Add sobol sampler as well as tests and demonstrating notebook

[Samuel-Vangu]

Adds a Sobol sampling class for integration on (hyper)cubic grids,
following up on the discussion in #322.

What's included

• src/grid/sobol.py: Sobol(n_points, dimension, seed=None, randomize=True, origin=None, axes=None). Wraps scipy.stats.qmc.Sobol (already a grid
  dependency), generating points via random_base2 for the balance
  properties of the digital-net construction; n_points must therefore be a
  power of 2. randomize=False gives the deterministic, unscrambled
  sequence (whose first point is always the origin); randomize=True
  applies Owen scrambling. Supports mapping onto an arbitrary parallelepiped
  via origin/axes.
• src/grid/tests/test_sobol.py: tests covering validation, weights, domain
  mapping, integration accuracy, reproducibility, and properties specific to
  Sobol' sequences (origin as first point when unrandomized, the nesting
  property between an N-point and a 2N-point design).

• An example notebook in examples/: compares Sobol against baselines
  already in grid (plain Monte Carlo, Tensor1DGrids/Trapezoidal) on two
  chemistry-motivated integrands -- a product of Gaussian-type functions
  (smooth) and a spherical interaction-cutoff indicator (discontinuous) --
  to show honestly where Sobol's low-discrepancy advantage holds and where
  it doesn't.