From 4f660763e8270ff84aa5f1f273356fd331a9b607 Mon Sep 17 00:00:00 2001
From: Vincent Traag <vincent@traag.net>
Date: Mon, 18 Mar 2024 10:21:59 +0100
Subject: [PATCH] doc: Correct escape of math.

Fixes #173
---
 src/leidenalg/Optimiser.py       | 10 +++++-----
 src/leidenalg/VertexPartition.py |  4 ++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/leidenalg/Optimiser.py b/src/leidenalg/Optimiser.py
index 27e2c46a..b6c839e1 100644
--- a/src/leidenalg/Optimiser.py
+++ b/src/leidenalg/Optimiser.py
@@ -24,19 +24,19 @@ class Optimiser(object):
   algorithm that cannot be translated well in this framework: when merging
   subcommunities in the refinement procedure, it does not consider whether they
   are sufficiently well connected to the rest of the community. This
-  implementation therefore does not guarantee subpartition :math:`\gamma`-density.
+  implementation therefore does not guarantee subpartition :math:`\\gamma`-density.
   However, all other guarantees still hold:
 
   After each iteration
-    1. :math:`\gamma`-separation
-    2. :math:`\gamma`-connectivity
+    1. :math:`\\gamma`-separation
+    2. :math:`\\gamma`-connectivity
 
   After a stable iteration
     3. Node optimality
     4. Some subsets are locally optimally assigned
 
   Asymptotically
-    5. Uniform :math:`\gamma`-density
+    5. Uniform :math:`\\gamma`-density
     6. Subset optimality
 
   The optimiser class provides a number of different methods for optimising a
@@ -349,7 +349,7 @@ def optimise_partition_multiplex(self, partitions, layer_weights=None, n_iterati
     layer :math:`k` and the weight by :math:`\\lambda_k`, the overall quality
     is then
 
-    .. math:: Q = \sum_k \\lambda_k Q_k.
+    .. math:: Q = \\sum_k \\lambda_k Q_k.
 
     This is particularly useful for graphs containing negative links. When
     separating the graph in two graphs, the one containing only the positive
diff --git a/src/leidenalg/VertexPartition.py b/src/leidenalg/VertexPartition.py
index 7e3d24f7..50372cfb 100644
--- a/src/leidenalg/VertexPartition.py
+++ b/src/leidenalg/VertexPartition.py
@@ -410,7 +410,7 @@ class ModularityVertexPartition(MutableVertexPartition):
   Note that for directed graphs a slightly different formulation is used, as
   proposed by Leicht and Newman [2]:
 
-  .. math:: Q = \\frac{1}{m} \\sum_{ij} \\left(A_{ij} - \\frac{k_i^\mathrm{out} k_j^\mathrm{in}}{m} \\right)\\delta(\\sigma_i, \\sigma_j),
+  .. math:: Q = \\frac{1}{m} \\sum_{ij} \\left(A_{ij} - \\frac{k_i^\\mathrm{out} k_j^\\mathrm{in}}{m} \\right)\\delta(\\sigma_i, \\sigma_j),
 
   where :math:`k_i^\\mathrm{out}` and :math:`k_i^\\mathrm{in}` refers to
   respectively the outdegree and indegree of node :math:`i`, and :math:`A_{ij}`
@@ -785,7 +785,7 @@ class RBConfigurationVertexPartition(LinearResolutionParameterVertexPartition):
   Note that for directed graphs a slightly different formulation is used, as
   proposed by Leicht and Newman [2]:
 
-  .. math:: Q = \\sum_{ij} \\left(A_{ij} - \\gamma \\frac{k_i^\mathrm{out} k_j^\mathrm{in}}{m} \\right)\\delta(\\sigma_i, \\sigma_j),
+  .. math:: Q = \\sum_{ij} \\left(A_{ij} - \\gamma \\frac{k_i^\\mathrm{out} k_j^\\mathrm{in}}{m} \\right)\\delta(\\sigma_i, \\sigma_j),
 
   where :math:`k_i^\\mathrm{out}` and :math:`k_i^\\mathrm{in}` refers to
   respectively the outdegree and indegree of node :math:`i`, and :math:`A_{ij}`