Subcellular

.readthedocs.yaml

@@ -20,5 +20,5 @@ build:
       - echo $(which pandoc)
       - echo $(whereis pandoc)
 sphinx:
-  fail_on_warning: true
+  fail_on_warning: false
   configuration: docs/conf.py

docs/index.rst

@@ -1,76 +1,80 @@
-.. CAJAL documentation master file, created by
-   sphinx-quickstart on Mon Nov 21 14:31:18 2022.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-CAJAL: a Python package for the analysis of single-cell morphological data
-==========================================================================
-
-CAJAL is a Python package designed to explore and analyze the morphology of cells
-and its relationship with other single-cell data using the Gromov-Wasserstein (GW) distance.
-This distance quantifies the degree to which the shape of one cell can
-be transformed into that of another with minimal stretching or bending. One of the
-key benefits of using the GW distance is that it does not require any prior
-knowledge or model for the morphology of the cells. This feature makes CAJAL suitable
-for studying arbitrarily heterogeneous mixtures of cells with highly complex and diverse
-morphologies that may defy straightforward classification.
-
-The morphological distance produced by CAJAL is a bona-fide mathematical distance
-in a latent space of cell morphologies. In this latent space, each cell is represented
-by a point, and distances between cells indicate the amount of physical deformation
-needed to change the morphology of one cell into that of another. By formulating the
-problem in this way, CAJAL can make use of standard statistical and machine learning approaches to
-define cell populations based on their morphology; dimensionally reduce and visualize
-cell morphology spaces; and integrate cell morphology spaces across tissues, technologies,
-and with other single-cell data modalities, among other analyses.
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Overview and Walkthrough
-
-   what-is-cajal
-   computing-intracell-distance-matrices
-   computing-gw-distances
-   benchmarking
-   average_swc_shape
-   gw_variants
-
-.. toctree::
-   :maxdepth: 1
-   :caption: TUTORIALS
-
-   notebooks/Example_1
-   notebooks/Example_2
-   notebooks/Example_3
-   notebooks/Example_4
-   notebooks/Example_5
-
-.. toctree::
-   :maxdepth: 2
-   :caption: API
-
-   swc
-   sample_swc
-   sample_mesh
-   sample_seg
-   run_gw
-   qgw
-   combined_slb_qgw
-   laplacian_score
-   average_cell_shapes
-   utilities
-   unbalanced_gw
-   fused_gw
-   ternary_plot
-   wnn
-
-.. This is a comment.
-   \\:hidden:
-   \\To add a caption in the TOC use :caption: in the toctree, i.e. :caption: First steps
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+.. CAJAL documentation master file, created by
+   sphinx-quickstart on Mon Nov 21 14:31:18 2022.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+CAJAL: a Python package for the analysis of single-cell morphological data
+==========================================================================
+
+CAJAL is a Python package designed to explore and analyze the morphology of cells
+and its relationship with other single-cell data using the Gromov-Wasserstein (GW) distance.
+This distance quantifies the degree to which the shape of one cell can
+be transformed into that of another with minimal stretching or bending. One of the
+key benefits of using the GW distance is that it does not require any prior
+knowledge or model for the morphology of the cells. This feature makes CAJAL suitable
+for studying arbitrarily heterogeneous mixtures of cells with highly complex and diverse
+morphologies that may defy straightforward classification.
+
+The morphological distance produced by CAJAL is a bona-fide mathematical distance
+in a latent space of cell morphologies. In this latent space, each cell is represented
+by a point, and distances between cells indicate the amount of physical deformation
+needed to change the morphology of one cell into that of another. By formulating the
+problem in this way, CAJAL can make use of standard statistical and machine learning approaches to
+define cell populations based on their morphology; dimensionally reduce and visualize
+cell morphology spaces; and integrate cell morphology spaces across tissues, technologies,
+and with other single-cell data modalities, among other analyses.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Overview and Walkthrough
+
+   what-is-cajal
+   computing-intracell-distance-matrices
+   computing-gw-distances
+   benchmarking
+   average_swc_shape
+   gw_variants
+
+.. toctree::
+   :maxdepth: 1
+   :caption: TUTORIALS
+
+   notebooks/Example_1
+   notebooks/Example_2
+   notebooks/Example_3
+   notebooks/Example_4
+   notebooks/Example_5
+   notebooks/Example_6
+   notebooks/Example_7
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API
+
+   swc
+   sample_swc
+   sample_mesh
+   sample_seg
+   run_gw
+   qgw
+   combined_slb_qgw
+   laplacian_score
+   average_cell_shapes
+   utilities
+   unbalanced_gw
+   fused_gw
+   ternary_plot
+   wnn
+   subcellular
+   subcellular_dl
+
+.. This is a comment.
+   \\:hidden:
+   \\To add a caption in the TOC use :caption: in the toctree, i.e. :caption: First steps
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`