如何使用Bokeh（或其他庫）在python中創建等高線圖？

Question

我有興趣在Bokeh中製作輪廓圖。到目前爲止，我還沒能在網上找到任何東西。

提醒一下，這是等高線圖：

任何幫助，將不勝感激。我也歡迎對其他圖書館提出建議，但必須先允許交互式/動畫圖，而不是僅渲染靜態輸出（圖像）。

Answer 1

Plotly一直支持等高線圖在Python在過去的一年：

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

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

密度圖（與相鄰的直方圖等高線圖）：

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

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

Plotly Python庫在pip上，100％免費且開放源代碼。

Answer 2

通過選擇一個調色板10個值如同在你提供的例子中，可以使用圖像（參見Bokeh image example）在背景虛化來模擬的等高線圖。黑色輪廓線和數字缺失，但顏色之間的邊界實際上是輪廓線。此外，據我所知，背景虛化不提供的彩條，但你可以把它作爲另一個圖像碼（更新：背景虛化的Latests版本都提供彩條）：

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) 

輸出的樣子：

Answer 3

您還可以使用matplotlibs輪廓圖計算輪廓數據，然後使用bokeh多線繪製輪廓。我也在繪製文本標籤（不幸有點難看）。

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) 

這是輸出：

Answer 4

我寫了基於matplotlib輸出填充輪廓刀具：

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