Put the math expression within $…$:

\(\LaTeX{}\)

$\Pi$

$ a * b = c ^ b $

$ 2^{\frac{n-1}{3}} $

$ \int_a^b f(x)\,dx. $

\( \int_a^b f(x)\,dx. \)

$ \rho {\rm{FOD}} = \sum\limits{\sigma ,i} {(\delta _1 - \delta _2 n_i^\sigma )

\phi _i^\sigma ({\bf{r}})

^2} $

$$ \rho {\rm{FOD}} = \sum\limits{\sigma ,i} {(\delta _1 - \delta _2 n_i^\sigma )

\phi _i^\sigma ({\bf{r}})

^2} $$

Here is a liquid filter.

`escape inline code`
inline code
Here is a capture block.

100 / 3 = 33

⁣1. 21312
⁣2. 21312
⁣4. 4214

>

import networkx as nx
from collections import Counter

diagrams = defaultdict(list)
particle_counts = defaultdict(Counter)

for (a, b), neighbors in common_neighbors.items():
    # Build up the graph of connections between the
    # common neighbors of a and b.
    g = nx.Graph()
    for i in neighbors:
        for j in set(nl.point_indices[
            nl.query_point_indices == i]).intersection(neighbors):
            g.add_edge(i, j)

    # Define the identifiers for a CNA diagram:
    # The first integer is 1 if the particles are bonded, otherwise 2
    # The second integer is the number of shared neighbors
    # The third integer is the number of bonds among shared neighbors
    # The fourth integer is an index, just to ensure uniqueness of diagrams
    diagram_type = 2-int(b in nl.point_indices[nl.query_point_indices == a])
    key = (diagram_type, len(neighbors), g.number_of_edges())
    # If we've seen any neighborhood graphs with this signature,
    # we explicitly check if the two graphs are identical to
    # determine whether to save this one. Otherwise, we add
    # the new graph immediately.
    if key in diagrams:
        isomorphs = [nx.is_isomorphic(g, h) for h in diagrams[key]]
        if any(isomorphs):
            idx = isomorphs.index(True)
        else:
            diagrams[key].append(g)
            idx = diagrams[key].index(g)
    else:
        diagrams[key].append(g)
        idx = diagrams[key].index(g)
    cna_signature = key + (idx,)
    particle_counts[a].update([cna_signature])