【数据展示】matplotlib子图设置子标题（subtitle for subplot）

「爱情、让人受尽委屈。」 2022-05-17 01:10 341阅读 0赞

# 简述 #

这个操作很常用。用于做对比区分

代码范式：

ax.set_title(str)

但是结合相对应的子图的设计却有多种的操作方式。比如 **实例2，实例3**

其中实例2的例子当中，不能将nrows改成10（虽然不知道为什么）。

*  实例1和实例2比较适用于特殊的情况（如果行或者列超过10第一方法就不太好了，但是第二种方法就可行）
 *  实例3会更加灵活。（**推荐使用**）

## 实例1 ##

![这里写图片描述][70]

import matplotlib.pyplot as plt
    import numpy as np
    plt.figure(figsize=(6, 6.5))
    for i in range(4):
        ax = plt.subplot(221+i)
        alpha = 0.98 / 4 * i + 0.01
        ax.set_title('%.3f' % alpha)
        t1 = np.arange(0.0, 1.0, 0.01)
        for n in [1, 2, 3, 4]:
            plt.plot(t1, t1 ** n, label="n=%d" % n)
        leg = plt.legend(loc='best', ncol=4, mode="expand", shadow=True)
        leg.get_frame().set_alpha(alpha)
    
    plt.savefig('1.png')
    plt.show()

## 实例2 ##

CIFAR10为例：

**导入包**

import torch
    import torchvision
    import os
    from torch.utils.data import Dataset, DataLoader
    import torchvision.utils as vutils
    import numpy as np 
    import matplotlib.pyplot as plt
    import pickle

**下载并加载数据**

DOWNLOAD_CIFAR10 = False
    cifar10_root = './cifar10/'
    if not (os.path.exists(cifar10_root)) or not os.listdir(cifar10_root):
        # not mnist dir or mnist is empyt dir
        DOWNLOAD_CIFAR10 = True
    train_data = torchvision.datasets.CIFAR10(
        root=cifar10_root,
        train=True,  # this is training data
        transform=torchvision.transforms.ToTensor(),  # Converts a PIL.Image or numpy.ndarray to
        # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
        download=DOWNLOAD_CIFAR10,
    )
    train_loader = DataLoader(dataset=train_data, batch_size=100, shuffle=True)
    with open('./cifar10/cifar-10-batches-py/batches.meta', 'rb') as f:
        data = pickle.load(f)

**调用数据**

![在这里插入图片描述][2020060814402097.png_pic_center]

for step, (x, y) in enumerate(train_loader):
        print(x.shape, y.shape)
        print(y)
        fig = plt.figure(figsize=(10, 10))
        fig, axs = plt.subplots(nrows=1, ncols=10, figsize=(10, 1.5))
        for i in range(10):
            ax = axs[i]
            ax.axis("off")
            ax.set_title(data['label_names'][y[i]])
            ax.imshow(np.transpose(x[i].numpy(), (1, 2, 0)))
        plt.savefig('cifar-10.png')
        plt.show()
        break

## 实例3 ##

**前面的步骤和实例2一模一样，就不重复了。**

**调用数据**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ExOTk5MDQxMg_size_16_color_FFFFFF_t_70_pic_center]

**注意`plt.axis()`不可以拿出到循环外面来。**

for step, (x, y) in enumerate(train_loader):
        print(x.shape, y.shape)
        print(y)
        fig = plt.figure(figsize=(20, 20))
        for i in range(100):
            ax = plt.subplot(10, 10, i+1)
            plt.axis("off")
            ax.set_title(data['label_names'][y[i]])
            plt.imshow(np.transpose(x[i].numpy(), (1, 2, 0)))
        plt.savefig('cifar-100.png')
        plt.show()
        break

效果类似于：

for step, (x, y) in enumerate(train_loader):
        print(x.shape, y.shape)
        print(y)
        fig = plt.figure(figsize=(20, 20))
        for i in range(100):
            ax = plt.subplot(10, 10, i+1)
            ax.axis("off")
            ax.set_title(data['label_names'][y[i]])
            plt.imshow(np.transpose(x[i].numpy(), (1, 2, 0)))
        plt.savefig('cifar-100.png')
        plt.show()
        break

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ExOTk5MDQxMg_size_16_color_FFFFFF_t_70]

[70]: /images/20220517/f9407ca757b04c8ea4ad85a63372dd80.png
[2020060814402097.png_pic_center]: /images/20220517/5ac50c1cabcf42e4a82f5c0a678098bd.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ExOTk5MDQxMg_size_16_color_FFFFFF_t_70_pic_center]: /images/20220517/094788b33ed04673bd1fe6138257beb2.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ExOTk5MDQxMg_size_16_color_FFFFFF_t_70]: /images/20220517/2943908b84f5430ab0ef6f06dfbdc3e0.png