Matplotlib底图+带不规则2D Numpy数组的Contourf

Question

从40 km Lambert保形圆锥投影中检索到的2d numpy数据数组，此matplotlib / basemap图中不规则间隔的坐标和数据看起来不正确。

值是正确的，但pcolormesh和contourf显示不连续。 使用正方形网格，不连续性消失。

编码：

from mpl_toolkits.basemap import Basemap, cm
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(8,8))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
lats = np.array([
        [ 41.30340576,  41.33528519,  41.36596298,  41.39544296,  41.42372513,   41.45080566,  41.47668457,  41.50136566,  41.52484131,  41.54711914],
        [ 41.56819153,  41.58806229,  41.60673141,  40.9545784,   40.98640823,   41.01704025,  41.04647827,  41.07471466,  41.10175323,  41.12759399],
        [ 41.15223694,  41.17567825,  41.19792175,  41.21896362,  41.23880768,   41.25744629,  40.60508347,  40.63686371,  40.66744995,  40.69684219],
        [ 40.72503662,  40.75203323,  40.77783585,  40.80244064,  40.82584381,   40.84805298,  40.86906433,  40.88887405,  40.90748978,  40.25493622],
        [ 40.28666687,  40.31720352,  40.34654999,  40.37469864,  40.4016571,   40.42741776,  40.45198059,  40.47535324,  40.49752426,  40.5185051 ],
        [ 40.5382843,   40.55686951,  39.90415192,  39.93583298,  39.96632004,   39.99561691,  40.0237236,   40.05063629,  40.07635498,  40.10087967],
        [ 40.12421417,  40.14635468,  40.16729736,  40.18704605,  40.20560074,   39.552742,  39.58436966,  39.61481094,  39.64405823,  39.67211914],
        [ 39.69898605,  39.72466278,  39.74915314,  39.77244568,  39.79454803,   39.81546021,  39.83517456,  39.85369873,  39.20072556,  39.2322998 ],
        [ 39.26268768,  39.29188919,  39.31990051,  39.34672546,  39.37236023,   39.39680481,  39.42006302,  39.44212723,  39.46300125,  39.4826889 ],
        [ 39.50118256,  38.8481102,   38.87963104,  38.90996933,  38.93911743,   38.96708298,  38.99386215,  39.01945496,  39.04385757,  39.06707382],
        [ 39.0891037,   39.10994339,  39.12959671,  39.14805984,  38.49491501,   38.52638245,  38.55666733,  38.58576584,  38.61368179,  38.64041519],
        [ 38.66596222,  38.69032669,  38.71350098,  38.73549271,  38.75629807,   38.77591324,  38.79434586,  38.14115524,  38.17256546,  38.20279694],
        [ 38.23184586,  38.25971222,  38.28639603,  38.31190109,  38.33621979,   38.35935593,  38.38130951,  38.40207672,  38.42165756,  38.44005585]])
lons = np.array([
        [-107.59836578, -107.13432312, -106.66999817, -106.20539093, -105.74052429, -105.27540588, -104.81004333, -104.3444519, -103.87863922, -103.41261292],
        [-102.94638824, -102.47998047, -102.01338959, -107.55528259, -107.09280396, -106.63005066, -106.16702271, -105.70373535, -105.24019623, -104.77642059],
        [-104.31240845, -103.84818268, -103.38375092, -102.91912079, -102.45429993, -101.98930359, -107.51248932, -107.05157471, -106.59037781, -106.12892151],
        [-105.66719818, -105.20523071, -104.74302673, -104.28059387, -103.81793976, -103.35508728, -102.89203644, -102.42880249, -101.96539307, -107.46998596],
        [-107.01062012, -106.55097961, -106.09107208, -105.63090515, -105.17050171, -104.70985413, -104.24898529, -103.78790283, -103.32661438, -102.86513519],
        [-102.4034729, -101.94163513, -107.42776489, -106.96994019, -106.51184082, -106.05348206, -105.59486389, -105.13600159, -104.6769104, -104.21759796],
        [-103.75806427, -103.29833984, -102.83841705, -102.37831879, -101.91804504, -107.38583374, -106.92954254, -106.47296906, -106.0161438, -105.55905914],
        [-105.10173798, -104.64418793, -104.18641663, -103.7284317, -103.27025604, -102.81188202, -102.35333252, -101.89461517, -107.34418488, -106.8894043 ],
        [-106.43435669, -105.97905731, -105.52349854, -105.06770325, -104.61168671, -104.15544891, -103.6989975, -103.24235535, -102.78552246, -102.3285141 ],
        [-101.87134552, -107.30281067, -106.84954071, -106.39600372, -105.94221497, -105.48817444, -105.03390503, -104.57939911, -104.12468719, -103.66976166],
        [-103.21464539, -102.75934601, -102.30387115, -101.84822845, -107.26171875, -106.80994415, -106.35791016, -105.90562439, -105.45308685, -105.00032043],
        [-104.54733276, -104.09413147, -103.64072418, -103.18712616, -102.7333374, -102.2793808, -101.82526398, -107.22089386, -106.77061462, -106.32006836],
        [-105.86927032, -105.41823578, -104.96696472, -104.51548004, -104.06378174, -103.61187744, -103.15978241, -102.7075119, -102.25506592, -101.80245209]])
data = np.array([
        [ 90.,  96.,  96.,  97.,  98.,  82.,  85.,  92.,  88.,  79.],
        [ 73.,  67.,  68.,  91.,  93.,  89.,  90.,  95.,  92.,  76.],
        [ 80.,  80.,  79.,  78.,  70.,  73.,  91.,  96.,  91.,  90.],
        [ 85.,  88.,  69.,  70.,  79.,  88.,  80.,  75.,  65.,  86.],
        [ 94.,  86.,  81.,  75.,  77.,  65.,  72.,  91.,  95.,  83.],
        [ 81.,  90.,  93.,  93.,  83.,  81.,  78.,  72.,  68.,  79.],
        [ 95.,  89.,  89.,  77.,  83.,  95.,  81.,  81.,  79.,  72.],
        [ 70.,  73.,  82.,  86.,  88.,  89.,  75.,  95.,  84.,  85.],
        [ 73.,  71.,  65.,  65.,  68.,  75.,  77.,  86.,  86.,  74.],
        [ 95.,  85.,  79.,  58.,  42.,  40.,  52.,  63.,  63.,  75.],
        [ 88.,  77.,  77.,  81.,  79.,  61.,  28.,  29.,  41.,  54.],
        [ 71.,  79.,  90.,  86.,  74.,  87.,  90.,  65.,  55.,  30.],
        [ 42.,  55.,  72.,  83.,  84.,  80.,  93.,  81.,  86.,  92.]])
fig = plt.figure()
m = Basemap(projection='cyl',
          resolution = 'c',
          llcrnrlon = lons.min(), llcrnrlat = lats.min(),
          urcrnrlon =lons.max(), urcrnrlat = lats.max())
m.drawcoastlines()
m.drawstates()
m.drawcountries()
cs = m.pcolormesh( lons, lats, data, shading='flat', latlon=True, vmin=0, vmax=100)
for i in range(len(lats)):
    for j in range(len(lats[i])):
        x,y = m(lons[i][j], lats[i][j])
        m.plot(x, y, 'bo', markersize=3, label=i)
plt.draw()

产生此图像：

Answer 1

我将数据插值到常规网格上。 例如，您可以使用LinearTriInterpolator 。

所以：

import matplotlib.tri as mtri

然后更换你的线

cs = m.pcolormesh( lons, lats, data, shading='flat', latlon=True, vmin=0, vmax=100)

用类似的东西

ngrid = 50
rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),
                  ngrid).reshape(ngrid, ngrid)
rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),
                  ngrid).reshape(ngrid, ngrid).T
tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(), lats.flatten()),
                                 data.flatten())
rdata = tli(rlons, rlats)
cs = m.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100)