memo: Python | Plotting

Difference between `plt` and `fig,ax`

object-oriented

Inverse the X-axis

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import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.invert_xaxis()

Add caption below the x-label

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fig, ax = plt.subplots()
txt = 'near and far are distance'
fig.text(x=.5, y=.001, s=txt, ha='center')

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fig, ax = plt.subplots()
ax.set_xlabel('''z_cam
near and far are distance''')

微分柱形图

Set xticks

xticks is the (fixed) number on the x-axis, while xticklabels can be customized strings.

For plt (Matplotlib.pyplot.xticks()): Matplotlib Set_xticks - Detailed Tutorial - Python Guides

Matplotlib.axes.Axes.set_xticklabels() Matplotlib Set_xticklabels -Python Guides

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fig, ax = plt.subplots()
ax.set_xticks([0, np.pi, 2*np.pi, 3*np.pi])
ax.set_xticklabels(labels=['0', r'$\pi$', r'2$\pi$', r'3$\pi$'], fontdict=None, fontsize=6, fontstyle='italic', color='red', verticalalignment='top', horizontalalignment='left', rotation=90, minor=True,

Change the number and text of the showing Ticks

GfG

For plt:

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plt.xticks(ticks=[1,2,3,4], labels=None, **kwargs)
plt.yticks(ticks=[7,13,24,22], labels=None,)

Use locator_param() to change the tightness and number of ticks.

plt.locator_params(axis='both', nbins=4)
Use xlim() to restrict the diplayed area, rather the whoel plot.
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plt.xlim(0,3) plt.locator_params(axis='x', nbins=3)
Use Matplotlib.ticker.MaxNLocator class

Set Number of Ticks in Matplotlib | Delft Stack

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from matplotlib.ticker import MaxNLocator
fig, ax = plt.subplots(1,1)
ax.yaxis.set_major_locator(MaxNLocator(5)) 

# hide specific ticks
for i, tick in enumerate(ax.xaxis.get_ticklabels()):
   if i%2 !=0:
      tick.set_visible(False)

Set y-axis view limits

Matplotlib.axes.Axes.set_ylim() in Python - GeeksforGeeks

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fig, ax = plt.subplots(facecolor ='# A0F0CC')
x, y = 4*(np.random.rand(2, 100) - .5)
ax.plot(x, y, 'g')
ax.set_ylim(-3, 3)

# 游标
cursor = Cursor(ax, useblit = True, color ='red', linewidth = 2)

Set the 1st x as 1 (not 0)

Python MatplotLib plot x-axis with first x-axis value labeled as 1 (instead of 0)

Add extra ticks and labels

Add extra ticks which must be number, not work for string SO

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fig, ax= plt.subplots()
extraticks = [2.1, 3, 7.6]
ax.set_xticks(list(ax.get_xticks()) + extraticks)

Use ax.set_xticklabels() for string Create label list - SO

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near, far = 1, 15
ax.set_xticks(list(ax.get_xticks()) + [-near,-far])
# Their labels also have to be appended at the end.
xticklabels= ['-16', '-14', '-12', '-10', '-8', '-6', '-4', '-2','0', '-near', '-far']
ax.set_xticklabels(xticklabels, rotation=90)

Example: Mark the yticks for the highest and lowest points:

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rmse = np.load("./error_list.npz")['Train_RMSE']
idx = np.arange(len(rmse))

fig, ax = plt.subplots()
ax.plot(idx, rmse)
extraticks = [rmse.min(), rmse.max()]
ax.set_yticks(list(ax.get_yticks()) + extraticks)

Set xticks as integer

(Not sure) ax.xaxis.set_major_locator(MaxNLocator(integer=True))

Python：使用f-string保留小数点位数 csdn

用decimal 模块：w3cschool

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from decimal import Decimal
a = 12.345
Decimal(a).quantize(Decimal("0.00")) # 使用默认的进位方式（同round）“0.00”表示保留小数点后两位

Margin

to leave more space around the figure to show the ticks of the boundary Geeksforgeeks

Or prevent the markers get clipped by the axes GfG plt xticks

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fig, ax = plt.subplots()
ax.margins(0.5)

Hide ticks

Legend

gfg Docs

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ax.plot([1, 2, 3], label='Inline label')
ax.legend(loc='best', bbox_to_anchor=(0.5, 0., 0.5, 0.5))

Put it below the subplots: ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.13),fancybox=False, shadow=False, ncol=5, fontsize=6)

How to add legend below subplots in matplotlib?

Draw a horizontal line

plt : gfg

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plt.axhline(y=0.5, color='r', linestyle='-')

ax: SO

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ax.hlines(y=0.2, xmin=0.01, xmax=20, linewidth=2, color='r')

Tight layout

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plt.tight_layout()

Or: plt.rcParams["savefig.bbox"] = 'tight'

Save as png

gfg

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ax.set_facecolor('pink') # inner background
plt.savefig('depth_buffer.png', bbox_inches='tight', dpi =100, facecolor="white") # bkg color

plt.savefig('output.jpg', bbox_inches='tight', dpi =40) #is smaller

Draw markers on axes

plt.plot(x,y, zorder=10, clip_on=False)

plotting markers on top of axes

Arrow of axis

matplotlib axis arrow tip

Change fonts

Plot in Ubuntu has ugly fonts (which is like ‘sans-serif’).

plt.rcParams["font.family"] = "cursive", This will change to your computer’s default monospace font. How to change fonts in matplotlib (python)?

散点图

plt.scatter(x,y, c='r', s=4), s can control marker size, docs
marker 是反着画的
matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)
x,y组成了散点的坐标；s为散点的面积；c为散点的颜色（默认为蓝色’b’）；marker为散点的标记；alpha为散点的透明度（0与1之间的数，0为完全透明，1为完全不透明）;linewidths为散点边缘的线宽；如果marker为None，则使用verts的值构建散点标记；edgecolors为散点边缘颜色。 csdn

拉长坐标间距

Python设置matplotlib.plot的坐标轴刻度间隔以及刻度范围

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import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator # 用于设置刻度间隔

x_vals = list(range(11))
y_vals = [x**2 for x in x_vals]
plt.plot(x_vals, y_vals, c='green')
plt.title('Squares', fontsize=24)

plt.tick_params(axis='both',which='major',labelsize=14)
plt.xlabel('Numbers',fontsize=14)
plt.ylabel('Squares',fontsize=14)
x_major_locator=MultipleLocator(1) #把x轴的刻度间隔设置为1，并存在变量里
y_major_locator=MultipleLocator(10) #把y轴的刻度间隔设置为10，并存在变量里
ax=plt.gca() #ax为两条坐标轴的实例
ax.xaxis.set_major_locator(x_major_locator) #把x轴的主刻度设置为1的倍数
ax.yaxis.set_major_locator(y_major_locator) #把y轴的主刻度设置为10的倍数
plt.xlim(-0.5,11) #把x轴的刻度范围设置为-0.5到11，因为0.5不满一个刻度间隔，所以数字不会显示出来，但是能看到一点空白
plt.ylim(-5,110) #把y轴的刻度范围设置为-5到110，同理，-5不会标出来，但是能看到一点空白
plt.show()

设置图片尺寸大小

如何指定matplotlib输出图片的尺寸？ - pythonic生物人的回答 - 知乎 https://www.zhihu.com/question/37221233/answer/2250419008

plt.rcParams['figure.figsize'] = (12.0, 8.0)
fig, ax = plt.subplots(figsize=(15,0.5),dpi=300) pool

设置图片分辨率

plt.figure(figsize=(a, b), dpi=dpi)

matplotlib设置分辨率

网格

plt.grid(visible=True,linestyle="--", color='gray', linewidth='1',)

绘制CDF (221211)

Repeat drawing

How to change a matplotlib figure in a different cell in Jupyter Notebook?

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fig.gca().scatter(x3, y3, color='g', linewidth=5)
fig

(2022-12-13)

Get the arguments name

use lib inspect with two .f_back: callers_local_vars = inspect.currentframe().f_back.f_back.f_locals.items(). Getting the name of a variable as a string

Get current color

How to get color of most recent plotted line in Python’s plt

plt.gca().lines[-1].get_color()

use ax Get default line colour cycle

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line = ax.plot(x,y)
ax.plot(x, y+.3, color = line.get_color())

Combine two figures

matplotlib: combine different figures and put them in a single subplot sharing a common legend

Multiple subplots

GfG

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fig, ax = plt.subplots(3, 3)

# draw graph
for i in ax:
    for j in i:
        j.plot(np.random.randint(0, 5, 5), np.random.randint(0, 5, 5))
plt.show()

title of subplots: ax[0][0].set_title("xxx")

hide x ticks for top subplots and y ticks for right plots: How to set a single, main title above all the subplots with Pyplot?

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plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)
plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)

Set xticks for subplots: How to set xticks in subplots

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fig, axes = plt.subplots(nrows=3, ncols=4)

# Set the ticks and ticklabels for all axes
plt.setp(axes, xticks=[0.1, 0.5, 0.9], xticklabels=['a', 'b', 'c'],
        yticks=[1, 2, 3])

# Use the pyplot interface to change just one subplot...
plt.sca(axes[1, 1])
plt.xticks(range(3), ['A', 'Big', 'Cat'], color='red')

fig.tight_layout()
plt.show()

Set the fontsize of numbers of xticks: plt.setp(ax.get_xticklabels(), fontsize=12, fontweight="normal", horizontalalignment="left", rotation=90) set_xticks() needs argument for ‘fontsize’ #12318
set_xticklabels-Docs

Scale y-axis

Docs

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x = np.arange(len(Train_RMSE))
fig, ax = plt.subplots()
plt.yscale('log',)
ax.plot(x, Train_RMSE)

Composite 2 imgs

(2023-12-20) Setting alpha:

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import cv2
import matplotlib.pyplot as plt

img1 = cv2.imread('rot1.png')
pts1 = [(24, 124), (49, 124), (98, 124), (104, 124), (120, 124),
        (18, 146), (37, 146), (65, 146), (102, 146), (133, 146)]
for pt in pts1:
  cv2.circle(img1, pt, 0, (0, 0, 255))
plt.figure(figsize=(20,10))
plt.imshow(img1[:,:, ::-1])

img2 = cv2.imread('rot2.png')
plt.imshow(img2[:,:,::-1], alpha=0.6)

An example

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import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator

epoch = np.array(
    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
     59])
train_loss = np.array(
    [0.27195, 0.07782, 0.05188, 0.03321, 0.03054, 0.02322, 0.01962, 0.01537, 0.01347, 0.01266, 0.00895, 0.01107,
     0.00755, 0.00633, 0.00915, 0.00872, 0.00431, 0.00733, 0.00584, 0.00477, 0.00502, 0.00384, 0.00282, 0.00506,
     0.00307, 0.00325, 0.00588, 0.00277, 0.00445, 0.00318, 0.00181, 0.00532, 0.00218, 0.00379, 0.00190, 0.00201,
     0.00242, 0.00164, 0.00215, 0.00176, 0.00374, 0.00149, 0.00145, 0.00170, 0.00481, 0.00136, 0.00184, 0.00165,
     0.00162, 0.00265, 0.00285, 0.00172, 0.00241, 0.00139, 0.00181, 0.00161, 0.00135, 0.00264, 0.00165, 0.00158])
valid_loss = np.array(
    [0.04518, 0.02028, 0.01517, 0.01853, 0.00928, 0.01604, 0.01256, 0.01068, 0.01229, 0.01066, 0.01251, 0.00756,
     0.00836, 0.00777, 0.00623, 0.00942, 0.00761, 0.00691, 0.00626, 0.00607, 0.00708, 0.00703, 0.01061, 0.00758,
     0.00807, 0.00541, 0.00521, 0.00570, 0.00844, 0.00610, 0.00735, 0.00448, 0.00466, 0.00734, 0.00633, 0.00511,
     0.00737, 0.00380, 0.00410, 0.00604, 0.00685, 0.00546, 0.00618, 0.00411, 0.00543, 0.00572, 0.00631, 0.00821,
     0.00578, 0.00525, 0.00391, 0.00529, 0.00621, 0.00606, 0.00979, 0.00515, 0.00555, 0.00712, 0.00535, 0.00439])

# 解决曲线图里面中文显示乱码问题

# plt.rcParams['font.sans-serif'] = ['Hiragino Sans GB']  # 用来正常显示中文标签
#
# plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(8,6))

# 增加曲线说明

A, = plt.plot(epoch, train_loss, lw=1, label='train_loss')
B, = plt.plot(epoch, valid_loss, lw=1, label='valid_loss')
font1 = {'family': 'Times New Roman',
         'weight': 'normal',
         'size': 18, }
# x轴label

plt.xlabel("epoch", labelpad=2, fontsize=18,)

# y轴label

plt.ylabel("loss", labelpad=4, fontsize=18)

# 设置标题

# plt.title("change in loss", x=0.5, y=-0.2, pad=0.3, fontsize=18)
plt.title("change in loss", fontsize=18)
fig=plt.gcf()
fig.set_facecolor('white')


# ------限制显示x,y轴最小-最大值范围（刻度不一定是多少）

plt.xlim(0, 60)

plt.ylim(0, 0.3)

# ------设置x,y轴刻度

x_major_locator = MultipleLocator(10)

y_major_locator = MultipleLocator(0.02)

ax = plt.gca()

# ax为两条坐标轴的实例

ax.xaxis.set_major_locator(x_major_locator)
# ax.xaxis.set_ticks_position('bottom')
# 把x轴的主刻度设置为1的倍数

ax.yaxis.set_major_locator(y_major_locator)

# 把y轴的主刻度设置为10的倍数

# ------显示网格

# plt.grid()

plt.grid(True, linestyle="--", color='gray', linewidth='1', axis='both')

plt.legend(handles=[A, B], prop=font1)
plt.tick_params(labelsize=18)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Time New Roman') for label in labels]

font2 = {'family': 'Time New Roman',
         'weight': 'normal',
         'size': 18}

plt.show()
plt.savefig('saved_figure.jpg')

3D interactive plot

(2024-03-23)

Make 3D interactive Matplotlib plot in Jupyter Notebook - GfG

3D Scatter plot:

Code

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# for creating a responsive plot 
%matplotlib widget 

# importing required libraries 
from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 

# creating random dataset 
xs = [14, 24, 43, 47, 54, 66, 74, 89, 12, 
      44, 1, 2, 3, 4, 5, 9, 8, 7, 6, 5] 

ys = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 3,
      5, 2, 4, 1, 8, 7, 0, 5] 

zs = [9, 6, 3, 5, 2, 4, 1, 8, 7, 0, 1, 2,
      3, 4, 5, 6, 7, 8, 9, 0] 

# creating figure 
fig = plt.figure() 
ax = fig.add_subplot(111, projection='3d') 

# creating the plot 
plot_geeks = ax.scatter(xs, ys, zs, color='green') 

# setting title and labels 
ax.set_title("3D plot") 
ax.set_xlabel('x-axis') 
ax.set_ylabel('y-axis') 
ax.set_zlabel('z-axis') 

# displaying the plot 
plt.show()

3D Bar plot:

Code

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%matplotlib widget 
from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 
import numpy as np 

# creating random dataset 
xs = [2, 3, 4, 5, 1, 6, 2, 1, 7, 2] 
ys = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 
zs = np.zeros(10) 
dx = np.ones(10) 
dy = np.ones(10) 
dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 

# creating figure 
figg = plt.figure() 
ax = figg.add_subplot(111, projection='3d') 

# creating the plot 
plot_geeks = ax.bar3d(xs, ys, zs, dx,  
                      dy, dz, color='green') 

# setting title and labels 
ax.set_title("3D bar plot") 
ax.set_xlabel('x-axis') 
ax.set_ylabel('y-axis') 
ax.set_zlabel('z-axis') 

plt.show()

Camera Poses

(2024-03-23)

Generate by ChatGPT with prompt: “How to plot multiple camera poses in a single figure?”

Code

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%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt

def plot_camera_poses(poses, axis_length=0.1):
    # Create a new figure
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')

    # Plot each camera pose
    for pose in poses:
        # Extract camera position and orientation
        cam_position = pose['position']
        cam_orientation = pose['rotation']

        # Plot camera position
        ax.scatter(cam_position[0], cam_position[1], cam_position[2], c='r', marker='o')

        # Plot camera axes
        axes_endpoints = cam_position + axis_length * cam_orientation
        ax.plot3D([cam_position[0], axes_endpoints[0,0]], 
                  [cam_position[1], axes_endpoints[0,1]], 
                  [cam_position[2], axes_endpoints[0,2]], 'r')
        ax.plot3D([cam_position[0], axes_endpoints[1,0]], 
                  [cam_position[1], axes_endpoints[1,1]], 
                  [cam_position[2], axes_endpoints[1,2]], 'g')
        ax.plot3D([cam_position[0], axes_endpoints[2,0]], 
                  [cam_position[1], axes_endpoints[2,1]], 
                  [cam_position[2], axes_endpoints[2,2]], 'b')

    # Set plot limits and labels
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')

    # Show plot
    plt.show()

# Example camera poses (positions and rotation matrices)
poses = [
    {
        'position': np.array([0, 0, 0]),
        'rotation': np.array([[1, 0, 0],
                              [0, 1, 0],
                              [0, 0, 1]])
    },
    {
        'position': np.array([1, 1, 1]),
        'rotation': np.array([[0, -1, 0],
                              [1, 0, 0],
                              [0, 0, 1]])
    }
]

# Plot camera poses
plot_camera_poses(poses)

3D arrow

(2024-03-23)

Putting arrowheads on vectors in a 3d plot - SO

Code

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%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d

class Arrow3D(FancyArrowPatch):
    def __init__(self, xs, ys, zs, *args, **kwargs):
        super().__init__((0,0), (0,0), *args, **kwargs)
        self._verts3d = xs, ys, zs

    def do_3d_projection(self, renderer=None):
        xs3d, ys3d, zs3d = self._verts3d
        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)
        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))

        return np.min(zs)

arrow_prop_dict = dict(mutation_scale=10, arrowstyle='-|>', color='k', shrinkA=0, shrinkB=0)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d') 
    
a = Arrow3D([0, 1], 
            [0, 0], 
            [0, 0], **arrow_prop_dict)
ax.add_artist(a)
a = Arrow3D([0, 0], 
            [0, 1], 
            [0, 0], **arrow_prop_dict)
ax.add_artist(a)
a = Arrow3D([0, 0], 
            [0, 0], 
            [0, 1], **arrow_prop_dict)
ax.add_artist(a)

Convex polygon on 2D

(2024-03-26)

How to fill an area within a polygon in Python using matplotlib? - SO

Example from Scipy:

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# Convex hull of a random set of points:
from scipy.spatial import ConvexHull, convex_hull_plot_2d
import numpy as np
rng = np.random.default_rng()
points = rng.random((30, 2))   # 30 random points in 2-D
hull = ConvexHull(points)

# Plot it:
import matplotlib.pyplot as plt
plt.plot(points[:,0], points[:,1], 'o')
for simplex in hull.simplices:
    plt.plot(points[simplex, 0], points[simplex, 1], 'k-')

plt.plot(points[hull.vertices,0], points[hull.vertices,1], 'r--', lw=2)
plt.plot(points[hull.vertices[0],0], points[hull.vertices[0],1], 'ro')
plt.show()

Fill 3D Polygon

(2024-03-26)

ax.fill is used for 2D, and doesn’t work for 3D.

ax.add_collection3d cannot follow the counter-clockwise order of convex hull to fill the polygon. For example, when drawing a rectangle, it will fill 2 triangles:

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%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d') 
verts = np.array([[ [0,0,0], 
                    [1,0,0], 
                    [0,1,0], 
                    [1,1,0] ]]) # (1,4,3)
rec = Poly3DCollection(verts, alpha=0.5, facecolors='cyan', edgecolors='k')
ax.add_collection3d(rec)

The Poly3DCollection code is generated by ChatGPT with prompt: “How to fill a 3D triangle with matlabplot”

So, I draw 2 triangles to form a quadrilateral:

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%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d') 
verts = np.array([[ [0,0,0], 
                    [1,0,0], 
                    [0,1,0],]])
tri = Poly3DCollection(verts, alpha=0.5, facecolors='cyan',)
ax.add_collection3d(tri)

verts = np.array([[ [1,0,0], 
                    [0,1,0],
                    [1,1,0] ]])
tri = Poly3DCollection(verts, alpha=0.5, facecolors='cyan',)
ax.add_collection3d(tri)

Plotting 3D Polygons - SO

Project 3D point onto plane

(2024-03-26)

How to project a point onto a plane in 3D? - SO

The distance from 3D point to the plane: dot product for 3D point and plane normal vector.
3D point minus the distance, resulting in the projection.

Code

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%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

R = np.array([[ 0.97026268,  0.00747991,  0.24193878], # cam x-axis
              [-0.01474287,  0.99949291,  0.02822342], # cam y-axis
              [-0.24160499, -0.030951  ,  0.96988095]]) #cam z-axis

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d') 

# fill rectangle
verts = np.array([[ [0,0,0], R[0]*2, R[1]*2, ]]) # (1,4,3)
tri = Poly3DCollection(verts, alpha=0.5, facecolors='cyan', )
ax.add_collection3d(tri)
verts = np.array([[ R[0]*2, R[1]*2, (R[0]+R[1])*2 ]]) # (1,4,3)
tri = Poly3DCollection(verts, alpha=0.5, facecolors='cyan', )
ax.add_collection3d(tri)

p = np.array([1,1,1])
ax.scatter(*p, c='r', marker='o')
plane_normal = R[2]

len2plane = R[2]@ p.T

projection = p-len2plane * plane_normal

ax.scatter(*projection, marker='*')
ax.plot3D([p[0], projection[0]], 
          [p[1], projection[1]], 
          [p[2], projection[2]], 'gray')

Difference between plt and fig,ax

Inverse the X-axis

Add caption below the x-label

微分柱形图

Set xticks

Change the number and text of the showing Ticks

Set y-axis view limits

Set the 1st x as 1 (not 0)

Add extra ticks and labels

Set xticks as integer

Margin

Hide ticks

Legend

Draw a horizontal line

Tight layout

Save as png

Draw markers on axes

Arrow of axis

Change fonts

散点图

拉长坐标间距

设置图片尺寸大小

设置图片分辨率

网格

绘制CDF (221211)

Repeat drawing

Get the arguments name

Get current color

Combine two figures

Multiple subplots

Scale y-axis

Composite 2 imgs

An example

3D interactive plot

Camera Poses

3D arrow

Convex polygon on 2D

Fill 3D Polygon

Project 3D point onto plane

Difference between `plt` and `fig,ax`