Merge pull request #158 from pynapple-org/new_io

New io

README.md

@@ -17,10 +17,10 @@ PYthon Neural Analysis Package.
 
 pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.
 
--   Free software: GNU General Public License v3
+-   Free software: MIT License
 -   __Documentation__: <https://pynapple-org.github.io/pynapple>
 -   __Notebooks and tutorials__ : <https://pynapple-org.github.io/pynapple/notebooks/pynapple-quick-start/>
--   __Collaborative repository__: <https://github.com/pynapple-org/pynacollada>
+<!-- -   __Collaborative repository__: <https://github.com/pynapple-org/pynacollada> -->
 
 
 > **Note**

docs/HISTORY.md

@@ -8,6 +8,14 @@ Around 2016-2017, Luke Sjulson started *TSToolbox2*, still in Matlab and which i
 In 2018, Francesco started neuroseries, a Python package built on Pandas. It was quickly adopted in Adrien's lab, especially by Guillaume Viejo, a postdoc in the lab. Gradually, the majority of the lab was using it and new functions were constantly added.
 In 2021, Guillaume and other trainees in Adrien's lab decided to fork from neuroseries and started *pynapple*. The core of pynapple is largely built upon neuroseries. Some of the original changes to TSToolbox made by Luke were included in this package, especially the *time_support* property of all ts/tsd objects.
 
+0.3.5 (2023-08-08)
+------------------
+
+- NWB reader class
+- NPZ reader class
+- Folder class for navigating a dataset.
+- Cross-correlograms function can take tuple
+- New doc with mkdocs-gallery
 
 0.3.4 (2023-06-29)
 ------------------

docs/index.md

@@ -11,10 +11,10 @@ PYthon Neural Analysis Package.
 
 pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.
 
--   Free software: GNU General Public License v3
+-   Free software: MIT License
 -   __Documentation__: <https://pynapple-org.github.io/pynapple>
 -   __Notebooks and tutorials__ : <https://pynapple-org.github.io/pynapple/notebooks/pynapple-quick-start/>
--   __Collaborative repository__: <https://github.com/PeyracheLab/pynacollada>
+<!-- -   __Collaborative repository__: <https://github.com/PeyracheLab/pynacollada> -->
 
 
 > **Note**

pynapple/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.3.4"
+__version__ = "0.3.5"
 from .core import *
 from .io import *
 from .process import *

pynapple/io/interface_nwb.py

@@ -2,7 +2,7 @@
 # @Author: Guillaume Viejo
 # @Date:   2023-08-01 11:54:45
 # @Last Modified by:   gviejo
-# @Last Modified time: 2023-08-07 21:38:08
+# @Last Modified time: 2023-08-07 22:34:26
 
 """
 Pynapple class to interface with NWB files.
@@ -195,6 +195,11 @@ def _make_tsd_frame(obj):
     else:
         columns = np.arange(obj.data.shape[1])
 
+    if len(columns) >= d.shape[1]:  # Weird sometimes if background ID added
+        columns = columns[0 : obj.data.shape[1]]
+    else:
+        columns = np.arange(obj.data.shape[1])
+
     data = nap.TsdFrame(t=t, d=d, columns=columns)
 
     return data

pyproject.toml

@@ -4,15 +4,15 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pynapple"
-version = "0.3.4"
+version = "0.3.5"
 description = "PYthon Neural Analysis Package Pour Laboratoires d’Excellence"
 readme = "README.md"
 authors = [{ name = "Guillaume Viejo", email = "[email protected]" }]
 license = { file = "LICENSE" }
 classifiers = [
         "Development Status :: 3 - Alpha",
         "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+        "License :: OSI Approved :: MIT License",
         "Natural Language :: English",
         "Programming Language :: Python :: 3",
         "Programming Language :: Python :: 3.8",
@@ -24,7 +24,7 @@ dependencies = [
     "numpy>=1.17.4",
     "scipy>=1.3.2",
     "numba>=0.46.0",
-    "pynwb",
+    "pynwb>=2.0.0",
     "tabulate",
     "h5py",
     "tifffile",

setup.py

@@ -15,7 +15,7 @@
         'numba>=0.46.0',
         'numpy>=1.17.4',
         'scipy>=1.3.2',
-        'pynwb',
+        'pynwb>=2.0.0',
         'tabulate',
         'h5py',
         'tifffile',
@@ -39,7 +39,7 @@
     classifiers=[
         'Development Status :: 3 - Alpha',
         'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
+        'License :: OSI Approved :: MIT License',
         'Natural Language :: English',
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.8',
@@ -62,8 +62,8 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/pynapple-org/pynapple',
-    version='v0.3.4',
+    version='v0.3.5',
     zip_safe=False,
     long_description_content_type='text/markdown',
-    download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.3.4.tar.gz'
+    download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.3.5.tar.gz'
 )

tests/test_nwb.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 # @Author: gviejo
 # @Date:   2022-04-04 21:32:10
-# @Last Modified by:   Guillaume Viejo
-# @Last Modified time: 2023-08-03 15:46:24
+# @Last Modified by:   gviejo
+# @Last Modified time: 2023-08-07 22:57:50
 
 """Tests of nwb reading for `pynapple` package."""
 
@@ -221,55 +221,58 @@ def test_add_Ecephys():
         mock_SpikeEventSeries,
     )
 
-    nwbfile = mock_NWBFile()
-    nwbfile.add_electrode_group(mock_ElectrodeGroup())
-    nwb = nap.NWBFile(nwbfile)
-    assert len(nwb) == 0
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
 
-    name_generator_registry.clear()
-    nwbfile = mock_NWBFile()
-    nwbfile.add_acquisition(mock_ElectricalSeries())
-    nwb = nap.NWBFile(nwbfile)
-    assert len(nwb) == 1
-    assert "ElectricalSeries" in nwb.keys()
-    data = nwb["ElectricalSeries"]
-    assert isinstance(data, nap.TsdFrame)
-    obj = nwbfile.acquisition["ElectricalSeries"]
-    np.testing.assert_array_almost_equal(data.values, obj.data[:])
-    np.testing.assert_array_almost_equal(
-        data.index.values, obj.starting_time + np.arange(obj.num_samples) / obj.rate
-    )
-    np.testing.assert_array_almost_equal(data.columns.values, obj.electrodes["id"][:])
+        nwbfile = mock_NWBFile()
+        nwbfile.add_electrode_group(mock_ElectrodeGroup())
+        nwb = nap.NWBFile(nwbfile)
+        assert len(nwb) == 0
 
-    # Try ElectrialSeries without channel mapping
-    name_generator_registry.clear()
-    nwbfile = mock_NWBFile()
-    nwbfile.add_acquisition(mock_ElectricalSeries(electrodes=None))
-    nwb = nap.NWBFile(nwbfile)
-    assert len(nwb) == 1
-    assert "ElectricalSeries" in nwb.keys()
-    data = nwb["ElectricalSeries"]
-    assert isinstance(data, nap.TsdFrame)
-    obj = nwbfile.acquisition["ElectricalSeries"]
-    np.testing.assert_array_almost_equal(data.values, obj.data[:])
-    np.testing.assert_array_almost_equal(
-        data.index.values, obj.starting_time + np.arange(obj.num_samples) / obj.rate
-    )
-    np.testing.assert_array_almost_equal(data.columns.values, np.arange(obj.data.shape[1]))
+        name_generator_registry.clear()
+        nwbfile = mock_NWBFile()
+        nwbfile.add_acquisition(mock_ElectricalSeries())
+        nwb = nap.NWBFile(nwbfile)
+        assert len(nwb) == 1
+        assert "ElectricalSeries" in nwb.keys()
+        data = nwb["ElectricalSeries"]
+        assert isinstance(data, nap.TsdFrame)
+        obj = nwbfile.acquisition["ElectricalSeries"]
+        np.testing.assert_array_almost_equal(data.values, obj.data[:])
+        np.testing.assert_array_almost_equal(
+            data.index.values, obj.starting_time + np.arange(obj.num_samples) / obj.rate
+        )
+        np.testing.assert_array_almost_equal(data.columns.values, obj.electrodes["id"][:])
 
+        # Try ElectrialSeries without channel mapping
+        name_generator_registry.clear()
+        nwbfile = mock_NWBFile()
+        nwbfile.add_acquisition(mock_ElectricalSeries(electrodes=None))
+        nwb = nap.NWBFile(nwbfile)
+        assert len(nwb) == 1
+        assert "ElectricalSeries" in nwb.keys()
+        data = nwb["ElectricalSeries"]
+        assert isinstance(data, nap.TsdFrame)
+        obj = nwbfile.acquisition["ElectricalSeries"]
+        np.testing.assert_array_almost_equal(data.values, obj.data[:])
+        np.testing.assert_array_almost_equal(
+            data.index.values, obj.starting_time + np.arange(obj.num_samples) / obj.rate
+        )
+        np.testing.assert_array_almost_equal(data.columns.values, np.arange(obj.data.shape[1]))
 
-    name_generator_registry.clear()
-    nwbfile = mock_NWBFile()
-    nwbfile.add_acquisition(mock_SpikeEventSeries())
-    nwb = nap.NWBFile(nwbfile)
-    assert len(nwb) == 1
-    assert "SpikeEventSeries" in nwb.keys()
-    data = nwb["SpikeEventSeries"]
-    assert isinstance(data, nap.TsdFrame)
-    obj = nwbfile.acquisition["SpikeEventSeries"]
-    np.testing.assert_array_almost_equal(data.values, obj.data[:])
-    np.testing.assert_array_almost_equal(data.index.values, obj.timestamps[:])
-    np.testing.assert_array_almost_equal(data.columns.values, obj.electrodes["id"][:])
+
+        name_generator_registry.clear()
+        nwbfile = mock_NWBFile()
+        nwbfile.add_acquisition(mock_SpikeEventSeries())
+        nwb = nap.NWBFile(nwbfile)
+        assert len(nwb) == 1
+        assert "SpikeEventSeries" in nwb.keys()
+        data = nwb["SpikeEventSeries"]
+        assert isinstance(data, nap.TsdFrame)
+        obj = nwbfile.acquisition["SpikeEventSeries"]
+        np.testing.assert_array_almost_equal(data.values, obj.data[:])
+        np.testing.assert_array_almost_equal(data.index.values, obj.timestamps[:])
+        np.testing.assert_array_almost_equal(data.columns.values, obj.electrodes["id"][:])
 
 
 def test_add_Icephys():
@@ -283,42 +286,45 @@ def test_add_Icephys():
             mock_IZeroClampSeries,
         )
 
-        name_generator_registry.clear()
-        nwbfile = mock_NWBFile()
-        nwbfile.add_icephys_electrode(mock_IntracellularElectrode())
-        nwb = nap.NWBFile(nwbfile)
-        assert len(nwb) == 0
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
 
-        for name, Series in zip(
-            [
-                "VoltageClampStimulusSeries",
-                "VoltageClampSeries",
-                "CurrentClampSeries",
-                "CurrentClampStimulusSeries",
-                "IZeroClampSeries",
-            ],
-            [
-                mock_VoltageClampStimulusSeries,
-                mock_VoltageClampSeries,
-                mock_CurrentClampSeries,
-                mock_CurrentClampStimulusSeries,
-                mock_IZeroClampSeries,
-            ],
-        ):
             name_generator_registry.clear()
             nwbfile = mock_NWBFile()
-            nwbfile.add_acquisition(Series())
+            nwbfile.add_icephys_electrode(mock_IntracellularElectrode())
             nwb = nap.NWBFile(nwbfile)
-            assert len(nwb) == 1
-            assert name in nwb.keys()
-            data = nwb[name]
-            assert isinstance(data, nap.Tsd)
-            obj = nwbfile.acquisition[name]
-            np.testing.assert_array_almost_equal(data.values, obj.data[:])
-            np.testing.assert_array_almost_equal(
-                data.index.values,
-                obj.starting_time + np.arange(obj.num_samples) / obj.rate,
-            )
+            assert len(nwb) == 0
+
+            for name, Series in zip(
+                [
+                    "VoltageClampStimulusSeries",
+                    "VoltageClampSeries",
+                    "CurrentClampSeries",
+                    "CurrentClampStimulusSeries",
+                    "IZeroClampSeries",
+                ],
+                [
+                    mock_VoltageClampStimulusSeries,
+                    mock_VoltageClampSeries,
+                    mock_CurrentClampSeries,
+                    mock_CurrentClampStimulusSeries,
+                    mock_IZeroClampSeries,
+                ],
+            ):
+                name_generator_registry.clear()
+                nwbfile = mock_NWBFile()
+                nwbfile.add_acquisition(Series())
+                nwb = nap.NWBFile(nwbfile)
+                assert len(nwb) == 1
+                assert name in nwb.keys()
+                data = nwb[name]
+                assert isinstance(data, nap.Tsd)
+                obj = nwbfile.acquisition[name]
+                np.testing.assert_array_almost_equal(data.values, obj.data[:])
+                np.testing.assert_array_almost_equal(
+                    data.index.values,
+                    obj.starting_time + np.arange(obj.num_samples) / obj.rate,
+                )
     except:
         # Doesn't work for all versions.
         pass
@@ -363,40 +369,42 @@ def test_add_Ophys():
             mock_DfOverF,
             mock_Fluorescence,
         )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
 
-        name_generator_registry.clear()
-        nwbfile = mock_NWBFile()
-        nwbfile.add_imaging_plane(mock_ImagingPlane())
-        nwb = nap.NWBFile(nwbfile)
-        assert len(nwb) == 0
-
-        for Series in [
-            # mock_OnePhotonSeries,
-            # mock_TwoPhotonSeries,
-            mock_RoiResponseSeries,
-            mock_DfOverF,
-            mock_Fluorescence,
-        ]:
             name_generator_registry.clear()
             nwbfile = mock_NWBFile()
-            nwbfile.add_acquisition(Series())
+            nwbfile.add_imaging_plane(mock_ImagingPlane())
             nwb = nap.NWBFile(nwbfile)
-            assert len(nwb) == 1
-            assert "RoiResponseSeries" in nwb.keys()
-            data = nwb["RoiResponseSeries"]
-            assert isinstance(data, nap.TsdFrame)
-            if "DfOverF" in nwbfile.acquisition.keys():
-                obj = nwbfile.acquisition["DfOverF"]["RoiResponseSeries"]
-            elif "Fluorescence" in nwbfile.acquisition.keys():
-                obj = nwbfile.acquisition["Fluorescence"]["RoiResponseSeries"]
-            else:
-                obj = nwbfile.acquisition[list(nwbfile.acquisition.keys())[0]]
-            np.testing.assert_array_almost_equal(data.values, obj.data[:])
-            np.testing.assert_array_almost_equal(
-                data.index.values,
-                obj.starting_time + np.arange(obj.num_samples) / obj.rate,
-            )
-            np.testing.assert_array_almost_equal(data.columns.values, obj.rois["id"][:])
+            assert len(nwb) == 0
+
+            for Series in [
+                # mock_OnePhotonSeries,
+                # mock_TwoPhotonSeries,
+                mock_RoiResponseSeries,
+                mock_DfOverF,
+                mock_Fluorescence,
+            ]:
+                name_generator_registry.clear()
+                nwbfile = mock_NWBFile()
+                nwbfile.add_acquisition(Series())
+                nwb = nap.NWBFile(nwbfile)
+                assert len(nwb) == 1
+                assert "RoiResponseSeries" in nwb.keys()
+                data = nwb["RoiResponseSeries"]
+                assert isinstance(data, nap.TsdFrame)
+                if "DfOverF" in nwbfile.acquisition.keys():
+                    obj = nwbfile.acquisition["DfOverF"]["RoiResponseSeries"]
+                elif "Fluorescence" in nwbfile.acquisition.keys():
+                    obj = nwbfile.acquisition["Fluorescence"]["RoiResponseSeries"]
+                else:
+                    obj = nwbfile.acquisition[list(nwbfile.acquisition.keys())[0]]
+                np.testing.assert_array_almost_equal(data.values, obj.data[:])
+                np.testing.assert_array_almost_equal(
+                    data.index.values,
+                    obj.starting_time + np.arange(obj.num_samples) / obj.rate,
+                )
+                np.testing.assert_array_almost_equal(data.columns.values, obj.rois["id"][:])
 
     except:
         pass  # some issues with pynwb version