import matplotlib.pyplot as plt
import numpy as np
import fastdataing as fd

def colorbar(orientation="vertical"):
	if orientation=="vertical":
		fig = fd.add_fig(figsize=(1.5, 6))
		ax = fig.add_axes([0.45, 0.2, 0.10, 0.6])
	else:
		fig = fd.add_fig(figsize=(6, 1.5))
		ax = fig.add_axes([0.2, 0.45, 0.6, 0.20])
	
	cmap = plt.get_cmap('jet')
	norm = plt.Normalize(vmin=0, vmax=1)
	cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, orientation=orientation)
	cb.set_ticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
	plt.savefig("./colorbar.png",dpi=600,transparent=True)
	# 显示图像
	plt.show()


def colorbar_T(orientation="vertical"):
	if orientation=="vertical":
		fig = fd.add_fig(figsize=(1.5, 6))
		ax = fig.add_axes([0.45, 0.2, 0.10, 0.6])
	else:
		fig = fd.add_fig(figsize=(6, 1.5))
		ax = fig.add_axes([0.2, 0.45, 0.6, 0.20])
	
	cmap = plt.get_cmap('jet')
	norm = plt.Normalize(vmin=257, vmax=317)
	cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, orientation=orientation)
	cb.set_ticks([257, 272 , 287, 302, 317])
	plt.savefig("./colorbar_temp.png",dpi=600,transparent=True)
	# 显示图像
	plt.show()

if __name__ == "__main__":
	# 调用函数以显示色棒
	orientation = "horizontal" # vertical horizontal
	# colorbar(orientation=orientation)
	colorbar_T(orientation=orientation)

import matplotlib.pyplot as plt

# 创建示例数据
x = [1, 2, 3, 4]
y1 = [1, 4, 9, 16]
y2 = [1, 2, 3, 4]
y3 = [2, 3, 5, 7]

# 创建绘图对象
fig, ax = plt.subplots()

# 绘制数据曲线
line1, = ax.plot(x, y1, label='y = x^2')
line2, = ax.plot(x, y2, label='y = x')
line3, = ax.plot(x, y3, label='y = prime')

# 获取图例句柄和标签
handles, labels = ax.get_legend_handles_labels()

# 重新排列图例顺序
order = [2, 0, 1]  # 按照你想要的顺序排列
ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order])

# 显示图形
plt.show()

import glob
import numpy as np

# 使用glob找到所有前缀相同的文件
file_prefix = "your_file_prefix"
files = glob.glob(f"{file_prefix}*")

# 初始化一个空的列表来存储所有文件的数据
all_data = []

# 遍历所有找到的文件并使用loadtxt读取数据
for file in files:
    data = np.loadtxt(file)
    all_data.append(data)

# 如果需要将所有数据合并成一个数组，可以使用np.vstack或np.hstack
# 例如，假设每个文件的结构相同
combined_data = np.vstack(all_data)

print(combined_data)

import numpy as np

# 假设你的一行向量和一列向量如下
row_vector = np.array([1, 2, 3])  # 1行
column_vector = np.array([4, 5, 6])  # 1列

# 使用外积生成 m行n列 矩阵
result_matrix = np.outer(column_vector, row_vector)

print(result_matrix)

first_legend = plt.legend(handles=plt.gca().get_lines()[:3], loc='upper center')
plt.gca().add_artist(first_legend).get_frame().set_alpha(0)
second_legend = plt.legend(handles=plt.gca().get_lines()[3:], loc='upper right').get_frame().set_alpha(0)

from pathlib import Path
Path(PATH+"msd/").mkdir(parents=True,exist_ok=True)