From 2defbc9d77413fc8141eb9745e2f462c454e91a6 Mon Sep 17 00:00:00 2001
From: Kristofers Solo <dev@kristofers.xyz>
Date: Tue, 22 Apr 2025 16:03:44 +0300
Subject: [PATCH] refactor: compact down function calls

---
 src/grovers_visualizer/main.py | 64 +++++++++++++++++++---------------
 1 file changed, 36 insertions(+), 28 deletions(-)

diff --git a/src/grovers_visualizer/main.py b/src/grovers_visualizer/main.py
index 181e3fb..13b5c38 100644
--- a/src/grovers_visualizer/main.py
+++ b/src/grovers_visualizer/main.py
@@ -9,9 +9,11 @@ using matplotlib.
 from collections.abc import Iterator
 from itertools import product
 from math import floor, pi, sqrt
+from typing import Callable
 
 import matplotlib.pyplot as plt
 import numpy as np
+import numpy.typing as npt
 from matplotlib.axes import Axes
 from matplotlib.container import BarContainer
 from qiskit import QuantumCircuit
@@ -77,6 +79,20 @@ def all_states(n_qubits: int) -> Iterator[QubitState]:
         yield QubitState("".join(bits))
 
 
+def optimal_grover_iterations(n_qubits: int) -> int:
+    """Return the optimal number of Grover iterations for n qubits."""
+    return floor(pi / 4 * sqrt(2**n_qubits))
+
+
+def get_bar_color(state: str, target_state: QubitState | None, iteration: int, optimal_iteration: int | None) -> str:
+    """Return the color for a bar based on state and iteration."""
+    if state != target_state:
+        return "skyblue"
+    if optimal_iteration and iteration % optimal_iteration == 0 and iteration != 0:
+        return "green"
+    return "orange"
+
+
 def plot_amplitudes_live(
     ax: Axes,
     bars: BarContainer,
@@ -87,25 +103,20 @@ def plot_amplitudes_live(
     target_state: QubitState | None = None,
     optimal_iteration: int | None = None,
 ) -> None:
-    amplitudes = statevector.data.real  # Real part of amplitudes
+    amplitudes: npt.NDArray[np.float64] = statevector.data.real  # Real part of amplitudes
     mean = np.mean(amplitudes)
+
     for bar, state, amp in zip(bars, basis_states, amplitudes, strict=False):
         bar.set_height(amp)
-        if state == target_state:
-            if optimal_iteration is not None and iteration == optimal_iteration:
-                bar.set_color("green")
-            else:
-                bar.set_color("orange")
-        else:
-            bar.set_color("skyblue")
+        bar.set_color(get_bar_color(state, target_state, iteration, optimal_iteration))
+
     ax.set_title(f"Iteration {iteration}: {step_label}")
     ax.set_ylim(-1, 1)
-    # Remove previous mean line(s)
 
-    for l in ax.lines:
+    for l in ax.lines:  # Remove previous mean line(s)
         l.remove()
 
-    ax.axhline(mean, color="red", linestyle="--", label="Mean")
+    ax.axhline(float(mean), color="red", linestyle="--", label="Mean")
 
     if not ax.get_legend():
         ax.legend(loc="upper right")
@@ -116,7 +127,7 @@ def main() -> None:
     target_state = QubitState("1010")
     n_qubits = len(target_state)
     basis_states = [str(bit) for bit in all_states(n_qubits)]
-    optimal_iterations = floor(pi / 4 * sqrt(2**n_qubits))
+    optimal_iterations = optimal_grover_iterations(n_qubits)
 
     plt.ion()
     fig, ax = plt.subplots(figsize=(8, 3))
@@ -126,28 +137,25 @@ def main() -> None:
     ax.set_ylim(-1, 1)
     ax.set_title("Grover Amplitudes")
 
+    def step_and_plot(
+        operation: Callable[[QuantumCircuit], None] | None,
+        step_label: str,
+        iteration: int,
+    ) -> None:
+        if operation is not None:
+            operation(qc)
+        sv = Statevector.from_instruction(qc)
+        plot_amplitudes_live(ax, bars, sv, basis_states, step_label, iteration, target_state, optimal_iterations)
+
     # Start with Hadamard
     qc = QuantumCircuit(n_qubits)
     qc.h(range(n_qubits))
-    sv = Statevector.from_instruction(qc)
-    plot_amplitudes_live(ax, bars, sv, basis_states, "Hadamard (Initialization)", 0, target_state, optimal_iterations)
+    step_and_plot(None, "Hadamard (Initialization)", 0)
 
     iteration = 1
     while plt.fignum_exists(fig.number):
-        # Oracle phase
-        oracle(qc, target_state)
-        sv = Statevector.from_instruction(qc)
-        plot_amplitudes_live(
-            ax, bars, sv, basis_states, "Oracle (Query Phase)", iteration, target_state, optimal_iterations
-        )
-
-        # Diffusion phase
-        diffusion(qc, n_qubits)
-        sv = Statevector.from_instruction(qc)
-        plot_amplitudes_live(
-            ax, bars, sv, basis_states, "Diffusion (Inversion Phase)", iteration, target_state, optimal_iterations
-        )
-
+        step_and_plot(lambda qc: oracle(qc, target_state), "Oracle (Query Phase)", iteration)
+        step_and_plot(lambda qc: diffusion(qc, n_qubits), "Diffusion (Inversion Phase)", iteration)
         iteration += 1
 
     plt.ioff()