请帮我编写一个python代码，用于手术排程，实现手术室黄金时间利用率最大化的效果，并进行可视化。 有10各手术间 25个医疗组，每个医疗组开展4种独有的术式，手术时间介于1-4个小时之间 其中医疗组1-5的手术日是周一，医疗组6-10的手术日是周二，医疗组11-15的手术日是周三，医疗组16-20的医疗组是周四，医疗组21-25的手术日是周五 每个手术医疗组最多可以使用两个手术间，比如医疗组1可以使用手术间1和手术间2，以此类推。

Sure, I can help you with that! Here's a Python code that can assist in scheduling surgeries and maximizing the utilization of the operating rooms:

import numpy as np
import matplotlib.pyplot as plt

num_operating_rooms = 10
num_medical_groups = 25
surgeries_per_group = 4
min_surgery_time = 1
max_surgery_time = 4

days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
groups_per_day = num_medical_groups // len(days_of_week)

surgery_times = np.random.randint(min_surgery_time, max_surgery_time+1, size=(num_medical_groups, surgeries_per_group))

schedule = np.zeros((len(days_of_week), num_operating_rooms))

for day_idx, day in enumerate(days_of_week):
    start_group_idx = day_idx * groups_per_day
    end_group_idx = start_group_idx + groups_per_day
    for group_idx in range(start_group_idx, end_group_idx):
        room_idx = group_idx % num_operating_rooms
        schedule[day_idx][room_idx] += np.sum(surgery_times[group_idx])

# Visualization
fig, ax = plt.subplots()
im = ax.imshow(schedule, cmap='YlOrRd')

# Show all ticks and label them with the respective list entries
ax.set_xticks(np.arange(num_operating_rooms))
ax.set_yticks(np.arange(len(days_of_week)))
ax.set_xticklabels(np.arange(1, num_operating_rooms+1))
ax.set_yticklabels(days_of_week)

# Loop over data dimensions and create text annotations
for i in range(len(days_of_week)):
    for j in range(num_operating_rooms):
        text = ax.text(j, i, int(schedule[i, j]),
                       ha="center", va="center", color="black")

ax.set_title("Operating Room Schedule")
fig.tight_layout()
plt.show()

This code uses numpy to generate random surgery times for each medical group and then creates a schedule matrix based on the given constraints. Finally, it visualizes the schedule using a heatmap.

Please note that this is a basic implementation and may not consider all possible constraints or optimization techniques. You can further customize and enhance the code based on your specific requirements.