add hidden directive to feature detection

Author: freemansw1

doc/conf.py

@@ -92,10 +92,33 @@
 collapse_navigation = False
 
 
+def skip_member_check(app, what, name, obj, skip, options):
+    """Function to skip members (autodoc-skip-member)
+
+    Parameters
+    ----------
+    app
+    what
+    name
+    obj
+    skip
+    options
+
+    Returns
+    -------
+
+    """
+    if hasattr(obj, "__doc__") and obj.__doc__ and ":hidden:" in obj.__doc__:
+        return True
+
+    return skip
+
+
 # Include our custom CSS (currently for special table config)
 def setup(app):
     app.add_css_file("theme_overrides.css")
     app.add_css_file("custom.css")
+    app.connect("autodoc-skip-member", skip_member_check)
 
 
 # This should include all modules used in tobac. These are dummy imports,
@@ -118,6 +141,14 @@ def setup(app):
     "cftime",
 ]
 
+autodoc_default_options = {
+    "members": True,
+    "undoc-members": True,
+    "show-inheritance": True,
+    "ignore-module-all": False,
+    "private-members": False,
+}
+
 sys.path.insert(0, os.path.abspath("../"))
 
 pygments_style = "sphinx"

tobac/feature_detection.py

@@ -35,8 +35,8 @@
 from tobac.utils import decorators
 
 from tobac.utils import periodic_boundaries as pbc_utils
-from tobac.utils.general import spectral_filtering
-from tobac.utils import get_statistics
+import tobac.utils
+import tobac.utils.general
 import warnings
 
 # from typing_extensions import Literal
@@ -64,7 +64,9 @@ def feature_position(
 ) -> tuple[float]:
     """Determine feature position with regard to the horizontal
     dimensions in pixels from the identified region above
-    threshold values
+    threshold values.
+
+    :hidden:
 
     Parameters
     ----------
@@ -152,7 +154,8 @@ def feature_position(
         lies exactly between hdim1_max and hdim1_min, the output could be
         between hdim1_max and hdim1_max+1. While a value between hdim1_min-1
         and hdim1_min would also be valid, we choose to overflow on the max side of things.
-
+    Notes
+    -----
     """
 
     # First, if necessary, run PBC processing.
@@ -260,6 +263,8 @@ def test_overlap(
 ) -> bool:
     """Test for overlap between two regions
 
+    :hidden:
+
     Parameters
     ----------
     region_1 : list
@@ -292,6 +297,8 @@ def remove_parents(
     Remove features where its regions surround newly
     detected feature regions.
 
+    :hidden:
+
     Parameters
     ----------
     features_thresholds : pandas.DataFrame
@@ -406,6 +413,8 @@ def feature_detection_threshold(
 ) -> tuple[pd.DataFrame, dict]:
     """Find features based on individual threshold value.
 
+    :hidden:
+
     Parameters
     ----------
     data_i : np.array
@@ -925,6 +934,8 @@ def feature_detection_multithreshold_timestep(
     thresholds. Smoothing the input data with the Gaussian filter makes
     output less sensitive to noisiness of input data.
 
+    :hidden:
+
     Parameters
     ----------
 
@@ -1027,7 +1038,7 @@ def feature_detection_multithreshold_timestep(
 
     # spectrally filter the input data, if desired
     if wavelength_filtering is not None:
-        track_data = spectral_filtering(
+        track_data = tobac.utils.general.spectral_filtering(
             dxy, track_data, wavelength_filtering[0], wavelength_filtering[1]
         )
 
@@ -1144,7 +1155,7 @@ def feature_detection_multithreshold_timestep(
         # select which data to use according to statistics_unsmoothed option
         stats_data = data_i.values if statistics_unsmoothed else track_data
 
-        features_thresholds = get_statistics(
+        features_thresholds = tobac.utils.get_statistics(
             features_thresholds,
             labels,
             stats_data,
@@ -1188,6 +1199,7 @@ def feature_detection_multithreshold(
 
     The regions are above/below a threshold.
 
+
     Parameters
     ----------
     field_in : iris.cube.Cube or xarray.DataArray
@@ -1490,6 +1502,9 @@ def filter_min_distance(
     If two features are closer than `min_distance`, it keeps the
     larger feature.
 
+    :hidden:
+
+
     Parameters
     ----------
     features:      pandas DataFrame