获得两条等高线 matplotlib 的单个图

Question

I am making a program that interpolates the points of some level curves, but when it comes to graphing, I am obtaining two individual graphs of the two level curves and not a single graph.

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d

pts1 = np.array([[19.02678991587782, -98.62426964439068] ,[19.02642477902292, -98.62396923697386],[19.02614078313657, -98.62409798300963],[19.025207650377993, -98.62439839042645],
[19.02378765569075, -98.62461296715276],[19.022692222926803, -98.62452713646223],[19.021393922893306, -98.62422672904542],[19.020866485607627, -98.6230680147234],
[19.020978059006985, -98.6220595041113],[19.020795484294528, -98.62195221574815],[19.02058248020984, -98.6220595041113],[19.019923180101493, -98.6228427091539],
[19.019923180101493, -98.62287489566285],[19.019426167537492, -98.6239799658033],[19.01909144395283, -98.62516013779798],[19.018533569789643, -98.62622229253545],
[19.01849299705195, -98.62694112456855],[19.019243591116275, -98.62830368671746],[19.019750747335433, -98.62919418013162],[19.019659459330185, -98.63011686005473],
[19.019618886877918, -98.63087860733337],[19.020136185037668, -98.63175837191123],[19.02097805899266, -98.632090965837],[19.02212421792218, -98.63189784679251],
[19.024102084177514, -98.63043872507744],[19.02554236171496, -98.62930146843671],[19.0258770723203, -98.62851826341256],[19.026232067679466, -98.6269303956773],
[19.02672905989373, -98.62547127397141]])

pts2 = np.array([[19.024832367299116, -98.62688748111249],[19.024548368691026, -98.62624375101424],[19.023899227192743, -98.62615792033446],[19.02260093658879, -98.62590042829517],
[19.0217489278678, -98.62568585159576],[19.02101863120187, -98.6252996135368],[19.020754912182237, -98.62528888442091],[19.020572337215178, -98.62560002091598],
[19.02024775901759, -98.62611500499459],[19.020085469681103, -98.62684456577261],[19.0204100481956, -98.62774578791017],[19.020815770447378, -98.62856117936796],
[19.021262063780405, -98.62911907878645],[19.021262063780405, -98.62976280888472],[19.021434494983918, -98.63030997918734],[19.022022788299633, -98.63035289452722],
[19.022692222987843, -98.62996665646827],[19.023665941356825, -98.62932292637001],[19.024477368972605, -98.62816421219316],[19.024680225257438, -98.6276277704446]])

for lst in pts1, pts2:

    ######## level curve interpolation #######################
    pad = 3       
    lst = np.pad(lst, [(pad,pad), (0,0)], mode='wrap')  
    y,x = lst.T                                                      
    i = np.arange(0, len(lst))
    interp_i = np.linspace(pad, i.max() - pad + 1, 5 * (i.size - 2*pad))
    xi = interp1d(i, x, kind='cubic')(interp_i)
    yi = interp1d(i, y, kind='cubic')(interp_i)

    #grafico de la interpolación
    plt.figure(figsize = (8,8))
    plt.plot(xi, yi, "k")
    plt.title("level curves")
    plt.xlabel("x")
    plt.ylabel("y")
    plt.show()

I would like to get this output:

Answer 1

You need to declare plt.figure() only once, outside of the for loop. Inside the for loop you add elements to the plot. Finally, outside of the loop you set axis labels and show the plot.

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d



pts1 = np.array([[19.02678991587782, -98.62426964439068] ,[19.02642477902292, -98.62396923697386],[19.02614078313657, -98.62409798300963],[19.025207650377993, -98.62439839042645],
[19.02378765569075, -98.62461296715276],[19.022692222926803, -98.62452713646223],[19.021393922893306, -98.62422672904542],[19.020866485607627, -98.6230680147234],
[19.020978059006985, -98.6220595041113],[19.020795484294528, -98.62195221574815],[19.02058248020984, -98.6220595041113],[19.019923180101493, -98.6228427091539],
[19.019923180101493, -98.62287489566285],[19.019426167537492, -98.6239799658033],[19.01909144395283, -98.62516013779798],[19.018533569789643, -98.62622229253545],
[19.01849299705195, -98.62694112456855],[19.019243591116275, -98.62830368671746],[19.019750747335433, -98.62919418013162],[19.019659459330185, -98.63011686005473],
[19.019618886877918, -98.63087860733337],[19.020136185037668, -98.63175837191123],[19.02097805899266, -98.632090965837],[19.02212421792218, -98.63189784679251],
[19.024102084177514, -98.63043872507744],[19.02554236171496, -98.62930146843671],[19.0258770723203, -98.62851826341256],[19.026232067679466, -98.6269303956773],
[19.02672905989373, -98.62547127397141]])

pts2 = np.array([[19.024832367299116, -98.62688748111249],[19.024548368691026, -98.62624375101424],[19.023899227192743, -98.62615792033446],[19.02260093658879, -98.62590042829517],
[19.0217489278678, -98.62568585159576],[19.02101863120187, -98.6252996135368],[19.020754912182237, -98.62528888442091],[19.020572337215178, -98.62560002091598],
[19.02024775901759, -98.62611500499459],[19.020085469681103, -98.62684456577261],[19.0204100481956, -98.62774578791017],[19.020815770447378, -98.62856117936796],
[19.021262063780405, -98.62911907878645],[19.021262063780405, -98.62976280888472],[19.021434494983918, -98.63030997918734],[19.022022788299633, -98.63035289452722],
[19.022692222987843, -98.62996665646827],[19.023665941356825, -98.62932292637001],[19.024477368972605, -98.62816421219316],[19.024680225257438, -98.6276277704446]])


plt.figure(figsize = (8,8))
for lst in pts1, pts2:
    ######## level curve interpolation #######################
    pad = 3       
    lst = np.pad(lst, [(pad,pad), (0,0)], mode='wrap')  
    y,x = lst.T                                                      
    i = np.arange(0, len(lst))
    interp_i = np.linspace(pad, i.max() - pad + 1, 5 * (i.size - 2*pad))
    xi = interp1d(i, x, kind='cubic')(interp_i)
    yi = interp1d(i, y, kind='cubic')(interp_i)

    #grafico de la interpolación
    plt.plot(xi, yi, "k")
    
plt.title("level curves")
plt.xlabel("x")
plt.ylabel("y")
plt.show()