Merge branch 'burst_analysis' of https://github.com/Gauravu2/MiV-OS i…

…nto burst_analysis

miv/statistics/burst.py

@@ -11,7 +11,6 @@ def burst(spiketrains: SpikestampsType, channel: float, min_isi: float, min_len:
     """
     Calculates parameters critical to characterize bursting phenomenon on a single channel
     Bursting is defined as the occurence of a specified number of spikes (usually >10), with a small interspike interval (usually < 100ms) [1]_, [2]_
-
     Parameters
     ----------
     spikes : SpikestampsType
@@ -22,7 +21,6 @@ def burst(spiketrains: SpikestampsType, channel: float, min_isi: float, min_len:
        Minimum Interspike Interval (in seconds) to be considered as bursting [standard = 0.1]
     min_len : float
        Minimum number of simultaneous spikes to be considered as bursting [standard = 10]
-
     Returns
     -------
     start_time: float
@@ -38,10 +36,10 @@ def burst(spiketrains: SpikestampsType, channel: float, min_isi: float, min_len:
     in cortical networks of neurons." Neurocomputing 65 (2005): 653-662.
     .. [2] Eisenman, Lawrence N., et al. "Quantification of bursting and synchrony in cultured
     hippocampal neurons." Journal of neurophysiology 114.2 (2015): 1059-1071.
-
     """
 
     spike_interval = interspike_intervals(spiketrains[channel].magnitude)
+    assert spike_interval.all() > 0, "Inter Spike Interval cannot be zero"
     burst_spike = (spike_interval <= min_isi).astype(
         np.bool_
     )  # Only spikes within specified min ISI are 1 otherwise 0 and are stored

tests/test_burst.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pytest
+from neo.core import AnalogSignal, Segment, SpikeTrain
+
+# Test set For Burst Module
+
+
+def test_burst_analysis_output():
+    from miv.statistics import burst
+
+    # Initialize the spiketrain as below
+    seg = Segment(index=1)
+    train0 = SpikeTrain(
+        times=[0.1, 1.2, 1.3, 1.4, 1.5, 1.6, 4, 5, 5.1, 5.2, 8, 9.5],
+        units="sec",
+        t_stop=10,
+    )
+    # train0 = SpikeTrain(times=[0.1,4,5,5.1,5.2,9.5], units='sec', t_stop=10)
+    seg.spiketrains.append(train0)
+
+    output = burst(seg.spiketrains, 0, 0.2, 2)
+    # The function above should return two burst events from 1.2 (duration 0.4, length 5, rate 12.5 )
+    # and 5(duration 0.2, length 3, rate 15)
+    np.testing.assert_allclose(output[0], [1.2, 5])
+    np.testing.assert_allclose(output[1], [0.4, 0.2])
+    np.testing.assert_allclose(output[2], [5, 3])
+    np.testing.assert_allclose(output[3], [12.5, 15])
+
+    seg1 = Segment(index=1)
+    train1 = SpikeTrain(
+        times=[0.1, 1.2, 1.2, 1.2, 5, 6],
+        units="sec",
+        t_stop=10,
+    )
+    seg1.spiketrains.append(train1)
+    with np.testing.assert_raises(AssertionError):
+        output = burst(seg1.spiketrains, 0, 0.2, 2)
+    # The function above should throw an error since the rate will become 1/0