使用"plotnine"库绘制3D表面图

Question

使用"plotnine"库绘制3D表面图

pythonplotplotlyplotnine

3

问题: 使用Python库'plotnine'，能否绘制交互式的3D表面图?

备用说明

What I'd like to do is, under python environment, creating an interactive 3D plot with R plot grammars like we do with ggplot2 library in R. It's because I have hard time remembering grammars of matplotlib and other libraries like seaborn.
An interactive 3D plot means a 3D plot that you can zoom in, zoom out, and scroll up and down, etc.
It seems like only Java supported plotting libraries scuh as bokeh or plotly can create interactive 3D plots. But I want to create it with the library 'plotnine' because the library supports ggplot-like grammar, which is easy to remember.

For example, can I draw a 3D surface plot like the one below with the library 'plotnine'?

import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd

# Read data from a csv
z_data =
pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/
master/api_docs/mt_bruno_elevation.csv')

 data = [
        go.Surface(
        z=z_data.as_matrix()
        )]
 layout = go.Layout(
 title='Mt Bruno Elevation',
 autosize=False,
 width=500,
 height=500,
 margin=dict(
 l=65,
 r=50,
 b=65,
 t=90
   )
 )
 fig = go.Figure(data=data, layout=layout)
 py.iplot(fig, filename='elevations-3d-surface')

上面的代码生成了如下图所示的图形。

你可以查看链接中的完整交互式3D表面图。

附：如果我可以用类似ggplot语法绘制交互式3D图，我们不必使用'plotnine'库。

感谢您抽出时间阅读此问题！

- Eiffelbear

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- krassowski · Accepted Answer

如果您愿意扩展plotnine，那么这是可能的，但需要注意。最终代码非常简单：

(
    ggplot_3d(mt_bruno_long)
    + aes(x='x', y='y', z='height')
    + geom_polygon_3d(size=0.01)
    + theme_minimal()
)

结果如下：

首先，您需要将数据转换为长格式：

z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv', index_col=0)
z = z_data.values
nrows, ncols = z.shape
x, y = np.linspace(0, 1, nrows), np.linspace(0, 1, ncols)
x, y = np.meshgrid(x, y)
mt_bruno_long = pd.DataFrame({'x': x.flatten(), 'y': y.flatten(), 'height': z.flatten()})

接下来，我们需要创建一个具有第三维意识的 ggplot 和 geom_polygon 的等效版本。

自从编写这个答案之后，代码现在已经在 plotnine3d 包中可用，因此您可以直接使用：

from plotnine3d import ggplot_3d, geom_polygon_3d

但为了完整起见，以下是相对简单的方法：

from plotnine import ggplot, geom_polygon
from plotnine.utils import to_rgba, SIZE_FACTOR


class ggplot_3d(ggplot):
    def _create_figure(self):
        figure = plt.figure()
        axs = [plt.axes(projection='3d')]
        
        figure._themeable = {}
        self.figure = figure
        self.axs = axs
        return figure, axs
    
    def _draw_labels(self):
        ax = self.axs[0]
        ax.set_xlabel(self.layout.xlabel(self.labels))
        ax.set_ylabel(self.layout.ylabel(self.labels))
        ax.set_zlabel(self.labels['z'])


class geom_polygon_3d(geom_polygon):
    REQUIRED_AES = {'x', 'y', 'z'}

    @staticmethod
    def draw_group(data, panel_params, coord, ax, **params):
        data = coord.transform(data, panel_params, munch=True)
        data['size'] *= SIZE_FACTOR

        grouper = data.groupby('group', sort=False)
        for i, (group, df) in enumerate(grouper):
            fill = to_rgba(df['fill'], df['alpha'])
            polyc = ax.plot_trisurf(
                df['x'].values,
                df['y'].values,
                df['z'].values,
                facecolors=fill if any(fill) else 'none',
                edgecolors=df['color'] if any(df['color']) else 'none',
                linestyles=df['linetype'],
                linewidths=df['size'],
                zorder=params['zorder'],
                rasterized=params['raster'],
            )
            # workaround for https://github.com/matplotlib/matplotlib/issues/9535
            if len(set(fill)) == 1:
                polyc.set_facecolors(fill[0])

如果你想要交互性，则可以使用 matplotlib 的任何后端，我选择了 ipympl（在 jupyter notebook 单元格中输入 pip install ipympl，然后输入 %matplotlib widget）。

需要注意的是：

虽然阴影效果很好，但 plot_trisurf 无法正确处理 facecolors（这里有一个解决 PR：链接）
你可能想添加一个参数以允许关闭阴影效果，请查看 matplotlib 3D 阴影效果示例
无法轻松实现分面、翻转坐标轴等操作，不过将来可能会解决，参见此 plotnine 问题讨论关于将 3D 绘图带入 plotnine 中。

编辑：如果数据集不可用，这里是一个基于 matplotlib 文档的自包含示例：

import numpy as np

n_radii = 8
n_angles = 36

radii = np.linspace(0.125, 1.0, n_radii)
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)[..., np.newaxis]

x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())

z = np.sin(-x*y)
df = pd.DataFrame(dict(x=x,y=y,z=z))

(
    ggplot_3d(df)
    + aes(x='x', y='y', z='z')
    + geom_polygon_3d(size=0.01)
    + theme_minimal()
)