Dask compatibility

[BalzaniEdoardo]

This PR introduces compatibility for pynapple Tsd objects with Dask arrays. Additionally, it modifies the __getattr__ method of Tsd to expose data attributes, providing seamless access to Dask-specific functionality, such as array.chunks and array.compute().

Key Changes

1. _load_array Flag

• New Attribute: Introduced a _load_array boolean flag in _BaseTsd. This flag tracks the lazy-load status of a Tsd.
• Purpose: Dask arrays support lazy computation. For example, when performing operations like tsd2 = np.exp(tsd), the resulting Tsd should maintain the same lazy-load behavior as the original.
• Alternative Consideration: Instead of using the _load_array flag, we could default lazy-loading to True .

2. __getattr__ for Data Attributes

• Behavior Changes:
  • If the requested attribute name exists in the NumPy namespace, the corresponding NumPy function is applied to the Tsd (existing behavior).
  • If the attribute name exists in tsd.d:
    • If callable, the attribute is invoked, and the result is returned as a Tsd if possible. Otherwise, the raw result is returned.
    • If not callable, the attribute is returned as-is.
  • If the attribute name does not match any of the above, an AttributeError is raised.
• Non-numpy array Integration: For Dask arrays, this change exposes attributes such as chunks and methods like compute(), enhancing user experience when working with lazy-loaded data. The getattr will expose other attributes of zarr arrays and other types we support

[codecov]

Codecov Report

Attention: Patch coverage is 89.55224% with 7 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
pynapple/core/time_series.py	90.90%	3 Missing and 2 partials ⚠️
pynapple/core/base_class.py	77.77%	1 Missing and 1 partial ⚠️

Files with missing lines	Coverage Δ
pynapple/core/utils.py	84.09% <100.00%> (-0.18%)	⬇️
pynapple/process/filtering.py	94.44% <100.00%> (-0.16%)	⬇️
pynapple/core/base_class.py	98.66% <77.77%> (-0.46%)	⬇️
pynapple/core/time_series.py	90.22% <90.90%> (+0.75%)	⬆️

... and 2 files with indirect coverage changes