如何使用 Bokeh（或其他库）在 python 中绘制等高线图？

Question

我有兴趣在 Bokeh 中制作等高线图。到目前为止我还没有在网上找到任何东西。

提醒一下，这是等高线图：

如有任何帮助，我们将不胜感激。我也欢迎向其他库提出建议，但前提是允许 interactive/animated 图，而不仅仅是渲染静态输出（图像）。

Answer 1

Plotly 在过去一年 Python 中支持等高线图：

https://plot.ly/python/contour-plots/

https://plot.ly/pandas/contour-plots/

密度图（具有相邻直方图的等值线图）：

https://plot.ly/python/2d-density-plots/

https://plot.ly/pandas/2d-density-plots/

Plotly Python 库在 pip 上，100% 免费和开源。

Answer 2

通过选择具有 10 个值的调色板，就像在您提供的示例中一样，可以在散景中使用图像（参见 Bokeh image example）来模拟等高线图。黑色的轮廓线和数字不见了，但颜色之间的边界实际上是轮廓线。另外，据我所知，Bokeh 不提供颜色条，但您可以将其编码为另一张图像 （更新：Bokeh 的最新版本确实提供了颜色条。）:

from bokeh.io import output_file
from bokeh.plotting import gridplot,figure, show
from bokeh.models import ColumnDataSource,FixedTicker
import numpy as np
from matplotlib import cm,colors

output_file("contour.html")
cmap = cm.get_cmap("jet") #choose any matplotlib colormap here
num_slabs = 10 # number of color steps
jet_10 = [colors.rgb2hex(m) for m in cmap(np.arange(0,cmap.N,cmap.N/(num_slabs-1)))]
vmin = 0
vmax = 1550
N = 200
x = np.linspace(0, 10, N)
y = np.linspace(0, 10, N)
xx, yy = np.meshgrid(x, y)
d = vmax * (1. + np.sin(xx)*np.cos(yy))

source = ColumnDataSource(data={'d': [d], 'xx': [x], 'yy': [y]})
p = figure(plot_width=400,plot_height=400,x_range=[0, 10], y_range=[0, 10],min_border_right=10)
p.image(image="d", x=[0], y=[0], dw=[10], dh=[10], palette=jet_10,source=source)

# The following code is for the colorbar:
pcb = figure(plot_width=80,plot_height=400,x_range=[0, 1], y_range=[0, vmax],min_border_right=10)
pcb.image(image=[np.linspace(vmin,vmax,100).reshape(100,1)],x=[0],y=[0],dw=[1],dh=[vmax-vmin], palette=jet_10)
pcb.xaxis.major_label_text_color = None
pcb.xaxis.major_tick_line_color = None
pcb.xaxis.minor_tick_line_color = None
pcb.yaxis[0].ticker=FixedTicker(ticks=np.linspace(vmin,vmax,num_slabs+1)) # 11 ticks
pgrid = gridplot([[p,pcb]])  # this places the colorbar next to the image
show(pgrid)

输出将如下所示：

Answer 3

您还可以使用 matplotlibs contour plot 计算等高线数据，然后使用 bokehs 绘制等高线 multi_line。我也在绘制文本标签（不幸的是有点难看）。

import numpy as np
import matplotlib.pyplot as plt
from bokeh.models import ColumnDataSource
from bokeh.io import output_file
from bokeh.plotting import gridplot,figure, show

def get_contour_data(X, Y, Z):
    cs = plt.contour(X, Y, Z)
    xs = []
    ys = []
    xt = []
    yt = []
    col = []
    text = []
    isolevelid = 0
    for isolevel in cs.collections:
        isocol = isolevel.get_color()[0]
        thecol = 3 * [None]
        theiso = str(cs.get_array()[isolevelid])
        isolevelid += 1
        for i in range(3):
            thecol[i] = int(255 * isocol[i])
        thecol = '#%02x%02x%02x' % (thecol[0], thecol[1], thecol[2])

        for path in isolevel.get_paths():
            v = path.vertices
            x = v[:, 0]
            y = v[:, 1]
            xs.append(x.tolist())
            ys.append(y.tolist())
            xt.append(x[len(x) / 2])
            yt.append(y[len(y) / 2])
            text.append(theiso)
            col.append(thecol)

    source = ColumnDataSource(data={'xs': xs, 'ys': ys, 'line_color': col,'xt':xt,'yt':yt,'text':text})
    return source


output_file("contour.html")

N = 400
x = np.linspace(-1, 1, N)
y = np.linspace(-1, 1, N)
X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2

source = get_contour_data(X,Y,Z)
plot = figure(plot_width=400,plot_height=400,x_range=[-1,1], y_range=[-1,1])
plot.multi_line(xs='xs', ys='ys', line_color='line_color', source=source)
plot.text(x='xt',y='yt',text='text',source=source,text_baseline='middle',text_align='center')
show(plot)

这是输出：

Answer 4

我写了一个基于matplotlib输出的填充轮廓工具：

https://github.com/asher-pembroke/bokeh-tools

Answer 5

谢谢 Pablo，你的回答对我很有帮助。

使用您的示例，我制作了一个类似的图，该图使用 skimage 库添加了等高线。我还打开了您引用的 Bokeh image example 中的悬停工具。希望其他人出现并添加行标签。

请注意，以下代码适用于 Jupyter Notebook 中的运行。

from skimage import measure
import numpy as np

from bokeh.plotting import figure, show, output_notebook
from bokeh.models import (ColorBar,
                          FixedTicker, 
                          LinearColorMapper, 
                          PrintfTickFormatter)

output_notebook()

N = 500
x = np.linspace(0, 10, N)
y = np.linspace(0, 10, N)
xx, yy = np.meshgrid(x, y)
d = np.sin(xx)*np.cos(yy)

mapper = LinearColorMapper(palette='Spectral11', low=-1, high=1)

p = figure(x_range=(0, 10), y_range=(0, 10),
           tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])

# must give a vector of image data for image parameter
p.image(image=[d], x=0, y=0, dw=10, dh=10, palette='Spectral11')

levels = np.linspace(-1, 1, 12)
color_bar = ColorBar(color_mapper=mapper, 
                     major_label_text_font_size="8pt",
                     ticker=FixedTicker(ticks=levels),
                     formatter=PrintfTickFormatter(format='%.2f'),
                     label_standoff=6, 
                     border_line_color=None, 
                     location=(0, 0))

p.add_layout(color_bar, 'right')

for level in levels:
  contours = measure.find_contours(d, level)
  for contour in contours:
    x = contour[:,1]/50
    y = contour[:,0]/50
    p.line(x, y, color='grey', line_width=2)

show(p)

如何使用 Bokeh（或其他库）在 python 中绘制等高线图？

How to make a contour plot in python using Bokeh (or other libs)?

python

plot

contour

bokeh