Reordered elephant functions by their original modules

neurotic/_elephant_tools.py

@@ -43,6 +43,9 @@
 from neo import SpikeTrain
 
 
+###############################################################################
+# elephant.signal_processing
+
 def butter(signal, highpass_freq=None, lowpass_freq=None, order=4,
            filter_function='filtfilt', fs=1.0, axis=-1):
     """
@@ -164,120 +167,6 @@ def butter(signal, highpass_freq=None, lowpass_freq=None, order=4,
     else:
         return filtered_data
 
-def isi(spiketrain, axis=-1):
-    """
-    Return an array containing the inter-spike intervals of the SpikeTrain.
-
-    Accepts a Neo SpikeTrain, a Quantity array, or a plain NumPy array.
-    If either a SpikeTrain or Quantity array is provided, the return value will
-    be a quantities array, otherwise a plain NumPy array. The units of
-    the quantities array will be the same as spiketrain.
-
-    Parameters
-    ----------
-
-    spiketrain : Neo SpikeTrain or Quantity array or NumPy ndarray
-                 The spike times.
-    axis : int, optional
-           The axis along which the difference is taken.
-           Default is the last axis.
-
-    Returns
-    -------
-
-    NumPy array or quantities array.
-
-    """
-    if axis is None:
-        axis = -1
-    if isinstance(spiketrain, neo.SpikeTrain):
-        intervals = np.diff(
-            np.sort(spiketrain.times.view(pq.Quantity)), axis=axis)
-    else:
-        intervals = np.diff(np.sort(spiketrain), axis=axis)
-    return intervals
-
-def peak_detection(signal, threshold=0.0 * mV, sign='above', format=None):
-    """
-    Return the peak times for all events that cross threshold.
-    Usually used for extracting spike times from a membrane potential.
-    Similar to spike_train_generation.threshold_detection.
-
-    Parameters
-    ----------
-    signal : neo AnalogSignal object
-        'signal' is an analog signal.
-    threshold : A quantity, e.g. in mV
-        'threshold' contains a value that must be reached
-        for an event to be detected.
-    sign : 'above' or 'below'
-        'sign' determines whether to count thresholding crossings that
-        cross above or below the threshold. Default: 'above'.
-    format : None or 'raw'
-        Whether to return as SpikeTrain (None) or as a plain array
-        of times ('raw'). Default: None.
-
-    Returns
-    -------
-    result_st : neo SpikeTrain object
-        'result_st' contains the spike times of each of the events
-        (spikes) extracted from the signal.
-    """
-    assert threshold is not None, "A threshold must be provided"
-
-    if sign == 'above':
-        cutout = np.where(signal > threshold)[0]
-        peak_func = np.argmax
-    elif sign == 'below':
-        cutout = np.where(signal < threshold)[0]
-        peak_func = np.argmin
-    else:
-        raise ValueError("sign must be 'above' or 'below'")
-
-    if len(cutout) <= 0:
-        events_base = np.zeros(0)
-    else:
-        # Select thr crossings lasting at least 2 dtps, np.diff(cutout) > 2
-        # This avoids empty slices
-        border_start = np.where(np.diff(cutout) > 1)[0]
-        border_end = border_start + 1
-        borders = np.concatenate((border_start, border_end))
-        borders = np.append(0, borders)
-        borders = np.append(borders, len(cutout)-1)
-        borders = np.sort(borders)
-        true_borders = cutout[borders]
-        right_borders = true_borders[1::2] + 1
-        true_borders = np.sort(np.append(true_borders[0::2], right_borders))
-
-        # Workaround for bug that occurs when signal goes below thr for 1 dtp,
-        # Workaround eliminates empy slices from np. split
-        backward_mask = np.absolute(np.ediff1d(true_borders, to_begin=1)) > 0
-        forward_mask = np.absolute(np.ediff1d(true_borders[::-1],
-                                              to_begin=1)[::-1]) > 0
-        true_borders = true_borders[backward_mask * forward_mask]
-        split_signal = np.split(np.array(signal), true_borders)[1::2]
-
-        maxima_idc_split = np.array([peak_func(x) for x in split_signal])
-
-        max_idc = maxima_idc_split + true_borders[0::2]
-
-        events = signal.times[max_idc]
-        events_base = events.magnitude
-
-        if events_base is None:
-            # This occurs in some Python 3 builds due to some
-            # bug in quantities.
-            events_base = np.array([event.magnitude for event in events])  # Workaround
-    if format is None:
-        result_st = SpikeTrain(events_base, units=signal.times.units,
-                               t_start=signal.t_start, t_stop=signal.t_stop)
-    elif 'raw':
-        result_st = events_base
-    else:
-        raise ValueError("Format argument must be None or 'raw'")
-
-    return result_st
-
 def rauc(signal, baseline=None, bin_duration=None, t_start=None, t_stop=None):
     '''
     Calculate the rectified area under the curve (RAUC) for an AnalogSignal.
@@ -395,3 +284,123 @@ def rauc(signal, baseline=None, bin_duration=None, t_start=None, t_stop=None):
             t_start=signal.t_start.rescale(bin_duration.units)+bin_duration/2,
             sampling_period=bin_duration)
         return rauc_sig
+
+###############################################################################
+# elephant.spike_train_generation
+
+def peak_detection(signal, threshold=0.0 * mV, sign='above', format=None):
+    """
+    Return the peak times for all events that cross threshold.
+    Usually used for extracting spike times from a membrane potential.
+    Similar to spike_train_generation.threshold_detection.
+
+    Parameters
+    ----------
+    signal : neo AnalogSignal object
+        'signal' is an analog signal.
+    threshold : A quantity, e.g. in mV
+        'threshold' contains a value that must be reached
+        for an event to be detected.
+    sign : 'above' or 'below'
+        'sign' determines whether to count thresholding crossings that
+        cross above or below the threshold. Default: 'above'.
+    format : None or 'raw'
+        Whether to return as SpikeTrain (None) or as a plain array
+        of times ('raw'). Default: None.
+
+    Returns
+    -------
+    result_st : neo SpikeTrain object
+        'result_st' contains the spike times of each of the events
+        (spikes) extracted from the signal.
+    """
+    assert threshold is not None, "A threshold must be provided"
+
+    if sign == 'above':
+        cutout = np.where(signal > threshold)[0]
+        peak_func = np.argmax
+    elif sign == 'below':
+        cutout = np.where(signal < threshold)[0]
+        peak_func = np.argmin
+    else:
+        raise ValueError("sign must be 'above' or 'below'")
+
+    if len(cutout) <= 0:
+        events_base = np.zeros(0)
+    else:
+        # Select thr crossings lasting at least 2 dtps, np.diff(cutout) > 2
+        # This avoids empty slices
+        border_start = np.where(np.diff(cutout) > 1)[0]
+        border_end = border_start + 1
+        borders = np.concatenate((border_start, border_end))
+        borders = np.append(0, borders)
+        borders = np.append(borders, len(cutout)-1)
+        borders = np.sort(borders)
+        true_borders = cutout[borders]
+        right_borders = true_borders[1::2] + 1
+        true_borders = np.sort(np.append(true_borders[0::2], right_borders))
+
+        # Workaround for bug that occurs when signal goes below thr for 1 dtp,
+        # Workaround eliminates empy slices from np. split
+        backward_mask = np.absolute(np.ediff1d(true_borders, to_begin=1)) > 0
+        forward_mask = np.absolute(np.ediff1d(true_borders[::-1],
+                                              to_begin=1)[::-1]) > 0
+        true_borders = true_borders[backward_mask * forward_mask]
+        split_signal = np.split(np.array(signal), true_borders)[1::2]
+
+        maxima_idc_split = np.array([peak_func(x) for x in split_signal])
+
+        max_idc = maxima_idc_split + true_borders[0::2]
+
+        events = signal.times[max_idc]
+        events_base = events.magnitude
+
+        if events_base is None:
+            # This occurs in some Python 3 builds due to some
+            # bug in quantities.
+            events_base = np.array([event.magnitude for event in events])  # Workaround
+    if format is None:
+        result_st = SpikeTrain(events_base, units=signal.times.units,
+                               t_start=signal.t_start, t_stop=signal.t_stop)
+    elif 'raw':
+        result_st = events_base
+    else:
+        raise ValueError("Format argument must be None or 'raw'")
+
+    return result_st
+
+###############################################################################
+# elephant.statistics
+
+def isi(spiketrain, axis=-1):
+    """
+    Return an array containing the inter-spike intervals of the SpikeTrain.
+
+    Accepts a Neo SpikeTrain, a Quantity array, or a plain NumPy array.
+    If either a SpikeTrain or Quantity array is provided, the return value will
+    be a quantities array, otherwise a plain NumPy array. The units of
+    the quantities array will be the same as spiketrain.
+
+    Parameters
+    ----------
+
+    spiketrain : Neo SpikeTrain or Quantity array or NumPy ndarray
+                 The spike times.
+    axis : int, optional
+           The axis along which the difference is taken.
+           Default is the last axis.
+
+    Returns
+    -------
+
+    NumPy array or quantities array.
+
+    """
+    if axis is None:
+        axis = -1
+    if isinstance(spiketrain, neo.SpikeTrain):
+        intervals = np.diff(
+            np.sort(spiketrain.times.view(pq.Quantity)), axis=axis)
+    else:
+        intervals = np.diff(np.sort(spiketrain), axis=axis)
+    return intervals