Matplotlib 箭袋图：参数顺序

Question

给定纵横比值的二维数组（罗盘方位，例如由高程模型的纵横比导数表示），我正在创建一个 quiver plot using matplotlib。这被放置在方面值的彩色矩阵上以充当检查。

代码之所以有效，是因为它创建了我想要的内容，但仅在参数与我的预期相反的地方创建。我犯了一个简单的错误，但无法发现它。

问题： 虽然 matplotlib.pyplot.quiver() 期望 quiver([X, Y], U, V, [C], **kw)，但为什么我的代码只给出 quiver([X, Y], V, U) 的预期答案（即 U 和 V是否反过来）被使用？

顺便说一句，在绘图时，我将 plt.imshow 的原点移到了 lower（如 here 所讨论的）。我认为问题出在与我的索引等相关的地方

下面的代码（使用 python 3.5 和 matplotlib v3.x）：

import numpy as np
import matplotlib.pyplot as plt


def compassBearing_to_standardPosition__degrees_counterClockwise(bearing_deg):
    """Vector magnitude and direction calculations assume angle is relative to the x axis 
          i.e. 0 degrees north is at 3 o'clock
    Adjust compass bearings to be relative to standard position
    """
    std_pos=(450 - bearing_deg) % 360
    return(std_pos)

def calculate_U_and_V__vector_magnitude_and_direction(angle_degrees, magnitude=1):
    """Calculates the components of a vector given in magnitude (U) and direction (V) form
    angle: Expected that angles are in standard position 
            i.e. relative to the x axis or where 3 o'clock is zero and not the compass bearing 
            where 12 o'clock is 0
    magnitude: defaults to 1
    """
    angle_rad=np.deg2rad(angle_degrees)
    x = magnitude * np.cos(angle_rad) # change in x == U
    y = magnitude * np.sin(angle_rad) # change in y == V
    return(x,y)

def array_indices(arr, indexing='xy'):
    """Calculates index positions of each cell in array
    These can be used to map to e.g. when creating a quiver plot

    indexing: Giving the string 'ij' returns a meshgrid with
        matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.
        In the 2-D case with inputs of length M and N, the outputs are of shape
        (N, M) for 'xy' indexing and (M, N) for 'ij' indexing.
    """
    nrows, ncols = arr.shape
    nx = 1
    ny = 1
    x = np.linspace(0, ncols-1, ncols)
    y = np.linspace(0, nrows-1, nrows)
    #y = np.linspace(nrows-1, 0, nrows) # note that the largest vlue is first
    xi, yi = np.meshgrid(x, y, indexing=indexing)
    return(xi, yi)

#Create a toy aspect grid (degrees North)
aspect_grid=np.array([[ 216,  226,  151],
       [  74,  323,  268],
       [ 177,  204,   84]])

#Get the array indices
x,y=array_indices(aspect_grid, indexing='xy')

#Get U and V 
x_change,y_change=calculate_U_and_V__vector_magnitude_and_direction(aspect_grid.flatten())

#Plot quiver over imshow
cmap = 'twilight_shifted' # this will expect matplotlib v3.x
plt.imshow(np.floor(aspect_grid), cmap=cmap, origin='lower')
plt.colorbar(label="Aspect (degrees N)")
plt.quiver(x, y, y_change, x_change, pivot='middle') # <<< why not x,y,x_change,y_change?
plt.title("Surface aspect values")
plt.show()

Answer 1

当您将 aspect_grid 数组传递给 calculate_U_and_V__vector_magnitude_and_direction 时，您并没有将它们从绝对方位角转换为逆时针度数，因为 compassBearing_to_standardPosition__degrees_counterClockwise 没有在 calculate_U_and_V__vector_magnitude_and_direction 中被调用。由于两个约定的 90 度错位，这导致 cos(angle) 对应于 y 组件，而 sin(angle) 对应于 x 组件（由于属性 cos(x - pi/2) == sin(x))。为了纠正这个问题，你只需要使用你设置的转换（它正确地从方位转换为标准位置）通过做类似

的事情

#...
angle_degrees = compassBearing_to_standardPosition__degrees_counterClockwise(angle_degrees)
angle_rad=np.deg2rad(angle_degrees)
#...

在 calculate_U_and_V__vector_magnitude_and_direction 中。这将允许您使用

plt.quiver(x, y, x_change, y_change, pivot='middle')

并得到正确的结果：

Matplotlib 箭袋图：参数顺序

Matploltib quiver plot: argument order

python

matplotlib

calculus