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Python 中常见的一些画图形式

article 2025/1/5 6:02:56

文章目录

Python 中常见的一些画图形式
- 颜色参数
- 直线参数
- 标记参数
- 画3D图

Python 中常见的一些画图形式

颜色参数

显示Python中不同的颜色

import math
from matplotlib.patches import Rectangle
import numpy as np
from matplotlib import pyplot as plt
# 颜色说明
import matplotlib.colors as mcolors

def plot_colortable(colors, *, ncols=4, sort_colors=True):

    cell_width = 212
    cell_height = 22
    swatch_width = 48
    margin = 12

    # Sort colors by hue, saturation, value and name.
    if sort_colors is True:
        names = sorted(
            colors, key=lambda c: tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(c))))
    else:
        names = list(colors)

    n = len(names)
    nrows = math.ceil(n / ncols)

    width = cell_width * ncols + 2 * margin
    height = cell_height * nrows + 2 * margin
    dpi = 72

    fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
    fig.subplots_adjust(margin/width, margin/height,
                        (width-margin)/width, (height-margin)/height)
    ax.set_xlim(0, cell_width * ncols)
    ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.)
    ax.yaxis.set_visible(False)
    ax.xaxis.set_visible(False)
    ax.set_axis_off()

    for i, name in enumerate(names):
        row = i % nrows
        col = i // nrows
        y = row * cell_height

        swatch_start_x = cell_width * col
        text_pos_x = cell_width * col + swatch_width + 7

        ax.text(text_pos_x, y, name, fontsize=14,
                horizontalalignment='left',
                verticalalignment='center')

        ax.add_patch(
            Rectangle(xy=(swatch_start_x, y-9), width=swatch_width,
                      height=18, facecolor=colors[name], edgecolor='0.7')
        )

    return fig


# 有8种颜色
# ['k', 'w', 'r', 'y', 'g', 'c', 'b', 'm']
# 黑色，白色，红色，黄色，绿色，青色，蓝色，品红色
# colors = mcolors.BASE_COLORS
# 有10种颜色
# ['gray','brown','orange','olive','green','cyan','blue','purple','pink','red']
# '灰色'， '棕色'， '橙色'， '橄榄色'， '绿色'， '青色'， '蓝色'， '紫色'， '粉红色'， '红色'
colors = mcolors.TABLEAU_COLORS
# 有 148 种颜色
# colors = mcolors.CSS4_COLORS
# 有 949 种颜色
# colors = mcolors.XKCD_COLORS
names = sorted(colors, key=lambda c: tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(c))))


plot_colortable(mcolors.BASE_COLORS, ncols=3, sort_colors=False)
plt.show()

plot_colortable(mcolors.TABLEAU_COLORS, ncols=3, sort_colors=False)
plt.show()

plot_colortable(mcolors.CSS4_COLORS, ncols=3, sort_colors=False)
plt.show()

举例说明

color_list = ['k', 'w', 'r', 'y', 'g', 'c', 'b', 'm', 'gray','brown','orange','olive','green','cyan','blue','purple','pink','red']
x = np.random.rand(len(color_list))
y = np.random.rand(len(color_list))
for i in range(len(color_list)):
	plt.plot(x[i],y[i],  color = color_list[i], markersize=12, marker='*')
plt.xlabel('x', fontsize=12)
plt.ylabel('y', fontsize=12)
plt.xlim(left = 0, right = 1)
plt.ylim(bottom = 0, top = 1)
# 设置轴刻度 ，matplotlib将刻度放在对应范围的哪个位置，默认情况下这些刻度就是刻度标签；
plt.xticks(np.linspace(0, 1, 5))
plt.yticks(np.linspace(0, 1, 5))
plt.show()

直线参数

显示Python中支持的不同直线线型

import matplotlib.pyplot as plt
import numpy as np

linestyle_str = [
     ('solid', 'solid'),      # Same as (0, ()) or '-'
     ('dotted', 'dotted'),    # Same as ':'
     ('dashed', 'dashed'),    # Same as '--'
     ('dashdot', 'dashdot')]  # Same as '-.'

linestyle_tuple = [
     ('loosely dotted',        (0, (1, 10))),
     ('dotted',                (0, (1, 5))),
     ('densely dotted',        (0, (1, 1))),

     ('long dash with offset', (5, (10, 3))),
     ('loosely dashed',        (0, (5, 10))),
     ('dashed',                (0, (5, 5))),
     ('densely dashed',        (0, (5, 1))),

     ('loosely dashdotted',    (0, (3, 10, 1, 10))),
     ('dashdotted',            (0, (3, 5, 1, 5))),
     ('densely dashdotted',    (0, (3, 1, 1, 1))),

     ('dashdotdotted',         (0, (3, 5, 1, 5, 1, 5))),
     ('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))),
     ('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))]


def plot_linestyles(ax, linestyles, title):
    X, Y = np.linspace(0, 100, 10), np.zeros(10)
    yticklabels = []

    for i, (name, linestyle) in enumerate(linestyles):
        ax.plot(X, Y+i, linestyle=linestyle, linewidth=1.5, color='black')
        yticklabels.append(name)

    ax.set_title(title)
    ax.set(ylim=(-0.5, len(linestyles)-0.5),
           yticks=np.arange(len(linestyles)),
           yticklabels=yticklabels)
    ax.tick_params(left=False, bottom=False, labelbottom=False)
    ax.spines[:].set_visible(False)

    # For each line style, add a text annotation with a small offset from
    # the reference point (0 in Axes coords, y tick value in Data coords).
    for i, (name, linestyle) in enumerate(linestyles):
        ax.annotate(repr(linestyle),
                    xy=(0.0, i), xycoords=ax.get_yaxis_transform(),
                    xytext=(-6, -12), textcoords='offset points',
                    color="blue", fontsize=8, ha="right", family="monospace")


fig, (ax0, ax1) = plt.subplots(2, 1, figsize=(7, 8), height_ratios=[1, 3],
                               layout='constrained')

plot_linestyles(ax0, linestyle_str[::-1], title='Named linestyles')
plot_linestyles(ax1, linestyle_tuple[::-1], title='Parametrized linestyles')

plt.show()

举例说明

line_list = ['-', '--', '-.', ':', 'solid', 'dashed', 'dashdot', 'dotted']
# line_list = ['solid', 'dashed', 'dashdot', 'dotted']
for i in range(len(line_list)):
	x = np.linspace(0, 10, 10)
	y = [i] * 10
	plt.plot(x, y, linestyle=line_list[i], linewidth=2,label = line_list[i])
plt.legend()
plt.show()

标记参数

import matplotlib.pyplot as plt

from matplotlib.lines import Line2D
from matplotlib.markers import MarkerStyle
from matplotlib.transforms import Affine2D

text_style = dict(horizontalalignment='right', verticalalignment='center',
                  fontsize=12, fontfamily='monospace')
marker_style = dict(linestyle=':', color='0.8', markersize=10,
                    markerfacecolor="tab:blue", markeredgecolor="tab:blue")


def format_axes(ax):
    ax.margins(0.2)
    ax.set_axis_off()
    ax.invert_yaxis()


def split_list(a_list):
    i_half = len(a_list) // 2
    return a_list[:i_half], a_list[i_half:]

fig, axs = plt.subplots(ncols=2)
fig.suptitle('Un-filled markers', fontsize=14)

# Filter out filled markers and marker settings that do nothing.
unfilled_markers = [m for m, func in Line2D.markers.items()
                    if func != 'nothing' and m not in Line2D.filled_markers]

for ax, markers in zip(axs, split_list(unfilled_markers)):
    for y, marker in enumerate(markers):
        ax.text(-0.5, y, repr(marker), **text_style)
        ax.plot([y] * 3, marker=marker, **marker_style)
    format_axes(ax)
plt.show()

fig, axs = plt.subplots(ncols=2)
fig.suptitle('Filled markers', fontsize=14)
for ax, markers in zip(axs, split_list(Line2D.filled_markers)):
    for y, marker in enumerate(markers):
        ax.text(-0.5, y, repr(marker), **text_style)
        ax.plot([y] * 3, marker=marker, **marker_style)
    format_axes(ax)
plt.show()


fig, ax = plt.subplots()
fig.suptitle('Marker fillstyle', fontsize=14)
fig.subplots_adjust(left=0.4)
filled_marker_style = dict(marker='o', linestyle=':', markersize=15,
                           color='darkgrey',
                           markerfacecolor='tab:blue',
                           markerfacecoloralt='lightsteelblue',
                           markeredgecolor='brown')

for y, fill_style in enumerate(Line2D.fillStyles):
    ax.text(-0.5, y, repr(fill_style), **text_style)
    ax.plot([y] * 3, fillstyle=fill_style, **filled_marker_style)
format_axes(ax)
plt.show()

举例说明

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)
unfilled_markers_list_1 = [',','1','2','3','4', '+', 'x', '|', '_']
unfilled_markers_list_2 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
x = np.random.rand(len(unfilled_markers_list_1))
y = np.random.rand(len(unfilled_markers_list_1))
for i in range(len(unfilled_markers_list_1)):
	plt.plot(x[i],y[i], markersize=12, marker=unfilled_markers_list_1[i])
x = np.random.rand(len(unfilled_markers_list_2))
y = np.random.rand(len(unfilled_markers_list_2))
for i in range(len(unfilled_markers_list_2)):
	plt.plot(x[i],y[i],  markersize=12, marker=unfilled_markers_list_2[i])
plt.show()

filled_markers_list = ['.','o','v','^','<','>','8','s','p','*','h','H','D','d','P','X']
x = np.random.rand(len(filled_markers_list))
y = np.random.rand(len(filled_markers_list))
for i in range(len(filled_markers_list)):
	plt.plot(x[i],y[i],  markersize=12, marker=filled_markers_list[i])
plt.show()

画3D图

import numpy as np
from matplotlib import pyplot as plt
# #方法一，利用关键字定义坐标轴 
fig = plt.figure()
ax1 = plt.axes(projection='3d')
# ``fig.add_subplot(235)`` is the same as
# ``fig.add_subplot(2, 3, 5)``. Note that this can only be used if there are no more than 9 subplots.
#ax = fig.add_subplot(211,projection='3d')  #这种方法也可以画多个子图
z = np.linspace(0,13,1000)
x = 5*np.sin(z)
y = 5*np.cos(z)
zd = 13*np.random.random(100)
xd = 5*np.sin(zd)
yd = 5*np.cos(zd)
ax1.scatter3D(xd,yd,zd, c = 'red',label='scatter')  #绘制散点图
ax1.plot3D(x,y,z,'gray',label ='plot')    #绘制空间曲线
# 设置标签的大小
ax1.set_xlabel('x', fontsize=12)
ax1.set_ylabel('y', fontsize=12)
ax1.set_zlabel('z', fontsize=12)
# ax1.set_xlim(left = -10, right = 10)
ax1.set_xbound(lower = -10, upper = 10)

# 设置轴刻度 ，matplotlib将刻度放在对应范围的哪个位置，默认情况下这些刻度就是刻度标签；
ax1.set_xticks(np.linspace(-10, 10, 20))
ax1.legend()
plt.show()

在这里插入图片描述

#方法二，利用三维轴方法
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(projection='3d')
x=np.arange(-8,8,0.25)     
y=np.arange(-8,8,0.25)
x,y=np.meshgrid(x,y)   
r=np.sqrt(x**2+y**2)
z=np.sin(r)/r               
ax.plot_surface(x,y,z,rstride=1,cstride=1,label='plot_surface')
ax.contourf(x,y,z,zdir='z')
# ax.contourf(x,y,z)
# 下面两种形式是一样的
# ax.set(xlabel='X', ylabel='Y', zlabel='Z')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('title')
# 下面两个效果是一样的，设置 X 轴的上下限
# ax.set_xlim(left = -10, right = 10)
ax.set_xbound(lower = -10, upper = 10)
# 设置 YZ 轴的上下限
# ax.set_ybound(bottom = 0, top = 1)
# ax.set_zbound(bottom = 0, top = 1)

# 设置轴刻度 ，matplotlib将刻度放在对应范围的哪个位置，默认情况下这些刻度就是刻度标签；
ax.set_xticks(np.linspace(-10, 10, 20))
# 可以将任何其他类型的值作为标签，可以赋值给之前已经设置过的set_xtick。
# ax.set_xticklabels()
# ax.legend()
plt.show()

在这里插入图片描述

import matplotlib.pyplot as plt
import numpy as np

ax = plt.figure().add_subplot(projection='3d')
# Plot a sin curve using the x and y axes.
x = np.linspace(0, 1, 100)
y = np.sin(x * 2 * np.pi) / 2 + 0.5
ax.plot(x, y, zs=0, zdir='z', label='curve in (x, y)')
# Plot scatterplot data (20 2D points per colour) on the x and z axes.
colors = ('r', 'g', 'b', 'k')
# Fixing random state for reproducibility
np.random.seed(19680801)
x = np.random.sample(20 * len(colors))
y = np.random.sample(20 * len(colors))
c_list = []
for c in colors:
    c_list.extend([c] * 20)
# By using zdir='y', the y value of these points is fixed to the zs value 0
# and the (x, y) points are plotted on the x and z axes.
ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x, z)')
# Make legend, set axes limits and labels
ax.legend()
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_zlim(0, 1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
# Customize the view angle so it's easier to see that the scatter points lie
# on the plane y=0
ax.view_init(elev=20., azim=-35, roll=0)
plt.show()