Merge branch 'NeuralEnsemble:master' into core-docstrings

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

environment_testing.yml

@@ -2,5 +2,6 @@ name: neo-test-env
 channels:
   - conda-forge
 dependencies:
+  - python=3.9
   - datalad
   - pip

examples/generated_data.py

@@ -3,17 +3,21 @@
 ===========================
 
 """
-
+###########################################################
+# Import our packages
 import os
 from urllib.request import urlretrieve
 import zipfile
 import matplotlib.pyplot as plt
 from neo.io import get_io
 
-
+#############################################################
+# Then download some data
 # Downloaded from Human Brain Project Collaboratory
 # Digital Reconstruction of Neocortical Microcircuitry (nmc-portal)
 # http://microcircuits.epfl.ch/#/animal/8ecde7d1-b2d2-11e4-b949-6003088da632
+
+
 datafile_url = "https://microcircuits.epfl.ch/data/released_data/B95.zip"
 filename_zip = "B95.zip"
 filename = "grouped_ephys/B95/B95_Ch0_IDRest_107.ibw"
@@ -23,9 +27,14 @@
 zip_ref.extract(path=".", member=filename)  # extract file to dir
 zip_ref.close()
 
-
+######################################################
+# Once we have our data we can use `get_io` to find an 
+# io (Igor in this case). Then we read the analogsignals
+# Finally we will make some nice plots
 reader = get_io(filename)
 signal = reader.read_analogsignal()
 plt.plot(signal.times, signal)
 plt.xlabel(signal.sampling_period.dimensionality)
 plt.ylabel(signal.dimensionality)
+
+plt.show()

examples/imageseq.py

@@ -0,0 +1,53 @@
+"""
+ImageSequences
+==============
+
+"""
+
+##########################################################
+# Let's import some packages
+
+from neo.core import ImageSequence
+from neo.core import RectangularRegionOfInterest, CircularRegionOfInterest, PolygonRegionOfInterest
+import matplotlib.pyplot as plt
+import quantities as pq
+
+import random
+
+
+############################################################
+# Now we need to generate some data
+# We will just make a nice box and then we can attach this 
+# ImageSequence to a variety of ROIs
+# our ImageSequence will be 50 frames of 100x100 pixel images
+
+l = []
+for frame in range(50):
+    l.append([])
+    for y in range(100):
+        l[frame].append([])
+        for x in range(100):
+            l[frame][y].append(random.randint(0, 50))
+
+#####################################################################
+# we then make our image sequence and pull out our results from the
+# image_seq
+
+image_seq = ImageSequence(l, sampling_rate=500 * pq.Hz, spatial_scale="m", units="V")
+
+result = image_seq.signal_from_region(
+    CircularRegionOfInterest(image_seq,50, 50, 25),
+    CircularRegionOfInterest(image_seq, 10, 10, 5),
+    PolygonRegionOfInterest(image_seq,(50, 25), (50, 45), (14, 65), (90, 80)),
+)
+
+###############################################################
+# It is easy to plot our results using matplotlib
+
+for i in range(len(result)):
+    plt.figure()
+    plt.plot(result[i].times, result[i])
+    plt.xlabel("seconde")
+    plt.ylabel("valeur")
+plt.tight_layout()
+plt.show()