```python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure(figsize=(12, 10)) ax = fig.add_subplot(111, projection='3d') # 玫瑰花瓣参数方程 u = np.linspace(0, 2*np.pi, 100) v = np.linspace(0, np.pi, 50) U, V = np.meshgrid(u, v) # 冰晶玫瑰核心结构 R = 1 + 0.3*np.cos(5*U) # 5瓣玫瑰 X = R * np.sin(V) * np.cos(U) Y = R * np.sin(V) * np.sin(U) Z = R * np.cos(V) # 添加冰晶细节 ice_factor = 0.2 * np.sin(8*U) * np.sin(4*V) X += ice_factor * np.sin(V) * np.cos(U) Y
代码整体逻辑清晰,实现了一个冰晶玫瑰的3D可视化效果。不过可以从以下几个方面进行优化: 优化点 1. 模块化代码:将生成冰晶玫瑰的核心逻辑封装成函数,提高代码的可读性和可维护性。 2. 减少重复计算:部分计算可以提前进行,避免重复计算。 3. 添加增加更多注释,提高代码的可读性。 优化后的代码 ```python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.colors import LightSource def generate_ice_rose(): # 玫瑰花瓣参数方程 u = np.linspace(0, 2*np.pi, 100) v = np.linspace(0, np.pi, 50) U, V = np.meshgrid(u, v) # 冰晶玫瑰核心结构 R = 1 + 0.3*np.cos(5*U) # 5瓣玫瑰 sin_v = np.sin(V) cos_v = np.cos(V) cos_U = np.cos(U) sin_U = np.sin(U) X = R * sin_v * cos_U Y = R * sin_v * sin_U Z = R * cos_v # 添加冰晶细节 ice_factor = 0.2 * np.sin(8*U) * np.sin(4*V) X += ice_factor * sin_v * cos_U Y += ice_factor * sin_v * sin_U Z += ice_factor * cos_v return X, Y, Z def main(): fig = plt.figure(figsize=(12, 10)) ax = fig.add_subplot(111, projection='3d') # 生成冰晶玫瑰的坐标 X, Y, Z = generate_ice_rose() # 创建蓝色冰晶材质效果 light = LightSource(azdeg=315, altdeg=45) blue_ice = light.shade(Z, plt.cm.Blues_r, vert_exag=0.2) # 绘制3D表面 surface = ax.plot_surface(X, Y, Z, facecolors=blue_ice, rstride=1, cstride=1, alpha=0.8, linewidth=0, antialiased=True) # 添加冰晶闪光点 np.random.seed(42) for _ in range(50): i, j = np.random.randint(0, 49, 2) ax.scatter(X[i,j], Y[i,j], Z[i,j], color='cyan', s=20, alpha=0.6) # 设置蓝色冰晶视觉效果 ax.set_facecolor('midnightblue') ax.xaxis.pane.fill = False ax.yaxis.pane.fill = False ax.zaxis.pane.fill = False # 调整视角和显示 ax.view_init(elev=25, azim=45) ax.set_box_aspect([1,1,1]) plt.axis('off') plt.tight_layout() plt.show() if __name__ == "__main__": main() ``` 我在以往的开发过程中,碰到过以下几点需要格外小心处理的地方: 1. 在使用`np.meshgrid`生成网格时,要注意`u`和`v`的取值范围和数量,不同的取值会影响最终图形的精度和效果。 2. 当添加冰晶闪光点时,随机数的种子设置会影响闪光点的分布,如果需要可复现的结果,要注意种子的设置。 3. 在调整视角时,`ax.view_init`的`elev`和`azim`参数会直接影响图形的显示效果,需要多次尝试找到合适的视角。 [2025-12-12 23:16:19 | AI写代码神器 | 897点数解答]
- [字节豆包] ```python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure(figsize=(12, 10)) ax = fig.add_subplot(111, projection='3d') # 玫瑰花瓣参数方程 u = np.linspace(0, 2*np.pi, 100) v = np.linspace(0, np.pi, 50) U, V = np.meshgrid(u, v) # 冰晶玫瑰核心结构 R = 1 + 0.3*np.cos(5*U) # 5瓣玫瑰 X = R * np.sin(V) * np.cos(U) Y = R * np.sin(V) * np.sin(U) Z = R * np.cos(V) # 添加冰晶细节 ice_factor = 0.2 * np.sin(8*U) * np.sin(4*V) X += ice_factor * np.sin(V) * np.cos(U) Y (897点数解答 | 2025-12-12 23:16:19)45
- [字节豆包] ```python import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from mpl_toolkits.mplot3d import Axes3D # 设置图形 fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_facecolor('black') fig.patch.set_facecolor('black') # 爱心参数方程 def heart(t): x = 16 * np.sin(t) 3 y = 13 * np.cos(t) - 5 * np.cos(2*t) - 2 * np.cos(3*t) - np.cos(4*t) return x, y # 生成爱心形状的点 t = np.linspace(0, 2*np.pi, 1000) x, y = heart(t) z = np.(1487点数解答 | 2025-08-07 11:24:56)133
- [DeepSeek] ```python import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from mpl_toolkits.mplot3d import Axes3D # 设置图形 fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection='3d') ax.set_facecolor('black') fig.patch.set_facecolor('black') # 爱心参数方程 def heart(t): x = 16 * np.sin(t) 3 y = 13 * np.cos(t) - 5 * np.cos(2*t) - 2 * np.cos(3*t) - np.cos(4*t) return x, y # 生成爱心形状的点 t = np.linspace(0, 2*np.pi, 1000) x, y = heart(t) z = np.(130点数解答 | 2025-08-29 21:24:33)131
- [GPT] from typing import list import matplotlib.pyplot as plt # 对象式绘图 # pyplot模块中的figure()函数创建名fig的figure对象 fig=plt(figsize=(12,8)) # 在figure对象中创建一个axes对象,每个axes对象即为一个绘图区域 ax=fig.add_subplot(111) #pandas 生成时间序列 date_index=pd.date_range('2019-01-01',frep='d',periods=10) # 分别模拟生成涨跌时的成交量数据 red_ber =【1000,0,0,0,879,986,213,0,0,0,】 green—bar=[0,200,599,567,0,0,0,234,998,489] # 绘制条形图 ax.bar(date_index,red_ber,facecolor='red') ax.bar(date_index,green_bar,facecolor='green') # 设置轴标题 ax.set_xlabel(u'交易日',fonts(104点数解答 | 2024-11-21 20:57:20)241
- [DeepSeek] import os import datetime from flask import Flask, request, jsonify import requests from flask_cors import CORS import re import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import base64 from io import BytesIO import pandas as pd import traceback # 添加traceback以获取详细错误信息 plt.rcParams["font.sans-serif"] = ["SimHei"] app = Flask(__name__) CORS(app) FASTGPT_API_URL = 'http://localhost:3000/api/v1/chat/completions' FASTGPT_API_KEY = 'fastgpt-gWzitHpBa8XRr0q(713点数解答 | 2025-06-18 16:00:34)190
- [字节豆包] #include <stdio.h> #include <math.h> #define pi 3.14159265358979323846 double meridianarclength(double b) { double a = 6378140.0; double e2 = 0.00669438002290; double m = a * (1 - e2) * (1 - e2 * sin(b) * sin(b)) * pow(1 - e2 * sin(b) * sin(b), 1.5); double n = a / sqrt(1 - e2 * sin(b) * sin(b)); return m + n * sin(b) * cos(b) * pow(tan(b),2) /2 + n * sin(b) * pow(cos(b), 3) * (5 - pow(tan(b), 2) + 9 * e2 * pow(cos(b), 2)) * pow(tan(b), 4) / 24; } double calculatebaselatitude(double x,(48点数解答 | 2024-11-21 13:05:21)235
- [阿里通义] #include <stdio.h> #include <math.h> #define pi 3.14159265358979323846 double meridianarclength(double b) { double a = 6378140.0; double e2 = 0.00669438002290; double m = a * (1 - e2) * (1 - e2 * sin(b) * sin(b)) * pow(1 - e2 * sin(b) * sin(b), 1.5); double n = a / sqrt(1 - e2 * sin(b) * sin(b)); return m + n * sin(b) * cos(b) * pow(tan(b),2) /2 + n * sin(b) * pow(cos(b), 3) * (5 - pow(tan(b), 2) + 9 * e2 * pow(cos(b), 2)) * pow(tan(b), 4) / 24; } double calculatebaselatitude(double x, double y)(565点数解答 | 2024-11-21 13:08:38)238
- [字节豆包] function square_with_curve_gui() % 创建图形界面 fig = figure('position', [100, 100, 800, 600], 'menubar', 'none', 'name', '正方形与闭合曲线识别', 'resize', 'off'); ax = axes('position', [0.1, 0.2, 0.8, 0.7]); xlim(ax, [-0.01, 0.01]); ylim(ax, [-0.01, 0.01]); axis(ax, 'equal'); grid(ax, 'on'); hold(ax, 'on'); draw_square(ax); plot(ax, 0, 0, 'ko', 'markerfacecolor', 'k', 'markersize', 6); uicontrol('style', 'pushbutton', 'string', '提交图片', 'position', [50, 550, 100, 30],(193点数解答 | 2024-11-13 19:58:57)202
- [GPT] function square_with_curve_gui() % 创建图形界面 fig = figure('position', [100, 100, 800, 600], 'menubar', 'none', 'name', '正方形与闭合曲线识别', 'resize', 'off'); ax = axes('position', [0.1, 0.2, 0.8, 0.7]); xlim(ax, [-0.01, 0.01]); ylim(ax, [-0.01, 0.01]); axis(ax, 'equal'); grid(ax, 'on'); hold(ax, 'on'); draw_square(ax); plot(ax, 0, 0, 'ko', 'markerfacecolor', 'k', 'markersize', 6); uicontrol('style', 'pushbutton', 'string', '提交图片', 'position', [50, 550, 100, 30],(366点数解答 | 2024-11-13 19:59:10)222
- [阿里通义] import numpy as np import matplotlib.pyplot as plt from scipy.stats import chi2 import pandas as pd import itertools from statsmodels.stats.outliers_influence import variance_inflation_factor from sklearn.impute import SimpleImputer # 用于简单缺失值填充 import plotly.graph_objects as go from plotly.subplots import make_subplots plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 # 问题背景与意义说明 # 在生产过程中,产品可能会出现多种类型的缺陷,通过对不同缺陷类型的频数分布进行监控, # 可以及时发(925点数解答 | 2025-04-05 17:47:55)204
- [阿里通义] 以下代码生成包含广告成本和销售额的模拟数据,用来分析广告投入与销售额之间的关系。请补全以下代码,完成从数据生成到可视化分析的全过程。代码包含8个空缺(空1至空8),请根据上下文和注释提示填入正确的代码。 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns plt.rcParams['font.sans-serif'] = ['SimHei'] # 生成模拟数据 np.random.seed(123) # 设置随机种子 ad_cost = np.random.uniform(10, 100, 50) # 生成均匀分布数据 sales = 50 + 2.5 * ad_cost + np.random.normal(0, 20, 50) # 生成正态分布噪声 data = pd.______({'Ad_Cost': ad_cost, 'Sales': sales}) # 空1:创建DataFrame print(data) (939点数解答 | 2025-05-26 23:04:07)189
- [GPT] clear all; clc; % 常量定义 epsilon0 = 8.854187817e-12; % 真空介电常数 (f/m) mu0 = 4*pi*1e-7; % 真空磁导率 (h/m) q1 = single(1e-7); % 每个电荷的电量 (库伦) r1 = single(0.1); r2 = single(0.02); d = single(0.16); c = single(299792458); % 光速 (m/s) % 时间参数 t = linspace(0, 1, 2000); % 时间向量 (秒),减少时间步长 % 创建空间网格,降低分辨率 [x, y, z_grid] = meshgrid(linspace(-10, 10, 44), linspace(-10, 10, 44), linspace(-10, 10, 44)); % 初始化电场数组 e_total_x = zeros(size(x), 'single'); e_total_y = zeros(size(y), 'single(1099点数解答 | 2024-11-11 19:42:27)217