the difference between import numpy as np and from numpy import pi

本文探讨了在Python中使用numpy库时,`import numpy as np`和`from numpy import array`两种导入方式的区别。`as`关键字用于创建别名,简化长模块名的使用,而`from...import`则允许选择性导入模块的部分内容。作者推荐在需要频繁调用numpy功能时使用`np`别名,以提高代码可读性和效率。

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import numpy as np 和 from numpy import pi的区别(转载自stack Overflow)

as keyword allows you to use shorthands and rename stuff. Particularly useful when you have long names or use things from that module a lot. In numpy you use a lot of stuff from it, so it’s certainly better to write np. than numpy. when there’s a lot of such calls, isn’t it?

from … import is just a way of importing only some components. If you use that lib a lot in your code, you do import . If you need only some elements and names don’t clash with yours, you do from import ,

Important: from … import still allows as notation! They are not counterparts. My fav shorthand is from pprint import pprint as pp when debugging in the console.

原链接:https://stackoverflow.com/questions/57889937/what-is-the-difference-between-import-numpy-as-np-and-from-numpy-import-array

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import quad from scipy.optimize import fsolve plt.rcParams['font.sans-serif'] = ['SimHei'] # 解决中文显示问题 plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题 # ============================== # 材料参数设定(以钆镓石榴石为例) # ============================== Tc = 2.0 # 居里温度 (K) H_max = 5.0 # 最大磁场强度 (T) C_m = 10.0 # 磁热容 (J/(kg·K)) mu0 = 4*np.pi*1e-7 # 真空磁导率 # ============================== # 磁熵变计算函数 (ΔS_M) # ============================== def delta_SM(T, H): """磁熵变积分公式 ΔS_M = ∫(∂M/∂T)_H dH""" # 简化假设:M = M_sat * (1 - (T/Tc)^1.5) ,在H足够大时近似 M_sat = 1.0 # 饱和磁化强度 (A/m) dMdT = -1.5 * M_sat * (T**(0.5))/Tc**1.5 # 导数项 return quad(lambda h: dMdT, 0, H)[0] # 对H积分 # ============================== # 温度-熵关系模型 # ============================== def entropy(T, H=0): """总熵 = 磁熵 + 晶格熵""" S_mag = delta_SM(T, H) S_lattice = C_m * np.log(T) if T > 0 else -np.inf # 处理边界情况 return S_mag + S_lattice def temperature(S_target, H): """求解给定熵和磁场时的温度""" def f(T): return entropy(T, H) - S_target try: return fsolve(f, x0=Tc)[0] except: return np.nan # ============================== # 磁制冷循环模拟 # ============================== # 初始状态 T_initial = 1.5 # 初始温度 (K) # 等温磁化 (A→B) T1 = T_initial S1 = entropy(T1, H=0) S2 = entropy(T1, H=H_max) # 绝热退磁 (B→C) H_steps_rev = np.linspace(H_max, 0, 100) S_BC = np.full_like(H_steps_rev, S2) T_BC = [temperature(s, h) for s, h in zip(S_BC, H_steps_rev)] # 等温退磁 (C→D) T2 = np.nanmin(T_BC) S3 = entropy(T2, H=0) S4 = entropy(T2, H=H_max) # 绝热磁化 (D→A) H_steps_mag = np.linspace(0, H_max, 100) S_DA = np.full_like(H_steps_mag, S3) T_DA = [temperature(s, h) for s, h in zip(S_DA, H_steps_mag)] # ============================== # 绘图 # ============================== plt.figure(figsize=(12, 6)) # 温熵图 (T-S图) plt.subplot(1,2,1) T_range = np.linspace(0.5, 3, 100) for H in [0, H_max]: S = [entropy(T, H) for T in T_range] plt.plot(S, T_range, label=f'H={H} T') # 绘制循环路径 plt.plot([S1, S2], [T1, T1], 'r--', label='等温磁化') # A→B plt.plot([entropy(t, h) for t, h in zip(T_BC, H_steps_rev)], T_BC, 'g--', label='绝热退磁') # B→C plt.plot([S3, S4], [T2, T2], 'b--', label='等温退磁') # C→D plt.plot([entropy(t, h) for t, h in zip(T_DA, H_steps_mag)], T_DA, 'm--', label='绝热磁化') # D→A plt.xlabel('熵 S (J/kg·K)') plt.ylabel('温度 T (K)') plt.title('温熵图 (T-S Diagram)') plt.grid(True) plt.legend() # 磁制冷循环图 plt.subplot(1,2,2) H_cycle = np.concatenate([ np.linspace(0, H_max, 100), np.linspace(H_max, 0, 100), np.linspace(0, -H_max, 100), np.linspace(-H_max, 0, 100) ]) T_cycle = np.concatenate([ [T1]*100, T_BC, [T2]*100, T_DA ]) plt.plot(H_cycle, T_cycle, 'r-') plt.xlabel('磁场强度 H (T)') plt.ylabel('温度 T (K)') plt.title('磁制冷循环温度变化') plt.grid(True) plt.tight_layout() plt.show() 帮我详细讲解这个代码中函数的作用
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05-15
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