Fix AssertionError of number of streams per layer for uniform_air

[linazsche]

Bug Fix for uniform_air

In 7e5af0b, the previously correct assert all(streams.n > 2) under compute_stream_uniform() was changed to assert all(np.size(n) > 2 for n in streams.n), which always fails since the size of a scalar is 1. This commit changes the assert back to its original purpose while keeping the element-wise comparison: assert all(n > 2 for n in streams.n).

Related: The assert under compute_stream_gaussian() seems to always pass following 7e5af0b, but n is apparently checked properly within gauss_legendre_quadrature().

Using stream_mode=uniform_air caused the following error:

  File "smrt\src\smrt\core\model.py", line 628, in run_single_simulation
    result = rtsolver.solve(
             ^^^^^^^^^^^^^^^
  File "smrt\src\smrt\rtsolver\dort.py", line 205, in solve
    self.prepare_streams()
  File "smrt\src\smrt\rtsolver\rtsolver_utils.py", line 89, in prepare_streams
    self.streams = compute_stream(self.n_max_stream, rtsolver.effective_permittivity, mode=self.stream_mode)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "smrt\src\smrt\rtsolver\streams.py", line 142, in compute_stream
    return compute_stream_uniform(n_max_stream, permittivity)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "smrt\src\smrt\rtsolver\streams.py", line 300, in compute_stream_uniform
    assert all(np.size(n) > 2 for n in streams.n)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError

Minimal code example:

from smrt import make_model, make_snowpack, sensor_list

sensor = sensor_list.passive(37e9, 55)
snowpack = make_snowpack(
    thickness=[1],
    microstructure_model="exponential",
    density=300,
    corr_length=1e-4,
    temperature=260,
)
model = make_model("iba", "dort", rtsolver_options={"stream_mode": "uniform_air"})

model.run(sensor, snowpack).TbV()

[ghislainp]

Thank you for the PR. Note that the "uniform_air" mode has never been well tested. Use with care.