Matplotlib底图+带不规则2d numpy数组的轮廓线 [英] Matplotlib basemap + contourf with irregular 2d numpy array

问题描述

使用从 40 公里 Lambert 等角圆锥投影中检索到的 2d numpy 数据数组，此不规则间隔坐标和数据的 matplotlib/底图图看起来不正确.

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.

代码:

来自mpl_toolkits.basemap的

 导入Basemap，cm导入matplotlib.pyplot作为plt将numpy导入为np无花果= 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, 461.50, 41.59, 41.56][ 41.56819153, 41.58806229, 41.60673141, 40.9545784, 40.98640823, 41.01704025, 41.04647827, 41.5919, 41.57827, 41.5919[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, 84,40.85, 40.85, 40.85[ 40.28666687, 40.31720352, 40.34654999, 40.37469864, 40.4016571, 40.42741776, 40.45198059, 40.55, 50.45, 50.45, 53247[ 40.5382843, 40.55686951, 39.90415192, 39.93583298, 39.96632004, 39.99561691, 40.0237236, 40.209, 40.096, 40.096, 7096][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, 39517456, 395, 395, 395.85[ 39.26268768, 39.29188919, 39.31990051, 39.34672546, 39.37236023, 39.39680481, 39.42006302, 32006302, 30051, 39.34.8, 39.8.[ 39.50118256, 38.8481102, 38.87963104, 38.90996933, 38.93911743, 38.96708298, 38.99386215, 359.707, 39.75, 359.07][ 39.0891037, 39.10994339, 39.12959671, 39.14805984, 38.49491501, 38.52638245, 38.55666733, 368.159, 38.159, 38.569[ 38.66596222, 38.69032669, 38.71350098, 38.73549271, 38.75629807, 38.77591324, 38.79434586, 35594, 28.76, 35594, 3594[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、-106.16702705066、-106.167027730、-106.167027730.1670273.59.59.59.59.59.59[-104.31240845, -103.84818268, -103.38375092, -102.91912079, -102.45429993, -101.98930359, -107.51248030359, -107.51248090, -107.51248090, -107.51248097,10,16,16,16,16,16,16.16.19[-105.66719818, -105.20523071, -104.74302673, -104.28059387, -103.81793976, -103.35508728, -102.8920386, -102.8920386, -102.89203640,95,940.95.95.940[-107.01062012, -106.55097961, -106.09107208, -105.63090515, -105.17050171, -104.70985413, -104.2489860,-104.248986,35,35,817,35.35.35.38.35.35.35.37[-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, -106.929519604, -106.92951960.59, -106.92954960.95,165.95.95.95.95.95.95.95.95.95.95.1879[-105.10173798, -104.64418793, -104.18641663, -103.7284317, -103.27025604, -102.81188202, -102.35333218.340,184,184,184,848.189.7027025604, -103.27025604, -102.81188202, -102.35333618.79[-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, -106.3579161010, -106.357916016, -106.35791610.30,59,506.30.59,506.30.35.30.40.35.30.350.350.40.30.35791022845, -107.26171875, -106.357916102[-104.54733276, -104.09413147, -103.64072418, -103.18712616, -102.7333374, -102.2793808, -101.825263796,160,160,160,160,160,160,160.7333376.73333276,-102.7333276,2016,18712616.[-105.86927032，-105.41823578，-104.96696472，-104.51548004，-104.06378174，-103.61187744，-103.15978241，-102.7075119，-102.25506592，-101.80245209]])数据= 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.]])无花果= plt.figure()m =底图(projection ='cyl'，分辨率 = '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)对于范围内的我(len(lats)):对于范围内的j(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:

解决方案

我将数据插值到常规网格上.例如，您可以使用

With 2d numpy arrays of data retrieved from a 40 km Lambert conformal conic projection, this matplotlib/basemap plot of irregularly-spaced coordinates and data does not look correct.

The values are correct but pcolormesh and contourf show discontinuities. With a square grid the discontinuities disappear.

The code:

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()

produces this image:

解决方案

I would interpolate the data on a regular grid. You can for instance use a LinearTriInterpolator.

So:

import matplotlib.tri as mtri

Then replace your line

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

with something like

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)

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