split test file into separate files

.gitignore

@@ -102,3 +102,4 @@ ENV/
 .mypy_cache/
 .Rhistory
 test.py
+try_stuff.py

docs/functions.rst

@@ -3,7 +3,7 @@ Functions
 
 This part of the documentation details the complete ``NeuroKit2`` for python API.
 
-.. automodule:: TruScanEEGpy.signal
+.. automodule:: neurokit2.signal
     :members:
 
 

neurokit2/__init__.py

@@ -5,3 +5,4 @@
 __version__ = '0.0.1'
 
 from .signal import *
+from .events import *

neurokit2/events/__init__.py

@@ -0,0 +1,4 @@
+"""Submodule for NeuroKit."""
+
+from .events_find import events_find
+from .plot_events_in_signal import plot_events_in_signal

neurokit2/events/events_find.py

@@ -0,0 +1,119 @@
+# -*- coding: utf-8 -*-
+import pandas as pd
+import numpy as np
+import itertools
+
+from ..signal import signal_binarize
+
+
+
+
+
+
+
+
+
+
+def _events_find(event_channel, threshold="auto", keep="above"):
+    """Internal function
+    """
+    binary = signal_binarize(np.array(event_channel), threshold=threshold)
+
+    if keep != 'above':
+        binary = np.abs(binary - 1)  # Reverse if events are below
+
+    # Initialize data
+    events = {"Onset":[], "Duration":[]}
+
+    index = 0
+    for event, group in (itertools.groupby(binary)):
+        duration = len(list(group))
+        if event == 1:
+            events["Onset"].append(index)
+            events["Duration"].append(duration)
+        index += duration
+
+    # Convert to array
+    events["Onset"] = np.array(events["Onset"])
+    events["Duration"] = np.array(events["Duration"])
+    return(events)
+
+
+
+
+
+
+
+
+
+
+
+
+
+# ==============================================================================
+# ==============================================================================
+# ==============================================================================
+# ==============================================================================
+# ==============================================================================
+# ==============================================================================
+# ==============================================================================
+# ==============================================================================
+def events_find(event_channel, threshold="auto", keep="above", start_at=0, end_at=None, duration_min=1, duration_max=None):
+    """
+    Find and select events based on a continuous signal.
+
+    Parameters
+    ----------
+    event_channel : array or list
+        The channel containing the events.
+    threshold : str or float
+        The threshold value by which to select the events. If "auto", takes the value between the max and the min.
+    keep : str
+        "above" or "below", define the events as above or under the treshold. For photosensors, a white screen corresponds usually to higher values. Therefore, if your events are signaled by a black colour, events values are the lower ones, and you should set the cut to "below".
+    start_at, end_at : int
+        Keep events which onset is after, or before a particular time point.
+    duration_min, duration_max : int
+        The minimum or maximum duration of an event to be considered as such (in time points). Useful when spurious events are created due to very high sampling rate.
+
+    Returns
+    ----------
+    dict
+        Dict containing two arrays, 'onset' for events onsets and 'duration' for events durations.
+
+    Example
+    ----------
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> import neurokit2 as nk
+    >>>
+    >>> signal = np.cos(np.linspace(start=0, stop=20, num=1000))
+    >>> nk.events_find(signal)
+    {'Onset': array([  0, 236, 550, 864]), 'Duration': array([ 79, 157, 157, 136])}
+
+    >>> nk.plot_events_in_signal(signal, events)
+    """
+    events = _events_find(event_channel, threshold="auto", keep="above")
+
+    # Warning when no events detected
+    if len(events["Onset"]) == 0:
+        print("NeuroKit warning: events_find(): No events found. Check your event_channel or adjust 'threhsold' or 'keep' arguments.")
+        return(events)
+
+    # Remove based on duration
+    to_keep = np.full(len(events["Onset"]), True)
+    to_keep[events["Duration"] < duration_min] = False
+    if duration_max is not None:
+        to_keep[events["Duration"] > duration_max] = False
+    events["Onset"] = events["Onset"][to_keep]
+    events["Duration"] = events["Duration"][to_keep]
+
+    # Remove based on index
+    events["Onset"] = events["Onset"][events["Onset"] >= start_at]
+    events["Duration"] = events["Duration"][events["Onset"] >= start_at]
+
+    if end_at is not None:
+        events["Onset"] = events["Onset"][events["Onset"] <= end_at]
+        events["Duration"] = events["Duration"][events["Onset"] <= end_at]
+
+
+    return(events)

neurokit2/events/plot_events_in_signal.py

@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+import pandas as pd
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+
+
+
+
+
+def plot_events_in_signal(signal, events, show=True, color="red", linestyle="--"):
+    """
+    Plot events in signal.
+
+    Parameters
+    ----------
+    signal : array or DataFrame
+        Signal array (can be a dataframe with many signals).
+    events_onsets : list or ndarray
+        Events onset location. If a dict is passed (e.g., from 'events_find()'), will select only the 'Onset' list.
+    show : bool
+        If True, will return a plot. If False, will return a DataFrame that can be plotted externally.
+    color, linestyle : str
+        Other arguments to pass to matplotlib plotting.
+
+    Examples
+    ----------
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> import neurokit2 as nk
+    >>>
+    >>> signal = np.cos(np.linspace(start=0, stop=20, num=1000))
+    >>> events = nk.events_find(signal)
+    >>> nk.plot_events_in_signal(signal, events)
+    """
+
+    if isinstance(events, dict):
+        events = events["Onset"]
+
+    if isinstance(signal, pd.DataFrame) is False:
+        df = pd.DataFrame({"Signal": signal})
+
+
+    # Plot if necessary
+    if show:
+        df.plot()
+        for event in events:
+            plt.axvline(event, color=color, linestyle=linestyle)
+
+    else:
+        df["Event_Onset"] = 0
+        df.iloc[events] = 1
+        return(df)
+
+
+

neurokit2/signal/__init__.py

@@ -1,4 +1,3 @@
 """Submodule for NeuroKit."""
 
-from .signal_binarize import *
-
+from .signal_binarize import signal_binarize

neurokit2/signal/signal_binarize.py

@@ -3,9 +3,7 @@
 import numpy as np
 
 
-
-
-def signal_binarize(signal, threshold="auto", cut="higher"):
+def signal_binarize(signal, threshold="auto"):
     """Binarize a continuous signal.
 
     Parameters
@@ -14,8 +12,6 @@ def signal_binarize(signal, threshold="auto", cut="higher"):
         The signal channel.
     threshold : float
         The threshold value by which to select the events. If "auto", takes the value between the max and the min.
-    cut : str
-        "higher" or "lower", define the events as above or under the threshold. For photosensors, a white screen corresponds usually to higher values. Therefore, if your events were signalled by a black colour, events values would be the lower ones, and you should set the cut to "lower".
 
     Returns
     -------
@@ -43,12 +39,8 @@ def signal_binarize(signal, threshold="auto", cut="higher"):
     if threshold == "auto":
         threshold = np.mean([np.max(signal), np.min(signal)])
 
-    if cut == "higher":
-        signal[signal > threshold] = 1
-        signal[signal <= threshold] = 0
-    else:
-        signal[signal > threshold] = 0
-        signal[signal <= threshold] = 1
+    signal[signal > threshold] = 1
+    signal[signal <= threshold] = 0
 
     if return_list:
         return(list(signal))

setup.py

@@ -21,9 +21,9 @@ def find_version():
 
 
 # Dependencies
-requirements = ['pandas', 'numpy', ]
+requirements = ['pandas', 'numpy', 'matplotlib', ]
 setup_requirements = ['pytest-runner', ]
-test_requirements = ['pytest', 'coverage', 'pandas', 'numpy', ]
+test_requirements = requirements + ['pytest', 'coverage', ]
 
 
 # Setup

tests/tests.py

@@ -10,20 +10,19 @@
     doctest.testmod()
     pytest.main()
 
-def test_foo():
-    assert 3 == 3
+from .tests_events import *
 
 # =============================================================================
 # Signal
 # =============================================================================
 
 
-    
+
 def test_signal_binarize():
 
     signal = np.cos(np.linspace(start=0, stop=20, num=1000))
     binary = nk.signal_binarize(signal)
     assert len(binary) == 1000
-    
+
     binary = nk.signal_binarize(list(signal))
     assert len(binary) == 1000

tests/tests_events.py

@@ -0,0 +1,24 @@
+import numpy as np
+import pandas as pd
+import neurokit2 as nk
+
+# =============================================================================
+# Events
+# =============================================================================
+
+
+
+def test_events_find():
+
+    signal = np.cos(np.linspace(start=0, stop=20, num=1000))
+    events = nk.events_find(signal)
+    assert list(events["Onset"]) == [0, 236, 550, 864]
+
+
+
+def test_plot_events_in_signal():
+
+    signal = np.cos(np.linspace(start=0, stop=20, num=1000))
+    events = nk.events_find(signal)
+    data = nk.plot_events_in_signal(signal, events, show=False)
+    assert len(data['Event_Onset']) == 1000