Question

我想可视化数学域或区间。同样，我想可视化一个布尔数组。有多个这样的数组，理想情况下，它们要一个放在另一个之上。

我所拥有的是一些数据：在一段100分钟的时间内录制了几张唱片。每个记录仅在部分时间内满足给定条件。我想可视化每个记录为“ True”的时间。一些更简单的变体：

就我而言，每个记录可以是多个间隔的并集。例如：

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb

sb.set_context("paper")

times = np.arange(0, 100)

mask1 = (times >= 0) * (times <= 30) + (times >= 70) * (times <= 100)
mask2 = (times >= 20) * (times <= 80)

我可以使用我编写的这两个功能分别绘制每个录音：

def bool2extreme(mask, times) :
    """return xmins and xmaxs for intervals in times"""
    binary = 1*mask
    slope = np.diff(binary)

    extr = (slope != 0)
    signs = slope[extr]

    mins = list(times[1:][slope==1])
    maxs = list(times[:-1][slope==-1])

    if signs[0]==-1:
        mins = [times[0]] + mins

    if signs[-1]==1:
        maxs = maxs + [times[-1]]

    return mins, maxs

def plot_interval(mask, times, y=0, color='k', ax=None) :

    if ax==None:
        print('None')
        ax = plt.gca()

    xmins, xmaxs = bool2extreme(mask, times)

    for xmin, xmax in zip(xmins, xmaxs):

        ax.plot([xmin, xmax], [y,y], lw=6, color=color)

    return ax

我的问题是控制各个间隔之间的垂直间隔。确实，当我绘制其中一个时，会有一个我不想要的垂直轴。即使我将其可见性设置为False，它也存在并占用空间。因此，当我将每个记录放在不同的子图中时，它们之间的垂直间距太大：

masks = [mask1, mask2]
labels = ['domain1', 'domain2']
n_plots = len(masks)

fig, axs = plt.subplots(n_plots, sharex=True)

for i, mask in enumerate(masks) :

    axs[i] = plot_interval(mask, times, ax=axs[i])

axs[-1].set_xlabel('Time (min)')

sb.despine()

我尝试过的另一种选择：将所有间隔都设置在同一轴上，但是使用不同的y值。但是间隔之间的垂直间距问题仍然存在。

masks = [mask1, mask2]
labels = ['domain1', 'domain2']
n_plots = len(masks)

fig, ax = plt.subplots(sharex=True)

for i, mask in enumerate(masks) :

    ax = plot_interval(mask, times, y=i, ax=ax)


ax.set_xlabel('Time (min)')

ax.set_yticks(range(n_plots))
ax.set_yticklabels(labels)
ax.grid(axis="x")

sb.despine(left=True)

如何控制这些间隔之间的垂直间隔？

Answer 1

一些想法：

以较小的高度显示尺寸； figsize的高度控制水平轴之间的距离：以英寸为单位时，它们将height/num_axes隔开
ax.yaxis.set_visible(False)隐藏y轴上的刻度线
ax.spines['left'].set_color('None')使y轴的书脊不可见
ax.spines['bottom'].set_position(('data', 0))将x轴放置在y=0高度
（可选）ax.tick_params(labelbottom=True)，以便在所有子图上（而不是仅在最后一个子图上）为xticks贴上标签。
使用矩形而不是粗线来更好地控制直线的确切起点和终点以及轴线上方和下方的厚度
要控制矩形的高度，需要固定ylim；我建议(-1.5, .5)，以便可以适当选择厚度。在下面留出更多空间来放置xtick的标签
由于绘制矩形不会自动更新xlim，因此需要对其进行显式设置
（可选）ax.tick_params(which='both', direction='in')，以在上方（而不是市长和次要的滴答声）上方获得刻度线

要在左侧添加标签，以下内容对我有用：

# ax.yaxis.set_visible(False)  # removed, as it also hides the ylabel
ax.set_ylabel('my ylabel', rotation=0, ha='right', labelpad=10)
ax.set_yticks([])  # to remove the ticks, the spine was already removed

在演示代码中，添加了更多xticks和末尾的某些类型的箭头。演示中有7个遮罩，以更好地查看轴之间的距离效果。尝试使轴尽可能接近，0.4英寸的距离似乎可行。（bool2extreme函数未更改，因为它与用作输入的格式密切相关。）

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Polygon
import matplotlib.ticker as plticker
import seaborn as sbs

sbs.set_context("paper")

times = np.arange(0, 101)
num_masks = 7
masks = [np.zeros_like(times, dtype=bool) for _ in range(num_masks)]
for i in range(num_masks):
    for j in range(50):
        masks[i] += (times >= (i+3)*j) * (times <= (i+3)*j+i+1)
masks = masks[::-1] # reverse to get the masks plotted from bottom to top

def bool2extreme(mask, times) :
    """return xmins and xmaxs for intervals in times"""
    binary = 1*mask
    slope = np.diff(binary)

    extr = (slope != 0)
    signs = slope[extr]
    mins = list(times[1:][slope==1])
    maxs = list(times[:-1][slope==-1])
    if signs[0]==-1:
        mins = [times[0]] + mins
    if signs[-1]==1:
        maxs = maxs + [times[-1]]
    return mins, maxs

def plot_interval(mask, times, xlim=None, y=0, thickness=0.4, color='k', ax=None):
    if ax is None:
        ax = plt.gca()
    ax.yaxis.set_visible(False)
    ax.spines['left'].set_color('None')
    ax.spines['right'].set_color('None')
    ax.spines['top'].set_color('None')
    ax.spines['bottom'].set_position(('data', 0))
    ax.tick_params(labelbottom=True)  # to get tick labels on all axes
    # ax.tick_params(which='both', direction='in')`  # tick marks above instead below the axis
    ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10)) # major ticks in steps of 10
    ax.xaxis.set_minor_locator(plticker.MultipleLocator(base=1))  # minor ticks in steps of 1
    ax.set_ylim(-1.5,.5)
    if xlim is None:
        xlim = (times[0]-0.9, times[-1]+0.9)
    ax.set_xlim(xlim)
    xmins, xmaxs = bool2extreme(mask, times)
    for xmin, xmax in zip(xmins, xmaxs):
        #ax.add_patch(Rectangle((xmin, y-thickness), xmax-xmin, 2*thickness, linewidth=0, color=color))
        ax.add_patch(Rectangle((xmin, y), xmax-xmin, thickness, linewidth=0, color=color))
    triangle1 = [(xlim[0]-0.5, y), (xlim[0], y-thickness), (xlim[0], y+thickness)]
    ax.add_patch(Polygon(triangle1, linewidth=0, color='black', clip_on=False))
    triangle2 = [(xlim[1]+0.5, y), (xlim[1], y-thickness), (xlim[1], y+thickness)]
    ax.add_patch(Polygon(triangle2, linewidth=0, color='black', clip_on=False))
    return ax

n_plots = len(masks)
dist_between_axis_in_inches = 0.4

fig, axs = plt.subplots(n_plots, sharex=True, figsize=(10, dist_between_axis_in_inches*len(masks)))
for i, mask in enumerate(masks) :
    axs[i] = plot_interval(mask, times, xlim=(times[0]-0.5, times[-1]+0.5), ax=axs[i], color='lime')
axs[-1].set_xlabel('Time (min)')
plt.show()

轴靠在一起的结果：

PS：This post包含有关添加箭头的更多建议。

如何创建用彩色线条显示间隔的多个一维轴？

1 个答案: