python作图

news2024/11/29 1:36:10

最重要的一张图了，有助于了解一下图的各个组成部分。最重要的一句话就是 Figure包含至少一个Axes，每个Axes可以被认为是一个模块（包含坐标轴，标题，图像内容等）。因此，创建单图的时候就是在Figure中唯一一个axes上进行设置；多图的时候就是分别对每一个axes进行设置。

fig = plt.figure()  # an empty figure with no Axes
fig, ax = plt.subplots()  # a figure with a single Axes
fig, axs = plt.subplots(2, 2)  # a figure with a 2x2 grid of Axes. Here axs contains two axes.
fig, (ax1, ax2) = plt.subplots(1, 2) # a figure with a 1*2 grid of Axes

其实 https://matplotlib.org/stable/users/explain/quick_start.html 中个介绍的比较清楚，但是比较长，这边就简化些。
需要用的包：

import matplotlib.pyplot as plt
import numpy as np

import matplotlib as mpl

基本单张图

x = np.linspace(0, 2, 100)  # Sample data.

plt.figure(figsize=(5, 2.7), layout='constrained')
plt.plot(x, x, label='linear')  # Plot some data on the (implicit) axes.
plt.plot(x, x**2, label='quadratic')  # etc.
plt.plot(x, x**3, label='cubic')
plt.xlabel('x label')
plt.ylabel('y label')
plt.title("Simple Plot")
plt.legend()
plt.show()

很简单，但是不太推荐这种形式，尤其是需要更改复杂的东西的时候。下面这种形式更加推荐。

x = np.linspace(0, 2, 100)  # Sample data.

# Note that even in the OO-style, we use `.pyplot.figure` to create the Figure.
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
ax.plot(x, x, label='linear')  # Plot some data on the axes.
ax.plot(x, x**2, label='quadratic')  # Plot more data on the axes...
ax.plot(x, x**3, label='cubic')  # ... and some more.
ax.set_xlabel('x label')  # Add an x-label to the axes.
ax.set_ylabel('y label')  # Add a y-label to the axes.
ax.set_title("Simple Plot")  # Add a title to the axes.
ax.legend()  # Add a legend.
plt.show()

在这里插入图片描述

多图排列

import matplotlib.pyplot as plt
import numpy as np

# Helper function used for visualization in the following examples
def identify_axes(ax_dict, fontsize=48):
    """
    Helper to identify the Axes in the examples below.
    Draws the label in a large font in the center of the Axes.
    Parameters
    ----------
    ax_dict : dict[str, Axes]
        Mapping between the title / label and the Axes.
    fontsize : int, optional
        How big the label should be.
    """
    kw = dict(ha="center", va="center", fontsize=fontsize, color="darkgrey")
    for k, ax in ax_dict.items():
        ax.text(0.5, 0.5, k, transform=ax.transAxes, **kw)


np.random.seed(19680801)
hist_data = np.random.randn(1_500)

fig = plt.figure(layout="constrained")
ax_array = fig.subplots(2, 2, squeeze=False)

ax_array[0, 0].bar(["a", "b", "c"], [5, 7, 9])
ax_array[0, 1].plot([1, 2, 3])
ax_array[1, 0].hist(hist_data, bins="auto")
ax_array[1, 1].imshow([[1, 2], [2, 1]])

identify_axes(
    {(j, k): a for j, r in enumerate(ax_array) for k, a in enumerate(r)},
)

在这里插入图片描述

ax_array = plt.figure(layout="constrained").subplot_mosaic(
    """
    .α.
    b☢ℝ
    ddd
    """,
    empty_sentinel=".", # default
    # set the height ratios between the rows
    height_ratios=[1, 3.5, 1],
    # set the width ratios between the columns
    width_ratios=[1, 3.5, 1],
    per_subplot_kw={
        "☢": {"projection": "polar"},
    },
)

np.random.seed(19680801)
hist_data = np.random.randn(1_500)

ax_array['α'].bar(["a", "b", "c"], [5, 7, 9])
ax_array['b'].plot([1, 2, 3])
ax_array['d'].hist(hist_data, bins="auto")
ax_array['ℝ'].imshow([[1, 2], [2, 1]])
identify_axes(ax_array)