pruning down warnings/errors in make html (#394)

* pruning down warnings/errors in make html

* remove pathlib import

* add missing docs images

* move notebook links to github

---------

Co-authored-by: Levi John Wolf <[email protected]>

Author: jGaboardi

docs/_static/images/missipq.png

@@ -304,3 +304,14 @@ @article{wolf2024confounded
     pages={0-0},
     journal={Annals of the American Association of Geographers}
 }
+
+@article{AnselinLi2019,
+  title={{Operational Local Join Count Statistics for Cluster Detection}},
+  author={Anselin, Luc and Li, Xun},
+  journal={Journal of Geographical Systems},
+  volume={21},
+  number={2},
+  pages={189--210},
+  year={2019},
+  publisher={Springer}
+}

docs/_static/images/mvmoran.png

@@ -1,28 +1,5 @@
-# -*- coding: utf-8 -*-
-#
-# giddy documentation build configuration file, created by
-# sphinx-quickstart on Wed Jun  6 15:54:22 2018.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-
 import sphinx_bootstrap_theme
 
-sys.path.insert(0, os.path.abspath("../"))
-
 # import your package to obtain the version info to display on the docs website
 import esda  # noqa E402
 
@@ -34,7 +11,7 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = [  # 'sphinx_gallery.gen_gallery',
+extensions = [
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.viewcode",
@@ -297,7 +274,7 @@ def setup(app):
 
 # This is processed by Jinja2 and inserted before each notebook
 nbsphinx_prolog = r"""
-{% set docname = env.doc2path(env.docname, base='docsrc/') %}
+{% set docname = env.doc2path(env.docname, base=None) %}
 
 .. only:: html
 
@@ -308,9 +285,9 @@ def setup(app):
 
         This page was generated from `{{ docname }}`__.
         Interactive online version:
-        :raw-html:`<a href="https://mybinder.org/v2/gh/pysal/esda/master?filepath={{ docname }}"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>`
+        :raw-html:`<a href="https://mybinder.org/v2/gh/pysal/esda/main?filepath={{ docname }}"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>`
 
-    __ https://github.com/pysal/esda/{{ docname }}
+    __ https://github.com/pysal/esda/blob/main/{{ docname }}
 
 
 .. raw:: latex
@@ -335,6 +312,6 @@ def setup(app):
     "--InlineBackend.rc={'figure.dpi': 96}",
 ]
 
-mathjax_config = {
+mathjax3_config = {
     "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}},
 }

docs/_static/images/silhouettes.png

@@ -10,7 +10,7 @@ ESDA is an open-source Python library for the exploratory analysis of spatial da
     <div class="container-fluid">
       <div class="row equal-height">
         <div class="col-12">
-            <a href="https://nbviewer.org/github/pysal/esda/blob/main/notebooks/spatial_autocorrelation_for_areal_unit_data.ipynb" class="thumbnail">
+            <a href="https://github.com/pysal/esda/blob/main/notebooks/spatial_autocorrelation_for_areal_unit_data.ipynb" class="thumbnail">
                 <img src="_static/images/prices.png" class="img-fluid center-block w-100">
                 <div class="caption text-center">
                 <h6>Spatial Autocorrelation for continuous and discrete data</h6>
@@ -20,15 +20,15 @@ ESDA is an open-source Python library for the exploratory analysis of spatial da
         </div>
         <div class="row equal-height">
         <div class="col-6">
-            <a href="https://nbviewer.org/github/pysal/esda/blob/main/notebooks/shape-measures.ipynb" class="thumbnail">
+            <a href="https://github.com/pysal/esda/blob/main/notebooks/shape-measures.ipynb" class="thumbnail">
                 <img src="_static/images/missipq.png" class="img-fluid center-block w-100">
                 <div class="caption text-center">
                 <h6>Shape Regularity Statistics</h6>
                 </div>
             </a>
         </div>
         <div class="col-6">
-            <a href="https://nbviewer.org/github/pysal/esda/blob/main/notebooks/geosilhouettes.ipynb" class="thumbnail">
+            <a href="https://github.com/pysal/esda/blob/main/notebooks/geosilhouettes.ipynb" class="thumbnail">
                 <img src="_static/images/silhouettes.png" class="img-fluid center-block w-100">
                 <div class="caption text-center">
                 <h6>Boundary and Region Statistics
@@ -39,7 +39,7 @@ ESDA is an open-source Python library for the exploratory analysis of spatial da
         </div>
         <div class='row equal-height'>
         <div class="col-6">
-        <a href="https://nbviewer.org/github/pysal/esda/blob/main/notebooks/multivariable_moran.ipynb" class="thumbnail">
+        <a href="https://github.com/pysal/esda/blob/main/notebooks/multivariable_moran.ipynb" class="thumbnail">
                 <img src="_static/images/mvmoran.png" class="img-fluid center-block w-100">
                 <div class="caption text-center">
                 <h6>Multivariable Moran Statistics
@@ -48,7 +48,7 @@ ESDA is an open-source Python library for the exploratory analysis of spatial da
             </a>
         </div>
         <div class="col-6">
-        <a href="https://nbviewer.org/github/pysal/esda/blob/main/notebooks/localjoincounts.ipynb" class="thumbnail">
+        <a href="https://github.com/pysal/esda/blob/main/notebooks/localjoincounts.ipynb" class="thumbnail">
                 <img src="_static/images/joincount.png" class="img-fluid center-block w-100">
                 <div class="caption text-center">
                 <h6>Discrete Spatial Autocorrelation with Join Counts
@@ -60,16 +60,6 @@ ESDA is an open-source Python library for the exploratory analysis of spatial da
     </div>
 
 
-.. toctree::
-   :hidden:
-   :maxdepth: 3
-   :caption: Contents:
-
-   Installation <installation>
-   API <api>
-   Tutorial <tutorial>
-   References <references>
-
 ************
 Introduction
 ************

docs/_static/references.bib

@@ -5,11 +5,12 @@ Tutorial
     :maxdepth: 1
     :caption: Contents:
 
-    notebooks/spatialautocorrelation.ipynb
     notebooks/localjoincounts.ipynb
     notebooks/LOSH.ipynb
     notebooks/geosilhouettes.ipynb
     notebooks/adbscan_berlin_example.ipynb
     notebooks/shape-measures.ipynb
     notebooks/spatial_autocorrelation_for_areal_unit_data.ipynb
     notebooks/multivariable_moran.ipynb
+    notebooks/correlogram.ipynb
+    notebooks/topo.ipynb

docs/conf.py

@@ -217,7 +217,7 @@ def __init__(
             self.EI_sim = sim.sum() / permutations
             self.seI_sim = np.array(sim).std()
             self.VI_sim = self.seI_sim**2
-            with np.errstate(divide='ignore'):
+            with np.errstate(divide="ignore"):
                 self.z_sim = (self.I - self.EI_sim) / self.seI_sim
             if self.z_sim > 0:
                 self.p_z_sim = stats.norm.sf(self.z_sim)
@@ -307,7 +307,7 @@ def by_col(
             documentation for the Moran statistic.
 
         Returns
-        --------
+        -------
         If inplace, None, and operation is conducted on dataframe
         in memory. Otherwise, returns a copy of the dataframe with
         the relevant columns attached.
@@ -540,7 +540,7 @@ def __init__(self, x, y, w, transformation="r", permutations=PERMUTATIONS):
             self.EI_sim = sim.sum() / permutations
             self.seI_sim = np.array(sim).std()
             self.VI_sim = self.seI_sim**2
-            with np.errstate(divide='ignore'):
+            with np.errstate(divide="ignore"):
                 self.z_sim = (self.I - self.EI_sim) / self.seI_sim
             if self.z_sim > 0:
                 self.p_z_sim = stats.norm.sf(self.z_sim)
@@ -603,7 +603,7 @@ def by_col(
             documentation for the Moran_BV statistic.
 
         Returns
-        --------
+        -------
         If inplace, None, and operation is conducted on dataframe
         in memory. Otherwise, returns a copy of the dataframe with
         the relevant columns attached.
@@ -1074,7 +1074,7 @@ def by_col(
             documentation for the Moran_Rate statistic.
 
         Returns
-        --------
+        -------
         If inplace, None, and operation is conducted on dataframe
         in memory. Otherwise, returns a copy of the dataframe with
         the relevant columns attached.
@@ -1217,13 +1217,13 @@ class Moran_Local:  # noqa: N801
         variance of Is from permutations
     EI : array
         analytical expectation of Is under total permutation,
-        from :cite:`Anselin1995`. Is the same at each site,
+        from :cite:`Anselin95`. Is the same at each site,
         and equal to the expectation of I itself when
         transformation='r'. We recommend using EI_sim, not EI,
         for analysis. This EI is only provided for reproducibility.
     VI : array
         analytical variance of Is under total permutation,
-        from :cite:`Anselin1995`. Varies according only to
+        from :cite:`Anselin95`. Varies according only to
         cardinality. We recommend using VI_sim, not VI, for
         analysis. This VI is only provided for reproducibility.
     EIc : array
@@ -1350,7 +1350,7 @@ def __init__(
                 self.EI_sim = self.sim.mean(axis=0)
                 self.seI_sim = self.sim.std(axis=0)
                 self.VI_sim = self.seI_sim * self.seI_sim
-                with np.errstate(divide='ignore'):
+                with np.errstate(divide="ignore"):
                     self.z_sim = (self.Is - self.EI_sim) / self.seI_sim
                 self.p_z_sim = stats.norm.sf(np.abs(self.z_sim))
             else:
@@ -1460,7 +1460,7 @@ def by_col(
             documentation for the Moran_Local statistic.
 
         Returns
-        --------
+        -------
         If inplace, None, and operation is conducted on dataframe
         in memory. Otherwise, returns a copy of the dataframe with
         the relevant columns attached.
@@ -1828,7 +1828,7 @@ def __init__(
                 self.EI_sim = sim.mean(axis=0)
                 self.seI_sim = sim.std(axis=0)
                 self.VI_sim = self.seI_sim * self.seI_sim
-                with np.errstate(divide='ignore'):
+                with np.errstate(divide="ignore"):
                     self.z_sim = (self.Is - self.EI_sim) / self.seI_sim
                 self.p_z_sim = stats.norm.sf(np.abs(self.z_sim))
 
@@ -1899,7 +1899,7 @@ def by_col(
             documentation for the Moran_Local_BV statistic.
 
         Returns
-        --------
+        -------
         If inplace, None, and operation is conducted on dataframe
         in memory. Otherwise, returns a copy of the dataframe with
         the relevant columns attached.
@@ -2284,7 +2284,7 @@ def by_col(
             documentation for the Moran_Local_Rate statistic.
 
         Returns
-        --------
+        -------
         If inplace, None, and operation is conducted on dataframe
         in memory. Otherwise, returns a copy of the dataframe with
         the relevant columns attached.
@@ -2782,8 +2782,8 @@ def _wikh_fast(W, sokal_correction=False):
     where this function is called, the defaults should always return
     the version in the original :cite:`Anselin1995 paper`.
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     W : scipy sparse matrix
         a sparse matrix describing the spatial relationships
         between observations.
@@ -2806,7 +2806,7 @@ def _wikh_fast(W, sokal_correction=False):
 def _wikh_numba(n, row, col, data, sokal_correction=False):
     """
     This is a fast implementation of the wi(kh) function from
-    :cite:`Anselin1995`.
+    :cite:`Anselin95`.
 
     This uses numpy to compute the outer product of each observation's
     weights, after removing the w_ii entry. Then, the sum of the outer
@@ -2831,7 +2831,7 @@ def _wikh_numba(n, row, col, data, sokal_correction=False):
 def _wikh_slow(W, sokal_correction=False):
     """
     This is a slow implementation of the wi(kh) function from
-    :cite:`Anselin1995`
+    :cite:`Anselin95`
 
     This does three nested for-loops over n, doing the literal operations
     stated by the expression.

docs/index.rst

@@ -58,7 +58,7 @@ def path_silhouette(
 
 
     Parameters
-    -----------
+    ----------
     data    :   np.ndarray (N,P)
                 matrix of data with N observations and P covariates.
     labels  :   np.ndarray (N,)
@@ -94,7 +94,7 @@ def path_silhouette(
                     asymmetry is ignored.
 
     Returns
-    --------
+    -------
     An (N_obs,) array of the path silhouette values for each observation.
     """
     if not HAS_REQUIREMENTS:
@@ -427,7 +427,7 @@ def silhouette_alist(data, labels, alist, indices=None, metric=skp.euclidean_dis
                 a function that takes an argument (data) and returns the all-pairs
                 distances/dissimilarity between observations.
 
-    Results
+    Returns
     -------
     pandas.DataFrame, copy of the adjacency list `alist`, with an additional
     column called `silhouette` that contains the pseudo-silhouette values