{"id":978757,"date":"2024-12-27T06:40:24","date_gmt":"2024-12-26T22:40:24","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/978757.html"},"modified":"2024-12-27T06:40:26","modified_gmt":"2024-12-26T22:40:26","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%88%b6%e4%bd%9c%e5%bb%ba%e7%ad%91","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/978757.html","title":{"rendered":"\u5982\u4f55\u7528python\u5236\u4f5c\u5efa\u7b51"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u5236\u4f5c\u5efa\u7b51\"<\/p>\n

\u4f7f\u7528Python\u5236\u4f5c\u5efa\u7b51\u7684\u6838\u5fc3\u5728\u4e8e\uff1a\u4f7f\u75283D\u5efa\u6a21\u5e93\u521b\u5efa\u5efa\u7b51\u6a21\u578b\u3001\u5b9e\u73b0\u53c2\u6570\u5316\u8bbe\u8ba1\u3001\u8fdb\u884c\u5efa\u7b51\u53ef\u89c6\u5316\u3001\u7ed3\u5408\u7b97\u6cd5\u4f18\u5316\u8bbe\u8ba1\u3002<\/strong>\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0cPython\u53ef\u4ee5\u5728\u5efa\u7b51\u8bbe\u8ba1\u4e2d\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u5c55\u5f00\u8fd9\u4e9b\u65b9\u9762\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u30013D\u5efa\u6a21\u5e93\u521b\u5efa\u5efa\u7b51\u6a21\u578b<\/p>\n<\/p>\n

Python\u62e5\u6709\u591a\u79cd\u5f3a\u5927\u76843D\u5efa\u6a21\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u521b\u5efa\u5efa\u7b51\u6a21\u578b\u3002\u6700\u5e38\u7528\u7684\u5e93\u4e4b\u4e00\u662fBlender\u7684Python API\u3002Blender\u662f\u4e00\u6b3e\u514d\u8d39\u5f00\u6e90\u76843D\u5efa\u6a21\u8f6f\u4ef6\uff0c\u5176Python API\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u811a\u672c\u521b\u5efa\u548c\u4fee\u65393D\u6a21\u578b\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    Blender\u548cPython API<\/strong><\/p>\n<\/p>\n

    Blender\u7684Python API\u63d0\u4f9b\u4e86\u4e00\u79cd\u7075\u6d3b\u7684\u65b9\u6cd5\u6765\u521b\u5efa\u590d\u6742\u7684\u5efa\u7b51\u6a21\u578b\u3002\u7528\u6237\u53ef\u4ee5\u7f16\u5199\u811a\u672c\u6765\u751f\u6210\u51e0\u4f55\u4f53\u3001\u6dfb\u52a0\u6750\u8d28\u548c\u7eb9\u7406\u3001\u4ee5\u53ca\u8bbe\u7f6e\u76f8\u673a\u548c\u706f\u5149\u7b49\u3002\u901a\u8fc7API\uff0c\u8bbe\u8ba1\u5e08\u53ef\u4ee5\u5b9e\u73b0\u81ea\u52a8\u5316\u7684\u5efa\u6a21\u8fc7\u7a0b\uff0c\u6781\u5927\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<\/p>\n

    \u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7Python\u811a\u672c\u751f\u6210\u4e00\u4e2a\u7b80\u5355\u7684\u5efa\u7b51\u7269\uff1a<\/p>\n<\/p>\n

    import bpy<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u65b0\u7acb\u65b9\u4f53\u4f5c\u4e3a\u5efa\u7b51\u7684\u57fa\u7840<\/strong><\/h2>\n
    bpy.ops.mesh.primitive_cube_add(size=2, location=(0, 0, 1))<\/p>\n
    \u5c06\u5176\u62c9\u4f38\u6210\u9ad8\u5c42\u5efa\u7b51<\/strong><\/h2>\n
    bpy.ops.transform.resize(value=(1, 1, 5))<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    OpenSCAD\u548cSolidPython<\/strong><\/p>\n<\/p>\n
    OpenSCAD\u662f\u4e00\u79cd\u7528\u4e8e\u521b\u5efa3D CAD\u6a21\u578b\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u800cSolidPython\u662f\u4e00\u4e2a\u5c06Python\u4e0eOpenSCAD\u7ed3\u5408\u7684\u5e93\u3002SolidPython\u5141\u8bb8\u7528\u6237\u4f7f\u7528Python\u7684\u8bed\u6cd5\u548c\u529f\u80fd\u6765\u6784\u5efa\u548c\u4fee\u6539OpenSCAD\u6a21\u578b\uff0c\u9002\u5408\u7528\u4e8e\u53c2\u6570\u5316\u8bbe\u8ba1\u3002<\/p>\n<\/p>\n
    \u901a\u8fc7SolidPython\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u53c2\u6570\u5316\u5efa\u7b51\u6a21\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    from solid import *<\/p>\n
    from solid.utils import *<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u623f\u5c4b\u6a21\u578b<\/strong><\/h2>\n
    def house(width, depth, height):<\/p>\n
        return cube([width, depth, height])<\/p>\n
    model = house(10, 15, 5)<\/p>\n
    scad_render_to_file(model, 'house.scad')<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u5b9e\u73b0\u53c2\u6570\u5316\u8bbe\u8ba1<\/p>\n<\/p>\n
    \u53c2\u6570\u5316\u8bbe\u8ba1\u662f\u73b0\u4ee3\u5efa\u7b51\u8bbe\u8ba1\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u8d8b\u52bf\uff0c\u5141\u8bb8\u8bbe\u8ba1\u5e08\u901a\u8fc7\u4fee\u6539\u53c2\u6570\u6765\u5feb\u901f\u63a2\u7d22\u4e0d\u540c\u7684\u8bbe\u8ba1\u65b9\u6848\u3002Python\u5728\u53c2\u6570\u5316\u8bbe\u8ba1\u4e2d\u626e\u6f14\u7740\u5173\u952e\u89d2\u8272\uff0c\u56e0\u4e3a\u5b83\u80fd\u591f\u8f7b\u677e\u5904\u7406\u590d\u6742\u7684\u6570\u5b66\u548c\u51e0\u4f55\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      Rhino\u548cGrasshopper\u7ed3\u5408Python<\/strong><\/p>\n<\/p>\n
      Rhino\u662f\u4e00\u6b3e\u5f3a\u5927\u76843D\u5efa\u6a21\u5de5\u5177\uff0c\u800cGrasshopper\u662f\u5176\u5185\u7f6e\u7684\u53c2\u6570\u5316\u8bbe\u8ba1\u63d2\u4ef6\u3002\u901a\u8fc7Python\u811a\u672c\uff0c\u8bbe\u8ba1\u5e08\u53ef\u4ee5\u5728Grasshopper\u4e2d\u5b9e\u73b0\u590d\u6742\u7684\u53c2\u6570\u5316\u8bbe\u8ba1\u3002<\/p>\n<\/p>\n
      \u5728Grasshopper\u4e2d\uff0cPython\u53ef\u4ee5\u7528\u4e8e\u7f16\u5199\u81ea\u5b9a\u4e49\u7ec4\u4ef6\u4ee5\u5904\u7406\u590d\u6742\u7684\u903b\u8f91\u548c\u8ba1\u7b97\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u6765\u8ba1\u7b97\u5efa\u7b51\u7269\u7684\u6700\u4f73\u671d\u5411\u6216\u4f18\u5316\u7a97\u6237\u7684\u5927\u5c0f\u548c\u4f4d\u7f6e\u4ee5\u6700\u5927\u5316\u81ea\u7136\u91c7\u5149\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u4f7f\u7528Python\u8fdb\u884c\u6570\u5b66\u548c\u51e0\u4f55\u8ba1\u7b97<\/strong><\/p>\n<\/p>\n
      Python\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5176\u4e30\u5bcc\u7684\u6570\u5b66\u548c\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5982NumPy\u548cSciPy\uff0c\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u590d\u6742\u7684\u51e0\u4f55\u53d8\u6362\u548c\u4f18\u5316\u7b97\u6cd5\u3002\u5728\u5efa\u7b51\u8bbe\u8ba1\u4e2d\uff0cPython\u53ef\u4ee5\u5e2e\u52a9\u8bbe\u8ba1\u5e08\u8fdb\u884c\u7ed3\u6784\u5206\u6790\u3001\u70ed\u529b\u5b66\u6a21\u62df\u7b49\u3002<\/p>\n<\/p>\n
      \u4f8b\u5982\uff0c\u901a\u8fc7Python\u8fdb\u884c\u7b80\u5355\u7684\u51e0\u4f55\u53d8\u6362\uff1a<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      \u5b9a\u4e49\u4e00\u4e2a\u70b9<\/strong><\/h2>\n
      point = np.array([1, 2, 3])<\/p>\n
      \u5b9a\u4e49\u4e00\u4e2a\u65cb\u8f6c\u77e9\u9635<\/strong><\/h2>\n
      angle = np.pi \/ 4  # 45\u5ea6<\/p>\n
      rotation_matrix = np.array([<\/p>\n
          [np.cos(angle), -np.sin(angle), 0],<\/p>\n
          [np.sin(angle), np.cos(angle), 0],<\/p>\n
          [0, 0, 1]<\/p>\n
      ])<\/p>\n
      \u5e94\u7528\u65cb\u8f6c\u77e9\u9635<\/strong><\/h2>\n
      rotated_point = np.dot(rotation_matrix, point)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u8fdb\u884c\u5efa\u7b51\u53ef\u89c6\u5316<\/p>\n<\/p>\n
      \u5efa\u7b51\u53ef\u89c6\u5316\u662f\u5efa\u7b51\u8bbe\u8ba1\u8fc7\u7a0b\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u4e00\u90e8\u5206\uff0c\u901a\u8fc7Python\uff0c\u8bbe\u8ba1\u5e08\u53ef\u4ee5\u5b9e\u73b0\u9ad8\u8d28\u91cf\u76843D\u6e32\u67d3\u548c\u52a8\u753b\uff0c\u5e2e\u52a9\u5ba2\u6237\u548c\u56e2\u961f\u66f4\u597d\u5730\u7406\u89e3\u8bbe\u8ba1\u65b9\u6848\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u4f7f\u7528Panda3D\u8fdb\u884c\u5b9e\u65f6\u6e32\u67d3<\/strong><\/p>\n<\/p>\n
        Panda3D\u662f\u4e00\u4e2a\u9762\u5411\u6e38\u620f\u548c\u53ef\u89c6\u5316\u76843D\u5f15\u64ce\uff0c\u652f\u6301Python\u7f16\u7a0b\u3002\u5b83\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u5b9e\u65f6\u5efa\u7b51\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u8bbe\u8ba1\u5e08\u548c\u5ba2\u6237\u5b9e\u65f6\u67e5\u770b\u5efa\u7b51\u8bbe\u8ba1\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
        \u901a\u8fc7Panda3D\uff0c\u8bbe\u8ba1\u5e08\u53ef\u4ee5\u5b9e\u73b0\u5efa\u7b51\u7684\u4ea4\u4e92\u5f0f\u6f14\u793a\uff0c\u5305\u62ec\u6f2b\u6e38\u3001\u5149\u5f71\u6548\u679c\u7b49\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
        from panda3d.core import Point3<\/p>\n
        from direct.showbase.ShowBase import ShowBase<\/p>\n
        class MyApp(ShowBase):<\/p>\n
            def __init__(self):<\/p>\n
                ShowBase.__init__(self)<\/p>\n
                # \u52a0\u8f7d\u5efa\u7b51\u6a21\u578b<\/p>\n
                self.scene = self.loader.loadModel("models\/my_building")<\/p>\n
                self.scene.reparentTo(self.render)<\/p>\n
                self.scene.setPos(Point3(0, 50, 0))<\/p>\n
        app = MyApp()<\/p>\n
        app.run()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u5229\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/strong><\/p>\n<\/p>\n
        \u5728\u5efa\u7b51\u8bbe\u8ba1\u4e2d\uff0c\u6570\u636e\u53ef\u89c6\u5316\u540c\u6837\u91cd\u8981\u3002Python\u7684Matplotlib\u5e93\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u4e8c\u7ef4\u548c\u4e09\u7ef4\u7684\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u8bbe\u8ba1\u5e08\u66f4\u597d\u5730\u7406\u89e3\u5efa\u7b51\u7269\u7684\u6027\u80fd\u6570\u636e\u3002<\/p>\n<\/p>\n
        \u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6765\u7ed8\u5236\u5efa\u7b51\u7269\u7684\u80fd\u8017\u66f2\u7ebf\uff1a<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u80fd\u8017\u6570\u636e<\/strong><\/h2>\n
        months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']<\/p>\n
        energy_usage = [200, 180, 220, 210, 230, 250]<\/p>\n
        plt.plot(months, energy_usage, marker='o')<\/p>\n
        plt.title('Monthly Energy Usage')<\/p>\n
        plt.xlabel('Month')<\/p>\n
        plt.ylabel('Energy (kWh)')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u7ed3\u5408\u7b97\u6cd5\u4f18\u5316\u8bbe\u8ba1<\/p>\n<\/p>\n
        Python\u5728\u4f18\u5316\u5efa\u7b51\u8bbe\u8ba1\u65b9\u9762\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u4f7f\u7528\u7b97\u6cd5\u4f18\u5316\u8bbe\u8ba1\u65b9\u6848\u65f6\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u79d1\u5b66\u8ba1\u7b97\u548c\u4f18\u5316\u5e93\uff0c\u8bbe\u8ba1\u5e08\u53ef\u4ee5\u81ea\u52a8\u5316\u5730\u63a2\u7d22\u4f18\u5316\u8bbe\u8ba1\u65b9\u6848\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u9057\u4f20\u7b97\u6cd5\u8fdb\u884c\u5efa\u7b51\u4f18\u5316<\/strong><\/p>\n<\/p>\n
          \u9057\u4f20\u7b97\u6cd5\u662f\u4e00\u79cd\u6a21\u62df\u81ea\u7136\u9009\u62e9\u7684\u4f18\u5316\u7b97\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5efa\u7b51\u8bbe\u8ba1\u7684\u591a\u76ee\u6807\u4f18\u5316\u95ee\u9898\u3002Python\u7684DEAP\u5e93\u63d0\u4f9b\u4e86\u5b9e\u73b0\u9057\u4f20\u7b97\u6cd5\u7684\u5de5\u5177\uff0c\u8bbe\u8ba1\u5e08\u53ef\u4ee5\u7528\u5b83\u6765\u4f18\u5316\u5efa\u7b51\u7269\u7684\u80fd\u6548\u3001\u7ed3\u6784\u7a33\u5b9a\u6027\u7b49\u3002<\/p>\n<\/p>\n
          \u4f8b\u5982\uff0c\u901a\u8fc7\u9057\u4f20\u7b97\u6cd5\u4f18\u5316\u5efa\u7b51\u7269\u7684\u671d\u5411\uff1a<\/p>\n<\/p>\n
          import random<\/p>\n
          from deap import base, creator, tools, algorithms<\/p>\n
          \u5b9a\u4e49\u9002\u5e94\u5ea6\u51fd\u6570<\/strong><\/h2>\n
          def eval_building(individual):<\/p>\n
              # \u793a\u4f8b\uff1a\u8ba1\u7b97\u5efa\u7b51\u7269\u671d\u5411\u4e0e\u592a\u9633\u7684\u5939\u89d2\u4f5c\u4e3a\u9002\u5e94\u5ea6<\/p>\n
              return (abs(individual[0] - 45),)<\/p>\n
          \u8bbe\u7f6e\u9057\u4f20\u7b97\u6cd5<\/strong><\/h2>\n
          creator.create("FitnessMin", base.Fitness, weights=(-1.0,))<\/p>\n
          creator.create("Individual", list, fitness=creator.FitnessMin)<\/p>\n
          toolbox = base.Toolbox()<\/p>\n
          toolbox.register("attr_float", random.uniform, 0, 90)<\/p>\n
          toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 1)<\/p>\n
          toolbox.register("population", tools.initRepeat, list, toolbox.individual)<\/p>\n
          toolbox.register("evaluate", eval_building)<\/p>\n
          toolbox.register("mate", tools.cxBlend, alpha=0.5)<\/p>\n
          toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)<\/p>\n
          toolbox.register("select", tools.selTournament, tournsize=3)<\/p>\n
          population = toolbox.population(n=100)<\/p>\n
          algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=50, verbose=True)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u673a\u5668\u5b66\u4e60<\/a>\u8f85\u52a9\u5efa\u7b51\u8bbe\u8ba1<\/strong><\/p>\n<\/p>\n
          \u673a\u5668\u5b66\u4e60\u5728\u5efa\u7b51\u8bbe\u8ba1\u4e2d\u4e5f\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u9884\u6d4b\u548c\u5206\u6790\u65b9\u9762\u3002Python\u7684scikit-learn\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5efa\u7b51\u8bbe\u8ba1\u7684\u6027\u80fd\u9884\u6d4b\u3001\u7528\u6237\u9700\u6c42\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
          \u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u9884\u6d4b\u5efa\u7b51\u7269\u7684\u80fd\u8017\uff1a<\/p>\n<\/p>\n
          from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
          from sklearn.linear_model import LinearRegression<\/p>\n
          \u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
          X = [[30, 200], [40, 250], [50, 300], [60, 350], [70, 400]]<\/p>\n
          y = [180, 220, 260, 300, 340]<\/p>\n
          \u62c6\u5206\u6570\u636e\u96c6<\/strong><\/h2>\n
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<\/p>\n
          \u521b\u5efa\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
          model = LinearRegression()<\/p>\n
          model.fit(X_train, y_train)<\/p>\n
          \u9884\u6d4b\u80fd\u8017<\/strong><\/h2>\n
          y_pred = model.predict(X_test)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0cPython\u53ef\u4ee5\u5728\u5efa\u7b51\u8bbe\u8ba1\u7684\u5404\u4e2a\u9636\u6bb5\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\uff0c\u4ece\u5efa\u6a21\u3001\u53c2\u6570\u5316\u8bbe\u8ba1\u3001\u53ef\u89c6\u5316\u5230\u4f18\u5316\u8bbe\u8ba1\uff0cPython\u7684\u5e94\u7528\u6781\u5927\u5730\u62d3\u5c55\u4e86\u5efa\u7b51\u8bbe\u8ba1\u7684\u53ef\u80fd\u6027\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u4f7f\u7528Python\u5236\u4f5c\u5efa\u7b51\u9700\u8981\u54ea\u4e9b\u57fa\u7840\u77e5\u8bc6\uff1f<\/strong>
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          Python\u6709\u54ea\u4e9b\u5e93\u9002\u5408\u7528\u4e8e\u5efa\u7b51\u8bbe\u8ba1\u548c\u5efa\u6a21\uff1f<\/strong>
          \u6709\u591a\u79cdPython\u5e93\u53ef\u4ee5\u7528\u4e8e\u5efa\u7b51\u8bbe\u8ba1\u548c\u5efa\u6a21\u3002Blender\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5f00\u6e903D\u521b\u4f5c\u5957\u4ef6\uff0c\u652f\u6301Python\u811a\u672c\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u590d\u6742\u7684\u5efa\u7b51\u6a21\u578b\u3002\u53e6\u4e00\u4e2a\u5e38\u7528\u7684\u5e93\u662fRhino\u4e0eGrasshopper\u7684\u7ed3\u5408\uff0c\u5b83\u4eec\u4e5f\u652f\u6301Python\u811a\u672c\uff0c\u9002\u5408\u8fdb\u884c\u53c2\u6570\u5316\u8bbe\u8ba1\u3002\u6b64\u5916\uff0cOpenSCAD\u662f\u4e00\u4e2a\u4e13\u6ce8\u4e8e3D\u8bbe\u8ba1\u7684\u5de5\u5177\uff0c\u4e5f\u80fd\u901a\u8fc7Python\u8fdb\u884c\u81ea\u52a8\u5316\u5efa\u6a21\u3002<\/p>\n
          \u5728Python\u4e2d\u5982\u4f55\u5b9e\u73b0\u5efa\u7b51\u7684\u53ef\u89c6\u5316\uff1f<\/strong>
          \u5728Python\u4e2d\u5b9e\u73b0\u5efa\u7b51\u53ef\u89c6\u5316\u901a\u5e38\u4f7f\u7528Matplotlib\u6216Mayavi\u7b49\u5e93\u3002Matplotlib\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u76842D\u56fe\u5f62\u7ed8\u5236\uff0c\u800cMayavi\u5219\u9002\u54083D\u53ef\u89c6\u5316\u3002\u5982\u679c\u5e0c\u671b\u521b\u5efa\u4ea4\u4e92\u5f0f\u7684\u5efa\u7b51\u6a21\u578b\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528PyOpenGL\u6216Pygame\u3002\u8fd9\u4e9b\u5de5\u5177\u80fd\u591f\u5e2e\u52a9\u4f60\u5c55\u793a\u5efa\u7b51\u6a21\u578b\u7684\u5404\u4e2a\u7ec6\u8282\uff0c\u5e76\u63d0\u4f9b\u7528\u6237\u4ea4\u4e92\u7684\u529f\u80fd\uff0c\u4f7f\u53ef\u89c6\u5316\u8fc7\u7a0b\u66f4\u52a0\u751f\u52a8\u548c\u76f4\u89c2\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u5236\u4f5c\u5efa\u7b51\u7684\u6838\u5fc3\u5728\u4e8e\uff1a\u4f7f\u75283D\u5efa\u6a21\u5e93\u521b\u5efa\u5efa\u7b51\u6a21\u578b\u3001\u5b9e\u73b0\u53c2\u6570\u5316\u8bbe\u8ba1\u3001\u8fdb\u884c\u5efa\u7b51\u53ef\u89c6\u5316\u3001\u7ed3\u5408\u7b97\u6cd5\u4f18\u5316\u8bbe […]","protected":false},"author":3,"featured_media":978758,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/978757"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=978757"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/978757\/revisions"}],"predecessor-version":[{"id":978760,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/978757\/revisions\/978760"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/978758"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=978757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=978757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=978757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}