0 共用模块
模块名称：example_utils.py，里面包括三个函数，各自功能如下：
代码语言：javascript复制import matplotlib.pyplot as plt
# 创建画图fig和axes
def setup_axes():
fig, axes = plt.subplots(ncols=3, figsize=(6.5,3))
for ax in fig.axes:
ax.set(xticks=[], yticks=[])
fig.subplots_adjust(wspace=0, left=0, right=0.93)
return fig, axes
# 图片标题
def title(fig, text, y=0.9):
fig.suptitle(text, size=14, y=y, weight='semibold', x=0.98, ha='right',
bbox=dict(boxstyle='round', fc='floralwhite', ec='#8B7E66',
lw=2))
# 为数据添加文本注释
def label(ax, text, y=0):
ax.annotate(text, xy=(0.5, 0.00), xycoords='axes fraction', ha='center',
style='italic',
bbox=dict(boxstyle='round', facecolor='floralwhite',
ec='#8B7E66'))1 基本绘图
R语言 - 线图绘制
对应代码：
代码语言：javascript复制import numpy as np
import matplotlib.pyplot as plt
import example_utils
x = np.linspace(0, 10, 100)
fig, axes = example_utils.setup_axes()
for ax in axes:
ax.margins(y=0.10)
# 子图1 默认plot多条线，颜色系统分配
for i in range(1, 6):
axes[0].plot(x, i * x)
# 子图2 展示线的不同linestyle
for i, ls in enumerate(['-', '--', ':', '-.']):
axes[1].plot(x, np.cos(x) i, linestyle=ls)
# 子图3 展示线的不同linestyle和marker
for i, (ls, mk) in enumerate(zip(['', '-', ':'], ['o', '^', 's'])):
axes[2].plot(x, np.cos(x) i * x, linestyle=ls, marker=mk, markevery=10)
# 设置标题
# example_utils.title(fig, '"ax.plot(x, y, ...)": Lines and/or markers', y=0.95)
# 保存图片
fig.savefig('plot_example.png', facecolor='none')
# 展示图片
plt.show()2 散点图
R语言 - 箱线图（小提琴图、抖动图、区域散点图）
对应代码：
代码语言：javascript复制"""
散点图的基本用法
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
# 随机生成数据
np.random.seed(1874)
x, y, z = np.random.normal(0, 1, (3, 100))
t = np.arctan2(y, x)
size = 50 * np.cos(2 * t)**2 10
fig, axes = example_utils.setup_axes()
# 子图1
axes[0].scatter(x, y, marker='o', color='darkblue', facecolor='white', s=80)
example_utils.label(axes[0], 'scatter(x, y)')
# 子图2
axes[1].scatter(x, y, marker='s', color='darkblue', s=size)
example_utils.label(axes[1], 'scatter(x, y, s)')
# 子图3
axes[2].scatter(x, y, s=size, c=z, cmap='gist_ncar')
example_utils.label(axes[2], 'scatter(x, y, s, c)')
# example_utils.title(fig, '"ax.scatter(...)": Colored/scaled markers',
# y=0.95)
fig.savefig('scatter_example.png', facecolor='none')
plt.show()3 柱状图
R语言 - 柱状图
对应代码：
代码语言：javascript复制import numpy as np
import matplotlib.pyplot as plt
import example_utils
def main():
fig, axes = example_utils.setup_axes()
basic_bar(axes[0])
tornado(axes[1])
general(axes[2])
# example_utils.title(fig, '"ax.bar(...)": Plot rectangles')
fig.savefig('bar_example.png', facecolor='none')
plt.show()
# 子图1
def basic_bar(ax):
y = [1, 3, 4, 5.5, 3, 2]
err = [0.2, 1, 2.5, 1, 1, 0.5]
x = np.arange(len(y))
ax.bar(x, y, yerr=err, color='lightblue', ecolor='black')
ax.margins(0.05)
ax.set_ylim(bottom=0)
example_utils.label(ax, 'bar(x, y, yerr=e)')
# 子图2
def tornado(ax):
y = np.arange(8)
x1 = y np.random.random(8) 1
x2 = y 3 * np.random.random(8) 1
ax.barh(y, x1, color='lightblue')
ax.barh(y, -x2, color='salmon')
ax.margins(0.15)
example_utils.label(ax, 'barh(x, y)')
# 子图3
def general(ax):
num = 10
left = np.random.randint(0, 10, num)
bottom = np.random.randint(0, 10, num)
width = np.random.random(num) 0.5
height = np.random.random(num) 0.5
ax.bar(left, height, width, bottom, color='salmon')
ax.margins(0.15)
example_utils.label(ax, 'bar(l, h, w, b)')
main()4 填充画图
对应代码：
代码语言：javascript复制"""
fill函数的各种用法
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
# -- 产生数据 ----------------------
def stackplot_data():
x = np.linspace(0, 10, 100)
y = np.random.normal(0, 1, (5, 100))
y = y.cumsum(axis=1)
y -= y.min(axis=0, keepdims=True)
return x, y
def sin_data():
x = np.linspace(0, 10, 100)
y = np.sin(x)
y2 = np.cos(x)
return x, y, y2
def fill_data():
t = np.linspace(0, 2*np.pi, 100)
r = np.random.normal(0, 1, 100).cumsum()
r -= r.min()
return r * np.cos(t), r * np.sin(t)
def fill_example(ax):
# fill一个多边形区域
x, y = fill_data()
ax.fill(x, y, color='lightblue')
ax.margins(0.1)
example_utils.label(ax, 'fill')
def fill_between_example(ax):
# 两条线间填充
x, y1, y2 = sin_data()
# fill_between的最常用法1
err = np.random.rand(x.size)**2 0.1
y = 0.7 * x 2
ax.fill_between(x, y err, y - err, color='orange')
# 最常用法2:两条曲线相交区域对应不同填充色
ax.fill_between(x, y1, y2, where=y1 > y2, color='lightblue')
ax.fill_between(x, y1, y2, where=y1 < y2, color='forestgreen')
# 最常用法3
ax.fill_betweenx(x, -y1, where=y1 > 0, color='red', alpha=0.5)
ax.fill_betweenx(x, -y1, where=y1 < 0, color='blue', alpha=0.5)
ax.margins(0.15)
example_utils.label(ax, 'fill_between/x')
def stackplot_example(ax):
# Stackplot就是多次调用 ax.fill_between
x, y = stackplot_data()
ax.stackplot(x, y.cumsum(axis=0), alpha=0.5)
example_utils.label(ax, 'stackplot')
def main():
fig, axes = example_utils.setup_axes()
fill_example(axes[0])
fill_between_example(axes[1])
stackplot_example(axes[2])
# example_utils.title(fig, 'fill/fill_between/stackplot: Filled polygons',
# y=0.95)
fig.savefig('fill_example.png', facecolor='none')
plt.show()
main()5 imshow
一个震撼的交互型3D可视化R包 - 可直接转ggplot2图为3D
对应代码：
代码语言：javascript复制import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cbook import get_sample_data
from mpl_toolkits import axes_grid1
import example_utils
def main():
fig, axes = setup_axes()
plot(axes, *load_data())
# example_utils.title(fig, '"ax.imshow(data, ...)": Colormapped or RGB arrays')
fig.savefig('imshow_example.png', facecolor='none')
plt.show()
def plot(axes, img_data, scalar_data, ny):
# 默认线性插值
axes[0].imshow(scalar_data, cmap='gist_earth', extent=[0, ny, ny, 0])
# 最近邻插值
axes[1].imshow(scalar_data, cmap='gist_earth', interpolation='nearest',
extent=[0, ny, ny, 0])
# 展示RGB/RGBA数据
axes[2].imshow(img_data)
def load_data():
img_data = plt.imread(get_sample_data('5.png'))
ny, nx, nbands = img_data.shape
scalar_data = np.load(get_sample_data('bivariate_normal.npy'))
return img_data, scalar_data, ny
def setup_axes():
fig = plt.figure(figsize=(6, 3))
axes = axes_grid1.ImageGrid(fig, [0, 0, .93, 1], (1, 3), axes_pad=0)
for ax in axes:
ax.set(xticks=[], yticks=[])
return fig, axes
main()6 pcolor
还在用PCA降维？快学学大牛最爱的t-SNE算法吧, 附Python/R代码
对应代码：
代码语言：javascript复制"""
pcolor/pcolormesh的基本用法
记住一点：假如数据在矩形区域内建议使用imshow，这样速度更快。此例子展示imshow不能使用的场景
"""
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cbook import get_sample_data
import example_utils
# 拿到数据 ...
z = np.load(get_sample_data('./bivariate_normal.npy'))
ny, nx = z.shape
y, x = np.mgrid[:ny, :nx]
y = (y - y.mean()) * (x 10)**2
mask = (z > -0.1) & (z < 0.1)
z2 = np.ma.masked_where(mask, z)
fig, axes = example_utils.setup_axes()
# pcolor 或 pcolormesh 都可，后者效率更高
axes[0].pcolor(x, y, z, cmap='gist_earth')
example_utils.label(axes[0], 'either')
# pcolor和pcolormesh的不同展示
# 使用pcolor
axes[1].pcolor(x, y, z2, cmap='gist_earth', edgecolor='black')
example_utils.label(axes[1], 'pcolor(x,y,z)')
# 使用pcolormesh
axes[2].pcolormesh(x, y, z2, cmap='gist_earth', edgecolor='black', lw=0.5,
antialiased=True)
example_utils.label(axes[2], 'pcolormesh(x,y,z)')
#example_utils.title(fig, 'pcolor/pcolormesh: Colormapped 2D arrays')
fig.savefig('pcolor_example.png', facecolor='none')
plt.show()7 contour
对应代码：
代码语言：javascript复制import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cbook import get_sample_data
import example_utils
z = np.load(get_sample_data('bivariate_normal.npy'))
fig, axes = example_utils.setup_axes()
axes[0].contour(z, cmap='gist_earth')
example_utils.label(axes[0], 'contour')
axes[1].contourf(z, cmap='gist_earth')
example_utils.label(axes[1], 'contourf')
axes[2].contourf(z, cmap='gist_earth')
cont = axes[2].contour(z, colors='black')
axes[2].clabel(cont, fontsize=6)
example_utils.label(axes[2], 'contourf contourn clabel')
# example_utils.title(fig, '"contour, contourf, clabel": Contour/label 2D data',
# y=0.96)
fig.savefig('contour_example.png', facecolor='none')
plt.show()8 向量场
对应代码：
代码语言：javascript复制import matplotlib.pyplot as plt
import numpy as np
import example_utils
# Generate data
n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
xi, yi = np.meshgrid(x, y)
z = (1 - xi / 2 xi**5 yi**3) * np.exp(-xi**2 - yi**2)
dy, dx = np.gradient(z)
mag = np.hypot(dx, dy)
fig, axes = example_utils.setup_axes()
# 单箭头
axes[0].arrow(0, 0, -0.5, 0.5, width=0.005, color='black')
axes[0].axis([-1, 1, -1, 1])
example_utils.label(axes[0], 'arrow(x, y, dx, dy)')
# ax.quiver
ds = np.s_[::16, ::16] # Downsample our array a bit...
axes[1].quiver(xi[ds], yi[ds], dx[ds], dy[ds], z[ds], cmap='gist_earth',
width=0.01, scale=0.25, pivot='middle')
axes[1].axis('tight')
example_utils.label(axes[1], 'quiver(x, y, dx, dy)')
# ax.streamplot
# 宽度和颜色变化
lw = 2 * (mag - mag.min()) / mag.ptp() 0.2
axes[2].streamplot(xi, yi, dx, dy, color=z, density=1.5, linewidth=lw,
cmap='gist_earth')
example_utils.label(axes[2], 'streamplot(x, y, dx, dy)')
# example_utils.title(fig, '"arrow/quiver/streamplot": Vector fields', y=0.96)
# fig.savefig('vector_example.png', facecolor='none')
plt.show()9 数据分布图
R语言 - 箱线图一步法
对应代码：
代码语言：javascript复制"""
Matplotlib 提供许多专业的绘制统计学相关的图函数
更多统计学相关图可使用 Seaborn 库，它基于Matplotlib编写。
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
def main():
colors = ['cyan', 'red', 'blue', 'green', 'purple']
dists = generate_data()
fig, axes = example_utils.setup_axes()
hist(axes[0], dists, colors)
boxplot(axes[1], dists, colors)
violinplot(axes[2], dists, colors)
# example_utils.title(fig, 'hist/boxplot/violinplot: Statistical plotting',
# y=0.9)
fig.savefig('statistical_example.png', facecolor='none')
plt.show()
def generate_data():
means = [0, -1, 2.5, 4.3, -3.6]
sigmas = [1.2, 5, 3, 1.5, 2]
# 每一个分布的样本个数
nums = [150, 1000, 100, 200, 500]
dists = [np.random.normal(*args) for args in zip(means, sigmas, nums)]
return dists
# 频率分布直方图
def hist(ax, dists, colors):
ax.set_color_cycle(colors)
for dist in dists:
ax.hist(dist, bins=20, density=True, edgecolor='none', alpha=0.5)
ax.margins(y=0.05)
ax.set_ylim(bottom=0)
example_utils.label(ax, 'ax.hist(dists)')
# 箱型图
def boxplot(ax, dists, colors):
result = ax.boxplot(dists, patch_artist=True, notch=True, vert=False)
for box, color in zip(result['boxes'], colors):
box.set(facecolor=color, alpha=0.5)
for item in ['whiskers', 'caps', 'medians']:
plt.setp(result[item], color='gray', linewidth=1.5)
plt.setp(result['fliers'], markeredgecolor='gray', markeredgewidth=1.5)
plt.setp(result['medians'], color='black')
ax.margins(0.05)
ax.set(yticks=[], ylim=[0, 6])
example_utils.label(ax, 'ax.boxplot(dists)')
#小提琴图
def violinplot(ax, dists, colors):
result = ax.violinplot(dists, vert=False, showmedians=True)
for body, color in zip(result['bodies'], colors):
body.set(facecolor=color, alpha=0.5)
for item in ['cbars', 'cmaxes', 'cmins', 'cmedians']:
plt.setp(result[item], edgecolor='gray', linewidth=1.5)
plt.setp(result['cmedians'], edgecolor='black')
ax.margins(0.05)
ax.set(ylim=[0, 6])
example_utils.label(ax, 'ax.violinplot(dists)')
main()本文参考：
https://nbviewer.jupyter.org/github/matplotlib/AnatomyOfMatplotlib/blob/master/AnatomyOfMatplotlib-Part2-Plotting_Methods_Overview.ipynb
