Technical review corrections

Author: daflack

src/CSET/operators/plot.py

@@ -1614,17 +1614,31 @@ def _calculate_CFAD(
     bin_edges: list[float]
         The bin edges for the histogram. The bins need to be specified to
         ensure consistency across the CFAD, otherwise it cannot be interpreted.
+
+    Notes
+    -----
+    Contour Frequency by Altitude Diagrams (CFADs) were first designed by
+    Yuter and Houze (1995)[YuterandHouze95]. They are calculated by binning the
+    data by altitude and then by variable bins (e.g. temperature). The variable
+    bins are then normalised by each altitude. This essenitally creates a
+    normalised frequency distribution for each altitude. These are then stacked
+    and combined in a single plot.
+
+    References
+    ----------
+    .. [YuterandHouze95] Yuter S.E., and Houze, R.A. (1995) "Three-Dimensional
+       Kinematic and Microphysical Evolution of Florida Cumulonimbus. Part II:
+       Frequency Distributions of Vertical Velocity, Reflectivity, and
+       Differential Reflectivity" Monthly Weather Review,  vol. 123, 1941-1963,
+       doi: 10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2
     """
     # Setup empty array for containing the CFAD data.
     CFAD_values = np.zeros(
         (len(cube.coord(vertical_coordinate).points), len(bin_edges) - 1)
     )
 
-    # Set iterator for CFAD values.
-    i = 0
-
     # Calculate the CFAD as a histogram summing to one for each level.
-    for level_cube in cube.slices_over(vertical_coordinate):
+    for i, level_cube in enumerate(cube.slices_over(vertical_coordinate)):
         # Note setting density to True does not produce the correct
         # normalization for a CFAD, where each row must sum to one.
         CFAD_values[i, :] = (
@@ -1633,8 +1647,7 @@ def _calculate_CFAD(
             ]
             / level_cube.data.size
         )
-        i += 1
-    # calculate central points for bins
+    # Calculate central points for bins.
     bins = (np.array(bin_edges[:-1]) + np.array(bin_edges[1:])) / 2.0
     bin_bounds = np.array((bin_edges[:-1], bin_edges[1:])).T
     # Now construct the coordinates for the cube.
@@ -1646,10 +1659,11 @@ def _calculate_CFAD(
     CFAD = iris.cube.Cube(
         CFAD_values,
         dim_coords_and_dims=[(vert_coord, 0), (bin_coord, 1)],
+        long_name=f"{cube.name()}_cfad",
         standard_name=cube.standard_name,
         units="1",
     )
-    CFAD.attributes["type"] = "Contour Frequency by Altitude Diagram (CFAD)"
+    CFAD.rename(f"{cube.name()}_cfad")
     return CFAD
 
 

tests/conftest.py

@@ -233,3 +233,15 @@ def ensemble_cube_read_only():
 def ensemble_cube(ensemble_cube_read_only):
     """Get ensemble cube to run tests on. It is safe to modify."""
     return ensemble_cube_read_only.copy()
+
+
+@pytest.fixture(scope="session")
+def xwind_read_only():
+    """Get regridded xwind to run tests on. It is NOT safe to modify."""
+    return read.read_cube("tests/test_data/ageofair/aoa_in_rgd.nc", "x_wind")
+
+
+@pytest.fixture()
+def xwind(xwind_read_only):
+    """Get regridded xwind to run tests on. It is safe to modify."""
+    return xwind_read_only.copy()

tests/operators/test_ageofair.py

@@ -22,12 +22,6 @@
 from CSET.operators import ageofair, read
 
 
-@pytest.fixture()
-def xwind() -> iris.cube.Cube:
-    """Get regridded xwind to run tests on."""
-    return read.read_cube("tests/test_data/ageofair/aoa_in_rgd.nc", "x_wind")
-
-
 @pytest.fixture()
 def ywind() -> iris.cube.Cube:
     """Get regridded ywind to run tests on."""

tests/operators/test_plot.py

@@ -18,6 +18,7 @@
 import logging
 from pathlib import Path
 
+import cf_units
 import iris.coords
 import iris.cube
 import matplotlib as mpl
@@ -28,12 +29,6 @@
 from CSET.operators import collapse, plot, read
 
 
-@pytest.fixture()
-def xwind() -> iris.cube.Cube:
-    """Get regridded xwind to run tests on."""
-    return iris.load_cube("tests/test_data/ageofair/aoa_in_rgd.nc", "x_wind")
-
-
 def test_check_single_cube():
     """Conversion to a single cube, and rejection where not possible."""
     cube = iris.cube.Cube([0.0])
@@ -955,13 +950,11 @@ def test_calculate_CFAD(xwind):
     """Test calculating a CFAD."""
     bins = np.array([-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50])
     calculated_CFAD = np.zeros((len(xwind.coord("pressure").points), len(bins) - 1))
-    j = 0
-    for level_cube in xwind.slices_over("pressure"):
+    for j, level_cube in enumerate(xwind.slices_over("pressure")):
         calculated_CFAD[j, :] = (
             np.histogram(level_cube.data.reshape(level_cube.data.size), bins=bins)[0]
             / level_cube.data.size
         )
-        j += 1
     assert np.allclose(
         plot._calculate_CFAD(
             xwind, "pressure", [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
@@ -970,3 +963,25 @@ def test_calculate_CFAD(xwind):
         rtol=1e-06,
         atol=1e-02,
     )
+
+
+def test_name_CFAD(xwind):
+    """Test naming of CFAD cube."""
+    expected_name = f"{xwind.name()}_cfad"
+    assert (
+        plot._calculate_CFAD(
+            xwind, "pressure", [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
+        ).name()
+        == expected_name
+    )
+
+
+def test_units_CFAD(xwind):
+    """Test units of CFAD cube."""
+    expected_units = cf_units.Unit("1")
+    assert (
+        plot._calculate_CFAD(
+            xwind, "pressure", [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
+        ).units
+        == expected_units
+    )