Merge pull request #1461 from NeuralEnsemble/black-formatting

Black formatting

Author: zm711

examples/plot_igorio.py

@@ -3,6 +3,7 @@
 ===========================
 
 """
+
 ###########################################################
 # Import our packages
 import os
@@ -28,7 +29,7 @@
 zip_ref.close()
 
 ######################################################
-# Once we have our data we can use `get_io` to find an 
+# 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)
@@ -37,4 +38,4 @@
 plt.xlabel(signal.sampling_period.dimensionality)
 plt.ylabel(signal.dimensionality)
 
-plt.show()
+plt.show()

examples/plot_imageseq.py

@@ -17,7 +17,7 @@
 
 ############################################################
 # Now we need to generate some data
-# We will just make a nice box and then we can attach this 
+# 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
 
@@ -36,9 +36,9 @@
 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, 50, 50, 25),
     CircularRegionOfInterest(image_seq, 10, 10, 5),
-    PolygonRegionOfInterest(image_seq,(50, 25), (50, 45), (14, 65), (90, 80)),
+    PolygonRegionOfInterest(image_seq, (50, 25), (50, 45), (14, 65), (90, 80)),
 )
 
 ###############################################################

examples/plot_multi_tetrode_example.py

@@ -2,6 +2,7 @@
 Analyzing and Plotting Data with Neo Structures
 ===============================================
 """
+
 ######################################################
 # First we import some packages. Since we are making simulated
 # data we will import quite a few neo features as well as use
@@ -31,7 +32,7 @@
 
 ##################################################################################
 # Neo can also have groups. Groups are structures within a block that can cross segments
-# for example we could group a neuron across trials or across probes. 
+# for example we could group a neuron across trials or across probes.
 
 # Create a group for each neuron, annotate each group with the tetrode from which it was recorded
 groups = []
@@ -86,7 +87,7 @@
 # since its data can be treated like numpy arrays
 # it is easy to use standard packages like matplotlib
 # for all your plotting needs
-# We do a classic in neuroscience and show various ways 
+# We do a classic in neuroscience and show various ways
 # to plot a PSTH (Peristimulus histogram)
 
 ###################################################

examples/plot_read_files_neo_io.py

@@ -19,7 +19,6 @@
 urllib.request.urlretrieve(distantfile, localfile)
 
 
-
 ###################################################
 # Now we can create our reader and read some data
 
@@ -31,7 +30,7 @@
 
 ######################################################
 # Once we have our blocks we can iterate through each
-# block of data and see the contents of all parts of 
+# block of data and see the contents of all parts of
 # that data
 
 # access to segments
@@ -45,7 +44,7 @@
 
 #######################################################
 # Let's look at another file type
-            
+
 # CED Spike2 files
 distantfile = url_repo + "spike2/File_spike2_1.smr"
 localfile = "./File_spike2_1.smr"

examples/plot_read_files_neo_rawio.py

@@ -4,6 +4,7 @@
 
 compare with read_files_neo_io.py
 """
+
 ###########################################################
 # First we import a RawIO from neo.rawio
 # For this example we will use PlexonRawIO
@@ -42,9 +43,9 @@
 ###############################################################
 # Read signal chunks
 # This is how we read raw data. We choose indices that we want or
-# we can use None to mean look at all channels. We also need to 
+# we can use None to mean look at all channels. We also need to
 # specify the block of data (block_index) as well as the segment
-# (seg_index). Then we give the index start and stop. Since we 
+# (seg_index). Then we give the index start and stop. Since we
 # often think in time: to go from time to index would just require
 # the sample rate (so index = time / sampling_rate)
 
@@ -82,7 +83,7 @@
 
 # Count units and spikes per unit
 nb_unit = reader.spike_channels_count()
-print(f"nb_unit: {nb_unit}\n") # nb_unit stands for number of units
+print(f"nb_unit: {nb_unit}\n")  # nb_unit stands for number of units
 print("spike_channel_index     nb_spike")
 for spike_channel_index in range(nb_unit):
     nb_spike = reader.spike_count(block_index=0, seg_index=0, spike_channel_index=spike_channel_index)
@@ -99,7 +100,7 @@
 
 #######################################################################
 # Some file formats can also give waveform information. We are lucky
-# again our file has waveform data!! We forms are a 3d dataset of 
+# again our file has waveform data!! We forms are a 3d dataset of
 # (nb_spike, nb_channel, nb_sample)
 
 # Read spike waveforms
@@ -134,7 +135,7 @@
 
 nb_event_channel = reader.event_channels_count()
 print(f"nb_event_channel: {nb_event_channel}")
-# now iterate through the channels 
+# now iterate through the channels
 for chan_index in range(nb_event_channel):
     nb_event = reader.event_count(block_index=0, seg_index=0, event_channel_index=chan_index)
     print(f"chan_index: {chan_index} nb_event: {nb_event}\n")

examples/plot_read_proxy_with_lazy_load.py

@@ -39,7 +39,7 @@
 
 ############################################################
 # as always we can look view some interesting information about the
-# metadata and structure of a file just by printing the reader and 
+# metadata and structure of a file just by printing the reader and
 # it's header
 print(reader)
 print(f"Header information: {reader.header}")
@@ -49,31 +49,33 @@
 # Now let's make a function that we want to apply to
 # look at lazy vs eager uses of the API
 
+
 def apply_my_fancy_average(sig_list):
     """basic average along triggers and then channels
     here we go back to numpy with magnitude
     to be able to use np.stack.
-    
+
     Because neo uses quantities to keep track of units
-    we can always get just the magnitude of an array 
+    we can always get just the magnitude of an array
     with `.magnitude`
     """
     sig_list = [s.magnitude for s in sig_list]
     sigs = np.stack(sig_list, axis=0)
     return np.mean(np.mean(sigs, axis=0), axis=1)
 
+
 #################################################
 # Let's set our limits for both cases. We will
-# use quantities to include time dimensions. 
+# use quantities to include time dimensions.
 
-lim_start = -20 * pq.ms # 20 milliseconds before
-lim_end = +20 * pq.ms # 20 milliseconds after
+lim_start = -20 * pq.ms  # 20 milliseconds before
+lim_end = +20 * pq.ms  # 20 milliseconds after
 
 ##################################################
 # We start with eager (where `lazy=False`.) Everything
 # is loaded into memory. We will read a segment of data.
-# This includes analog signal data and events data 
-# (final contents of a segment are dependent on the 
+# This includes analog signal data and events data
+# (final contents of a segment are dependent on the
 # underlying IO being used)
 
 
@@ -86,7 +88,7 @@ def apply_my_fancy_average(sig_list):
     anasig_chunk = anasig.time_slice(t0, t1)
     all_sig_chunks.append(anasig_chunk)
 
-# After pulling all data into memory and then iterating through triggers 
+# After pulling all data into memory and then iterating through triggers
 # we end by doing our average
 m1 = apply_my_fancy_average(all_sig_chunks)
 
@@ -111,7 +113,7 @@ def apply_my_fancy_average(sig_list):
 m2 = apply_my_fancy_average(all_sig_chunks)
 
 ##########################################################
-# We see that either way the result is the same, but 
+# We see that either way the result is the same, but
 # we do not exhaust our RAM/memory
 print(f"Eagerly loading data and averaging: {m1}")
 print(f"Lazy loading data and average {m2}")

neo/rawio/axonrawio.py

@@ -481,12 +481,12 @@ def parse_axon_soup(filename):
             # brittle. pyABF believes that looking for the \x00\x00 is more
             # robust. We find these values, replace mu->u, then split into
             # a set of strings
-            indexed_string = big_string[big_string.rfind(b'\x00\x00'):]
+            indexed_string = big_string[big_string.rfind(b"\x00\x00") :]
             # replace mu -> u for easy display
-            indexed_string = indexed_string.replace(b'\xb5', b'\x75')
+            indexed_string = indexed_string.replace(b"\xb5", b"\x75")
             # we need to remove one of the \x00 to have the indices be
             # the correct order
-            indexed_string = indexed_string.split(b'\x00')[1:]
+            indexed_string = indexed_string.split(b"\x00")[1:]
             strings = indexed_string
 
             # ADC sections

neo/rawio/spikegadgetsrawio.py

@@ -177,7 +177,9 @@ def _parse_header(self):
             chan_ind = 0
             self.is_scaleable = "spikeScalingToUv" in sconf[0].attrib
             if not self.is_scaleable:
-                self.logger.warning("Unable to read channel gain scaling (to uV) from .rec header. Data has no physical units!")
+                self.logger.warning(
+                    "Unable to read channel gain scaling (to uV) from .rec header. Data has no physical units!"
+                )
 
             for trode in sconf:
                 if "spikeScalingToUv" in trode.attrib:

neo/test/iotest/test_get_io.py

@@ -3,8 +3,10 @@
 import platform
 from neo.io import get_io, list_candidate_ios, NixIO
 import pytest
+
 try:
     import nixio
+
     HAVE_NIX = True
 except:
     HAVE_NIX = False
@@ -35,7 +37,8 @@ def test_list_candidate_ios_filename_stub():
 
         assert NixIO in ios
 
-@pytest.mark.skipif(not HAVE_NIX or platform.system()=='Windows', reason='Need nixio in order to return NixIO class')
+
+@pytest.mark.skipif(not HAVE_NIX or platform.system() == "Windows", reason="Need nixio in order to return NixIO class")
 def test_get_io_non_existant_file_writable_io():
     # use nixio for testing with writable io
     non_existant_file = Path("non_existant_file.nix")
@@ -45,4 +48,4 @@ def test_get_io_non_existant_file_writable_io():
     assert isinstance(io, NixIO)
 
     # cleanup
-    non_existant_file.unlink(missing_ok=True) # cleanup will fail on Windows so need to skip
+    non_existant_file.unlink(missing_ok=True)  # cleanup will fail on Windows so need to skip

neo/test/rawiotest/tools.py

@@ -17,6 +17,7 @@ def can_use_network():
         return False
     try:
         import datalad
+
         HAVE_DATALAD = True
     except:
         HAVE_DATALAD = False