如何解决Python：根据条件为多边形着色并赋予多边形恒定宽度
抱歉我的英语一般，如果我使用了不正确的技术术语，那只是一种爱好。
我用 MatPlotLib 创建了一个 3d 图来显示来自 Garmin 设备的 GPS 数据。根据轨道的坡度，有多个不同颜色的多边形。我对结果很满意，但在我看来，我这样做的方式效率不高：创建一个多边形，用嵌套循环将其拆分为多个多边形，从每个多边形的颜色图中获取特定颜色， ...
有没有更好的方法可以根据条件在 3d 绘图中的线下设置彩色窗帘？
我还想给线下的多边形（或对象）一些恒定的宽度。现在它只是房间里的一张卷曲的纸。
带有一些数据的代码如下。
感谢您的建议！
### MatPlotLib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from matplotlib import cm
### NumPy
import numpy as np
###############################################################################
###############################################################################
### Function 3D Plot
def plot3d():
### Data
longitude_values = [11.576064582914114,11.576026361435652,11.576014794409275,11.576004149392247,11.576001718640327,11.576027199625969,11.576056201010942,11.576067600399256,11.576094757765532,11.57611663453281,11.576139349490404,11.57617086544633,11.576194670051336,11.576198861002922,11.576215121895075,11.576283434405923,11.576311262324452,11.576398182660341,11.57647730782628,11.576566155999899,11.5767318662256,11.576855918392539,11.576994303613901,11.577197313308716,11.57743594609201,11.577681535854936,11.577779268845916,11.577828470617533,11.578180091455579,11.578272627666593,11.578320823609829,11.578463232144713,11.578651322051883,11.578963128849864,11.579086510464549,11.579212574288249,11.579246940091252,11.579429917037487,11.579581461846828,11.579634770751,11.579778268933296,11.579825794324279,11.580003574490547,11.580156711861491,11.580243296921253,11.580299204215407,11.580498274415731,11.580694830045104,11.580774206668139,11.581002613529563,11.581115433946252,11.581309894099832,11.58138264901936,11.581614827737212,11.581870140507817,11.582055632025003,11.58235888928175,11.582465004175901,11.582796508446336,11.582908742129803,11.583242090418935,11.583574851974845,11.583913648501039,11.584140378981829,11.584253534674644,11.584567939862609,11.584863569587469,11.58501603640616,11.585076469928026,11.585230696946383,11.585316862910986,11.585504114627838,11.58591315150261,11.586121441796422,11.586430566385388,11.586537770926952,11.586651345714927,11.586767183616757,11.587000954896212,11.58712257631123,11.587248807772994,11.587378475815058,11.587510323151946,11.587645690888166,11.587918773293495,11.58805238083005,11.588179199025035,11.588295456022024,11.588397463783622,11.588555546477437,11.588752688840032,11.5888268686831,11.588897863402963,11.588967936113477,11.589034656062722,11.589296171441674,11.58970738761127,11.590109383687377,11.59031230956316,11.590717826038599]
latitude_values = [50.813197530806065,50.81328931264579,50.81333616748452,50.81352811306715,50.81367521546781,50.81386431120336,50.81405625678599,50.814157761633396,50.81435976549983,50.814510472118855,50.814800234511495,50.815149592235684,50.815400294959545,50.81545150838792,50.81555485725403,50.815920643508434,50.81602206453681,50.81627863459289,50.81647929735482,50.816682474687696,50.81698715686798,50.81719318404794,50.817399797961116,50.817658212035894,50.817910423502326,50.818164898082614,50.81826958805323,50.81832197494805,50.81869128160179,50.818797228857875,50.81885020248592,50.819008788093925,50.81922546029091,50.81962091848254,50.81979132257402,50.81996449269354,50.82002593204379,50.82036984153092,50.82065708935261,50.820768317207694,50.821055732667446,50.82117106765509,50.821617655456066,50.82200339064002,50.82222467288375,50.82232089713216,50.82287737168372,50.823250114917755,50.82335136830807,50.82357818260789,50.82365839742124,50.82376576960087,50.823795190081,50.823875991627574,50.82394262775779,50.82397263497114,50.82399501465261,50.82399694249034,50.82397900521755,50.823965510353446,50.82390231080353,50.82382595166564,50.82374590449035,50.82369200885296,50.8236638456583,50.82357415929437,50.82347131334245,50.82339361310005,50.82337877713144,50.823350781574845,50.823336532339454,50.82330359145999,50.823213988915086,50.82316478714347,50.823096726089716,50.82307283766568,50.8230491168797,50.823029754683375,50.82298390567303,50.822960855439305,50.82293713465333,50.822912994772196,50.82288592122495,50.82285641692579,50.82280000671744,50.82276840694249,50.82273077219725,50.82268391735852,50.822632955387235,50.82253513857722,50.82242164760828,50.82242097705603,50.822448721155524,50.82249356433749,50.82254729233682,50.8227676525712,50.82304618321359,50.82331448793411,50.823451951146126,50.82371891476214]
altitude_values = [176.60000000000002,177.39999999999998,177.79999999999995,179.0,179.39999999999998,180.60000000000002,181.0,181.20000000000005,181.79999999999995,182.60000000000002,184.0,184.39999999999998,184.60000000000002,184.79999999999995,185.60000000000002,186.20000000000005,186.39999999999998,186.60000000000002,185.79999999999995,185.20000000000005,185.0,184.20000000000005,183.39999999999998,183.0,183.60000000000002,186.0,182.39999999999998,180.39999999999998,180.20000000000005,180.0,179.79999999999995,178.39999999999998,178.20000000000005,178.0,177.0,176.79999999999995,176.60000000000002,176.20000000000005,175.79999999999995,174.79999999999995,174.0,173.39999999999998,172.39999999999998,171.0,168.79999999999995,165.39999999999998,164.0,162.60000000000002,162.0,161.0,159.20000000000005,158.60000000000002,158.39999999999998,158.60000000000002]
slope_values = [4.98,6.2,6.16,6.76,5.11,4.48,3.26,3.04,2.08,1.47,1.66,1.96,2.43,2.81,2.23,2.42,1.25,1.35,0.97,1.24,0.56,0.28,0.0,-0.4,-0.8,-0.39,-0.42,-0.9,-1.13,-1.14,-1.68,-1.03,-1.17,-0.63,0.4,0.53,0.13,0.21,0.36,0.38,1.13,0.72,1.76,2.25,1.52,1.37,0.6,-0.55,-1.86,-2.65,-3.17,-2.38,-3.08,-4.27,-5.21,-4.93,-2.96,-1.21,-0.46,-0.68,-1.35,-3.47,-4.22,-3.87,-2.64,-3.3,-3.21,-2.76,-2.68,-3.31,-4.3,-5.56,-6.24,-8.39,-11.78,-15.3,-18.2,-12.22,-13.44,-13.14,-15.85,-10.25,-5.26,-3.04,-2.24,-0.65]
### Normal Zero Polygon
h = 0
### Position Data for Polygon
xs = longitude_values
ys = latitude_values
zs = altitude_values
### Polygon
polygon = []
for k in range(0,len(xs) - 1):
x = [xs[k],xs[k+1],xs[k]]
y = [ys[k],ys[k+1],ys[k]]
z = [zs[k],zs[k+1],h,h]
polygon.append(list(zip(x,y,z)))
### Round Slope Values
slope_values = [int(round(value,0)) for value in slope_values]
### Number of Colors,Color Map,Interval for Color Map
num_colors = 31
cmap = cm.get_cmap("jet")
min_slope_col = -15
max_slope_col = 15
### Slope
slopes = list(np.linspace(min_slope_col,max_slope_col,num = num_colors))
### List Splited Polygons
list_poly_slopes = [[] for x in range(0,num_colors)]
for i,slo in enumerate(slopes):
for index,percent in enumerate(slope_values):
if percent == slo:
list_poly_slopes[i].append(index)
elif percent < min_slope_col:
list_poly_slopes[0].append(index)
elif percent > max_slope_col:
list_poly_slopes[-1].append(index)
### List with Colors
list_poly_colors = []
for item in np.linspace(0,1,num = num_colors):
rgba = cmap(item)
color = [rgba[0],rgba[1],rgba[2]]
list_poly_colors.append(color)
### 3D Plot
fig = plt.figure()
ax = Axes3D(fig)
### Polygons
for poly_slope,poly_color in zip(list_poly_slopes,list_poly_colors):
if len(poly_slope) != 0:
ax.add_collection3d(Poly3DCollection([element for index,element in enumerate(polygon) if index in poly_slope],facecolors = poly_color,alpha = 0.5))
else:
pass
### Line on Top of Polygon
ax.plot(xs,ys,zs,color = "black",alpha = 1)
### X,Y,Z Limits
ax.set_xlim([0.9999 * min(xs),1.0001 * max(xs)])
ax.set_ylim([0.9999 * min(ys),1.0001 * max(ys)])
ax.set_zlim([min(zs),max(zs)])
### Axes and Grid Off
plt.axis("off")
plt.grid(b = None)
### Zoom
ax.dist = 50
### Plot Show
plt.show()
###############################################################################
###############################################################################
### Plot
plot3d()
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