Add pyclass intersphinx references

.coveragerc

@@ -10,6 +10,7 @@ omit =
 	gpm/retrievals/*
 	gpm/visualization/animation.py
 	gpm/utils/pyresample.py
+	gpm/utils/collocation.py
 	gpm/_version.py
 
 [report]

.gitignore

@@ -36,10 +36,10 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
-_version.py
 gpm/_version.py
+docs/source/tutorials/
 docs/build/
-
+lcov.info
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

.pre-commit-config.yaml

@@ -69,13 +69,3 @@ repos:
       - id: rst-backticks
       - id: rst-directive-colons
       - id: rst-inline-touching-normal
-
-  # - repo: https://github.com/adrienverge/yamllint.git
-  #   rev: v1.35.1
-  #   hooks:
-  #     - id: yamllint
-  #       args: [-d relaxed]
-  # - repo: https://github.com/lyz-code/yamlfix/
-  #   rev: 1.16.0
-  #   hooks:
-  #     - id: yamlfix

docs/environment.yaml

@@ -16,4 +16,3 @@ dependencies:
   - sphinx==7.2.6
   - sphinxcontrib-youtube
   - ximage
-  - pip: [pycolorbar]

gpm/bucket/partitioning.py

@@ -441,8 +441,8 @@ def quadmesh(self, origin="bottom"):
         A quadrilateral mesh is a grid of M by N adjacent quadrilaterals that are defined via a (M+1, N+1)
         grid of vertices.
 
-        The quadrilateral mesh is accepted by `matplotlib.pyplot.pcolormesh`, `matplotlib.collections.QuadMesh`
-        `matplotlib.collections.PolyQuadMesh`.
+        The quadrilateral mesh is accepted by :py:class:`matplotlib.pyplot.pcolormesh`,
+        :py:class:`matplotlib.collections.QuadMesh` and :py:class:`matplotlib.collections.PolyQuadMesh`.
 
         Parameters
         ----------
@@ -464,7 +464,7 @@ def vertices(self, origin="bottom", ccw=True):
         """Return the partitions vertices in an array of shape (N, M, 4, 2).
 
         The output vertices, once the first 2 dimension are flattened,
-        can be passed directly to a `matplotlib.PolyCollection`.
+        can be passed directly to a :py:class:`matplotlib.PolyCollection`.
         For plotting with cartopy, the polygon order must be "counterclockwise".
 
         Parameters
@@ -587,7 +587,7 @@ def add_labels(self, df, x, y, remove_invalid_rows=True):
 
         Parameters
         ----------
-        df : `pandas.DataFrame`, `dask.DataFrame`, `polars.DataFrame`, `pyarrow.Table` or `polars.LazyFrame`
+        df : pandas.DataFrame, dask.DataFrame, polars.DataFrame, pyarrow.Table or polars.LazyFrame
             Dataframe to which add partitions centroids.
         x : str
             Column name with the x coordinate.
@@ -599,7 +599,7 @@ def add_labels(self, df, x, y, remove_invalid_rows=True):
 
         Returns
         -------
-        df : `pandas.DataFrame`, `dask.DataFrame`, `polars.DataFrame`, `pyarrow.Table` or `polars.LazyFrame`
+        df : pandas.DataFrame, dask.DataFrame, polars.DataFrame, pyarrow.Table or polars.LazyFrame
             Dataframe with the partitions label(s) column(s).
 
         """
@@ -631,7 +631,7 @@ def add_centroids(self, df, x, y, x_coord=None, y_coord=None, remove_invalid_row
 
         Parameters
         ----------
-        df : `pandas.DataFrame`, `dask.DataFrame`, `polars.DataFrame`, `pyarrow.Table` or `polars.LazyFrame`
+        df : pandas.DataFrame, dask.DataFrame, polars.DataFrame, pyarrow.Table or polars.LazyFrame
             Dataframe to which add partitions centroids.
         x : str
             Column name with the x coordinate.
@@ -649,7 +649,7 @@ def add_centroids(self, df, x, y, x_coord=None, y_coord=None, remove_invalid_row
 
         Returns
         -------
-        df : `pandas.DataFrame`, `dask.DataFrame`, `polars.DataFrame`, `pyarrow.Table` or `polars.LazyFrame`
+        df : pandas.DataFrame, dask.DataFrame, polars.DataFrame, pyarrow.Table or polars.LazyFrame
             Dataframe with the partitions centroids x and y coordinates columns.
 
         """
@@ -832,8 +832,14 @@ def __init__(
     # -----------------------------------------------------------------------------------.
     def _custom_labels_function(self, x_indices, y_indices):
         """Return the partition labels as function of the specified 2D partitions indices."""
-        x_labels = self.x_centroids[x_indices].round(self._labels_decimals[0]).astype(str)
-        y_labels = self.y_centroids[y_indices].round(self._labels_decimals[1]).astype(str)
+        x_labels_value = self.x_centroids[x_indices].round(self._labels_decimals[0])
+        y_labels_value = self.y_centroids[y_indices].round(self._labels_decimals[1])
+        if self._labels_decimals[0] == 0:
+            x_labels_value = x_labels_value.astype(int)
+        if self._labels_decimals[1] == 0:
+            y_labels_value = y_labels_value.astype(int)
+        x_labels = x_labels_value.astype(str)
+        y_labels = y_labels_value.astype(str)
         return x_labels, y_labels
 
     def to_dict(self):

gpm/bucket/readers.py

@@ -204,7 +204,7 @@ def read_bucket(
 
     Returns
     -------
-    df : `pandas.DataFrame`, `polars.DataFrame`, `polars.LazyFrame` or `pyarrow.Table`
+    df : pandas.DataFrame, polars.DataFrame, polars.LazyFrame or pyarrow.Table
         Bucket dataframe.
 
     """

gpm/bucket/routines.py

@@ -67,7 +67,7 @@ def write_granule_bucket(
         File path of the granule to store in the bucket archive.
     bucket_dir: str
         Base directory of the per-granule bucket archive.
-    partitioning: `gpm.bucket.SpatialPartitioning`
+    partitioning: gpm.bucket.SpatialPartitioning
         A spatial partitioning class.
     granule_to_df_func : Callable
         Function taking a granule filepath, opening it and returning a pandas or dask dataframe.
@@ -143,7 +143,7 @@ def write_granules_bucket(
         File paths of the GPM granules to store in the bucket archive.
     bucket_dir: str
         Base directory of the per-granule bucket archive.
-    partitioning: `gpm.bucket.SpatialPartitioning`
+    partitioning: gpm.bucket.SpatialPartitioning
         A spatial partitioning class.
         Carefully consider the size of the partitions.
         Earth partitioning by:
@@ -243,11 +243,11 @@ def write_bucket(
 
     Parameters
     ----------
-    ds : `pandas.DataFrame` or `dask.DataFrame`
+    ds : pandas.DataFrame or dask.DataFrame
         Pandas or Dask dataframe to be written into a geographic bucket.
     bucket_dir: str
         Base directory of the geographic bucket archive.
-    partitioning: `gpm.bucket.SpatialPartitioning`
+    partitioning: gpm.bucket.SpatialPartitioning
         A spatial partitioning class.
         Carefully consider the size of the partitions.
         Earth partitioning by: