).x.to_numpy()
data = (u, y)
else:
raise ValueError("Invalid dataset argument. Should be 'continous' or 'binary'.")
return data
