过了一会儿，我发现了一种解决方法，可以应用Flabetvibes在此excellent answer中解释的巧妙技巧。

我使用此处显示的代码来镜像数据，如上述答案的第一张图所示。我介绍的唯一修改是将镜像数据修剪为perc填充（我默认将其设置为10％），以免携带很多不必要的值。

结果显示在此处，原始非镜像数据在左侧，镜像数据在右侧：

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

def in_box(towers,bounding_box):
    return np.logical_and(np.logical_and(bounding_box[0] <= towers[:,0],towers[:,0] <= bounding_box[1]),np.logical_and(bounding_box[2] <= towers[:,1],1] <= bounding_box[3]))


def dataMirror(towers,bounding_box,perc=.1):
    # Select towers inside the bounding box
    i = in_box(towers,bounding_box)
    # Mirror points
    points_center = towers[i,:]
    points_left = np.copy(points_center)
    points_left[:,0] = bounding_box[0] - (points_left[:,0] - bounding_box[0])
    points_right = np.copy(points_center)
    points_right[:,0] = bounding_box[1] + (bounding_box[1] - points_right[:,0])
    points_down = np.copy(points_center)
    points_down[:,1] = bounding_box[2] - (points_down[:,1] - bounding_box[2])
    points_up = np.copy(points_center)
    points_up[:,1] = bounding_box[3] + (bounding_box[3] - points_up[:,1])
    points = np.append(points_center,np.append(np.append(points_left,points_right,axis=0),np.append(points_down,points_up,axis=0)

    # Trim mirrored frame to withtin a 'perc' pad
    xr,yr = np.ptp(towers.T[0]) * perc,np.ptp(towers.T[1]) * perc
    xmin,xmax = bounding_box[0] - xr,bounding_box[1] + xr
    ymin,ymax = bounding_box[2] - yr,bounding_box[3] + yr
    msk = (points[:,0] > xmin) & (points[:,0] < xmax) &\
        (points[:,1] > ymin) & (points[:,1] < ymax)
    points = points[msk]

    return points.T


def KDEplot(xmin,xmax,ymin,ymax,values):
    # Gaussian KDE.
    kernel = stats.gaussian_kde(values,bw_method=.2)
    # Grid density (number of points).
    gd_c = complex(0,50)
    # Define x,y grid.
    x_grid,y_grid = np.mgrid[xmin:xmax:gd_c,ymin:ymax:gd_c]
    positions = np.vstack([x_grid.ravel(),y_grid.ravel()])
    # Evaluate kernel in grid positions.
    k_pos = kernel(positions)

    ext_range = [xmin,ymax]
    kde = np.reshape(k_pos.T,x_grid.shape)

    plt.imshow(np.rot90(kde),cmap=plt.get_cmap('RdYlBu_r'),extent=ext_range)


x_data = np.random.uniform(1.,2000.,1000)
y_data = np.random.uniform(1.,1000)

xmin,xmax = np.min(x_data),np.max(x_data)
ymin,ymax = np.min(y_data),np.max(y_data)
values = np.vstack([x_data,y_data])

# Plot non-mirrored data
plt.subplot(121)
KDEplot(xmin,values)
plt.scatter(*values,s=3,c='k')
plt.xlim(xmin,xmax)
plt.ylim(ymin,ymax)

# Plot mirrored data
bounding_box = (xmin,ymax)
values = dataMirror(values.T,bounding_box)
plt.subplot(122)
KDEplot(xmin,ymax)

plt.show()