Fix/197 198 (NeuralEnsemble#199)

* Replaced wrong rescaling with simplified and introduced magnitude instead of base

* Changed t_stop of spiketrain in test to cover the case of different units

* Cumulative fix for various Neo 0.7.x related issues (NeuralEnsemble#203)

* Change signal's `duplicate_with_new_array` to `duplicate_with_new_data`

For compatibility with Neo 0.7.0

* Fixed error by replacing deprecated pandas function

* Quick fix due to event labels being treated as array annotations

* Quick fix due to event labels being treated as array annotations

* Fixed handling of neo 0.7 array_annotations

* Fixed requirements for neo

* Removed left-over print statement

* Added a hot fix to circumtvent Neo problem in generating fake epochs

* Added fixed Neo Version 0.7.x

* Fixed failing test in spike_train_correlation by alternate numpy construct.

* Removes static dependencies on readthedocs envioronment file conflicting with readthedocs.org (NeuralEnsemble#205)

* Removed setuptools from requirements

* Removed all fixed packages from doc environment

* Further simplified new environment.yml for RTD

* Changes for compatibility release 0.6.1 (NeuralEnsemble#204)

* Changes for compatibility release 0.6.1

* Added release notes

* Add rauc and derivative to signal_processing (NeuralEnsemble#194)

* Fix typo in docstring

* Replace some deprecated aliases

* Add rauc and derivative to signal_processing

* Apply suggestions from code review

Co-Authored-By: jpgill86 <jeffrey.p.gill@gmail.com>

* Similar changes based on code review

* Ensure rauc output signal has same time units as input

* Replaced wrong rescaling with simplified and introduced magnitude instead of base

* Changed t_stop of spiketrain in test to cover the case of different units

* Removed print

elephant/spike_train_surrogates.py

@@ -111,23 +111,22 @@ def dither_spikes(spiketrain, dither, n=1, decimals=None, edges=True):
     # Main: generate the surrogates
     surr = data.reshape((1, len(data))) + 2 * dither * np.random.random_sample(
         (n, len(data))) - dither
-
     # Round the surrogate data to decimal position, if requested
     if decimals is not None:
         surr = surr.round(decimals)
 
     if edges is False:
         # Move all spikes outside [spiketrain.t_start, spiketrain.t_stop] to
         # the range's ends
-        surr = np.minimum(np.maximum(surr.base,
-            (spiketrain.t_start / spiketrain.units).base),
-            (spiketrain.t_stop / spiketrain.units).base) * spiketrain.units
+        surr = np.minimum(np.maximum(surr.simplified.magnitude,
+            spiketrain.t_start.simplified.magnitude),
+            spiketrain.t_stop.simplified.magnitude) * pq.s
     else:
         # Leave out all spikes outside [spiketrain.t_start, spiketrain.t_stop]
-        tstart, tstop = (spiketrain.t_start / spiketrain.units).base, \
-                        (spiketrain.t_stop / spiketrain.units).base
-        surr = [np.sort(s[np.all([s >= tstart, s < tstop], axis=0)]) * spiketrain.units
-                for s in surr.base]
+        tstart, tstop = spiketrain.t_start.simplified.magnitude, \
+                        spiketrain.t_stop.simplified.magnitude
+        surr = [np.sort(s[np.all([s >= tstart, s < tstop], axis=0)]) * pq.s
+                for s in surr.simplified.magnitude]
 
     # Return the surrogates as SpikeTrains
     return [neo.SpikeTrain(s,
@@ -347,15 +346,15 @@ def dither_spike_train(spiketrain, shift, n=1, decimals=None, edges=True):
     if edges is False:
         # Move all spikes outside [spiketrain.t_start, spiketrain.t_stop] to
         # the range's ends
-        surr = np.minimum(np.maximum(surr.base,
-            (spiketrain.t_start / spiketrain.units).base),
-            (spiketrain.t_stop / spiketrain.units).base) * spiketrain.units
+        surr = np.minimum(np.maximum(surr.simplified.magnitude,
+            spiketrain.t_start.simplified.magnitude),
+            spiketrain.t_stop.simplified.magnitude) * pq.s
     else:
         # Leave out all spikes outside [spiketrain.t_start, spiketrain.t_stop]
-        tstart, tstop = (spiketrain.t_start / spiketrain.units).base,\
-                        (spiketrain.t_stop / spiketrain.units).base
-        surr = [s[np.all([s >= tstart, s < tstop], axis=0)] * spiketrain.units
-                for s in surr.base]
+        tstart, tstop = spiketrain.t_start.simplified.magnitude, \
+                        spiketrain.t_stop.simplified.magnitude
+        surr = [np.sort(s[np.all([s >= tstart, s < tstop], axis=0)]) * pq.s
+                for s in surr.simplified.magnitude]
 
     # Return the surrogates as SpikeTrains
     return [neo.SpikeTrain(s, t_start=spiketrain.t_start,

elephant/test/test_spike_train_surrogates.py

@@ -19,8 +19,8 @@ class SurrogatesTestCase(unittest.TestCase):
 
     def test_dither_spikes_output_format(self):
 
-        st = neo.SpikeTrain([90, 150, 180, 350] * pq.ms, t_stop=500 * pq.ms)
-
+        st = neo.SpikeTrain([90, 150, 180, 350] * pq.ms, t_stop=5 * pq.s)
+        st.t_stop = .5 * pq.s
         nr_surr = 2
         dither = 10 * pq.ms
         surrs = surr.dither_spikes(st, dither=dither, n=nr_surr)