pyplot.imshow прямоугольники

Question

Я в настоящее время использую matplotlib.pyplot визуализировать некоторые 2D данных:

from matplotlib import pyplot as plt 
import numpy as np 
A=np.matrix("1 2 1;3 0 3;1 2 0") # 3x3 matrix with 2D data 
plt.imshow(A, interpolation="nearest") # draws one square per matrix entry 
plt.show() 

Теперь я переехал данные из квадратов в прямоугольники, то есть у меня есть два дополнительные массивы, например:

grid_x = np.array([0.0, 1.0, 4.0, 5.0]) # points on the x-axis 
grid_x = np.array([0.0, 2.5, 4.0, 5.0]) # points on the y-axis 

теперь я хочу сетку с прямоугольниками:

• верхний левый угол: (grid_x[i], grid_y[j])
• правый нижний угол: (grid_x[i+1], grid_y[j+1])
• данные (цвет): A[i,j]

Что такое простой способ построить данные на новой сетке? imshow, по-видимому, можно использовать, я посмотрел на pcolormesh, но его сбивает с сеткой как 2D-массив, используя две матрицы, такие как np.mgrid[0:5:0.5,0:5:0.5] для обычной сетки и построение чего-то похожего на нерегулярное.

Что такое простой способ визуализации прямоугольников?

Answer 1

import matplotlib.pyplot as plt 
from matplotlib.patches import Rectangle 
import matplotlib.cm as cm 
from matplotlib.collections import PatchCollection 
import numpy as np 

A = np.matrix("1 2 1;3 0 3;1 2 0;4 1 2") # 4x3 matrix with 2D data 

grid_x0 = np.array([0.0, 1.0, 4.0, 6.7]) 
grid_y0 = np.array([0.0, 2.5, 4.0, 7.8, 12.4]) 

grid_x1, grid_y1 = np.meshgrid(grid_x0, grid_y0) 
grid_x2 = grid_x1[:-1, :-1].flat 
grid_y2 = grid_y1[:-1, :-1].flat 
widths = np.tile(np.diff(grid_x0)[np.newaxis], (len(grid_y0)-1, 1)).flat 
heights = np.tile(np.diff(grid_y0)[np.newaxis].T, (1, len(grid_x0)-1)).flat 

fig = plt.figure() 
ax = fig.add_subplot(111) 
ptchs = [] 
for x0, y0, w, h in zip(grid_x2, grid_y2, widths, heights): 
    ptchs.append(Rectangle(
     (x0, y0), w, h, 
    )) 
p = PatchCollection(ptchs, cmap=cm.viridis, alpha=0.4) 
p.set_array(np.ravel(A)) 
ax.add_collection(p) 
plt.xlim([0, 8]) 
plt.ylim([0, 13]) 
plt.show() 

Вот еще один способ, с помощью изображения и R-дерево и imshow с colorbar, вам необходимо изменить x-ticks и y-ticks (Есть много SO Q & А о том, как это сделать).

from rtree import index 
import matplotlib.pyplot as plt 
import numpy as np 

eps = 1e-3 

A = np.matrix("1 2 1;3 0 3;1 2 0;4 1 2") # 4x3 matrix with 2D data 
grid_x0 = np.array([0.0, 1.0, 4.0, 6.7]) 
grid_y0 = np.array([0.0, 2.5, 4.0, 7.8, 12.4]) 

grid_x1, grid_y1 = np.meshgrid(grid_x0, grid_y0) 
grid_x2 = grid_x1[:-1, :-1].flat 
grid_y2 = grid_y1[:-1, :-1].flat 
grid_x3 = grid_x1[1:, 1:].flat 
grid_y3 = grid_y1[1:, 1:].flat 

fig = plt.figure() 

rows = 100 
cols = 200 
im = np.zeros((rows, cols), dtype=np.int8) 
grid_j = np.linspace(grid_x0[0], grid_x0[-1], cols) 
grid_i = np.linspace(grid_y0[0], grid_y0[-1], rows) 
j, i = np.meshgrid(grid_j, grid_i) 

i = i.flat 
j = j.flat 

idx = index.Index() 

for m, (x0, y0, x1, y1) in enumerate(zip(grid_x2, grid_y2, grid_x3, grid_y3)): 
    idx.insert(m, (x0, y0, x1, y1)) 


for k, (i0, j0) in enumerate(zip(i, j)): 
    ind = next(idx.intersection((j0-eps, i0-eps, j0+eps, i0+eps))) 

    im[np.unravel_index(k, im.shape)] = A[np.unravel_index(ind, A.shape)] 
plt.imshow(im) 
plt.colorbar() 
plt.show() 

Answer 2

Вот многоразовая функция, основанная на коде от @ Офир-Карми:

import matplotlib.pyplot as plt 
from matplotlib.patches import Rectangle 
from matplotlib.collections import PatchCollection 
import itertools 
import numpy as np 

def gridshow(grid_x, grid_y, data, **kwargs): 
    vmin = kwargs.pop("vmin", None) 
    vmax = kwargs.pop("vmax", None) 
    data = np.array(data).reshape(-1) 
    # there should be data for (n-1)x(m-1) cells 
    assert (grid_x.shape[0] - 1) * (grid_y.shape[0] - 1) == data.shape[0], "Wrong number of data points. grid_x=%s, grid_y=%s, data=%s" % (grid_x.shape, grid_y.shape, data.shape) 
    ptchs = [] 
    for j, i in itertools.product(xrange(len(grid_y) - 1), xrange(len(grid_x) - 1)): 
     xy = grid_x[i], grid_y[j] 
     width = grid_x[i+1] - grid_x[i] 
     height = grid_y[j+1] - grid_y[j] 
     ptchs.append(Rectangle(xy=xy, width=width, height=height, rasterized=True, linewidth=0, linestyle="None")) 
    p = PatchCollection(ptchs, linewidth=0, **kwargs) 
    p.set_array(np.array(data)) 
    p.set_clim(vmin, vmax) 
    ax = plt.gca() 
    ax.set_aspect("equal") 
    plt.xlim([grid_x[0], grid_x[-1]]) 
    plt.ylim([grid_y[0], grid_y[-1]]) 
    ret = ax.add_collection(p) 
    plt.sci(ret) 
    return ret 

if __name__ == "__main__": 
    grid_x = np.linspace(0, 20, 21) + np.random.randn(21)/5.0 
    grid_y = np.linspace(0, 18, 19) + np.random.randn(19)/5.0 
    grid_x = np.round(grid_x, 2) 
    grid_y = np.round(grid_y, 2) 
    data = np.random.randn((grid_x.shape[0] -1) * (grid_y.shape[0] -1)) 

    fig = plt.figure() 
    ax = fig.add_subplot(111) 
    gridshow(grid_x, grid_y, data, alpha=1.0) 
    plt.savefig("test.png") 

Я не совсем уверен, что производительность для больших сетей и если **kwargs следует наложить на PatchCollection. И между некоторыми прямоугольниками, кажется, 1px пробелы, вероятно, из-за плохого округления. Возможно, dx, width, height необходимо согласовать floor/ceil со следующим полным пикселом.

Другое решение с использованием rtree и imshow:

import matplotlib.pyplot as plt 
import numpy as np 
from rtree import index 

def gridshow(grid_x, grid_y, data, rows=200, cols=200, eps=1e-3, **kwargs): 
    grid_x1, grid_y1 = np.meshgrid(grid_x, grid_y) 
    grid_x2 = grid_x1[:-1, :-1].flat 
    grid_y2 = grid_y1[:-1, :-1].flat 
    grid_x3 = grid_x1[1:, 1:].flat 
    grid_y3 = grid_y1[1:, 1:].flat 

    grid_j = np.linspace(grid_x[0], grid_x[-1], cols) 
    grid_i = np.linspace(grid_y[0], grid_y[-1], rows) 
    j, i = np.meshgrid(grid_j, grid_i) 
    i = i.flat 
    j = j.flat 

    im = np.empty((rows, cols), dtype=np.float64) 

    idx = index.Index() 
    for m, (x0, y0, x1, y1) in enumerate(zip(grid_x2, grid_y2, grid_x3, grid_y3)): 
     idx.insert(m, (x0, y0, x1, y1)) 
    for k, (i0, j0) in enumerate(zip(i, j)): 
     ind = next(idx.intersection((j0-eps, i0-eps, j0+eps, i0+eps))) 
     im[np.unravel_index(k, im.shape)] = data[np.unravel_index(ind, data.shape)] 

    fig = plt.gca() 
    return plt.imshow(im, interpolation="nearest") 


if __name__ == "__main__": 
    grid_x = np.linspace(0, 200, 201) + np.random.randn(201)/5.0 
    grid_y = np.linspace(0, 108, 109) + np.random.randn(109)/5.0 
    grid_x = np.round(grid_x, 2) 
    grid_y = np.round(grid_y, 2) 
    data = np.random.randn((grid_x.shape[0] -1) * (grid_y.shape[0] -1)) 

    fig = plt.figure() 
    ax = fig.add_subplot(111) 
    gridshow(grid_x, grid_y, data, alpha=1.0) 
    plt.savefig("test.png")