Merge branch 'master' into fix_openephys_stream

h-mayorquin · web-flow · commit 19b76e3af9b1 · 2025-01-20T13:04:53.000-06:00
diff --git a/codemeta.json b/codemeta.json
@@ -4,15 +4,15 @@
   "license": "https://spdx.org/licenses/BSD-3-Clause",
   "codeRepository": "https://github.com/NeuralEnsemble/python-neo",
   "contIntegration": "https://github.com/NeuralEnsemble/python-neo/actions",
-  "dateModified": "2024-10-14",
-  "downloadUrl": "https://files.pythonhosted.org/packages/e0/0d/e973b7e8464b6f1d88022c46040f203d93c0b080af0e33702bb11873dbbb/neo-0.13.4.tar.gz",
+  "dateModified": "2025-01-20",
+  "downloadUrl": "https://files.pythonhosted.org/packages/3b/91/c3630d766b3b959f8e604d8d41580c78973ece5f1e070b13bd9755dba60b/neo-0.14.0.tar.gz",
   "issueTracker": "https://github.com/NeuralEnsemble/python-neo/issues",
   "name": "Neo",
-  "version": "0.13.4",
+  "version": "0.14.0",
   "identifier": "RRID:SCR_000634",
   "description": "Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5.\n\nThe goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.\n\nNeo is used by a number of other software tools, including SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing), PyNN (simulations), tridesclous_ (spike sorting) and ephyviewer (data visualization).\n\nNeo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo's data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion.",
   "applicationCategory": "neuroscience",
-  "releaseNotes": "https://neo.readthedocs.io/en/latest/releases/0.13.4.html",
+  "releaseNotes": "https://neo.readthedocs.io/en/latest/releases/0.14.0.html",
   "funding": "https://cordis.europa.eu/project/id/945539",
   "developmentStatus": "active",
   "referencePublication": "https://doi.org/10.3389/fninf.2014.00010",
@@ -113,6 +113,7 @@
     { "@type": "Person", "givenName": "Xin", "familyName": "Niu"},
     { "@type": "Person", "givenName": "Anthony", "familyName": "Pinto"},
     { "@type": "Person", "givenName": "Chris", "familyName": "Heydrick"},
-    {"@type": "Person", "givenName": "Nikhil", "familyName": "Chandra"}
+    {"@type": "Person", "givenName": "Nikhil", "familyName": "Chandra"},
+    {"@type": "Person", "givenName": "Luigi", "familyName": "Petrucco"}
   ]
 }
diff --git a/doc/source/authors.rst b/doc/source/authors.rst
@@ -89,6 +89,7 @@ and may not be the current affiliation of a contributor.
 * Anthony Pinto [41]
 * Xin Niu
 * Nikhil Chandra [40]
+* Luigi Petrucco [42]
 
 1. Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292 - INSERM U1028 - Universite Claude Bernard Lyon 1
 2. Unité de Neuroscience, Information et Complexité, CNRS UPR 3293, Gif-sur-Yvette, France
@@ -131,6 +132,7 @@ and may not be the current affiliation of a contributor.
 39. Massachusetts General Hospital, Department of Molecular Biology
 40. Plexon Inc.
 41. Paris Brain Institute
+42. Istituto Italiano di Tecnologia (IIT), Italy
 
 
 
diff --git a/doc/source/releases.rst b/doc/source/releases.rst
@@ -6,6 +6,7 @@ Release notes
 .. toctree::
    :maxdepth: 1
 
+   releases/0.14.0.rst
    releases/0.13.4.rst
    releases/0.13.3.rst
    releases/0.13.2.rst
diff --git a/doc/source/releases/0.14.0.rst b/doc/source/releases/0.14.0.rst
@@ -0,0 +1,40 @@
+========================
+Neo 0.14.0 release notes
+========================
+
+17 January 2025
+
+This release of Neo is now compatible with NumPy 2.0 for core and IOs (with the exception of :class:`MedIO`) as well as Python 3.13, 
+and includes IO bug fixes with an eye toward a 1.0 release.
+
+See all `pull requests`_ included in this release and the `list of closed issues`_.
+
+Updated dependencies
+--------------------
+
+Neo now has a limit of NumPy >= 1.22.4
+
+CI Improvements
+---------------
+
+To ensure compatiblility between pre- and post- NumPy 2.0 the CI was changed to test on the lowest supported Python (3.9) and
+the highest supported Python (3.13) each with NumPy 1.26 as well as NumPy 2.0 for all :code:`RawIO` and :code:`IO` tests.
+
+We also no longer use a cached conda env for testing as we see that there is no speed benefit to caching and we had some issues
+with the caches getting corrupted.
+
+Testing of additional Python-NumPy combinations for core tests were added (NumPy 2.0 and 2.1 with their respective Python versions).
+
+Bug fixes and improvements in IO modules
+----------------------------------------
+
+Bug fixes and/or improvements have been made to :class:`NeuroNexusIO`, :class:`OpenEphysBinaryIO`, :class:`MicromedIO`, :class:`IntanIO` and :class:`SpikeGLX`.
+
+Acknowledgements
+----------------
+
+Thanks to Zach McKenzie, Heberto Mayorquin, Andrew Davison, Luigi Petrucco, Alessio Buccino, and Samuel Garcia.
+
+.. _`pull requests` : https://github.com/NeuralEnsemble/python-neo/pulls?q=is%3Apr+is%3Aclosed+milestone%3A0.14.0
+
+.. _`list of closed issues` : https://github.com/NeuralEnsemble/python-neo/issues?q=is%3Aissue%20state%3Aclosed%20milestone%3A0.14.0
diff --git a/examples/plot_igorio.py b/examples/plot_igorio.py
@@ -1,6 +1,6 @@
 """
-IgorProIO Demo
-===========================
+IgorProIO Demo (BROKEN)
+=======================
 
 """
 
@@ -17,25 +17,25 @@
 # 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"
-urlretrieve(datafile_url, filename_zip)
-
-zip_ref = zipfile.ZipFile(filename_zip)  # create zipfile object
-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()
+# NOTE: this dataset is not found as the link is broken.
+
+# datafile_url = "https://microcircuits.epfl.ch/data/released_data/B95.zip"
+# filename_zip = "B95.zip"
+# filename = "grouped_ephys/B95/B95_Ch0_IDRest_107.ibw"
+# urlretrieve(datafile_url, filename_zip)
+
+# zip_ref = zipfile.ZipFile(filename_zip)  # create zipfile object
+# 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()
diff --git a/neo/core/spiketrain.py b/neo/core/spiketrain.py
@@ -199,7 +199,7 @@ def normalize_times_array(times, units=None, dtype=None, copy=None):
             "In order to facilitate the deprecation copy can be set to None but will raise an "
             "error if set to True/False since this will silently do nothing. This argument will be completely "
             "removed in Neo 0.15.0. Please update your code base as necessary."
-            )
+        )
 
     if dtype is None:
         if not hasattr(times, "dtype"):
diff --git a/neo/rawio/blackrockrawio.py b/neo/rawio/blackrockrawio.py
@@ -681,8 +681,7 @@ def _get_timestamp_slice(self, timestamp, seg_index, t_start, t_stop):
             if t_start is None:
                 t_start = self._seg_t_starts[seg_index]
             if t_stop is None:
-                t_stop = self._seg_t_stops[seg_index] + 1 / float(
-                    self.__nev_basic_header['timestamp_resolution'])
+                t_stop = self._seg_t_stops[seg_index] + 1 / float(self.__nev_basic_header["timestamp_resolution"])
 
         if t_start is None:
             ind_start = None
@@ -715,15 +714,16 @@ def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start,
         )
         unit_spikes = all_spikes[mask]
 
-        wf_dtype = self.__nev_params('waveform_dtypes')[channel_id]
-        wf_size = self.__nev_params('waveform_size')[channel_id]
+        wf_dtype = self.__nev_params("waveform_dtypes")[channel_id]
+        wf_size = self.__nev_params("waveform_size")[channel_id]
         wf_byte_size = np.dtype(wf_dtype).itemsize * wf_size
 
         dt1 = [
-            ('extra', 'S{}'.format(unit_spikes['waveform'].dtype.itemsize - wf_byte_size)),
-            ('ch_waveform', 'S{}'.format(wf_byte_size))]
+            ("extra", "S{}".format(unit_spikes["waveform"].dtype.itemsize - wf_byte_size)),
+            ("ch_waveform", "S{}".format(wf_byte_size)),
+        ]
 
-        waveforms = unit_spikes['waveform'].view(dt1)['ch_waveform'].flatten().view(wf_dtype)
+        waveforms = unit_spikes["waveform"].view(dt1)["ch_waveform"].flatten().view(wf_dtype)
 
         waveforms = waveforms.reshape(int(unit_spikes.size), 1, int(wf_size))
 
@@ -1365,7 +1365,9 @@ def __match_nsx_and_nev_segment_ids(self, nsx_nb):
 
                     # Show warning if spikes do not fit any segment (+- 1 sampling 'tick')
                     # Spike should belong to segment before
-                    mask_outside = (ev_ids == i) & (data["timestamp"] < int(seg["timestamp"]) - int(nsx_offset) - int(nsx_period))
+                    mask_outside = (ev_ids == i) & (
+                        data["timestamp"] < int(seg["timestamp"]) - int(nsx_offset) - int(nsx_period)
+                    )
 
                     if len(data[mask_outside]) > 0:
                         warnings.warn(f"Spikes outside any segment. Detected on segment #{i}")
@@ -1995,7 +1997,6 @@ def __get_nsx_param_variant_a(self, nsx_nb):
         else:
             units = "uV"
 
-        
         nsx_parameters = {
             "nb_data_points": int(
                 (self.__get_file_size(filename) - bytes_in_headers)
diff --git a/neo/rawio/intanrawio.py b/neo/rawio/intanrawio.py
@@ -92,7 +92,7 @@ class IntanRawIO(BaseRawIO):
       one long vector, which must be post-processed to extract individual digital channel information.
       See the intantech website for more information on performing this post-processing.
 
-    
+
     Examples
     --------
     >>> import neo.rawio
diff --git a/neo/rawio/micromedrawio.py b/neo/rawio/micromedrawio.py
@@ -52,7 +52,6 @@ def __init__(self, filename=""):
 
     def _parse_header(self):
 
-
         with open(self.filename, "rb") as fid:
             f = StructFile(fid)
 
@@ -99,7 +98,6 @@ def _parse_header(self):
                 if zname != zname2.decode("ascii").strip(" "):
                     raise NeoReadWriteError("expected the zone name to match")
 
-
             # "TRONCA" zone define segments
             zname2, pos, length = zones["TRONCA"]
             f.seek(pos)
@@ -114,7 +112,7 @@ def _parse_header(self):
                     break
                 else:
                     self.info_segments.append((seg_start, trace_offset))
-            
+
             if len(self.info_segments) == 0:
                 # one unique segment = general case
                 self.info_segments.append((0, 0))
@@ -152,8 +150,9 @@ def _parse_header(self):
                 (sampling_rate,) = f.read_f("H")
                 sampling_rate *= Rate_Min
                 chan_id = str(c)
-                signal_channels.append((chan_name, chan_id, sampling_rate, sig_dtype, units, gain, offset, stream_id, buffer_id))
-
+                signal_channels.append(
+                    (chan_name, chan_id, sampling_rate, sig_dtype, units, gain, offset, stream_id, buffer_id)
+                )
 
             signal_channels = np.array(signal_channels, dtype=_signal_channel_dtype)
 
@@ -166,31 +165,31 @@ def _parse_header(self):
             self._sampling_rate = float(np.unique(signal_channels["sampling_rate"])[0])
 
             # memmap traces buffer
-            full_signal_shape = get_memmap_shape(self.filename, sig_dtype, num_channels=Num_Chan, offset=Data_Start_Offset)
+            full_signal_shape = get_memmap_shape(
+                self.filename, sig_dtype, num_channels=Num_Chan, offset=Data_Start_Offset
+            )
             seg_limits = [trace_offset for seg_start, trace_offset in self.info_segments] + [full_signal_shape[0]]
             self._t_starts = []
-            self._buffer_descriptions = {0 :{}}
+            self._buffer_descriptions = {0: {}}
             for seg_index in range(nb_segment):
                 seg_start, trace_offset = self.info_segments[seg_index]
                 self._t_starts.append(seg_start / self._sampling_rate)
 
                 start = seg_limits[seg_index]
                 stop = seg_limits[seg_index + 1]
-                
+
                 shape = (stop - start, Num_Chan)
-                file_offset = Data_Start_Offset + ( start * np.dtype(sig_dtype).itemsize * Num_Chan)
+                file_offset = Data_Start_Offset + (start * np.dtype(sig_dtype).itemsize * Num_Chan)
                 self._buffer_descriptions[0][seg_index] = {}
                 self._buffer_descriptions[0][seg_index][buffer_id] = {
-                    "type" : "raw",
-                    "file_path" : str(self.filename),
-                    "dtype" : sig_dtype,
+                    "type": "raw",
+                    "file_path": str(self.filename),
+                    "dtype": sig_dtype,
                     "order": "C",
-                    "file_offset" : file_offset,
-                    "shape" : shape,
+                    "file_offset": file_offset,
+                    "shape": shape,
                 }
 
-
-
             # Event channels
             event_channels = []
             event_channels.append(("Trigger", "", "event"))
@@ -217,14 +216,9 @@ def _parse_header(self):
                 for seg_index in range(nb_segment):
                     left_lim = seg_limits[seg_index]
                     right_lim = seg_limits[seg_index + 1]
-                    keep = (
-                        (rawevent["start"] >= left_lim)
-                        & (rawevent["start"] < right_lim)
-                        & (rawevent["start"] != 0)
-                    )
+                    keep = (rawevent["start"] >= left_lim) & (rawevent["start"] < right_lim) & (rawevent["start"] != 0)
                     self._raw_events[-1].append(rawevent[keep])
 
-
             # No spikes
             spike_channels = []
             spike_channels = np.array(spike_channels, dtype=_spike_channel_dtype)
diff --git a/neo/rawio/neuronexusrawio.py b/neo/rawio/neuronexusrawio.py
@@ -73,7 +73,7 @@ def __init__(self, filename: str | Path = ""):
             * The *.xdat.json metadata file
             * The *_data.xdat binary file of all raw data
             * The *_timestamps.xdat binary file of the timestamp data
-            
+
         From the metadata the other two files are located within the same directory
         and loaded.
 
diff --git a/neo/rawio/spikeglxrawio.py b/neo/rawio/spikeglxrawio.py
@@ -85,7 +85,7 @@ class SpikeGLXRawIO(BaseRawWithBufferApiIO):
     * This IO reads the entire folder and subfolders locating the `.bin` and `.meta` files
     * Handles gates and triggers as segments (based on the `_gt0`, `_gt1`, `_t0` , `_t1` in filenames)
     * Handles all signals coming from different acquisition cards ("imec0", "imec1", etc) in a typical
-        PXIe chassis setup and also external signal like "nidq". 
+        PXIe chassis setup and also external signal like "nidq".
     * For imec devices both "ap" and "lf" are extracted so even a one device setup will have several "streams"
 
     Examples
@@ -227,22 +227,19 @@ def _parse_header(self):
 
         self._t_starts = {stream_name: {} for stream_name in stream_names}
         self._t_stops = {seg_index: 0.0 for seg_index in range(nb_segment)}
-        
+
         for stream_name in stream_names:
             for seg_index in range(nb_segment):
                 info = self.signals_info_dict[seg_index, stream_name]
 
                 frame_start = float(info["meta"]["firstSample"])
                 sampling_frequency = info["sampling_rate"]
                 t_start = frame_start / sampling_frequency
-                                
-                self._t_starts[stream_name][seg_index] = t_start 
+
+                self._t_starts[stream_name][seg_index] = t_start
                 t_stop = info["sample_length"] / info["sampling_rate"]
                 self._t_stops[seg_index] = max(self._t_stops[seg_index], t_stop)
 
-                
-
-
         # fille into header dict
         self.header = {}
         self.header["nb_block"] = 1
@@ -361,24 +358,23 @@ def scan_files(dirname):
         raise FileNotFoundError(f"No appropriate combination of .meta and .bin files were detected in {dirname}")
 
     # This sets non-integers values before integers
-    normalize = lambda x: x if isinstance(x, int) else -1  
+    normalize = lambda x: x if isinstance(x, int) else -1
 
     # Segment index is determined by the gate_num and trigger_num in that order
     def get_segment_tuple(info):
         # Create a key from the normalized gate_num and trigger_num
         gate_num = normalize(info.get("gate_num"))
         trigger_num = normalize(info.get("trigger_num"))
         return (gate_num, trigger_num)
-    
+
     unique_segment_tuples = {get_segment_tuple(info) for info in info_list}
     sorted_keys = sorted(unique_segment_tuples)
 
     # Map each unique key to a corresponding index
     segment_tuple_to_segment_index = {key: idx for idx, key in enumerate(sorted_keys)}
 
     for info in info_list:
-        info["seg_index"] = segment_tuple_to_segment_index[get_segment_tuple(info)]    
-    
+        info["seg_index"] = segment_tuple_to_segment_index[get_segment_tuple(info)]
 
     # Probe index calculation
     # The calculation is ordered by slot, port, dock in that order, this is the number that appears in the filename
@@ -409,7 +405,7 @@ def get_probe_tuple(info):
         stream_name = f"{device_kind}{device_index}{stream_kind}"
 
         info["stream_name"] = stream_name
-        
+
     return info_list
 
 
diff --git a/neo/test/rawiotest/test_micromedrawio.py b/neo/test/rawiotest/test_micromedrawio.py
diff --git a/neo/test/rawiotest/test_spikeglxrawio.py b/neo/test/rawiotest/test_spikeglxrawio.py
diff --git a/pyproject.toml b/pyproject.toml
