[Feature] Allow read_xdf to read from URLs, and some typing/documentation improvements

neurokit2/data/read_xdf.py

@@ -1,17 +1,27 @@
 # -*- coding: utf-8 -*-
+import io
+import urllib
+
 import numpy as np
 import pandas as pd
+import requests
 
 
-def read_xdf(filename, upsample=2, fillmissing=None):
+def read_xdf(
+    filename: str, upsample: float = 2.0, fillmissing: float | None = None
+) -> tuple[pd.DataFrame, dict]:
     """**Read and tidy an XDF file**
 
     Reads and tidies an XDF file with multiple streams into a Pandas DataFrame.
     The function outputs both the dataframe and the information (such as the sampling rate).
 
     Note that, as XDF can store streams with different sampling rates and different time stamps,
     **the function will resample all streams to 2 times (default) the highest sampling rate** (to
-    minimize aliasing). The final sampling rate can be found in the ``info`` dictionary.
+    minimize aliasing) and then interpolate based on an evenly spaced index. While this is generally safe, it
+    may produce unexpected results, particularly if the original stream has large gaps in its time series.
+    For more discussion, see `here <https://github.com/xdf-modules/pyxdf/pull/1>`_.
+
+    The final upsampled sampling rate can be found in the ``info`` dictionary.
 
     .. note::
 
@@ -21,11 +31,12 @@ def read_xdf(filename, upsample=2, fillmissing=None):
     Parameters
     ----------
     filename :  str
-        Path (with the extension) of an XDF file (e.g., ``"data.xdf"``).
+        Path (with the extension) or URL pointing to an XDF file (e.g., ``"data.xdf"``).
     upsample : float
-        Factor by which to upsample the data. Default is 2, which means that the data will be
+        Factor by which to upsample the data. Default is 2.0, which means that the data will be
         resampled to 2 times the highest sampling rate. You can increase that to further reduce
-        edge-distortion, especially for high frequency signals like EEG.
+        edge-distortion, especially for high frequency signals like EEG. ``1.0`` disables upsampling
+        (but not interpolation).
     fillmissing : float
         The maximum duration in seconds of missing data to fill. ``None`` (default) will
         interpolate all missing values and prevent issues with NaNs. However, it might be important
@@ -35,9 +46,9 @@ def read_xdf(filename, upsample=2, fillmissing=None):
 
     Returns
     ----------
-    df : DataFrame, dict
-        The BITalino file as a pandas dataframe if one device was read, or a dictionary
-        of pandas dataframes (one dataframe per device) if multiple devices are read.
+    df : DataFrame
+        The XDF data as a pandas dataframe. If multiple streams are read,
+        they will be merged into one dataframe.
     info : dict
         The metadata information containing the sampling rate(s).
 
@@ -53,21 +64,32 @@ def read_xdf(filename, upsample=2, fillmissing=None):
 
       # data, info = nk.read_xdf("data.xdf")
       # sampling_rate = info["sampling_rate"]
+
     """
     try:
         import pyxdf
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "The 'pyxdf' module is required for this function to run. ",
             "Please install it first (`pip install pyxdf`).",
-        )
+        ) from e
 
     # Load file
-    # TODO: would be nice to be able to stream a file from URL
+    # if filename is a URL, stream bytes from file
+    if urllib.parse.urlparse(filename).scheme != "":
+        try:
+            req = requests.get(filename, stream=True, timeout=10)
+            req.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
+
+            req.raw.decode_content = True
+            filename = io.BytesIO(req.content)
+        except requests.exceptions.RequestException as e:
+            raise IOError(f"Failed to read XDF file from URL: {filename}") from e
+
     streams, header = pyxdf.load_xdf(filename)
 
     # Get smaller time stamp to later use as offset (zero point)
-    min_ts = min([min(s["time_stamps"]) for s in streams])
+    min_ts = min(min(s["time_stamps"]) for s in streams)
 
     # Loop through all the streams and convert to dataframes
     dfs = []