Merge branch 'dev' into pr/939

neurokit2/data/database.py

@@ -1,9 +1,7 @@
 import pathlib
-import urllib.parse
+import urllib
 import zipfile
 
-import requests
-
 
 def download_from_url(url, destination_path=None):
     """**Download Files from URLs**
@@ -26,7 +24,7 @@ def download_from_url(url, destination_path=None):
     destination_path = _download_path_sanitize(url, destination_path)
 
     # Download the file
-    response = requests.get(url)
+    response = urllib.request.urlopen(url)
 
     if response.status_code == 200:
         with destination_path.open("wb") as file:

neurokit2/data/read_xdf.py

@@ -79,10 +79,16 @@ def read_xdf(filename, upsample=2, fillmissing=None):
 
         # Special treatment for some devices
         if stream["info"]["name"][0] == "Muse":
-            # Rename GYRO channels and add ACCelerometer
+            # Rename GYRO channels
             if stream["info"]["type"][0] == "GYRO":
                 dat = dat.rename(columns={"X": "GYRO_X", "Y": "GYRO_Y", "Z": "GYRO_Z"})
-                dat["ACC"] = np.sqrt(dat["GYRO_X"] ** 2 + dat["GYRO_Y"] ** 2 + dat["GYRO_Z"] ** 2)
+                # Compute movement
+                dat["GYRO"] = np.sqrt(dat["GYRO_X"] ** 2 + dat["GYRO_Y"] ** 2 + dat["GYRO_Z"] ** 2)
+
+            if stream["info"]["type"][0] == "ACC":
+                dat = dat.rename(columns={"X": "ACC_X", "Y": "ACC_Y", "Z": "ACC_Z"})
+                # Compute acceleration
+                dat["ACC"] = np.sqrt(dat["ACC_X"] ** 2 + dat["ACC_Y"] ** 2 + dat["ACC_Z"] ** 2)
 
             # Muse - PPG data has three channels: ambient, infrared, red
             if stream["info"]["type"][0] == "PPG":

neurokit2/epochs/epochs_create.py

@@ -113,6 +113,15 @@ def epochs_create(
       # Chunk into n blocks of 1 second
       epochs = nk.epochs_create(data, sampling_rate=100, epochs_end=1)
 
+    * **Example 5**: Epochs with list of starting points
+
+    .. ipython:: python
+
+      epochs = nk.epochs_create(data, events, sampling_rate=100,
+                                epochs_start=[0, -1, -1, 0],
+                                epochs_end=[1, 0, 0, 1])
+      [len(epochs[i]) for i in epochs.keys()]
+
     """
 
     # Santize data input
@@ -129,9 +138,7 @@ def epochs_create(
     if isinstance(events, int):
         events = np.linspace(0, len(data), events + 2)[1:-1]
     if isinstance(events, dict) is False:
-        events = _events_find_label(
-            {"onset": events}, event_labels=event_labels, event_conditions=event_conditions
-        )
+        events = _events_find_label({"onset": events}, event_labels=event_labels, event_conditions=event_conditions)
 
     event_onsets = list(events["onset"])
     event_labels = list(events["label"])
@@ -160,9 +167,7 @@ def epochs_create(
     length_buffer = epoch_max_duration
 
     # First createa buffer of the same dtype as data and fill with it 0s
-    buffer = pd.DataFrame(0, index=range(length_buffer), columns=data.columns).astype(
-        dtype=data.dtypes
-    )
+    buffer = pd.DataFrame(0, index=range(length_buffer), columns=data.columns).astype(dtype=data.dtypes)
     # Only then, we convert the non-integers to nans (because regular numpy's ints cannot be nan)
     buffer.select_dtypes(exclude="int64").replace({0.0: np.nan}, inplace=True)
     # Now we can combine the buffer with the data
@@ -173,7 +178,6 @@ def epochs_create(
 
     epochs = {}
     for i, label in enumerate(parameters["label"]):
-
         # Find indices
         start = parameters["onset"][i] + (parameters["start"][i] * sampling_rate)
         end = parameters["onset"][i] + (parameters["end"][i] * sampling_rate)
@@ -201,9 +205,7 @@ def epochs_create(
 
     # Sanitize dtype of individual columns
     for i in epochs:
-
         for colname, column in epochs[i].select_dtypes(include=["object"]).items():
-
             # Check whether columns are indices or label/condition
             values = column.unique().tolist()
             zero_or_one = not (False in [x in [0, 1] for x in values])

neurokit2/misc/__init__.py

@@ -5,7 +5,7 @@
 """
 
 from ._warnings import NeuroKitWarning
-from .random import check_random_state, check_random_state_children, spawn_random_state
+from .check_random_state import check_random_state, check_random_state_children, spawn_random_state
 from .check_type import check_type
 from .copyfunction import copyfunction
 from .expspace import expspace
@@ -21,6 +21,7 @@
 from .replace import replace
 from .type_converters import as_vector
 from .report import create_report
+from .fig2img import fig2img
 
 
 __all__ = [
@@ -43,4 +44,5 @@
     "check_random_state_children",
     "spawn_random_state",
     "create_report",
+    "fig2img",
 ]

neurokit2/misc/random.py → neurokit2/misc/check_random_state.py

@@ -25,6 +25,7 @@ def check_random_state(seed=None):
     rng: numpy.random.Generator or numpy.random.RandomState
         Random number generator.
     """
+
     # If seed is an integer, use the legacy RandomState class
     if isinstance(seed, numbers.Integral):
         return np.random.RandomState(seed)
@@ -77,7 +78,10 @@ def spawn_random_state(rng, n_children=1):
         if rng._bit_generator._seed_seq is not None:
             rng_class = type(rng)
             bit_generator_class = type(rng._bit_generator)
-            return [rng_class(bit_generator_class(seed=s)) for s in rng._bit_generator._seed_seq.spawn(n_children)]
+            return [
+                rng_class(bit_generator_class(seed=s))
+                for s in rng._bit_generator._seed_seq.spawn(n_children)
+            ]
     except TypeError:
         # The rng does not support spawning through SeedSequence, see below
         pass
@@ -94,7 +98,9 @@ def spawn_random_state(rng, n_children=1):
     return [np.random.RandomState(seed=s) for s in temp_rng.random_raw(n_children)]
 
 
-def check_random_state_children(random_state_parent, random_state_children, n_children=1):
+def check_random_state_children(
+    random_state_parent, random_state_children, n_children=1
+):
     """**Create new independent children random number generators to be used in sub-functions**
 
     Parameters

neurokit2/misc/fig2img.py

@@ -0,0 +1,46 @@
+import io
+
+
+def fig2img(fig):
+    """Matplotlib Figure to PIL Image
+
+    Convert a Matplotlib figure to a PIL Image
+
+    Parameters
+    ----------
+    fig : plt.figure
+        Matplotlib figure.
+
+    Returns
+    ----------
+    list
+        The rescaled values.
+
+
+    Examples
+    ----------
+    .. ipython:: python
+
+      import neurokit2 as nk
+      import matplotlib.pyplot as plt
+
+      plt.plot([1, 2, 3, 4, 5])  # Make plot
+      fig = plt.gcf()  # Get current figure
+      nk.fig2img(fig)  # Convert to PIL Image
+      plt.close(fig)  # Close figure
+
+    """
+
+    try:
+        import PIL.Image
+    except ImportError as e:
+        raise ImportError(
+            "fig2img(): the 'PIL' (Pillow) module is required for this function to run. ",
+            "Please install it first (`pip install pillow`).",
+        ) from e
+
+    buffer = io.BytesIO()
+    fig.savefig(buffer)
+    buffer.seek(0)
+    img = PIL.Image.open(buffer)
+    return img