Update app.py

app.py

@@ -3,6 +3,11 @@ import marimo
 __generated_with = "0.11.5"
 app = marimo.App(width="medium")
 
+# /// script
+# [tool.marimo.display]
+# theme = "dark"
+# ///
+
 
 @app.cell(hide_code=True)
 def _(mo):
@@ -131,8 +136,6 @@ def _(mo):
 
 @app.cell
 def _(errors, plt, scalar_products):
-
-
     # Create a figure with two subplots side by side
     fig, axs = plt.subplots(1, 2, figsize=(12, 5))
 
@@ -171,8 +174,7 @@ def _(mo):
 
         1. Prove the convergence of the algorithm for generic initializations.
         2. Find the speed of convergence for arbitrary $n$
-        3. What is the distribution of the scalar products for (large / given) $n$? How about the maximal norm of a commutator
-        $$[u_{ij}, u_{kl}] \, ?$$
+        3. What is the distribution of the scalar products for (large / given) $n$? How about the maximal norm of a commutator $[u_{ij}, u_{kl}]$?
         """
     )
     return
@@ -187,7 +189,7 @@ def _(mo):
 
         1. S. Wang, “Quantum symmetry groups of finite spaces,” _Communications in mathematical physics_, vol. 195, no. 1, pp. 195–211, 1998.
         2. T. Banica, J. Bichon, and B. Collins, “Quantum permutation groups: a survey,” _Banach Center Publications_, vol. 78, no. 1, pp. 13–34, 2007.
-        3. T. Banica, I. Nechita, "Flat matrix models for quantum permutation groups," _Adv. Appl. Math._ 83, 24-46 (2017)
+        3. T. Banica, I. Nechita, "Flat matrix models for quantum permutation groups," _Adv. Appl. Math._ 83, 24-46 (2017).
         """
     )
     return
@@ -230,8 +232,6 @@ def _(np):
                         scalar_products.append(scalar_product)
 
         return scalar_products
-        
-
     return (
         error_QPM,
         generate_random_complex_gaussian_matrix,
@@ -306,4 +306,4 @@ def _():
 
 
 if __name__ == "__main__":
-    app.run()
+    app.run()