# 使用该法，不用写plt.show()，以及可以边写边运行

%matplotlib notebook

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号

import pandas as pd

import numpy as np

标题及轴标签

def f(t):

s1 = np.cos(2*np.pi*t)

e1 = np.exp(-t)

return s1 * e1

t1 = np.arange(0.0, 5.0, 0.1)

t2 = np.arange(0.0, 5.0, 0.02)

t3 = np.arange(0.0, 2.0, 0.01)

box = dict(facecolor='yellow', pad=5, alpha=0.2)

# 整个画板的标题

plt.suptitle('我的画板标题', fontsize=16, fontweight='bold')

plt.subplots_adjust(left=0.2, wspace=0.8, top=0.8) #位置调整

plt.subplot(121)

plt.plot(t1, f(t1), 'o', t2, f(t2), '-')

plt.title('画板1',color='r')

plt.ylabel('Y轴',bbox=box)

plt.subplot(122)

plt.plot(t3, np.cos(2*np.pi*t3), '--')

plt.title('画板2', color='b')

plt.xlabel('X 轴',bbox=box)

plt.ylabel('Y 轴',bbox=box)

plt.show()

样式

x1 = np.linspace(0.0, 5.0)

x2 = np.linspace(0.0, 2.0)

y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)

y2 = np.cos(2 * np.pi * x2)

plt.subplot(2, 1, 1)

# "o-r"中r表示红色，o表示实点，-表示连接线

# 可以写成 ro- 或 or- 或 -or 顺序不要求

plt.plot(x1, y1, 'o-r')

plt.subplot(2, 1, 2)

plt.plot(x2, y2, '.-')

plt.show()

t = np.arange(0.0, 2.0, 0.01)

s = np.sin(2 * np.pi * t)

fig, ax = plt.subplots()

ax.plot(t, s)

# 网格

ax.grid(True, linestyle='-.')

# 坐标

# ax.tick_params(axis='both',labelcolor='r', labelsize='medium', width=3)

ax.tick_params(axis='x',labelcolor='gold', labelsize='medium', width=3)

ax.tick_params(axis='y',labelcolor='b', labelsize='medium', width=2)

# 注释

ax.annotate("这是注释\n"

"这是注释", (0.5, 0.5),

xycoords="axes fraction", va="center", ha="center",

bbox=dict(boxstyle="round, pad=1", fc="w"))

plt.show()

data = np.random.randn(30).cumsum()

plt.plot(data, 'r--', label='Default',marker='o')

# 写这步运行直接添加到上图中

plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')

plt.legend(loc='best')

使用内置样式

# 全部内置样式

from matplotlib import style print(plt.style.available)

‘bmh’, ‘classic’, ‘dark_background’, ‘fast’, ‘fivethirtyeight’, ‘ggplot’, ‘grayscale’, ‘seaborn-bright’, ‘seaborn-muted’, ‘seaborn-notebook’, ‘seaborn-paper’, ‘seaborn-pastel’, ‘seaborn-poster’, ‘seaborn-talk’, ‘seaborn-ticks’, ‘seaborn-white’, ‘seaborn-whitegrid’, ‘seaborn’, ‘Solarize_Light2’, ‘tableau-colorblind10’, ‘_classic_test’

plt.style.use('dark_background')

fig, ax = plt.subplots()

L = 6

x = np.linspace(0, L)

ncolors = len(plt.rcParams['axes.prop_cycle'])

shift = np.linspace(0, L, ncolors, endpoint=False)

for s in shift:

ax.plot(x, np.sin(x + s), 'o-')

ax.set_xlabel('x-axis')

ax.set_ylabel('y-axis')

ax.set_title("'dark_background' style sheet")

plt.show()

plt.style.use('fivethirtyeight')

x = np.linspace(0, 10)

# Fixing random state for reproducibility

np.random.seed(19680801)

fig, ax = plt.subplots()

ax.plot(x, np.sin(x) + x + np.random.randn(50))

ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))

ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50))

ax.plot(x, np.sin(x) - 0.5 * x + np.random.randn(50))

ax.plot(x, np.sin(x) - 2 * x + np.random.randn(50))

ax.plot(x, np.sin(x) + np.random.randn(50))

ax.set_title("'fivethirtyeight' style sheet")

plt.show()

线条及填充

t = np.arange(-1, 2, .01)

s = np.sin(2 * np.pi * t)

#曲线

plt.plot(t, s)

# 以y轴0点画横线

plt.axhline(linewidth=8, color='#d62728')

# 画横线

plt.axhline(y=1)

# 画纵线

plt.axvline(x=1)

# Draw a thick blue vline at x=0 that spans the upper quadrant of the yrange

# plt.axvline(x=0, ymin=0.75, linewidth=8, color='#1f77b4')

# 画线段

plt.axhline(y=.5, xmin=0.25, xmax=0.75)

# 平行填充

plt.axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)

# 垂直填充

plt.axvspan(1.25, 1.55, facecolor='#2ca02c', alpha=0.5)

# 坐标轴

plt.axis([-1, 2, -1, 2])

plt.show()

交差及填充

x = np.arange(0.0, 2, 0.01)

y1 = np.sin(2*np.pi*x)

y2 = 1.2*np.sin(4*np.pi*x)

fig, ax = plt.subplots()

ax.plot(x, y1, x, y2, color='black')

ax.fill_between(x, y1, y2, where=y2>y1, facecolor='green')

ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='red')

ax.set_title('fill between where')

plt.show()

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.collections as collections

t = np.arange(0.0, 2, 0.01)

s1 = np.sin(2*np.pi*t)

s2 = 1.2*np.sin(4*np.pi*t)

fig, ax = plt.subplots()

ax.set_title('using span_where')

ax.plot(t, s1, color='black')

ax.axhline(0, color='black', lw=2)

collection = collections.BrokenBarHCollection.span_where(

t, ymin=0, ymax=1, where=s1 > 0, facecolor='green', alpha=0.5)

ax.add_collection(collection)

collection = collections.BrokenBarHCollection.span_where(

t, ymin=-1, ymax=0, where=s1 < 0, facecolor='red', alpha=0.5)

ax.add_collection(collection)

plt.show()

图例

ax = plt.subplot(111)

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,))

# plt.legend()

leg = plt.legend(loc='best', ncol=2, mode="expand", shadow=True, fancybox=True)

leg.get_frame().set_alpha(0.5)

plt.show()

# Make some fake data.

a = b = np.arange(0, 3, .02)

c = np.exp(a)

d = c[::-1]

# Create plots with pre-defined labels.

fig, ax = plt.subplots()

ax.plot(a, c, 'k--', label='Model length')

ax.plot(a, d, 'r:', label='Data length')

ax.plot(a, c + d, 'b', label='Total message length')

legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')

# Put a nicer background color on the legend.

legend.get_frame().set_facecolor('#00FFCC')

plt.show()

颜色

%matplotlib inline

from cycler import cycler

import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi)

offsets = np.linspace(0, 2*np.pi, 4, endpoint=False)

# Create array with shifted-sine curve along each column

yy = np.transpose([np.sin(x + phi) for phi in offsets])

# 1. Setting prop cycle on default rc parameter

plt.rc('lines', linewidth=4)

plt.rc('axes', prop_cycle=(cycler('color', ['r', 'g', 'b', 'y']) +

cycler('linestyle', ['-', '--', ':', '-.'])))

fig, (ax0, ax1) = plt.subplots(nrows=2, facecolor='darkslategray')

ax0.plot(yy)

ax0.set_title('Set default color cycle to rgby')

# 2. Define prop cycle for single set of axes

ax1.set_prop_cycle(cycler('color', ['c', 'm', 'y', 'k']) +

cycler('lw', [1, 2, 3, 4]))

ax1.plot(yy)

ax1.set_title('Set axes color cycle to cmyk')

# Tweak spacing between subplots to prevent labels from overlapping

fig.subplots_adjust(hspace=0.3)

plt.show()

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号

import pandas as pd

import numpy as np

把图保存为文件

我们可以用plt.savefig来保存图。这个方法等同于直接在figure对象上调用savefig方法。例如，想要保存一个SVG版本的图片，键入：

`plt.savefig('figpath.svg)`

保存的文件类型通过文件名后缀来指定。即如果使用 .pdf做为后缀，就会得到一个PDF文件。这里有一些重要的设置，作者经常用来刊印图片：dpi，控制每英寸长度上的分辨率

bbox_inches, 能删除figure周围的空白部分

比如我们想要得到一幅PNG图，有最小的空白，400 DPI，键入：

plt.savefig('figpath.png', dpi=400, bbox_inches='tight')

savefig不仅可以写入磁盘，还可以导出为任意像是文件一样的对象，比如BytesIO：

from io import BytesIO

buffer = BytesIO()

plt.savefig(buffer)

plot_data = buffer.getvalue()

看下图关于savefig更多的选项：

普通图形

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1)

rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)

circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)

pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],

color='g', alpha=0.5)

ax.add_patch(rect)

ax.add_patch(circ)

ax.add_patch(pgon)

地图

# 地形

plt.figure(dpi=128, figsize = (8,4))

m = Basemap(projection = 'mill',

llcrnrlat = -90, # 左下角的纬度

llcrnrlon = -180, # 左下角经度

urcrnrlat = 90, # 右上角的纬度

urcrnrlon = 180, # 右上角的经度

resolution ='l' #分辨率

)

m.drawcoastlines()

m.drawcountries(linewidth=2)

m.drawcounties(color='darkred')

m.etopo() #地形

# m.bluemarble() # 大理石样式

plt.title('Basemap Tutorial')

plt.show()

# 绘制坐标

plt.figure(dpi=128, figsize = (8,4))

m = Basemap(projection = 'mill',

llcrnrlat = 25, # 左下角的纬度

llcrnrlon = -130, # 左下角经度

urcrnrlat = 50, # 右上角的纬度

urcrnrlon = -60, # 右上角的经度

resolution ='l' #分辨率

)

m.drawcoastlines()

m.drawcountries(linewidth=2)

m.drawstates(color='b')

xs = []

ys = []

# 指定坐标坐五角星

NYClat, NYClon = 40.7127, -74.0059

xpt, ypt = m(NYClon, NYClat)

xs.append(xpt)

ys.append(ypt)

m.plot(xpt, ypt, 'r*', markersize=15)

# 指定坐标坐三角形

LAlat, LAlon = 34.05, -118.25

xpt, ypt = m(LAlon, LAlat)

xs.append(xpt)

ys.append(ypt)

m.plot(xpt, ypt, 'g^', markersize=15)

# 画直线

m.plot(xs, ys, color='r', linewidth=3, label='Flight 98')

# 画弧线

m.drawgreatcircle(NYClon, NYClat, LAlon, LAlat, color ='c', linewidth=3, label='Arc')

plt.legend(loc=4)

plt.title('Basemap Tutorial')

plt.show()

地理相关

# 地理相关

fig = plt.figure(figsize=(14,8))

ax1 = fig.add_subplot(221, projection="aitoff")

ax1.set_title("Aitoff")

ax1.grid(True)

ax2 = fig.add_subplot(222, projection="hammer")

ax2.set_title("hammer")

ax2.grid(True)

ax3 = fig.add_subplot(223, projection="lambert")

ax3.set_title("lambert")

ax3.grid(True)

ax4 = fig.add_subplot(224, projection="mollweide")

ax4.set_title("mollweide")

ax4.grid(True)

x = np.linspace(0, 2 * np.pi, 400)

y = np.sin(x ** 2)

f, axarr = plt.subplots(2, 2, subplot_kw=dict(projection='polar'))

axarr[0, 0].plot(x, y)

axarr[0, 0].set_title('Axis [0,0]')

axarr[0, 1].scatter(x, y)

axarr[0, 1].set_title('Axis [0,1]')

axarr[1, 0].plot(x, y ** 2)

axarr[1, 0].set_title('Axis [1,0]')

axarr[1, 1].scatter(x, y ** 2)

axarr[1, 1].set_title('Axis [1,1]')

# Fine-tune figure; make subplots farther from each other.

f.subplots_adjust(hspace=0.8)

3D

%matplotlib inline

from mpl_toolkits.mplot3d.axes3d import Axes3D

from matplotlib import cm

from matplotlib.ticker import LinearLocator, FixedLocator, FormatStrFormatter

import matplotlib.pyplot as plt

import numpy as np

# 画板大小

fig = plt.figure(figsize=(18,10))

#画布1

ax = fig.add_subplot(1, 2, 1, projection='3d')

X = np.arange(-5, 5, 0.25)

Y = np.arange(-5, 5, 0.25)

X, Y = np.meshgrid(X, Y)

R = np.sqrt(X**2 + Y**2)

Z = np.sin(R)

surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,

linewidth=0, antialiased=False)

ax.set_zlim3d(-1.01, 1.01)

ax.w_zaxis.set_major_locator(LinearLocator(10))

ax.w_zaxis.set_major_formatter(FormatStrFormatter('%.03f'))

# 颜色条

fig.colorbar(surf, shrink=0.5, aspect=5)

#画布2

from mpl_toolkits.mplot3d.axes3d import get_test_data

ax = fig.add_subplot(1, 2, 2, projection='3d')

X, Y, Z = get_test_data(0.05)

ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)

plt.show()

import numpy as np

import matplotlib.pyplot as plt

from matplotlib import cm

from mpl_toolkits.mplot3d import Axes3D

X = np.arange(-5, 5, 0.25)

Y = np.arange(-5, 5, 0.25)

X, Y = np.meshgrid(X, Y)

R = np.sqrt(X**2 + Y**2)

Z = np.sin(R)

fig = plt.figure()

ax = Axes3D(fig)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.viridis)

plt.show()

雷达图

%matplotlib inline

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.path import Path

from matplotlib.spines import Spine

from matplotlib.projections.polar import PolarAxes

from matplotlib.projections import register_projection

def radar_factory(num_vars, frame='circle'):

"""Create a radar chart with `num_vars` axes.

This function creates a RadarAxes projection and registers it.

Parameters

----------

num_vars : int

Number of variables for radar chart.

frame : {'circle' | 'polygon'}

Shape of frame surrounding axes.

"""

# calculate evenly-spaced axis angles

theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)

def draw_poly_patch(self):

# rotate theta such that the first axis is at the top

verts = unit_poly_verts(theta + np.pi / 2)

return plt.Polygon(verts, closed=True, edgecolor='k')

def draw_circle_patch(self):

# unit circle centered on (0.5, 0.5)

return plt.Circle((0.5, 0.5), 0.5)

patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}

if frame not in patch_dict:

raise ValueError('unknown value for `frame`: %s' % frame)

class RadarAxes(PolarAxes):

name = 'radar'

# use 1 line segment to connect specified points

RESOLUTION = 1

# define draw_frame method

draw_patch = patch_dict[frame]

def __init__(self, *args, **kwargs):

super(RadarAxes, self).__init__(*args, **kwargs)

# rotate plot such that the first axis is at the top

self.set_theta_zero_location('N')

def fill(self, *args, **kwargs):

"""Override fill so that line is closed by default"""

closed = kwargs.pop('closed', True)

return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)

def plot(self, *args, **kwargs):

"""Override plot so that line is closed by default"""

lines = super(RadarAxes, self).plot(*args, **kwargs)

for line in lines:

self._close_line(line)

def _close_line(self, line):

x, y = line.get_data()

# FIXME: markers at x[0], y[0] get doubled-up

if x[0] != x[-1]:

x = np.concatenate((x, [x[0]]))

y = np.concatenate((y, [y[0]]))

line.set_data(x, y)

def set_varlabels(self, labels):

self.set_thetagrids(np.degrees(theta), labels)

def _gen_axes_patch(self):

return self.draw_patch()

def _gen_axes_spines(self):

if frame == 'circle':

return PolarAxes._gen_axes_spines(self)

# The following is a hack to get the spines (i.e. the axes frame)

# to draw correctly for a polygon frame.

# spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.

spine_type = 'circle'

verts = unit_poly_verts(theta + np.pi / 2)

# close off polygon by repeating first vertex

verts.append(verts[0])

path = Path(verts)

spine = Spine(self, spine_type, path)

spine.set_transform(self.transAxes)

return {'polar': spine}

register_projection(RadarAxes)

return theta

def unit_poly_verts(theta):

"""Return vertices of polygon for subplot axes.

This polygon is circumscribed by a unit circle centered at (0.5, 0.5)

"""

x0, y0, r = [0.5] * 3

verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]

return verts

def example_data():

# The following data is from the Denver Aerosol Sources and Health study.

# See doi:10.1016/j.atmosenv.2008.12.017

#

# The data are pollution source profile estimates for five modeled

# pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical

# species. The radar charts are experimented with here to see if we can

# nicely visualize how the modeled source profiles change across four

# scenarios:

# 1) No gas-phase species present, just seven particulate counts on

# Sulfate

# Nitrate

# Elemental Carbon (EC)

# Organic Carbon fraction 1 (OC)

# Organic Carbon fraction 2 (OC2)

# Organic Carbon fraction 3 (OC3)

# Pyrolized Organic Carbon (OP)

# 2)Inclusion of gas-phase specie carbon monoxide (CO)

# 3)Inclusion of gas-phase specie ozone (O3).

# 4)Inclusion of both gas-phase species is present...

data = [

['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],

('Basecase', [

[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],

[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],

[0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],

[0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],

[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),

('With CO', [

[0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],

[0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],

[0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],

[0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],

[0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),

('With O3', [

[0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],

[0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],

[0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],

[0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],

[0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),

('CO & O3', [

[0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],

[0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],

[0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],

[0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],

[0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])

]

return data

if __name__ == '__main__':

N = 9

theta = radar_factory(N, frame='polygon')

data = example_data()

spoke_labels = data.pop(0)

fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,

subplot_kw=dict(projection='radar'))

fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)

colors = ['b', 'r', 'g', 'm', 'y']

# Plot the four cases from the example data on separate axes

for ax, (title, case_data) in zip(axes.flatten(), data):

ax.set_rgrids([0.2, 0.4, 0.6, 0.8])

ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),

horizontalalignment='center', verticalalignment='center')

for d, color in zip(case_data, colors):

ax.plot(theta, d, color=color)

ax.fill(theta, d, facecolor=color, alpha=0.25)

ax.set_varlabels(spoke_labels)

# add legend relative to top-left plot

ax = axes[0, 0]

labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')

legend = ax.legend(labels, loc=(0.9, .95),

labelspacing=0.1, fontsize='small')

fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',

horizontalalignment='center', color='black', weight='bold',

size='large')

plt.show()

其它

Matplotlib Logos

%matplotlib inline

import numpy as np

import matplotlib as mpl

import matplotlib.pyplot as plt

import matplotlib.cm as cm

mpl.rcParams['xtick.labelsize'] = 10

mpl.rcParams['ytick.labelsize'] = 12

mpl.rcParams['axes.edgecolor'] = 'gray'

axalpha = 0.05

figcolor = 'white'

dpi = 80

fig = plt.figure(figsize=(6, 1.1), dpi=dpi)

fig.patch.set_edgecolor(figcolor)

fig.patch.set_facecolor(figcolor)

# 绘制背景

def add_math_background():

ax = fig.add_axes([0., 0., 1., 1.])

text = []

text.append(

(r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "

r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2}"

r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 "

r"\left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - "

r"\alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} "

r"}{U^{0\beta}_{\rho_1 \sigma_2}}\right]$", (0.7, 0.2), 20))

text.append((r"$\frac{d\rho}{d t} + \rho \vec{v}\cdot\nabla\vec{v} "

r"= -\nabla p + \mu\nabla^2 \vec{v} + \rho \vec{g}$",

(0.35, 0.9), 20))

text.append((r"$\int_{-\infty}^\infty e^{-x^2}dx=\sqrt{\pi}$",

(0.15, 0.3), 25))

text.append((r"$F_G = G\frac{m_1m_2}{r^2}$",

(0.85, 0.7), 30))

for eq, (x, y), size in text:

ax.text(x, y, eq, ha='center', va='center', color="#11557c",

alpha=0.25, transform=ax.transAxes, fontsize=size)

ax.set_axis_off()

return ax

# 文字

def add_matplotlib_text(ax):

ax.text(0.95, 0.5, 'matplotlib', color='#11557c', fontsize=65,

ha='right', va='center', alpha=1.0, transform=ax.transAxes)

# 前面的圆

def add_polar_bar():

ax = fig.add_axes([0.025, 0.075, 0.2, 0.85], projection='polar')

ax.patch.set_alpha(axalpha)

ax.set_axisbelow(True)

N = 7

arc = 2. * np.pi

theta = np.arange(0.0, arc, arc/N)

radii = 10 * np.array([0.2, 0.6, 0.8, 0.7, 0.4, 0.5, 0.8])

width = np.pi / 4 * np.array([0.4, 0.4, 0.6, 0.8, 0.2, 0.5, 0.3])

bars = ax.bar(theta, radii, width=width, bottom=0.0)

for r, bar in zip(radii, bars):

bar.set_facecolor(cm.jet(r/10.))

bar.set_alpha(0.6)

ax.tick_params(labelbottom=False, labeltop=False,

labelleft=False, labelright=False)

ax.grid(lw=0.8, alpha=0.9, ls='-', color='0.5')

ax.set_yticks(np.arange(1, 9, 2))

ax.set_rmax(9)

if __name__ == '__main__':

main_axes = add_math_background()

add_polar_bar()

add_matplotlib_text(main_axes)

plt.show()

# 贝塞尔曲线

import matplotlib.path as mpath

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt

Path = mpath.Path

fig, ax = plt.subplots()

pp1 = mpatches.PathPatch(

Path([(0, 0), (1, 0), (1, 1), (0, 0)],

[Path.MOVETO, Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY]),

fc="none", transform=ax.transData)

ax.add_patch(pp1)

ax.plot([0.75], [0.25], "ro")

ax.set_title('The red point should be on the path')

plt.show()

python用matplotlib画五角星_基于Matplotlib的Python绘图相关推荐

1. python中turtle画笑脸_基于turtle的Python作画

   2018年6月12日笔记 按win+q键换出搜索界面,输入path,进入系统属性,选择高级,选择环境变量.在系统变量中的PATHEXT这个变量中文本内容为.COM;.EXE;.BAT;.CMD;.VB ...

2. python问题化教学设计_基于IPO的Python教学设计

   冯艳茹 陈平 摘要:程序设计基础课程是培养大学生解决计算问题的思维和能力的课程,使用Python作为大学生的首门编程语言课程,可操作性强,入门容易,上手快.该文提出了基于IPO的教学设计新思维,使教学 ...

3. python应用如何实现升级_基于esky实现python应用的自动升级详解

   基于 esky 实现 python 应用的自动升级 一. esky 介绍 Esky is an auto-update framework for frozen Python applications ...

4. python用matplotlib画五角星_绘图：Matplotlib

   用于绘制一些数据图,同学推荐的,挺好用.非常好的官网文档:http://matplotlib.org/contents.html 0. 安装 可以直接pip install,还有一些依赖就按照提示来吧 ...

5. 如何用python的turtle画五角星_海龟编辑器五角星怎么画 绘制五角星就是这么简单...

   海龟编辑器作为一款面向少儿的Python编辑器,它可以让孩子通过图形化的方式学习Python,很多用户在刚开始使用时不知道怎么绘制最基本的图形,小编将绘制五角星的方式通过两种方法进行讲解,想知道的赶快 ...

6. python 3d游戏记录路径_基于osg的python三维程序开发（五）------沿路径运动

   在上一节中, 我们演示了如何更新节点的状态, 这是动画的基本的技巧. 这一小节里,我们看一个稍微复杂一点的例子------让物体沿着固定的路径运动. 在osg 中,使得物体沿着固定路径运动, 会用到几 ...

7. python识别视频中火焰_基于yolov3和python框架的火焰识别检测算法

   本算法识别的效果如下:有兴趣学习交流python 编程的伙伴可加群:1026352781 下面开始实际操作啦 一.配置环境 算法所需环境如下: Python: 3.7.4 Tensorflow-GPU ...

8. Python实践：画个动图玩玩，Python绘制GIF图总结

   Python实践:画个动图玩玩,Python绘制GIF图总结 文章目录 Python实践:画个动图玩玩,Python绘制GIF图总结 具体实现 Python代码 参考资料 上期博客< Pytho ...

9. python用matplotlib画球_用Python的Matplotlib 画一个足球场

   我们可能想要在图表上绘制线条或圆圈的原因有很多. 我们可以寻找添加平均线,突出显示关键数据点甚至绘制图片. 本文将展示如何使用足球场地图的示例添加线条,圆圈和圆弧,然后可以使用它来显示热图,传球或比赛 ...

最新文章

1. 微信第一行代码曝光！从「扫地僧」到「地成佛」，张小龙10年磨一剑
2. opencv教程大全
3. openssh升级sftp_CentOS6.5升级OpenSSH 8.3版本
4. MySQL之日期时间处理函数_MySQL之日期时间处理函数
5. linux vi刷新页面,vim的神级配置 - bubifengyun的个人页面 - OSCHINA - 中文开源技术交流社区...
6. Eigen的基础使用-C++
7. linux下rust编译环境搭建
8. html5小游戏抓包,wireshark抓包分析
9. java中 移位运算符_java中的移位运算符心得总结
10. UE4读取scv文件 -- 数据驱动游戏性元素
11. matlab 求矩阵奇异值,MATLAB矩阵特征值和奇异值.
12. 2020科目一考试口诀_2020年驾考科目一考试技巧与口诀
13. 自定义Behavior
14. 关于mathtype中的等号=和括号
15. 家庭NAS服务器(2)VM测试-Ubuntu组建Raid10
16. 2021-11-04 Spring
17. 前端 google maps 地理编码(geocode),模糊搜索(search)
18. 大家的人工智能——线性回归
19. Spyder python文件抬头默认内容自定义
20. 全国计算机考试照片格式错了,全国计算机等级考试的报名照片终于可以换了！...

热门文章

1. php中平方代码_php 做出平方代码，用类来实现的接口，初学者请大侠们出手啊。...
2. 静态工作点是什么？有什么作用？
3. 如何在面试中介绍自己的项目
4. 汉字提取首字母（包括多音字处理）
5. 硬盘上有损坏区域，该如何重新分区
6. 【2020秋招】提前批陌陌机器学习算法工程师面试经验
7. angular ng zorro框架日期框无法自适应宽度的解决方法
8. RSD 教程 —— 2 开始运行RSD
9. kali linux 入门（1） 基于win10和docker的环境搭建
10. spark sql优化：小表大表关联优化 union替换or broadcast join