Matplotlib Layout and Configuration Functions

Matplotlib 参考文档Matplotlib Reference Documentation

Layout functions control subplot spacing and arrangement, configuration functions manage Matplotlib global parameters.

Function Overview

Layout Functions

FunctionDescription
subplots_adjust()Manually adjust subplot spacing
tight_layout()Automatic compact layout
margins()Set data margins (whitespace at both ends of the axes)
subplot_tool()Launch interactive subplot adjustment tool

Configuration Functions

FunctionDescription
rc()Set rc parameters (supports batch setting)
rc_context()Context manager for temporarily setting rc parameters
rcdefaults()Restore all rc parameters to default values

Colormap Functions

FunctionDescription
clim()Set the data range for color mapping
get_cmap()Get the colormap with the specified name
set_cmap()Set the default colormap
gci()Get the current color-mappable object
sci()Set the current color-mappable object
imread()Read an image from a file into an array
imsave()Save an array as an image file

Output and Interaction Functions

FunctionDescription
show()Display all opened Figures
draw()Force re-render the current Figure
draw_if_interactive()Render Figure in interactive mode
pause()Pause for the specified number of seconds (processes GUI events during the pause)
ion() / ioff()Enable/disable interactive mode
isinteractive()Check whether currently in interactive mode
install_repl_displayhook()Install REPL display hook
uninstall_repl_displayhook()Uninstall REPL display hook
switch_backend()Switch Matplotlib backend

Layout Function Examples

subplots_adjust() / tight_layout() / margins()

matplotlib.pyplot.subplots_adjust(left=None, bottom=None, right=None,
    top=None, wspace=None, hspace=None)
matplotlib.pyplot.tight_layout(*, pad=1.08, h_pad=None, w_pad=None,
    rect=None)
matplotlib.pyplot.margins(*margins, x=None, y=None, tight=True)

Example

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(2, 2, figsize=(10, 8))

# Manual control with subplots_adjust
fig.subplots_adjust(left=0.1, right=0.95,
                     top=0.9, bottom=0.1,
                     hspace=0.4, wspace=0.3)

for i, ax in enumerate(axes.flatten()):
    ax.plot(x, np.sin(x + i))
    ax.set_title(f'Plot {i+1}')
    ax.set_xlabel('A long x label')
    ax.set_ylabel('A long y label')
    # Set margins
    ax.margins(x=0.1, y=0.15)

fig.suptitle('subplots_adjust() + margins()', fontsize=14)
plt.show()
print("example: layout example")

Configuration Function Examples

rc() / rc_context() / rcdefaults()

matplotlib.pyplot.rc(group, **kwargs)
matplotlib.pyplot.rc_context(rc=None, fname=None)
matplotlib.pyplot.rcdefaults()

Example

import matplotlib.pyplot as plt
import numpy as np

# Method 1: Set rcParams globally
plt.rcParams['font.size'] = 12
plt.rcParams['axes.grid'] = True

# Method 2: Use rc() to set in batch
plt.rc('lines', linewidth=2, color='steelblue')
plt.rc('axes', titlesize=14, grid=True)

x = np.linspace(0, 10, 50)

# rc_context temporary setting (only valid within the with block)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4),
                                layout='constrained')

# ax1 uses global rc settings
ax1.plot(x, np.sin(x))
ax1.set_title('Global rc settings')

# ax2 uses temporary style override
with plt.rc_context({'lines.color': 'coral',
                      'lines.linestyle': '--',
                      'axes.facecolor': '#f0f0f0'}):
    ax2.plot(x, np.cos(x))
    ax2.set_title('rc_context() overrides')

plt.show()

# Restore defaults
plt.rcdefaults()
print("example: rc settings restored to defaults")

Colormap Function Examples

clim() / get_cmap() / set_cmap()

matplotlib.pyplot.clim(vmin=None, vmax=None)
matplotlib.pyplot.get_cmap(name=None, lut=None)
matplotlib.pyplot.set_cmap(cmap)

Example

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10, 10)

# View the current default colormap
print('Default cmap:', plt.rcParams['image.cmap'])

# Get a specific colormap
cmap = plt.get_cmap('plasma')
print('Got cmap:', cmap.name)

fig, ax = plt.subplots(figsize=(5, 4), layout='constrained')

im = ax.imshow(data, cmap='hot', vmin=0.2, vmax=0.8)
# clim() can also be used to modify the range
plt.clim(0.1, 0.9)

fig.colorbar(im, ax=ax, label='Value')
ax.set_title('imshow with clim()')
plt.show()

# Set the global default colormap
plt.set_cmap('Blues')
print('New default cmap:', plt.rcParams['image.cmap'])
Default cmap: viridis
Got cmap: plasma
New default cmap: Blues

imread() / imsave()

matplotlib.pyplot.imread(fname, format=None)
matplotlib.pyplot.imsave(fname, arr, **kwargs)

Example

import matplotlib.pyplot as plt
import numpy as np

# Create image data
img = np.random.rand(50, 50)

# Save as PNG (no Figure required)
plt.imsave('example_temp_image.png', img,
           cmap='viridis')
print("example: image saved via imsave()")

# Read it back
loaded = plt.imread('example_temp_image.png')
print("Loaded shape:", loaded.shape)
print("example: image loaded via imread()")
Loaded shape: (50, 50, 4)

Output and Interaction Function Examples

ion() / ioff() / pause() / draw()

matplotlib.pyplot.ion()    # 开启交互模式
matplotlib.pyplot.ioff()   # 关闭交互模式
matplotlib.pyplot.isinteractive()  # 检查状态
matplotlib.pyplot.pause(interval)  # 暂停并处理事件
matplotlib.pyplot.draw()           # 强制重绘

Example

import matplotlib.pyplot as plt
import numpy as np

# Interactive mode example (run in an environment that supports interaction)
print('Interactive mode:', plt.isinteractive())

# Manual control of rendering in non-interactive mode
plt.ioff()
fig, ax = plt.subplots()
x = np.linspace(0, 10, 100)

for i in range(3):
    ax.clear()
    ax.plot(x, np.sin(x + i * 0.5))
    ax.set_title(f'Frame {i+1}')
    plt.draw()              # Manually render
    plt.pause(0.5)          # Pause for 0.5 seconds

plt.show()
print("example: animation complete")

switch_backend()

matplotlib.pyplot.switch_backend(newbackend)

switch_backend() can only be called after the first import of pyplot and before creating any Figure. To switch backends after creating a Figure, you need to use the experimentalmatplotlib.use()。


Matplotlib 参考文档Matplotlib Reference Documentation

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