防止GridSpec子图分离随图形大小而变化

Question

我想创建一个没有中间空间的地块网格。

看起来像这样：

代码1

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure()

gs = GridSpec(2, 2, wspace=0.0, hspace=0.0)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])

fig.show()

但是，当我添加数据时，子图之间的间距取决于图的尺寸。 （通过改变fig.show（）打开的窗口的尺寸可以看出。）

举个例子：

代码2

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

import numpy as np

fig = plt.figure()

gs = GridSpec(2, 2, wspace=0.0, hspace=0.0)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])

for axis in [ax1, ax2, ax3, ax4]:
    axis.imshow(np.random.random((10,10)))

fig.show()

因此，最好还是使用GridSpec，是否可以强制将图块保持在一起？ 我能想到的唯一其他选择是访问图表的大小并在plt.figure(figsize=(##,##))中使用这些维度，但我似乎无法访问这些数字。

注意：图表的数量以及高/宽比率会有所不同。 （例如GridSpec(2, 3, width_ratios=[10,10,1], wspace=0.0, hspace=0.0)我将使用最后一列来保存用于所有绘图的颜色条。）

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~ Python 2.7.10，Matplotlib 1.4.3 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~

Answer 1

我找到了两种快速而肮脏的方法：

方法1：使用figsize

在figsize中设置plt.figure关键字参数，其宽度和高度与数据的相同宽高比相匹配，相当好的工作量。

Result from Method 1

方法1

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

import numpy as np

length_x_axis = 30
length_y_axis = 10

rows  = 3
columns = 2

fig_height = 5.

height = length_y_axis * rows
width = length_x_axis  * columns

plot_aspect_ratio= float(width)/float(height)

fig = plt.figure(figsize=(fig_height  * plot_aspect_ratio, fig_height ))

gs = GridSpec(rows, columns, wspace=0.0, hspace=0.0)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])
ax5 = fig.add_subplot(gs[2, 0])
ax6 = fig.add_subplot(gs[2, 1])

for axis in [ax1, ax2, ax3, ax4, ax5, ax6]:
    axis.imshow(np.random.random((length_y_axis , length_x_axis )))

fig.savefig("testing.png")

方法2：使用set_anchor

对每个轴使用set_anchor方法可以获得更好的结果，但需要更多的努力，并且通过一些快速测试，它不适用于大于3x2的绘图数组。

Result from Method 2

方法2

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

import numpy as np

fig = plt.figure()
gs = GridSpec(2, 3, wspace=0.0, hspace=0.0)

ax1 = fig.add_subplot(gs[0, 0])
ax1.set_anchor("SE")

ax2 = fig.add_subplot(gs[0, 1])
ax2.set_anchor("S")

ax3 = fig.add_subplot(gs[0, 2])
ax3.set_anchor("SW")

ax4 = fig.add_subplot(gs[1, 0])
ax4.set_anchor("NE")

ax5 = fig.add_subplot(gs[1, 1])
ax5.set_anchor("N")

ax6 = fig.add_subplot(gs[1, 2])
ax6.set_anchor("NW")

for axis in [ax1, ax2, ax3, ax4, ax5, ax6]:
    axis.imshow(np.random.random((10 , 10 )))

fig.show()

Answer 2

据我所知，C Complex Type = 10.000000 20.000000 不会覆盖您设置的默认gridspec：figsize。

Answer 3

使用：width_ratios = [2,1]

例如，

import matplotlib.gridspec as gridspec
import numpy as np


fig = plt.figure(figsize=(18, 6))
outer_grid = gridspec.GridSpec(1, 2, wspace=0.0, hspace=0.0,width_ratios=[2,1])

# first 
ax = fig.add_subplot(outer_grid[0])
ax.plot(gan.arr_g_loss,label='Generator Loss')
ax.plot(gan.arr_d_loss_real,label='Discriminator Loss on Real data')
ax.plot(gan.arr_d_loss_fake,label='Discriminator Loss on Fake data')
ax.set_title('Loss')
ax.legend()
fig.add_subplot(ax,figsize=(15, 6))

    
# second
inner_grid = gridspec.GridSpecFromSubplotSpec(5, 5,subplot_spec=outer_grid[1], wspace=0.0, hspace=0.0)
images = gan.generate_images().cpu().numpy()[:,0,:,:]
for j, i in enumerate(images):
    ax = fig.add_subplot(inner_grid[j])
    ax.imshow(i,cmap='gray')
#     ax.plot(x)
    ax.axis('off')
    ax.autoscale(False)
    
    fig.add_subplot(ax)

Answer 4

你可以做Nested GridSpec using SubplotSpec：

上面链接的matplotlib示例代码产生了这个：

代码，来自here：

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np

try:
    from itertools import product
except ImportError:
    # product is new in v 2.6
    def product(*args, **kwds):
        pools = map(tuple, args) * kwds.get('repeat', 1)
        result = [[]]
        for pool in pools:
            result = [x+[y] for x in result for y in pool]
        for prod in result:
            yield tuple(prod)


def squiggle_xy(a, b, c, d, i=np.arange(0.0, 2*np.pi, 0.05)):
    return np.sin(i*a)*np.cos(i*b), np.sin(i*c)*np.cos(i*d)

fig = plt.figure(figsize=(8, 8))

# gridspec inside gridspec
outer_grid = gridspec.GridSpec(4, 4, wspace=0.0, hspace=0.0)

for i in range(16):
    inner_grid = gridspec.GridSpecFromSubplotSpec(3, 3,
            subplot_spec=outer_grid[i], wspace=0.0, hspace=0.0)
    a, b = int(i/4)+1,i%4+1
    for j, (c, d) in enumerate(product(range(1, 4), repeat=2)):
        ax = plt.Subplot(fig, inner_grid[j])
        ax.plot(*squiggle_xy(a, b, c, d))
        ax.set_xticks([])
        ax.set_yticks([])
        fig.add_subplot(ax)

all_axes = fig.get_axes()

#show only the outside spines
for ax in all_axes:
    for sp in ax.spines.values():
        sp.set_visible(False)
    if ax.is_first_row():
        ax.spines['top'].set_visible(True)
    if ax.is_last_row():
        ax.spines['bottom'].set_visible(True)
    if ax.is_first_col():
        ax.spines['left'].set_visible(True)
    if ax.is_last_col():
        ax.spines['right'].set_visible(True)

plt.show()