ax.set(xlabel='time (s)', ylabel='voltage (mV)',
title='About as simple as it gets, folks') #设置一些属性
ax.grid() #显示网格
#fig.savefig("test.png") 保存图片在当前目录下
plt.show()
matplotlib.axes.Axes.plot
matplotlib.pyplot.plot
matplotlib.pyplot.subplots
matplotlib.figure.Figure.savefig
#First create some toy data:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
#Creates just a figure and only one subplot
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
#Creates two subplots and unpacks the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
#Creates four polar axes, and accesses them through the returned array
fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
axes[0, 0].plot(x, y)
axes[1, 1].scatter(x, y)
#Share a X axis with each column of subplots
plt.subplots(2, 2, sharex='col')
#Share a Y axis with each row of subplots
plt.subplots(2, 2, sharey='row')
#Share both X and Y axes with all subplots
plt.subplots(2, 2, sharex='all', sharey='all')
#Note that this is the same as
plt.subplots(2, 2, sharex=True, sharey=True)
#Creates figure number 10 with a single subplot
#and clears it if it already exists.
fig, ax=plt.subplots(num=10, clear=True)
这样写也可以达到同样的目的：
fig, (ax1, ax2) = plt.subplots(2, 1) # one figure, 2 * 1 axes
ax1.plot(x1, y1, 'o-')
ax1.set(title='A tale of 2 subplots',
ylabel='Damped oscillation')
ax2.plot(x2, y2, '.-')
ax2.set(**{'xlabel':'times(s)',
'ylabel':'Undamped'})
均为下图：
Image 展示图片
imshow()
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
from matplotlib.path import Path
from matplotlib.patches import PathPatch
展示二元正态分布
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
fig, ax = plt.subplots()
im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
origin='lower', extent=[-3, 3, -3, 3],
vmax=abs(Z).max(), vmin=-abs(Z).max())
plt.show()
np.meshgrid()-千千Sama
A sample image
# A sample image
with cbook.get_sample_data('Lenna.png') as image_file:
image = plt.imread(image_file)
fig, ax = plt.subplots()
ax.imshow(image)
ax.axis('off') # clear x-axis and y-axis
Interpolating images 插值图像？
A = np.random.rand(5, 5)
fig, axs = plt.subplots(1, 3, figsize=(10, 3))
for ax, interp in zip(axs, ['nearest', 'bilinear', 'bicubic']):
ax.imshow(A, interpolation=interp)
ax.set_title(interp.capitalize())
ax.grid(True)
plt.show()
x = np.arange(120).reshape((10, 12))
interp = 'bilinear'
fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(3, 5))
axs[0].set_title('blue should be up')
axs[0].imshow(x, origin='upper', interpolation=interp)
axs[1].set_title('blue should be down')
axs[1].imshow(x, origin='lower', interpolation=interp)
plt.show()
Clip path
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
patch = PathPatch(path, facecolor='none')
fig, ax = plt.subplots()
ax.add_patch(patch)
im = ax.imshow(Z, interpolation='bilinear', cmap=cm.gray,
origin='lower', extent=[-3, 3, -3, 3],
clip_path=patch, clip_on=True)
im.set_clip_path(patch)
plt.show()
matplotlib.axes.Axes.imshow
matplotlib.pyplot.imshow
matplotlib.artist.Artist.set_clip_path
matplotlib.patches.PathPatch
Contouring and Pseudocolor 轮廓与伪彩色
# make these smaller to increase the resolution
dx, dy = 0.05, 0.05
# generate 2 2d grids for the x & y bounds
y, x = np.mgrid[slice(1, 5 + dy, dy),
slice(1, 5 + dx, dx)]
z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)
# x and y are bounds, so z should be the value *inside* those bounds.
# Therefore, remove the last value from the z array.
z = z[:-1, :-1]
levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())
# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap = plt.get_cmap('PiYG')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) #压缩到[0-1]，因为颜色的要求？
fig, (ax0, ax1) = plt.subplots(nrows=2)
im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax0) # 添加右边的颜色的标度
ax0.set_title('pcolormesh with levels')
# contours are *point* based plots, so convert our bound into point
# centers
cf = ax1.contourf(x[:-1, :-1] + dx/2., #在轮廓里面填充颜色？
y[:-1, :-1] + dy/2., z, levels=levels,
cmap=cmap)
fig.colorbar(cf, ax=ax1)
ax1.set_title('contourf with levels')
# adjust spacing between subplots so `ax1` title and `ax0` tick labels
# don't overlap
fig.tight_layout()
plt.show()
numpy.mgrid j表示末端数字也包括
matplotlib.axes.Axes.pcolormesh
matplotlib.pyplot.pcolormesh
matplotlib.axes.Axes.contourf
matplotlib.pyplot.contourf
matplotlib.figure.Figure.colorbar
matplotlib.pyplot.colorbar
matplotlib.colors.BoundaryNorm
matplotlib.ticker.MaxNLocator
Histograms hist()
np.random.seed(19680801)
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(437)#x ~ N(mu, sigma^2)
num_bins = 50 #柱状图数目
fig, ax = plt.subplots()
# the histogram of the data
n, bins, patches = ax.hist(x, num_bins, density=1)
#n: 每个bin在图上对应的y轴，这里就是概率密度
#bins:如果没猜错，是bin所对应的x轴（中间的那个值）
#patches：应该是画bin所用的一些信息？
#density:一个可选参数，如果为True，那么bins下的面积和会被调整(scale)为1,这样#就类似一个密度函数了
# add a 'best fit' line
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabel
fig.tight_layout()
plt.show()
matplotlib.axes.Axes.hist
matplotlib.axes.Axes.set_title
matplotlib.axes.Axes.set_xlabel
matplotlib.axes.Axes.set_ylabel
Paths
你可以使用matplotlib.path模块添加任意路径。
fig, ax = plt.subplots()
Path = mpath.Path #import matplotlib.path as mpath
path_data = [
(Path.MOVETO, (1.58, -2.57)),
(Path.CURVE4, (0.35, -1.1)),
(Path.CURVE4, (-1.75, 2.0)),
(Path.CURVE4, (0.375, 2.0)),
(Path.LINETO, (0.85, 1.15)),
(Path.CURVE4, (2.2, 3.2)),
(Path.CURVE4, (3, 0.05)),
(Path.CURVE4, (2.0, -0.5)),
(Path.CLOSEPOLY, (1.58, -2.57)),
codes, verts = zip(*path_data)
#codes:(1, 4, 4, 4, 2, 4, 4, 4, 79) 大概是对应的path的方法，曲线啊，直线啊啥的
#verts:((1.58, -2.57), (0.35, -1.1), (-1.75, 2.0), (0.375, 2.0), (0.85, 1.15),
#(2.2, 3.2), (3, 0.05), (2.0, -0.5), (1.58, -2.57))
path = mpath.Path(verts, codes)
patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
ax.add_patch(patch)
# plot control points and connecting lines
x, y = zip(*path.vertices)
#x:(1.58, 0.35, -1.75, 0.375, 0.85, 2.2, 3.0, 2.0, 1.58)
#y:(-2.57, -1.1, 2.0, 2.0, 1.15, 3.2, 0.05, -0.5, -2.57)
line, = ax.plot(x, y, 'go-')
ax.grid()
ax.axis('equal')
for i, (item1, item2) in enumerate(path_data, 1):
plt.text(item2[0], item2[1], str(i), size=20)
plt.show()
zip() ，传入迭代子，讲不同迭代子相同位置的元素“捏”在一起返回一个新的迭代子。注意，视情况，zip不仅可以解压，而且可以反解。
matplotlib.path
matplotlib.path.Path
matplotlib.patches
matplotlib.patches.PathPatch
matplotlib.axes.Axes.add_patch
Three-dimensional plotting
mplot3d 工具包支持3d作图，比如曲面，线框，散点等。
# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d') #.gca() 获取当前的axes，如果有必要（大概是没有？），就创建一个
#projection 设置工程为3d？
# Make data.
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y) #生成二维坐标点
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# Customize the z axis.
ax.set_zlim(-1.01, 1.01) #设置z轴标度的表示范围
ax.zaxis.set_major_locator(LinearLocator(10)) #大概就是将z轴分成10格
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))#2位小数
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5) #添加颜色标度
plt.show()
Streamplot 流线图？
streamplot()函数，可以画向量场的流线，还可以。。。
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
#并没有采用Demo里面的模型，而是选择了:
#F=(-yi+xj)/(x^2+y^2)^(1/2)
#缺点是，线宽啥的都体现不出来了（因为速度大小均为1）
w = 3
Y, X = np.mgrid[-w:w:100j, -w:w:100j] # 生成10000个[-3, 3] * [-3, 3]的均匀网格坐标
U = -Y / np.sqrt(X ** 2 + Y ** 2)
V = X / np.sqrt(X ** 2 + Y ** 2)
speed = np.sqrt(U*U + V*V)
print(np.where(speed == np.sqrt(2)))
fig = plt.figure(figsize=(7, 9)) #7行9列
#应该是把分好的块再重新分了, height_ratios, 从下面的图片中可以看到
#3行图片的高度比例是1：1：2
#不出意外，len(height_ratios) == nrows
gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])
# Varying density along a streamline
ax0 = fig.add_subplot(gs[0, 0])
ax0.streamplot(X, Y, U, V, density=[0.5, 1])
ax0.set_title('Varying Density')
#x,y: 位置坐标
#U, V： x and y-velocities(x轴分速度，y-轴分速度)
# Varying color along a streamline
ax1 = fig.add_subplot(gs[0, 1])
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn') #颜色的区别
fig.colorbar(strm.lines)
ax1.set_title('Varying Color')
# Varying line width along a streamline
ax2 = fig.add_subplot(gs[1, 0])
lw = 5*speed / speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw) #不同密度线宽不同
ax2.set_title('Varying Line Width')
# Controlling the starting points of the streamlines
seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1, 0, 1, 2, 2]])
#与图中蓝色点对应，好像是只有经过上述某点的流线才被保留
ax3 = fig.add_subplot(gs[1, 1])
strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,
cmap='autumn', start_points=seed_points.T)
fig.colorbar(strm.lines)
ax3.set_title('Controlling Starting Points')
# Displaying the starting points with blue symbols.
ax3.plot(seed_points[0], seed_points[1], 'bo')
ax3.axis((-w, w, -w, w))
# Create a mask
mask = np.zeros(U.shape, dtype=bool)
mask[40:60, 40:60] = True
U[:20, :20] = np.nan
U = np.ma.array(U, mask=mask) #掩码数组
ax4 = fig.add_subplot(gs[2:, :])
ax4.streamplot(X, Y, U, V, color='r')
ax4.set_title('Streamplot with Masking')
ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
interpolation='nearest', cmap='gray', aspect='auto')
ax4.set_aspect('equal')
plt.tight_layout()
plt.show()
matplotlib.axes.Axes.streamplot
matplotlib.pyplot.streamplot
matplotlib.gridspec
matplotlib.gridspec.GridSpec
Ellipses 椭圆
#这个例子画了很多椭圆
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse
NUM = 250 #250个
ells = [Ellipse(xy=np.random.rand(2) * 10, #（x, y） [0,10)
width=np.random.rand(), height=np.random.rand(),
angle=np.random.rand() * 360) #看来angle的表示是用度
for i in range(NUM)]
fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})
for e in ells:
ax.add_artist(e)
e.set_clip_box(ax.bbox)#
e.set_alpha(np.random.rand())
e.set_facecolor(np.random.rand(3)) #rgb?
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse
delta = 45.0 # degrees 每次偏转的角度
angles = np.arange(0, 360 + delta, delta)
ells = [Ellipse((1, 1), 4, 2, a) for a in angles]
a = plt.subplot(111, aspect='equal')
for e in ells:
e.set_clip_box(a.bbox)
e.set_alpha(0.1)
a.add_artist(e) #和上面的例子进行比较，可以发现，这行代码是绑定
plt.xlim(-2, 4)
plt.ylim(-1, 3)
plt.show()
matplotlib.patches
matplotlib.patches.Ellipse
matplotlib.axes.Axes.add_artist
matplotlib.artist.Artist.set_clip_box
matplotlib.artist.Artist.set_alpha
matplotlib.patches.Patch.set_facecolor
Bar charts 条形图
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
n_groups = 5
means_men = (20, 35, 30, 35, 27)
std_men = (2, 3, 4, 1, 2)
means_women = (25, 32, 34, 20, 25)
std_women = (3, 5, 2, 3, 3)
fig, ax = plt.subplots()
index = np.arange(n_groups)
bar_width = 0.35 #条宽
opacity = 0.4 #不透明度
error_config = {'ecolor': '#0000FF'} #设置误差的一些属性
error_config2 = {'ecolor': '#00FF00'}
rects1 = ax.bar(index, means_men, bar_width,
alpha=opacity, color='b',
yerr=std_men, error_kw=error_config, #有yerr才会有那个一竖
label='Men')
rects2 = ax.bar(index + bar_width, means_women, bar_width,
alpha=opacity, color='r',
yerr=std_women, error_kw=error_config2,
label='Women')
ax.set_xlabel('Group')
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(('A', 'B', 'C', 'D', 'E')) #设置x轴标签
ax.legend()
fig.tight_layout()
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
Student = namedtuple('Student', ['name', 'grade', 'gender']) #利用name来存取的tuple
Score = namedtuple('Score', ['score', 'percentile'])
# GLOBAL CONSTANTS
testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility',
'Push Ups'] #测试项目的名称
testMeta = dict(zip(testNames, ['laps', 'sec', 'min:sec', 'sec', ''])) #项目与单位匹配
def attach_ordinal(num):
"""helper function to add ordinal string to integers
1 -> 1st
56 -> 56th
suffixes = {str(i): v
for i, v in enumerate(['th', 'st', 'nd', 'rd', 'th',
'th', 'th', 'th', 'th', 'th'])} #名次缩写的匹配
v = str(num)
# special case early teens
if v in {'11', '12', '13'}:
return v + 'th' #特殊情况
return v + suffixes[v[-1]] #普通情况依照最后一位数字分配缩写
def format_score(scr, test):
Build up the score labels for the right Y-axis by first
appending a carriage return to each string and then tacking on
the appropriate meta information (i.e., 'laps' vs 'seconds'). We
want the labels centered on the ticks, so if there is no meta
info (like for pushups) then don't add the carriage return to
the string
md = testMeta[test] #获取对应的单位
if md:
return '{0}\n{1}'.format(scr, md)
else:
return scr
def format_ycursor(y):
y = int(y)
if y < 0 or y >= len(testNames):
return ''
else:
return testNames[y]
def plot_student_results(student, scores, cohort_size):
# create the figure
fig, ax1 = plt.subplots(figsize=(9, 7))
fig.subplots_adjust(left=0.115, right=0.88)
fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')
pos = np.arange(len(testNames))
rects = ax1.barh(pos, [scores[k].percentile for k in testNames],
align='center',
height=0.5, color='m', #height 就是bar的高（用宽可能更加贴切）占的百分比
tick_label=testNames)
ax1.set_title(student.name)
ax1.set_xlim([0, 100]) #设置x轴的范围
ax1.xaxis.set_major_locator(MaxNLocator(11)) #MaxNLocator：就是x轴最多的标度数目为11
ax1.xaxis.grid(True, linestyle='--', which='major', #which???
color='grey', alpha=.25)
# Plot a solid vertical gridline to highlight the median position
ax1.axvline(50, color='grey', alpha=0.25) #画一条实线，在50坐标处
# set X-axis tick marks at the deciles
cohort_label = ax1.text(.5, -.07, 'Cohort Size: {0}'.format(cohort_size),
horizontalalignment='center', size='small',
transform=ax1.transAxes)
# Set the right-hand Y-axis ticks and labels
ax2 = ax1.twinx() #twinx, 拥有同一个x？
scoreLabels = [format_score(scores[k].score, k) for k in testNames]
# set the tick locations
ax2.set_yticks(pos)
# make sure that the limits are set equally on both yaxis so the
# ticks line up
ax2.set_ylim(ax1.get_ylim()) #ax2设置为同ax1一样的y的范围
# set the tick labels
ax2.set_yticklabels(scoreLabels)
ax2.set_ylabel('Test Scores')
ax2.set_xlabel(('Percentile Ranking Across '
'{grade} Grade {gender}s').format(
grade=attach_ordinal(student.grade),
gender=student.gender.title()))
rect_labels = []
# Lastly, write in the ranking inside each bar to aid in interpretation
for rect in rects: #rect 条 每个bar
# Rectangle widths are already integer-valued but are floating
# type, so it helps to remove the trailing decimal point and 0 by
# converting width to int type
width = int(rect.get_width())
rankStr = attach_ordinal(width)
# The bars aren't wide enough to print the ranking inside
if width < 5: #如果bar的宽度(可能叫长度更为贴切)不够容纳排名，那么就写在右边
# Shift the text to the right side of the right edge
xloc = width + 1
# Black against white background
clr = 'black'
align = 'left'#应该是指，xloc是文本的左贴边
else:
# Shift the text to the left side of the right edge
xloc = 0.98*width
# White on magenta
clr = 'white'
align = 'right'
# Center the text vertically in the bar
yloc = rect.get_y() + rect.get_height()/2.0 #居中显示
label = ax1.text(xloc, yloc, rankStr, horizontalalignment=align,
verticalalignment='center', color=clr, weight='bold',
clip_on=True)
rect_labels.append(label)
# make the interactive mouse over give the bar title
ax2.fmt_ydata = format_ycursor #这个交互信息，从图片没法直接体现
# return all of the artists created
return {'fig': fig,
'ax': ax1,
'ax_right': ax2,
'bars': rects,
'perc_labels': rect_labels,
'cohort_label': cohort_label}
student = Student('Johnny Doe', 2, 'boy')
scores = dict(zip(testNames,
(Score(v, p) for v, p in
zip(['7', '48', '12:52', '17', '14'],
np.round(np.random.uniform(0, 1,
len(testNames))*100, 0)))))
cohort_size = 62 # The number of other 2nd grade boys
arts = plot_student_results(student, scores, cohort_size)
plt.show()
Pie charts 饼图
本节的例子同时对下面的功能进行了展式：
自动标记百分比
用“爆炸”来偏移切片
自定义起始角度
import matplotlib.pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 43, 10] #sum == 98 not 100
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') hogs被孤立了
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90) #startangle :起始角度
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
matplotlib.axes.Axes.pie
matplotlib.pyplot.pie
Tables
import numpy as np
import matplotlib.pyplot as plt
data = [[ 66386, 174296, 75131, 577908, 32015],
[ 58230, 381139, 78045, 99308, 160454],
[ 89135, 80552, 152558, 497981, 603535],
[ 78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]
values = np.arange(0, 2500, 500)
value_increment = 1000
# Get some pastel shades for the colors
colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))#colormap?
n_rows = len(data)
index = np.arange(len(columns)) + 0.3
bar_width = 0.4
# Initialize the vertical-offset for the stacked bar chart.
y_offset = np.zeros(len(columns))
# Plot bars and create text labels for the table
cell_text = []
for row in range(n_rows):
plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row]) #bottom!!!!!!!!!!!!!
y_offset = y_offset + data[row]
cell_text.append(['%1.1f' % (x / 1000.0) for x in y_offset]) #记录下每层的数据
# Reverse colors and text labels to display the last value at the top.
colors = colors[::-1]
cell_text.reverse()
# Add a table at the bottom of the axes
the_table = plt.table(cellText=cell_text, #在图片下方加一个表格
rowLabels=rows,
rowColours=colors,
colLabels=columns,
loc='bottom')#loc location 位置
# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.2, bottom=0.2)
plt.ylabel("Loss in ${0}'s".format(value_increment))
plt.yticks(values * value_increment, ['%d' % val for val in values])
plt.xticks([])
plt.title('Loss by Disaster')
plt.show()
Scatter plots 散点图
下面代码我并没有执行，图片是直接照搬的。
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data('goog.npz') as datafile:
price_data = np.load(datafile)['price_data'].view(np.recarray)
price_data = price_data[-250:] # get the most recent 250 trading days
delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]
# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
ax.set_xlabel(r'$\Delta_i$', fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
ax.set_title('Volume and percent change')
ax.grid(True)
fig.tight_layout()
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25) #左边右边都留点空
t = np.arange(0.0, 1.0, 0.001)
a0 = 5 #幅度
f0 = 3 #频率相关
delta_f = 5.0 #频率的幅度吧
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10]) # 设置axis的范围 x:[0, 1], y:[-10, 10]
axcolor = 'lightgoldenrodyellow' #调整框的颜色
axfreq = plt.axes([0.25, 0.3, 0.65, 0.03], facecolor=axcolor) #[left, bottom, length, height]
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f) #valstep?
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
def update(val):
amp = samp.val #获取调节后的amp 幅度
freq = sfreq.val #获取调节后的 freq 频率
l.set_ydata(amp*np.sin(2*np.pi*freq*t)) #画新的图
fig.canvas.draw_idle() #？？？
sfreq.on_changed(update) #绑定更新函数
samp.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
#设定button reset的一些属性
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
sfreq.reset()
samp.reset()
button.on_clicked(reset) #button reset点击动作绑定reset
rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0) # 倒是有点像网页 选择按钮
def colorfunc(label): #改变曲线颜色
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc) #绑定colorfunc到选择按钮
plt.show()
matplotlib.pyplot.axes
matplotlib.widgets
Filled curves 填充曲线？
import numpy as np
import matplotlib.pyplot as plt
x = [0, 1, 2, 1]
y = [1, 2, 1, 0]
fig, ax = plt.subplots()
ax.fill(x, y)
plt.show()
下面的例子要稍微复杂一些，涉及多条曲线，设置填充颜色，设置透明度等。
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1.5 * np.pi, 500)
y1 = np.sin(x)
y2 = np.sin(3 * x)
fig, ax = plt.subplots()
ax.fill(x, y1, 'b', x, y2, 'r', alpha=0.3)
# Outline of the region we've filled in
ax.plot(x, y1, c='b', alpha=0.8)
ax.plot(x, y2, c='r', alpha=0.8)
ax.plot([x[0], x[-1]], [y1[0], y1[-1]], c='b', alpha=0.8)
ax.plot([x[0], x[-1]], [y2[0], y2[-1]], c='r', alpha=0.8)
plt.show()
matplotlib.pyplot.fill
Date handling 处理时间（序列?）
下面代码并没有跑，只是照搬
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data('goog.npz') as datafile:
r = np.load(datafile)['price_data'].view(np.recarray)
fig, ax = plt.subplots()
ax.plot(r.date, r.adj_close)
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
# round to nearest years...
datemin = np.datetime64(r.date[0], 'Y')
datemax = np.datetime64(r.date[-1], 'Y') + np.timedelta64(1, 'Y')
ax.set_xlim(datemin, datemax)
# format the coords message box
def price(x):
return '$%1.2f' % x
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = price
ax.grid(True)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()
plt.show()
Log plots
import numpy as np
import matplotlib.pyplot as plt
# Data for plotting
t = np.arange(0.01, 20.0, 0.01)
# Create figure
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# log y axis
ax1.semilogy(t, np.exp(-t / 5.0))
ax1.set(title='semilogy')
ax1.grid()
# log x axis
ax2.semilogx(t, np.sin(2 * np.pi * t))
ax2.set(title='semilogx')
ax2.grid()
# log x and y axis
ax3.loglog(t, 20 * np.exp(-t / 10.0), basex=2)
ax3.set(title='loglog base 2 on x')
ax3.grid()
# With errorbars: clip non-positive values
# Use new data for plotting
x = 10.0**np.linspace(0.0, 2.0, 20)
y = x**2.0
ax4.set_xscale("log", nonposx='clip')
ax4.set_yscale("log", nonposy='clip')
ax4.set(title='Errorbars go negative')
ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
# ylim must be set after errorbar to allow errorbar to autoscale limits
ax4.set_ylim(bottom=0.1)
fig.tight_layout()
plt.show()
matplotlib.axes. Axes.semilogx
matplotlib.axes. Axes.semilogy
Polar plots 极坐标
import numpy as np
import matplotlib.pyplot as plt
r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r #看来采用的是弧度制
ax = plt.subplot(111, projection='polar')
ax.plot(theta, r)
ax.set_rmax(2) #数字大了 整体会缩小？明白了，rmax就是极坐标最大半径，最大半径设置得越大，相应得原本的内容就显得越小。
ax.set_rticks([0.5, 1, 1.5, 2]) # Less radial ticks
ax.set_rlabel_position(-12.5) # 0.5 1 1.5 2位于-12.5度处
ax.grid(True)
ax.set_title("A line plot on a polar axis", va='bottom')
plt.show()
matplotlib.axes.Axes.plot
matplotlib.projections.polar
matplotlib.projections.polar.PolarAxes
matplotlib.projections.polar.PolarAxes.set_rticks
matplotlib.projections.polar.PolarAxes.set_rmax
matplotlib.projections.polar.PolarAxes.set_rlabel_position
Legends 图例
import numpy as np
import matplotlib.pyplot as plt
# 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, 'k:', label='Data length')
ax.plot(a, c + d, 'k', label='Total message length')
legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
#loc: location best:0, upper right:1 upper left:2 ...
#shadow 是否添加阴影
#fontsize 字体大小
# xx-small, x-small, small, medium, large, x-large, xx-large, larger, smaller, None
#或者，数字 fontsize=15
# Put a nicer background color on the legend.
legend.get_frame().set_facecolor('C0')
plt.show()
matplotlib.axes.Axes.plot
matplotlib.pyplot.plot
matplotlib.axes.Axes.legend
matplotlib.pyplot.legend
TeX-notation for text objects
import matplotlib.pyplot as plt
import subprocess
import sys
import re
# Selection of features following "Writing mathematical expressions" tutorial
mathtext_titles = {
0: "Header demo",
1: "Subscripts and superscripts",
2: "Fractions, binomials and stacked numbers",
3: "Radicals",
4: "Fonts",
5: "Accents",
6: "Greek, Hebrew",
7: "Delimiters, functions and Symbols"}
n_lines = len(mathtext_titles)
# Randomly picked examples
mathext_demos = {
0: r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "
r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} "
r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ "
r"U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_"
r"{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$",
1: r"$\alpha_i > \beta_i,\ "
r"\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ "
r"\ldots$",
2: r"$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ "
r"\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$",
3: r"$\sqrt{2},\ \sqrt[3]{x},\ \ldots$",
4: r"$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ "
r"\mathrm{or}\ \mathcal{CALLIGRAPHY}$",
5: r"$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ "
r"\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ "
r"\ldots$",
6: r"$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ "
r"\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ "
r"\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$",
7: r"$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ "
r"\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ "
r"\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$"}
def doall():
# Colors used in mpl online documentation.
mpl_blue_rvb = (191. / 255., 209. / 256., 212. / 255.)
mpl_orange_rvb = (202. / 255., 121. / 256., 0. / 255.)
mpl_grey_rvb = (51. / 255., 51. / 255., 51. / 255.)
# Creating figure and axis.
plt.figure(figsize=(6, 7))
plt.axes([0.01, 0.01, 0.98, 0.90], facecolor="white", frameon=True)
plt.gca().set_xlim(0., 1.)
plt.gca().set_ylim(0., 1.)
plt.gca().set_title("Matplotlib's math rendering engine",
color=mpl_grey_rvb, fontsize=14, weight='bold')
plt.gca().set_xticklabels("", visible=False)
plt.gca().set_yticklabels("", visible=False)
# Gap between lines in axes coords
line_axesfrac = (1. / (n_lines))
# Plotting header demonstration formula
full_demo = mathext_demos[0]
plt.annotate(full_demo,
xy=(0.5, 1. - 0.59 * line_axesfrac),
xycoords='data', color=mpl_orange_rvb, ha='center',
fontsize=20)
# Plotting features demonstration formulae
for i_line in range(1, n_lines):
baseline = 1 - (i_line) * line_axesfrac
baseline_next = baseline - line_axesfrac
title = mathtext_titles[i_line] + ":"
fill_color = ['white', mpl_blue_rvb][i_line % 2]
plt.fill_between([0., 1.], [baseline, baseline],
[baseline_next, baseline_next],
color=fill_color, alpha=0.5)
plt.annotate(title,
xy=(0.07, baseline - 0.3 * line_axesfrac),
xycoords='data', color=mpl_grey_rvb, weight='bold')
demo = mathext_demos[i_line]
plt.annotate(demo,
xy=(0.05, baseline - 0.75 * line_axesfrac),
xycoords='data', color=mpl_grey_rvb,
fontsize=16)
for i in range(n_lines):
s = mathext_demos[i]
print(i, s)
plt.show()
if '--latex' in sys.argv:
# Run: python mathtext_examples.py --latex
# Need amsmath and amssymb packages.
fd = open("mathtext_examples.ltx", "w")
fd.write("\\documentclass{article}\n")
fd.write("\\usepackage{amsmath, amssymb}\n")
fd.write("\\begin{document}\n")
fd.write("\\begin{enumerate}\n")
for i in range(n_lines):
s = mathext_demos[i]
s = re.sub(r"(?<!\\)\$", "$$", s)
fd.write("\\item %s\n" % s)
fd.write("\\end{enumerate}\n")
fd.write("\\end{document}\n")
fd.close()
subprocess.call(["pdflatex", "mathtext_examples.ltx"])
else:
doall()
Native TeX rendering
不晓得，为啥使用原始的TeX,我的程序会挂，不过，matplotlib提供的已经足够了吧。下面的直接照搬了。
import numpy as np
import matplotlib
matplotlib.rcParams['text.usetex'] = True
import matplotlib.pyplot as plt
t = np.linspace(0.0, 1.0, 100)
s = np.cos(4 * np.pi * t) + 2
fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
ax.plot(t, s)
ax.set_xlabel(r'\textbf{time (s)}')
ax.set_ylabel('\\textit{Velocity (\N{DEGREE SIGN}/sec)}', fontsize=16)
ax.set_title(r'\TeX\ is Number $\displaystyle\sum_{n=1}^\infty'
r'\frac{-e^{i\pi}}{2^n}$!', fontsize=16, color='r')
plt.show()
EEG GUI
你可以把matplotlib嵌入到pygtk,wx,Tk或者Qt应用中。
这部分姑且先跳过吧。
import tkinter
from matplotlib.backends.backend_tkagg import (
FigureCanvasTkAgg, NavigationToolbar2Tk)
# Implement the default Matplotlib key bindings.
from matplotlib.backend_bases import key_press_handler
from matplotlib.figure import Figure
import numpy as np
root = tkinter.Tk()
root.wm_title("Embedding in Tk")
fig = Figure(figsize=(5, 4), dpi=100)
t = np.arange(0, 3, .01)
fig.add_subplot(111).plot(t, 2 * np.sin(2 * np.pi * t))
canvas = FigureCanvasTkAgg(fig, master=root) # A tk.DrawingArea.
canvas.draw()
canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
toolbar = NavigationToolbar2Tk(canvas, root)
toolbar.update()
canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
def on_key_press(event):
print("you pressed {}".format(event.key))
key_press_handler(event, canvas, toolbar)
canvas.mpl_connect("key_press_event", on_key_press)
def _quit():
root.quit() # stops mainloop
root.destroy() # this is necessary on Windows to prevent
# Fatal Python Error: PyEval_RestoreThread: NULL tstate
button = tkinter.Button(master=root, text="Quit", command=_quit)
button.pack(side=tkinter.BOTTOM)
tkinter.mainloop()
# If you put root.destroy() here, it will cause an error if the window is
# closed with the window manager.
XKCD-style sketch plots
单纯为了好玩，matplotlib支持xkcd-style.
import matplotlib.pyplot as plt
import numpy as np
with plt.xkcd():
# Based on "Stove Ownership" from XKCD by Randall Munroe
# http://xkcd.com/418/
fig = plt.figure()
ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.xticks([])
plt.yticks([])
ax.set_ylim([-30, 10])
data = np.ones(100)
data[70:] -= np.arange(30)
plt.annotate(
'THE DAY I REALIZED\nI COULD COOK BACON\nWHENEVER I WANTED',
xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))
plt.plot(data)
plt.xlabel('time')
plt.ylabel('my overall health')
fig.text(
0.5, 0.05,
'"Stove Ownership" from xkcd by Randall Munroe',
ha='center')
with plt.xkcd():
# Based on "The Data So Far" from XKCD by Randall Munroe
# http://xkcd.com/373/
fig = plt.figure()
ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
ax.bar([0, 1], [0, 100], 0.25)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks([0, 1])
ax.set_xlim([-0.5, 1.5])
ax.set_ylim([0, 110])
ax.set_xticklabels(['CONFIRMED BY\nEXPERIMENT', 'REFUTED BY\nEXPERIMENT'])
plt.yticks([])
plt.title("CLAIMS OF SUPERNATURAL POWERS")
fig.text(
0.5, 0.05,
'"The Data So Far" from xkcd by Randall Munroe',
ha='center')
plt.show()
Subplot examples
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
data = np.random.randn(2, 100)
fig, axs = plt.subplots(2, 2, figsize=(5, 5))
axs[0, 0].hist(data[0])
axs[1, 0].scatter(data[0], data[1])
axs[0, 1].plot(data[0], data[1])
axs[1, 1].hist2d(data[0], data[1])
plt.show()
