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本文内容:介绍 ProPlot9大亮点+python代码

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让Matplotlib爱好者拥有更加smoother plotting experience。

  1. 代码更简洁,图形更好看

将Matplotlib一行代码设置一个参数的繁琐行为直接通过format方法一次搞定,比如下图,

Proplot代码

import proplot as ppltfig, 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')

一个完整的使用案例:

import proplot as ppltimport numpy as npfig, axs = pplt.subplots(ncols=2, nrows=2, refwidth=2, share=False)state = np.random.RandomState(51423)N = 60x = 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,)

  1. 更友好的类构造函数

将Matplotlib中类名书写不友好的类进行封装,可通过简洁的关键字参数调用。例如,mpl_toolkits.basemap.Basemap()、matplotlib.ticker.LogFormatterExponent()、ax.xaxis.set_major_locator(MultipleLocator(1.000))等等,封装后,

  1. 图形大小、子图间距自适应

proplot通过refwidthrefheightrefaspect、refheight、proplot.gridspec.GridSpec等控制图形大小和子图间距,替代Matplotlib自带的tightlayout,避免图形重叠、标签不完全等问题

案例:

proplot控制图形大小:

import proplot as ppltimport numpy as npstate = 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')

proplot如何控制子图间距?

import proplot as ppltfig, 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\nTitle\nTitle')axs[:, 0].format(ylabel='ylabel')

4.多子图个性化设置

  • 子图灵活设置坐标轴标签:sharex, sharey, spanx, spany, alignxaligny参数控制,效果见下图(相同颜色比较来看)

  • 子图灵活添加编号

import proplot as ppltimport numpy as npN = 20state = 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')

  • 子图灵活设置Panels

  • 子图各自外观灵活自定义

import proplot as ppltimport numpy as npstate = np.random.RandomState(51423)# Selected subplots in a simple gridfig, 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)  # eauivalentaxs[:, 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)

5.图例、colorbar灵活设置

  • 图例、colorbar位置指定

  • 图例、colorbar:On-the-fly,

  • 图例、colorbar:Figure-wide

  • 图例外观个性化:可轻松设置图例顺序、位置、颜色等等,

  • colorbar外观个性化:可轻松设置colorbar的刻度、标签、宽窄等,

6.更加优化的绘图指令

7、整合地图库Cartopy和basemap

Cartopybasemap是Python里非常强大的地图库,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。

个性化设置,

支持cartopy中的各种投影,’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’

当然,也支持basemap中的各种投影,’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

  • 将多个colormaps融合

  • ContinuousColormap和DiscreteColormap方法修改colormaps

  • proplot添加字体

自定义的.ttc、.ttf等格式字体保存~/.proplot/fonts文件中。

9、全局参数设置更灵活

新的rc方法更新全局参数

import proplot as ppltimport 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, 7state = 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()

全局设置’ggplot’, ‘seaborn’的style

import proplot as ppltimport numpy as npstate = np.random.RandomState(51423)data = state.rand(10, 5)# Set up figurefig, 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)

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来源:https://github.com/lukelbd/proplot

参考:https://mp.weixin.qq.com/s/8M5GRGj13hcfnnruqz7kmA

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