Disable filter_convert function

While this was useful early in the v3 redesign, we’ve converged to a design where this is no longer used.

floris/simulation/turbine.py

@@ -36,51 +36,6 @@
 from floris.utilities import cosd
 
 
-def _filter_convert(
-    ix_filter: NDArrayFilter | Iterable[int] | None, sample_arg: NDArrayFloat | NDArrayInt
-) -> NDArrayFloat | None:
-    """This function selects turbine indeces from the given array of turbine properties
-    over the simulation's atmospheric conditions (wind directions / wind speeds).
-    It converts the ix_filter to a standard format of `np.ndarray`s for filtering
-    certain arguments.
-
-    Args:
-        ix_filter (NDArrayFilter | Iterable[int] | None): The indices, or truth
-            array-like object to use for filtering. None implies that all indeces in the
-            sample_arg should be selected.
-        sample_arg (NDArrayFloat | NDArrayInt): Any argument that will be filtered, to be used for
-            creating the shape. This should be of shape:
-            (n wind directions, n wind speeds, n turbines)
-
-    Returns:
-        NDArrayFloat | None: Returns an array of a truth or index list if a list is
-            passed, a truth array if ix_filter is None, or None if ix_filter is None
-            and the `sample_arg` is a single value.
-    """
-    # Check that the ix_filter is either None or an Iterable. Otherwise,
-    # there's nothing we can do with it.
-    if not isinstance(ix_filter, Iterable) and ix_filter is not None:
-        raise TypeError("Expected ix_filter to be an Iterable or None")
-
-    # Check that the sample_arg is a Numpy array. If it isn't, we
-    # can't get its shape.
-    if not isinstance(sample_arg, np.ndarray):
-        raise TypeError("Expected sample_arg to be a float or integer np.ndarray")
-
-    # At this point, the arguments have this type:
-    # ix_filter: Union[Iterable, None]
-    # sample_arg: np.ndarray
-
-    # Return all values in the turbine-dimension
-    # if the index filter is None
-    if ix_filter is None:
-        return np.ones(sample_arg.shape[-1], dtype=bool)
-
-    # Finally, we should have an index filter list of type Iterable,
-    # so cast it to Numpy array and return
-    return np.array(ix_filter)
-
-
 def _rotor_velocity_yaw_correction(
     pP: float,
     yaw_angle: NDArrayFloat,
@@ -178,7 +133,6 @@ def rotor_effective_velocity(
 
     # Down-select inputs if ix_filter is given
     if ix_filter is not None:
-        ix_filter = _filter_convert(ix_filter, yaw_angle)
         velocities = velocities[:, :, ix_filter]
         yaw_angle = yaw_angle[:, :, ix_filter]
         tilt_angle = tilt_angle[:, :, ix_filter]
@@ -255,7 +209,6 @@ def power(
 
     # Down-select inputs if ix_filter is given
     if ix_filter is not None:
-        ix_filter = _filter_convert(ix_filter, rotor_effective_velocities)
         rotor_effective_velocities = rotor_effective_velocities[:, :, ix_filter]
         turbine_type_map = turbine_type_map[:, :, ix_filter]
 
@@ -322,7 +275,6 @@ def Ct(
 
     # Down-select inputs if ix_filter is given
     if ix_filter is not None:
-        ix_filter = _filter_convert(ix_filter, yaw_angle)
         velocities = velocities[:, :, ix_filter]
         yaw_angle = yaw_angle[:, :, ix_filter]
         tilt_angle = tilt_angle[:, :, ix_filter]
@@ -425,7 +377,6 @@ def axial_induction(
     )
 
     # Then, process the input arguments as needed for this function
-    ix_filter = _filter_convert(ix_filter, yaw_angle)
     if ix_filter is not None:
         yaw_angle = yaw_angle[:, :, ix_filter]
         tilt_angle = tilt_angle[:, :, ix_filter]

floris/simulation/turbine_multi_dim.py

@@ -32,7 +32,6 @@
     TiltTable,
     Turbine,
 )
-from floris.simulation.turbine import _filter_convert
 from floris.type_dec import (
     NDArrayBool,
     NDArrayFilter,
@@ -79,7 +78,6 @@ def power_multidim(
 
     # Down-select inputs if ix_filter is given
     if ix_filter is not None:
-        ix_filter = _filter_convert(ix_filter, rotor_effective_velocities)
         power_interp = power_interp[:, :, ix_filter]
         rotor_effective_velocities = rotor_effective_velocities[:, :, ix_filter]
     # Loop over each turbine to get power for all turbines
@@ -141,7 +139,6 @@ def Ct_multidim(
 
     # Down-select inputs if ix_filter is given
     if ix_filter is not None:
-        ix_filter = _filter_convert(ix_filter, yaw_angle)
         velocities = velocities[:, :, ix_filter]
         yaw_angle = yaw_angle[:, :, ix_filter]
         tilt_angle = tilt_angle[:, :, ix_filter]
@@ -240,7 +237,6 @@ def axial_induction_multidim(
     )
 
     # Then, process the input arguments as needed for this function
-    ix_filter = _filter_convert(ix_filter, yaw_angle)
     if ix_filter is not None:
         yaw_angle = yaw_angle[:, :, ix_filter]
         tilt_angle = tilt_angle[:, :, ix_filter]

tests/turbine_unit_test.py

@@ -26,7 +26,6 @@
     Turbine,
 )
 from floris.simulation.turbine import (
-    _filter_convert,
     _rotor_velocity_tilt_correction,
     _rotor_velocity_yaw_correction,
     compute_tilt_angles_for_floating_turbines,
@@ -132,50 +131,6 @@ def test_rotor_area():
     assert turbine.rotor_area == np.pi
 
 
-def test_filter_convert():
-    N = 4
-
-    # When the index filter is not None or a Numpy array,
-    # the function should return None
-    ix_filter = 1
-    sample_arg = np.arange(N)
-    with pytest.raises(TypeError):
-        _filter_convert(ix_filter, sample_arg)
-
-    # When the sample_arg is not a Numpy array, the function
-    # should return None
-    ix_filter = None
-    sample_arg = [1, 2, 3]
-    with pytest.raises(TypeError):
-        _filter_convert(ix_filter, sample_arg)
-
-    # When the sample_arg is a Numpy array and the index filter
-    # is None, a boolean array containing all True should be
-    # returned with the same shape as the sample_arg.
-    ix_filter = None
-    sample_arg = np.arange(N)
-    ix_filter = _filter_convert(ix_filter, sample_arg)
-    assert ix_filter.sum() == N
-    assert ix_filter.shape == (N,)
-
-    # When the index filter is given as a Python list, the
-    # function should return the values cast to a Numpy array
-    ix_filter = [1, 2]
-    sample_arg = np.arange(N).reshape(1, 1, N)
-    ix_filter = _filter_convert(ix_filter, sample_arg)
-    np.testing.assert_array_equal(ix_filter, np.array([1, 2]))
-
-    # Test that a 1-D boolean truth array is returned
-    # When the index filter is None and the sample_arg
-    # is a Numpy array of values, the returned filter indices
-    # should be all True and have the shape of the turbine-dimension
-    ix_filter = None
-    sample_arg = np.arange(N).reshape(1, 1, N)
-    ix_filter = _filter_convert(ix_filter, sample_arg)
-    assert ix_filter.sum() == N
-    assert ix_filter.shape == (N,)
-
-
 def test_average_velocity():
     # TODO: why do we use cube root - mean - cube (like rms) instead of a simple average (np.mean)?
     # Dimensions are (n wind directions, n wind speeds, n turbines, grid x, grid y)