Matplotlib是一个非常通用的绘图包，被科学家和工程师广泛使用，但是，Matplotlib也存在不足
，例如：

，ProPlot是Matplotlib面向对象绘图方法(object-oriented interface)的高级封装，整合了cartopy/basemap地图库、xarray和pandas，可弥补Matplotlib的部分缺陷，
ProPlot

Proplot的9大亮点：

## 1、更简简洁的代码，更好看的图形

Proplot中代码

``````import proplot as pplt
fig, axs = pplt.subplots(ncols=2)
axs.format(color='gray', linewidth=1) #format设置所有子图属性
axs[0].bar([10, 50, 80], [0.2, 0.5, 1])
axs[0].format(xlim=(0, 100), #format设置子图1属性
xticks=10,
xtickminor=True,
xlabel='foo',
ylabel='bar')
``````

Matplotlib中代码，

``````import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib as mpl
with mpl.rc_context(rc={'axes.linewidth': 1, 'axes.edgecolor': 'gray'}):
fig, axs = plt.subplots(ncols=2, sharey=True)
axs[0].set_ylabel('bar', color='gray')
axs[0].bar([10, 50, 80], [0.2, 0.5, 1], width=14)
for ax in axs:
#每一行代码设置一个图形参数
ax.set_xlim(0, 100)
ax.xaxis.set_major_locator(mticker.MultipleLocator(10))
ax.tick_params(width=1, color='gray', labelcolor='gray')
ax.tick_params(axis='x', which='minor', bottom=True)
ax.set_xlabel('foo', color='gray')
``````

``````import proplot as pplt
import numpy as np
fig, axs = pplt.subplots(ncols=2, nrows=2, refwidth=2, share=False)
state = np.random.RandomState(51423)
N = 60
x = np.linspace(1, 10, N)
y = (state.rand(N, 5) - 0.5).cumsum(axis=0)
axs[0].plot(x, y, linewidth=1.5)
# 图表诸多属性可在format中设置
axs.format(
suptitle='Format command demo',
abc='A.',
abcloc='ul',
title='Main',
ltitle='Left',
rtitle='Right',  # different titles
ultitle='Title 1',
urtitle='Title 2',
lltitle='Title 3',
lrtitle='Title 4',
toplabels=('Column 1', 'Column 2'),
leftlabels=('Row 1', 'Row 2'),
xlabel='xaxis',
ylabel='yaxis',
xscale='log',
xlim=(1, 10),
xticks=1,
ylim=(-3, 3),
yticks=pplt.arange(-3, 3),
yticklabels=('a', 'bb', 'c', 'dd', 'e', 'ff', 'g'),
ytickloc='both',
yticklabelloc='both',
xtickdir='inout',
xtickminor=False,
ygridminor=True,
)
``````

## 2、更友好的类构造函数

。例如，mpl_toolkits.basemap.Basemap()、matplotlib.ticker.LogFormatterExponent()、ax.xaxis.set_major_locator(MultipleLocator(1.000))等等，封装后，

## 3、图形大小、子图间距自适应

proplot通过`refwidth`
`refheight`
`refaspect、refheight、`
proplot.gridspec.GridSpec`等控制`

。推荐阅读

:point_right:图中文本标记太多，挤在一起咋办了？

:point_right:图太多，挤在一起了，哦多开？

``````import proplot as pplt
import numpy as np
state = np.random.RandomState(51423)
colors = np.tile(state.rand(8, 12, 1), (1, 1, 3))
fig, axs = pplt.subplots(ncols=3, nrows=2, refwidth=1.7) #refwidth的使用
fig.format(suptitle='Auto figure dimensions for grid of images')
for ax in axs:
ax.imshow(colors)
# 结合上文第2部分看，使用proj='robin'关键字参数调用cartopy projections'
fig, axs = pplt.subplots(ncols=2, nrows=3, proj='robin')
axs.format(land=True, landcolor='k')
fig.format(suptitle='Auto figure dimensions for grid of cartopy projections')
``````

``````import proplot as pplt
fig, axs = pplt.subplots(
ncols=4, nrows=3, refwidth=1.1, span=False,
bottom='5em', right='5em',
wspace=(0, 0, None), hspace=(0, None),
) # proplot新的子图间距控制算法
axs.format(
grid=False,
xlocator=1, ylocator=1, tickdir='inout',
xlim=(-1.5, 1.5), ylim=(-1.5, 1.5),
suptitle='Tight layout with user overrides',
toplabels=('Column 1', 'Column 2', 'Column 3', 'Column 4'),
leftlabels=('Row 1', 'Row 2', 'Row 3'),
)
axs[0, :].format(xtickloc='top')
axs[2, :].format(xtickloc='both')
axs[:, 1].format(ytickloc='neither')
axs[:, 2].format(ytickloc='right')
axs[:, 3].format(ytickloc='both')
axs[-1, :].format(xlabel='xlabel', title='Title
Title
Title')
axs[:, 0].format(ylabel='ylabel')
``````

## 4、多子图个性化设置

Matplotlib对于多子图轴标签、legend和colorbar等处理存在冗余问题，proplot使用Figure、colorbar和legend方法处理这种情况，使多子图绘图更简洁。

`sharex`
`sharey`
`spanx`
`spany`
`alignx`
`aligny`

``````import proplot as pplt
import numpy as np
N = 20
state = np.random.RandomState(51423)
data = N + (state.rand(N, N) - 0.55).cumsum(axis=0).cumsum(axis=1)
cycle = pplt.Cycle('greys', left=0.2, N=5)
fig, axs = pplt.subplots(ncols=2, nrows=2, figwidth=5, share=False)
axs[0].plot(data[:, :5], linewidth=2, linestyle='--', cycle=cycle)
axs[1].scatter(data[:, :5], marker='x', cycle=cycle)
axs[2].pcolormesh(data, cmap='greys')
m = axs[3].contourf(data, cmap='greys')
axs.format(
abc='a.', titleloc='l', title='Title',
xlabel='xlabel', ylabel='ylabel', suptitle='Quick plotting demo'
) #abc='a.'为各子图添加编号
fig.colorbar(m, loc='b', label='label')
``````

``````import proplot as pplt
import numpy as np
state = np.random.RandomState(51423)
# Selected subplots in a simple grid
fig, axs = pplt.subplots(ncols=4, nrows=4, refwidth=1.2, span=True)
axs.format(xlabel='xlabel', ylabel='ylabel', suptitle='Simple SubplotGrid')
axs.format(grid=False, xlim=(0, 50), ylim=(-4, 4))
# 使用axs[:, 0].format自定义某个子图外观
axs[:, 0].format(facecolor='blush', edgecolor='gray7', linewidth=1)  # eauivalent
axs[:, 0].format(fc='blush', ec='gray7', lw=1)
axs[0, :].format(fc='sky blue', ec='gray7', lw=1)
axs[0].format(ec='black', fc='gray5', lw=1.4)
axs[1:, 1:].format(fc='gray1')
for ax in axs[1:, 1:]:
ax.plot((state.rand(50, 5) - 0.5).cumsum(axis=0), cycle='Grays', lw=2)
# 使用axs[1, 1:].format自定义某个子图外观
fig = pplt.figure(refwidth=1, refnum=5, span=False)
axs = fig.subplots([[1, 1, 2], [3, 4, 2], [3, 4, 5]], hratios=[2.2, 1, 1])
axs.format(xlabel='xlabel', ylabel='ylabel', suptitle='Complex SubplotGrid')
axs[0].format(ec='black', fc='gray1', lw=1.4)
axs[1, 1:].format(fc='blush')
axs[1, :1].format(fc='sky blue')
axs[-1, -1].format(fc='gray4', grid=False)
axs[0].plot((state.rand(50, 10) - 0.5).cumsum(axis=0), cycle='Grays_r', lw=2)
``````

colorbar外观个性化

## 7、整合地图库`Cartopy`和`basemap`

`Cartopy`
`basemap`

proplot将cartopy和basemap进行了整合，解决了basemap使用需要创建新的axes、cartopy使用时代码冗长等缺陷。

，’cyl’, ‘merc’, ‘mill’, ‘lcyl’, ‘tmerc’, ‘robin’, ‘hammer’, ‘moll’, ‘kav7’, ‘aitoff’, ‘wintri’, ‘sinu’, ‘geos’, ‘ortho’, ‘nsper’, ‘aea’, ‘eqdc’, ‘lcc’, ‘gnom’, ‘npstere’, ‘nplaea’, ‘npaeqd’, ‘npgnom’, ‘igh’, ‘eck1’, ‘eck2’, ‘eck3’, ‘eck4’, ‘eck5’, ‘eck6′

，’cyl’, ‘merc’, ‘mill’, ‘cea’, ‘gall’, ‘sinu’, ‘eck4’, ‘robin’, ‘moll’, ‘kav7’, ‘hammer’, ‘mbtfpq’, ‘geos’, ‘ortho’, ‘nsper’, ‘vandg’, ‘aea’, ‘eqdc’, ‘gnom’, ‘cass’, ‘lcc’, ‘npstere’, ‘npaeqd’, ‘nplaea’。

## 8、更美观的colormaps, colors和fonts

proplot除了整合seaborn, cmocean, SciVisColor及Scientific Colour Maps projects中的colormaps之外，还增加了新的colormaps，同时增加PerceptualColormap方法来制作colormaps(貌似比Matplotlib的ListedColormap、LinearSegmentedColormap好用)，ContinuousColormap和DiscreteColormap方法修改colormaps等等。

proplot中可非常便利的添加字体。

proplot新增colormaps

PerceptualColormap制作colormaps

ContinuousColormap和DiscreteColormap方法修改colormaps

proplot添加字体

## 9、全局参数设置更灵活

``````import proplot as pplt
import numpy as np
# 多种方法Update全局参数
pplt.rc.metacolor = 'gray6'
pplt.rc.update({'fontname': 'Source Sans Pro', 'fontsize': 11})
pplt.rc['figure.facecolor'] = 'gray3'
pplt.rc.axesfacecolor = 'gray4'
# 使用Update后的全局参数：with pplt.rc.context法
with pplt.rc.context({'suptitle.size': 13}, toplabelcolor='gray6', metawidth=1.5):
fig = pplt.figure(figwidth=6, sharey='limits', span=False)
axs = fig.subplots(ncols=2)
N, M = 100, 7
state = np.random.RandomState(51423)
values = np.arange(1, M + 1)
cycle = pplt.get_colors('grays', M - 1) + ['red']
for i, ax in enumerate(axs):
data = np.cumsum(state.rand(N, M) - 0.5, axis=0)
lines = ax.plot(data, linewidth=3, cycle=cycle)
# 使用Update后的全局参数：format()法
axs.format(
grid=False, xlabel='xlabel', ylabel='ylabel',
toplabels=('Column 1', 'Column 2'),
suptitle='Rc settings demo',
suptitlecolor='gray7',
abc='[A]', abcloc='l',
title='Title', titleloc='r', titlecolor='gray7'
)
# 恢复设置
pplt.rc.reset()
``````

``````import proplot as pplt
import numpy as np
state = np.random.RandomState(51423)
data = state.rand(10, 5)
# Set up figure
fig, axs = pplt.subplots(ncols=2, nrows=2, span=False, share=False)
axs.format(suptitle='Stylesheets demo')
styles = ('ggplot', 'seaborn', '538', 'bmh')
# 直接使用format()方法
for ax, style in zip(axs, styles):
ax.format(style=style, xlabel='xlabel', ylabel='ylabel', title=style)
ax.plot(data, linewidth=3)
``````

proplot的皮毛，更多学习：https://github.com/lukelbd/proplot