Merge branch 'NeuralEnsemble:master' into assert-errors

Author: zm711

.gitignore

@@ -76,4 +76,8 @@ doc/*.plx
 doc/*.nev
 doc/*.ns5
 doc/*.nix
-doc/*.nwb
+doc/*.nwb
+*.plx
+*.smr
+B95.zip
+grouped_ephys

doc/source/conf.py

@@ -252,6 +252,7 @@
 )
 
 sphinx_gallery_conf = {
+    # 'only_warn_on_example_error': True, # helps with debugging broken examples
     "examples_dirs": "../../examples",  # path to your example scripts
     "gallery_dirs": "examples",  # path to where to save gallery generated output
 }

doc/source/contributing.rst

@@ -208,6 +208,28 @@ The first time this is run, all of the Neo test files will be downloaded to your
 so the run time can be an hour or more.
 For subsequent runs, the files are already there, so the tests will run much faster.
 
+Because Neo downloads datasets this can lead to issues in the course of offline development or
+for packaging Neo (e.g. for a Linux distribution). In order to not download datasets and to skip
+tests which require downloaded datasets the environment variable :code:`NEO_TESTS_NO_NETWORK` can
+be set to any truthy value (e.g. :code:`'True'``).
+
+For macOS/Linux this can be done by doing:
+
+.. code-block:: bash
+
+    NEO_TESTS_NO_NETWORK='True' pytest .
+
+For Windows this can be done by doing:
+
+.. code-block:: bat
+
+    set NEO_TESTS_NO_NETWORK=true
+
+    pytest . 
+
+This can also be done with a conda environment variable if developing in a conda env. To configure these
+see the docs at `conda env vars documentation`_.
+
 It is often helpful to run only parts of the test suite. To test only the :mod:`neo.core` module,
 which is much quicker than testing :mod:`neo.io`, run::
 
@@ -465,4 +487,5 @@ Making a release
 .. _PyPI: https://pypi.org/project/neo
 .. _`continuous integration server`: https://github.com/NeuralEnsemble/python-neo/actions
 .. _`Read the Docs`: https://neo.readthedocs.io/en/latest/
-.. _`docs configuration page`: https://readthedocs.org/projects/neo/
+.. _`docs configuration page`: https://readthedocs.org/projects/neo/
+.. _`conda env vars documentation`: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment

examples/generated_data.py

@@ -29,3 +29,5 @@
 plt.plot(signal.times, signal)
 plt.xlabel(signal.sampling_period.dimensionality)
 plt.ylabel(signal.dimensionality)
+
+plt.show()

examples/igorio.py examples/plot_igorio.pyexamples/igorio.py renamed to examples/plot_igorio.py

@@ -24,9 +24,9 @@
 image_seq = ImageSequence(l, sampling_rate=500 * pq.Hz, spatial_scale="m", units="V")
 
 result = image_seq.signal_from_region(
-    CircularRegionOfInterest(50, 50, 25),
-    CircularRegionOfInterest(10, 10, 5),
-    PolygonRegionOfInterest((50, 25), (50, 45), (14, 65), (90, 80)),
+    CircularRegionOfInterest(image_seq,50, 50, 25),
+    CircularRegionOfInterest(image_seq, 10, 10, 5),
+    PolygonRegionOfInterest(image_seq,(50, 25), (50, 45), (14, 65), (90, 80)),
 )
 
 for i in range(len(result)):

examples/imageseq.py examples/plot_imageseq.pyexamples/imageseq.py renamed to examples/plot_imageseq.py

@@ -0,0 +1,95 @@
+"""
+Example for usecases.rst
+"""
+
+from itertools import cycle
+import numpy as np
+from quantities import ms, mV, kHz
+import matplotlib.pyplot as plt
+from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal
+
+store_signals = False
+
+block = Block(name="probe data", tetrode_ids=["Tetrode #1", "Tetrode #2"])
+block.segments = [
+    Segment(name="trial #1", index=0),
+    Segment(name="trial #2", index=1),
+    Segment(name="trial #3", index=2),
+]
+
+n_units = {"Tetrode #1": 2, "Tetrode #2": 5}
+
+# Create a group for each neuron, annotate each group with the tetrode from which it was recorded
+groups = []
+counter = 0
+for tetrode_id, n in n_units.items():
+    groups.extend([Group(name=f"neuron #{counter + i + 1}", tetrode_id=tetrode_id) for i in range(n)])
+    counter += n
+block.groups.extend(groups)
+
+iter_group = cycle(groups)
+
+# Create dummy data, one segment at a time
+for segment in block.segments:
+
+    # create two 4-channel AnalogSignals with dummy data
+    signals = {
+        "Tetrode #1": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #1"),
+        "Tetrode #2": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #2"),
+    }
+    if store_signals:
+        segment.analogsignals.extend(signals.values())
+
+    # create spike trains with dummy data
+    # we will pretend the spikes have been extracted from the dummy signal
+    for tetrode_id in ("Tetrode #1", "Tetrode #2"):
+        for i in range(n_units[tetrode_id]):
+            spiketrain = SpikeTrain(np.random.uniform(0, 100, size=30) * ms, t_stop=100 * ms)
+            # assign each spiketrain to the appropriate segment
+            segment.spiketrains.append(spiketrain)
+            # assign each spiketrain to a given neuron
+            current_group = next(iter_group)
+            current_group.add(spiketrain)
+            if store_signals:
+                # add to the group a reference to the signal from which the spikes were obtained
+                # this does not give a 1:1 correspondance between spike trains and signals,
+                # for that we could use additional groups (and have groups of groups)
+                current_group.add(signals[tetrode_id])
+
+
+# Now plot the data
+
+# .. by trial
+plt.figure()
+for seg in block.segments:
+    print(f"Analyzing segment {seg.index}")
+    stlist = [st - st.t_start for st in seg.spiketrains]
+    plt.subplot(len(block.segments), 1, seg.index + 1)
+    count, bins = np.histogram(stlist)
+    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
+    plt.title(f"PSTH in segment {seg.index}")
+plt.show()
+
+# ..by neuron
+
+plt.figure()
+for i, group in enumerate(block.groups):
+    stlist = [st - st.t_start for st in group.spiketrains]
+    plt.subplot(len(block.groups), 1, i + 1)
+    count, bins = np.histogram(stlist)
+    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
+    plt.title(f"PSTH of unit {group.name}")
+plt.show()
+
+# ..by tetrode
+
+plt.figure()
+for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]):
+    stlist = []
+    for unit in block.filter(objects=Group, tetrode_id=tetrode_id):
+        stlist.extend([st - st.t_start for st in unit.spiketrains])
+    plt.subplot(2, 1, i + 1)
+    count, bins = np.histogram(stlist)
+    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
+    plt.title(f"PSTH blend of tetrode {tetrode_id}")
+plt.show()