Merge pull request #412 from pynapple-org/dev

Bumping 0.8.4

doc/releases.md

@@ -18,6 +18,13 @@ of the Flatiron institute.
 
 ## Releases
 
+### 0.8.4 (2025-02-07)
+
+- Fix value printing of IntervalSet when rows are collapsed 
+- Backward compatibility fix for loading npz files with TsGroup
+- Fix indexing of IntervalSet to be able to use -1
+- Add column names for compute_wavelet_transform 
+
 ### 0.8.3 (2025-01-24)
 
 - `compute_mean_power_spectral_density` computes the mean periodogram.

pynapple/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.8.3"
+__version__ = "0.8.4"
 from .core import (
     IntervalSet,
     Ts,

pynapple/core/_core_functions.py

@@ -1,10 +1,10 @@
 """
-    This module holds the core function of pynapple as well as 
-    the dispatch between numba and jax.
+This module holds the core function of pynapple as well as
+the dispatch between numba and jax.
 
-    If pynajax is installed and `nap.nap_config.backend` is set 
-    to `jax`, the module will call the functions within pynajax.
-    Otherwise the module will call the functions within `_jitted_functions.py`.
+If pynajax is installed and `nap.nap_config.backend` is set
+to `jax`, the module will call the functions within pynajax.
+Otherwise the module will call the functions within `_jitted_functions.py`.
 
 """
 

pynapple/core/base_class.py

@@ -1,5 +1,5 @@
 """
-    Abstract class for `core` time series.
+Abstract class for `core` time series.
 
 """
 

pynapple/core/config.py

@@ -2,12 +2,12 @@
 
 ## Backend configuration
 
-By default, pynapple core functions are compiled with [Numba](https://numba.pydata.org/). 
-It is possible to change the backend to [Jax](https://jax.readthedocs.io/en/latest/index.html) 
+By default, pynapple core functions are compiled with [Numba](https://numba.pydata.org/).
+It is possible to change the backend to [Jax](https://jax.readthedocs.io/en/latest/index.html)
 through the [pynajax package](https://github.com/pynapple-org/pynajax).
 
 While numba core functions runs on CPU, the `jax` backend allows pynapple to use GPU accelerated core functions.
-For some core functions, the `jax` backend offers speed gains (provided that Jax runs on the GPU). 
+For some core functions, the `jax` backend offers speed gains (provided that Jax runs on the GPU).
 
 See the example below to update the backend. Don't forget to install [pynajax](https://github.com/pynapple-org/pynajax).
 
@@ -16,7 +16,7 @@
 import numpy as np
 nap.nap_config.set_backend("jax") # Default option is 'numba'.
 
-You can view the current backend with 
+You can view the current backend with
 
 >>> print(nap.nap_config.backend)
 'jax'

pynapple/core/interval_set.py

@@ -1,4 +1,4 @@
-"""        
+"""
 The class `IntervalSet` deals with non-overlaping epochs. `IntervalSet` objects can interact with each other or with the time series objects.
 """
 
@@ -294,7 +294,7 @@ def __init__(
         self._class_attributes = self.__dir__()  # get list of all attributes
         self._class_attributes.append("_class_attributes")  # add this property
         self._initialized = True
-        if drop_meta is False:
+        if (drop_meta is False) and (metadata is not None):
             self.set_info(metadata)
 
     def __repr__(self):
@@ -336,7 +336,7 @@ def __repr__(self):
                     np.hstack(
                         (
                             self.index[-n_rows:, None],
-                            self.values[0:n_rows],
+                            self.values[-n_rows:],
                             _convert_iter_to_str(metadata.values[-n_rows:]),
                         ),
                         dtype=object,
@@ -406,11 +406,6 @@ def __setitem__(self, key, value):
             )
 
     def __getitem__(self, key):
-        try:
-            metadata = _MetadataMixin.__getitem__(self, key)
-        except Exception:
-            metadata = pd.DataFrame(index=self.index)
-
         if isinstance(key, str):
             # self[str]
             if key == "start":
@@ -435,9 +430,15 @@ def __getitem__(self, key):
         elif isinstance(key, Number):
             # self[Number]
             output = self.values.__getitem__(key)
+            metadata = self._metadata.iloc[key]
             return IntervalSet(start=output[0], end=output[1], metadata=metadata)
-        elif isinstance(key, (slice, list, np.ndarray, pd.Series)):
-            # self[array_like]
+        elif isinstance(key, (slice, list, np.ndarray)):
+            # self[array_like], use iloc for metadata
+            output = self.values.__getitem__(key)
+            metadata = self._metadata.iloc[key].reset_index(drop=True)
+            return IntervalSet(start=output[:, 0], end=output[:, 1], metadata=metadata)
+        elif isinstance(key, pd.Series):
+            # use loc for metadata
             output = self.values.__getitem__(key)
             metadata = _MetadataMixin.__getitem__(self, key).reset_index(drop=True)
             return IntervalSet(start=output[:, 0], end=output[:, 1], metadata=metadata)
@@ -487,7 +488,7 @@ def __getitem__(self, key):
                     if key[1] == slice(None, None, None):
                         # self[Any, :]
                         output = self.values.__getitem__(key[0])
-                        metadata = _MetadataMixin.__getitem__(self, key[0])
+                        metadata = self._metadata.iloc[key[0]]
 
                         if isinstance(key[0], Number):
                             return IntervalSet(
@@ -500,7 +501,9 @@ def __getitem__(self, key):
                                 metadata=metadata.reset_index(drop=True),
                             )
 
-                    elif key[1] == slice(0, 2, None):
+                    elif (key[1] == slice(0, 2, None)) or (
+                        key[1] == slice(None, 2, None)
+                    ):
                         # self[Any, :2]
                         # allow number indexing for start and end times for backward compatibility
                         output = self.values.__getitem__(key[0])

pynapple/core/metadata_class.py

@@ -313,7 +313,7 @@ def get_info(self, key):
                 )
             )
         ):
-            # assume key is index, or tupe of index and column name
+            # assume key is index, or tuple of index and column name
             # metadata[Number], metadata[array_like], metadata[Any, str], or metadata[Any, [*str]]
             return self._metadata.loc[key]
 

pynapple/core/time_index.py

@@ -1,12 +1,12 @@
 """
 
-    Similar to pandas.Index, `TsIndex` holds the timestamps associated with the data of a time series.
-    This class deals with conversion between different time units for all pynapple objects as well
-    as making sure that timestamps are property sorted before initializing any objects.
-    
-        - `us`: microseconds
-        - `ms`: milliseconds
-        - `s`: seconds  (overall default)
+Similar to pandas.Index, `TsIndex` holds the timestamps associated with the data of a time series.
+This class deals with conversion between different time units for all pynapple objects as well
+as making sure that timestamps are property sorted before initializing any objects.
+
+    - `us`: microseconds
+    - `ms`: milliseconds
+    - `s`: seconds  (overall default)
 """
 
 from warnings import warn

pynapple/core/time_series.py

@@ -1,18 +1,18 @@
 """
-    
-    Pynapple time series are containers specialized for neurophysiological time series.
 
-    They provides standardized time representation, plus various functions for manipulating times series with identical sampling frequency.
+Pynapple time series are containers specialized for neurophysiological time series.
 
-    Multiple time series object are avaible depending on the shape of the data.
+They provides standardized time representation, plus various functions for manipulating times series with identical sampling frequency.
 
-    - `TsdTensor` : for data with of more than 2 dimensions, typically movies.
-    - `TsdFrame` : for column-based data. It can be easily converted to a pandas.DataFrame. Columns can be labelled and selected similar to pandas.
-    - `Tsd` : One-dimensional time series. It can be converted to a pandas.Series.
-    - `Ts` : For timestamps data only.
+Multiple time series object are avaible depending on the shape of the data.
 
-    Most of the same functions are available through all classes. Objects behaves like numpy.ndarray. Slicing can be done the same way for example 
-    `tsd[0:10]` returns the first 10 rows. Similarly, you can call any numpy functions like `np.mean(tsd, 1)`.
+- `TsdTensor` : for data with of more than 2 dimensions, typically movies.
+- `TsdFrame` : for column-based data. It can be easily converted to a pandas.DataFrame. Columns can be labelled and selected similar to pandas.
+- `Tsd` : One-dimensional time series. It can be converted to a pandas.Series.
+- `Ts` : For timestamps data only.
+
+Most of the same functions are available through all classes. Objects behaves like numpy.ndarray. Slicing can be done the same way for example
+`tsd[0:10]` returns the first 10 rows. Similarly, you can call any numpy functions like `np.mean(tsd, 1)`.
 """
 
 import abc

pynapple/core/ts_group.py

@@ -1,6 +1,6 @@
 """
 
-The class `TsGroup` helps group objects with different timestamps 
+The class `TsGroup` helps group objects with different timestamps
 (i.e. timestamps of spikes of a population of neurons).
 
 """
@@ -1419,13 +1419,31 @@ def _from_npz_reader(cls, file):
 
         tsgroup = cls(group, time_support=time_support, bypass_check=True)
 
-        # do we need to enforce that these keys are not in metadata?
-        # not_info_keys = {"start", "end", "t", "index", "d", "rate", "keys"}
-
         if "_metadata" in file:  # load metadata if it exists
             if file["_metadata"]:  # check that metadata is not empty
                 metainfo = pd.DataFrame.from_dict(file["_metadata"].item())
                 tsgroup.set_info(metainfo)
+
+        metainfo = {}
+        not_info_keys = {
+            "start",
+            "end",
+            "t",
+            "index",
+            "d",
+            "rate",
+            "keys",
+            "_metadata",
+            "type",
+        }
+
+        for k in set(file.keys()) - not_info_keys:
+            tmp = file[k]
+            if len(tmp) == len(tsgroup):
+                metainfo[k] = tmp
+
+        tsgroup.set_info(**metainfo)
+
         return tsgroup
 
     @add_meta_docstring("set_info")

pynapple/core/utils.py

@@ -1,5 +1,5 @@
 """
-    Utility functions
+Utility functions
 """
 
 import os

pynapple/process/_process_functions.py

@@ -1,10 +1,10 @@
 """
-    This module holds some process function of pynapple that can be
-    called with numba or pynajax as backend    
+This module holds some process function of pynapple that can be
+called with numba or pynajax as backend
 
-    If pynajax is installed and `nap.nap_config.backend` is set 
-    to `jax`, the module will call the functions within pynajax.
-    Otherwise the module will call the functions within `_jitted_functions.py`.
+If pynajax is installed and `nap.nap_config.backend` is set
+to `jax`, the module will call the functions within pynajax.
+Otherwise the module will call the functions within `_jitted_functions.py`.
 
 """
 

pynapple/process/perievent.py

@@ -1,6 +1,4 @@
-"""Functions to realign time series relative to a reference time.
-
-"""
+"""Functions to realign time series relative to a reference time."""
 
 import numpy as np
 

pynapple/process/wavelets.py

@@ -127,7 +127,10 @@ def compute_wavelet_transform(
 
     if len(output_shape) == 2:
         return nap.TsdFrame(
-            t=sig.index, d=np.squeeze(cwt, axis=1), time_support=sig.time_support
+            t=sig.index,
+            d=np.squeeze(cwt, axis=1),
+            time_support=sig.time_support,
+            columns=freqs,
         )
     else:
         return nap.TsdTensor(

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pynapple"
-version = "0.8.3"
+version = "0.8.4"
 description = "PYthon Neural Analysis Package Pour Laboratoires d’Excellence"
 readme = "README.md"
 authors = [{ name = "Guillaume Viejo", email = "[email protected]" }]

setup.py

@@ -59,7 +59,7 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/pynapple-org/pynapple',
-    version='v0.8.3',
+    version='v0.8.4',
     zip_safe=False,
     long_description_content_type='text/markdown',
     download_url='https://github.com/pynapple-org/pynapple/archive/refs/tags/v0.8.3.tar.gz'