Switch to furo theme + add docs

Author: MaximLippeveld

.readthedocs.yaml

@@ -14,4 +14,4 @@ python:
   install:
     - requirements: requirements.txt
     - method: pip
-      path: .
+      path: .

docs/source/conf.py

@@ -47,7 +47,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+html_theme = 'furo'
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,

requirements.txt

@@ -9,3 +9,4 @@ types-PyYAML
 types-setuptools
 sphinx
 versioneer
+furo

setup.py

@@ -9,7 +9,7 @@
     name="scip",
     version=versioneer.get_version(),
     cmdclass=versioneer.get_cmdclass(),
-    package_dir={"scip": "src/scip"},
+    package_dir={"": "src"},
     packages=setuptools.find_packages(where="src"),
     install_requires=[
         'numpy',

src/scip/features/feature_extraction.py

@@ -1,3 +1,8 @@
+"""Functions for extracting features from images.
+"""
+
+from typing import Iterable, Mapping, Any
+
 import dask
 import dask.bag
 import dask.dataframe
@@ -7,7 +12,7 @@
 from .texture import texture_features, _texture_features_meta
 
 
-def _bbox_features_meta(channel_names):
+def _bbox_features_meta(channel_names: Iterable[str]) -> Mapping[str, type]:
     d = {
         "bbox_minr": float,
         "bbox_minc": float,
@@ -18,7 +23,19 @@ def _bbox_features_meta(channel_names):
     return d
 
 
-def bbox_features(p, channel_names):
+def bbox_features(p: Mapping, channel_names: Iterable[str]) -> Mapping[str, Any]:
+    """Extracts bbox features from image.
+
+    The bbox consist of four values: bbox_minr, bbox_minc, bbox_maxr, bbox_maxc.
+
+    Args:
+        p (Mapping): Contains a sequence of 4 numbers under key bbox.
+        channel_names (Iterable[str]): names of channels in the image.
+
+    Returns:
+        Mapping[str, Any]: extracted features.
+    """
+
     d = {
         "bbox_minr": p["bbox"][0],
         "bbox_minc": p["bbox"][1],
@@ -35,16 +52,25 @@ def extract_features(  # noqa: C901
     channel_names: list,
     types: list,
     maximum_pixel_value: int,
-    loader_meta: dict = {}
+    loader_meta: Mapping[str, type] = {}
 ) -> dask.dataframe.DataFrame:
-    """
-    Extract features from pixel data
-
-    Args:
-        images (dask.bag): bag containing dictionaries of image data
+    """Extracts requested features from pixel values in images.
+
+    Keyword Args:
+        images (dask.bag.Bag): bag of mappings containing image data. Check each feature
+          extraction method (:func:`bbox_features`, :func:`scip.features.intensity.intensity_features`,
+          :func:`shape_features` and :func:`texture_features`) to see what keys
+          must be present in each mapping.
+        channel_names (list): names of channels in the image.
+        types (list): feature types to be extracted from the image.
+        maximum_pixel_value (int): theoretical maximal value in the image.
+        loader_meta (Mapping[str, type], optional): data type mapping of meta keys extracted
+          by the loader. Defaults to {}.
 
     Returns:
-        dask.bag: bag containing dictionaries of image features
+        dask.dataframe.DataFrame:
+          dataframe containing all extracted features (columns) for all
+          images (rows) in the input bag.
     """
 
     def features_partition(part):

src/scip/features/intensity.py

@@ -1,3 +1,5 @@
+from typing import Mapping, List, Any
+
 import numpy
 import scipy.stats
 from scipy.ndimage import convolve
@@ -17,7 +19,7 @@
 ]
 
 
-def _intensity_features_meta(channel_names):
+def _intensity_features_meta(channel_names: List[str]) -> Mapping[str, type]:
     out = {}
     for i in channel_names:
         out.update({f"{p}_{i}": float for p in props})
@@ -31,7 +33,7 @@ def _intensity_features_meta(channel_names):
     return out
 
 
-def _row(pixels, i):
+def _row(pixels: numpy.ndarray, i: int) -> Mapping[str, Any]:
     quartiles = numpy.quantile(pixels, q=(0.25, 0.75))
 
     d = {
@@ -51,7 +53,7 @@ def _row(pixels, i):
     return d
 
 
-def intensity_features(sample, channel_names):
+def intensity_features(sample: Mapping[str, Any], channel_names: List[str]) -> Mapping[str, Any]:
     """Compute intensity features.
 
     Find following intensity features based on masked pixel values:
@@ -76,10 +78,12 @@ def intensity_features(sample, channel_names):
         8. Background substracted edge values of union of masks
 
     Args:
-        sample (dict): dictionary including image data
+        sample (Mapping): mapping with pixels, mask, combined_mask, background and
+          combined background key.
+        channel_names (List[str]): names of channels in the image.
 
     Returns:
-        dict: dictionary including computed intensity features
+        Mapping[str, Any]: extracted features
     """
 
     features_dict = {}