def cartesian_to_polar(x, y): r = math.sqrt(x2 + y2) theta_rad = math.atan2(y, x) theta_deg = math.degrees(theta_rad) return r, theta_deg 示例:将笛卡尔坐标(3, 4)转换为极坐标 r, theta = cartesian_to_polar(3, 4) print(f"Polar coordinates: r = {r}, θ = {theta} deg...
import numpy as np def polar_to_affine(r, theta, transform_matrix): x = r * np.cos(theta) y = r * np.sin(theta) point = np.array([x, y, 1]) transformed_point = np.dot(transform_matrix, point) return transformed_point[:2] 示例 r = 5 theta = np.pi / 4 # 45度 transfor...
def polar_to_cartesian(location): # 极坐标转换为直角坐标 f = np.pi / 180 return [location[0] * np.cos(location[1] * f),location[0] * np.sin(location[1] * f)] def cartesian_to_polar(location): # 直角坐标转换为极坐标 cn = list(cmath.polar(complex(location[0], location[1]))...
这里的r是距离,θ是角度(以弧度为单位): defpolar_to_cartesian(r,theta):""" 将极坐标(r, θ)转换为直角坐标(x, y) r: 极坐标的距离 theta: 极坐标的角度(以弧度为单位) return: 返回直角坐标(x, y) """x=r*np.cos(theta)# x坐标y=r*np.sin(theta)# y坐标returnx,y 1. 2. 3. 4. ...
""" Generate the angles in spherical coordinates corresponding to a vector whose components are all equal """ # N = Number of angles required to specify sphere arg_for_sqrt = np.arange(N+1, 3-0.5, -1) polar_list = np.arccos( 1 / ...
radii = np.linspace(0.125, 1.0, n_radii) angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) # Repeat all angles for each radius. angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) # Convert polar (radii, angles) coords to cartesian (x, y) coords. # (0...
笛卡尔坐标:笛卡尔坐标系(Cartesian coordinates,法语:les coordonnées cartésiennes)就是直角坐标系和斜坐标系的统称,相交于原点的两条数轴,构成了平面仿射坐标系 。如两条数轴上的度量单位相等,则称此仿射坐标系为笛卡尔坐标系。两条数轴互相垂直的笛卡尔坐标系,称为笛卡尔直角坐标系,否则称为笛卡尔斜角坐标系。点(...
同样,polar_to_cartesian 返回的“元组”(tuple类型)实际上并不是作为 Python 对象而创建的,而是临时表示为结构体(struct类型),然后由编译器对其进行优化。 GPU 所支持的 Python Numba 在 CPU 上受到限制无法编译所有函数,GPU 上同样如此。GPU 所支持的 Python 操作包括: if/elif/else while 和for 循环 基础...
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False) # Repeat all angles for each radius angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) # Convert polar (radii, angles) coords to cartesian (x, y) coords ...
import numpy as npfrom bokeh.io import output_file, showfrom bokeh.plotting import figurefrom bokeh.util.hex import axial_to_cartesianoutput_file("hex_coords.html")q = np.array([0, 0, 0, -1, -1, 1, 1])r = np.array([0, -1, 1, 0, 1, -1, 0])p = figure(plot_width=400...