Matplotlib basemap + contour with 2d numpy irregular matrix
With 2d numpy arrays of data derived from a 40 km long Lambert conformal cone, this matplotlib / basemap plot with irregularly spaced coordinates and data looks wrong.
The values are correct, but pcolormesh and contourf show breaks. With a square grid, the gaps disappear.
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()
creates this image:
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1 answer
I would interpolate the data on a regular grid. For example, you can use LinearTriInterpolator .
So:
import matplotlib.tri as mtri
Then replace the 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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