fix pdf docs

deepmd/descriptor/se_a.py

@@ -96,8 +96,8 @@ class DescrptSeA(DescrptSe):
     .. math::
         (\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji}))
 
-    :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2`$` columns of
-    :math:`\mathcal{G}^i`$`. The equation of embedding network :math:`\mathcal{N}` can be found at
+    :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2` columns of
+    :math:`\mathcal{G}^i`. The equation of embedding network :math:`\mathcal{N}` can be found at
     :meth:`deepmd.utils.network.embedding_net`.
 
     Parameters

deepmd/descriptor/se_a_mask.py

@@ -70,8 +70,8 @@ class DescrptSeAMask(DescrptSeA):
     .. math::
         (\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji}))
 
-    :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2`$` columns of
-    :math:`\mathcal{G}^i`$`. The equation of embedding network :math:`\mathcal{N}` can be found at
+    :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2` columns of
+    :math:`\mathcal{G}^i`. The equation of embedding network :math:`\mathcal{N}` can be found at
     :meth:`deepmd.utils.network.embedding_net`.
     Specially for descriptor se_a_mask is a concise implementation of se_a.
     The difference is that se_a_mask only considered a non-pbc system.

deepmd/fit/ener.py

@@ -59,9 +59,9 @@ class EnerFitting(Fitting):
         \mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})=
             \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})
 
-    where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
+    where :math:`\mathbf{x} \in \mathbb{R}^{N_1}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
     is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and
-    :math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively,
+    :math:`\mathbf{b} \in \mathbb{R}^{N_2}` are weights and biases, respectively,
     both of which are trainable if `trainable[i]` is `True`. :math:`\boldsymbol{\phi}`
     is the activation function.
 
@@ -71,9 +71,9 @@ class EnerFitting(Fitting):
         \mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})=
             \mathbf{x}^T\mathbf{w}+\mathbf{b}
 
-    where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}`
+    where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}`
     is the output scalar. :math:`\mathbf{w} \in \mathbb{R}^{N_{n-1}}` and
-    :math:`\mathbf{b} \in \mathbb{R}`$` are weights and bias, respectively,
+    :math:`\mathbf{b} \in \mathbb{R}` are weights and bias, respectively,
     both of which are trainable if `trainable[n]` is `True`.
 
     Parameters

deepmd/utils/network.py

@@ -144,9 +144,9 @@ def embedding_net(
             \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}), & \text{otherwise} \\
         \end{cases}
 
-    where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
+    where :math:`\mathbf{x} \in \mathbb{R}^{N_1}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
     is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and
-    :math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively,
+    :math:`\mathbf{b} \in \mathbb{R}^{N_2}` are weights and biases, respectively,
     both of which are trainable if `trainable` is `True`. :math:`\boldsymbol{\phi}`
     is the activation function.
 

doc/conf.py

@@ -305,6 +305,10 @@ def setup(app):
     "fontpkg": r"""
 \usepackage{fontspec}
 \setmainfont{Symbola}
+""",
+    "preamble": r"""
+\usepackage{enumitem}
+\setlistdepth{99}
 """,
 }