[Types] Add datetime64 support

Overview

Add support for datetime64 type to NumSharp for time series and data science workloads.

Problem

NumSharp lacks datetime support, which is essential for:

>>> import numpy as np
>>> np.array(['2024-01-15', '2024-06-30'], dtype='datetime64[D]')
array(['2024-01-15', '2024-06-30'], dtype='datetime64[D]')

>>> np.datetime64('2024-01-15') + np.timedelta64(30, 'D')
numpy.datetime64('2024-02-14')

>>> np.arange('2024-01', '2024-06', dtype='datetime64[M]')
array(['2024-01', '2024-02', '2024-03', '2024-04', '2024-05'], dtype='datetime64[M]')

Use cases:

• Time series analysis — Financial data, sensor data, logs
• Pandas interop — Pandas uses NumPy datetime64 internally
• Data science — Date ranges, filtering, aggregation
• File I/O — Many datasets have datetime columns

Proposal

Task List

• Design datetime64 representation (epoch + unit)
• Create DateTime64 struct with unit awareness:

  public readonly struct DateTime64
  {
      public readonly long Value;  // Ticks from epoch
      public readonly DateTimeUnit Unit;  // ns, us, ms, s, m, h, D, W, M, Y
  }
  

• Add NPTypeCode.DateTime64 to NPTypeCode.cs
• Implement unit conversion logic
• Implement parsing from strings ("2024-01-15", "2024-01-15T12:30:00")
• Implement arithmetic (datetime + timedelta)
• Implement comparison operators
• Add np.datetime64() constructor
• Update type promotion tables
• Add tests with NumPy verification

Units (matching NumPy)

C# Type Mapping Options

Implementation Effort

MEDIUM — Requires unit handling, parsing, arithmetic with timedelta64.

Estimated: ~500 lines for struct, parsing, and operations.

Related

• Part of NumPy 2.x alignment: #529
• Will benefit from DynamicMethod IL emission: #544
• Depends on: timedelta64 support for full arithmetic

References

• NumPy datetime64 docs: https://numpy.org/doc/stable/reference/arrays.datetime.html