Question

这是一个示例图，如图所示。

该图显示了欧洲部分地区的卫星SO2柱数据。

由于卫星和经度之间的差异，符合卫星扫描原理的网格网络与经度并不平行。

我不知道是否可以在matplotlib.basemap中使用pcolor或pcolormesh绘制这种网格网络。所以，我在这里发表我的问题。

Answer 1

我偶然发现了这个问题，因为我也在寻找一种方法，使用matplotlib和basemap在地图上绘制网格化卫星测量值。
我不确定我的想法是否与你的问题相关，因为我的像素只能假设非常有限的离散数值（4），但我还是决定回答，也是为了找出你最终是否找到了有效的解决方案。我所做的是通过使用方法Polygon将每个单个像素直接绘制为地图上的多边形。我将alpha值设置为基础物理测量的函数。就我而言 - 一个云面具情节 - 这个策略运作得很好这是为每个要绘制的像素调用的函数：

def draw_cloud_pixel(lats, lons, index, mapplot):
    """Draw a pixel on the map. The fill color alpha level depends on the cloud index, 
    ranging from 0.1 (almost fully transparent) for confidently clear pixels to 1 (fully opaque)
    for confidently cloudy pixels.

    Keyword arguments:
    lats -- Array of latitude values for the pixel 4 corner points (numpy array)
    lons -- Array of longitudes values for the pixel 4 corner points (numpy array)
    index -- Cloud mask index for given pixel: 
        0: confidently_cloudy
        1: probably_cloudy
        2: probably_clear
        3: confidently_clear
    mapplot -- Map object for coordinate transformation

    Returns:
    None
    """
    x, y = mapplot(lons, lats)
    xy = zip(x,y)
    poly = Polygon(xy, facecolor='white', alpha=1-0.3*index)
    plt.gca().add_patch(poly)

在我的主要绘图程序中，我然后为所选区域中的每个像素调用draw_cloud_pixel函数：

# draw plot, each pixel at the time
for scanline in xrange(select_cp_lat.shape[0]):
    for pixel in xrange(select_cp_lat.shape[1]):
        draw_cloud_pixel(select_cp_lat[scanline, pixel,:], 
                         select_cp_lon[scanline, pixel,:], 
                         cloud_mask[scanline, pixel], 
                         mapplot)

我得到像这样的情节：

Answer 2

查看此页面中的不同示例：http://www.uvm.edu/~jbagrow/dsv/heatmap_basemap.html

示例的主要思想是在pcolormesh上绘制basemap：

import csv
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

# load earthquake epicenters:
# http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/1.0_week.csv
lats, lons = [], []
with open('earthquake_data.csv') as f:
    reader = csv.reader(f)
    next(reader) # Ignore the header row.
    for row in reader:
        lat = float(row[1])
        lon = float(row[2])
        # filter lat,lons to (approximate) map view:
        if -130 <= lon <= -100 and 25 <= lat <= 55:
            lats.append( lat )
            lons.append( lon )

# Use orthographic projection centered on California with corners
# defined by number of meters from center position:
m  = Basemap(projection='ortho',lon_0=-119,lat_0=37,resolution='l',\
             llcrnrx=-1000*1000,llcrnry=-1000*1000,
             urcrnrx=+1150*1000,urcrnry=+1700*1000)
m.drawcoastlines()
m.drawcountries()
m.drawstates()

# ######################################################################
# bin the epicenters (adapted from 
# http://stackoverflow.com/questions/11507575/basemap-and-density-plots)

# compute appropriate bins to chop up the data:
db = 1 # bin padding
lon_bins = np.linspace(min(lons)-db, max(lons)+db, 10+1) # 10 bins
lat_bins = np.linspace(min(lats)-db, max(lats)+db, 13+1) # 13 bins

density, _, _ = np.histogram2d(lats, lons, [lat_bins, lon_bins])

# Turn the lon/lat of the bins into 2 dimensional arrays ready
# for conversion into projected coordinates
lon_bins_2d, lat_bins_2d = np.meshgrid(lon_bins, lat_bins)

# convert the bin mesh to map coordinates:
xs, ys = m(lon_bins_2d, lat_bins_2d) # will be plotted using pcolormesh
# ######################################################################

# define custom colormap, white -> nicered, #E6072A = RGB(0.9,0.03,0.16)
cdict = {'red':  ( (0.0,  1.0,  1.0),
                   (1.0,  0.9,  1.0) ),
         'green':( (0.0,  1.0,  1.0),
                   (1.0,  0.03, 0.0) ),
         'blue': ( (0.0,  1.0,  1.0),
                   (1.0,  0.16, 0.0) ) }
custom_map = LinearSegmentedColormap('custom_map', cdict)
plt.register_cmap(cmap=custom_map)

# add histogram squares and a corresponding colorbar to the map:
plt.pcolormesh(xs, ys, density, cmap="custom_map")

cbar = plt.colorbar(orientation='horizontal', shrink=0.625, aspect=20, fraction=0.2,pad=0.02)
cbar.set_label('Number of earthquakes',size=18)
#plt.clim([0,100])

# translucent blue scatter plot of epicenters above histogram:    
x,y = m(lons, lats)
m.plot(x, y, 'o', markersize=5,zorder=6, markerfacecolor='#424FA4',markeredgecolor="none", alpha=0.33)


# http://matplotlib.org/basemap/api/basemap_api.html#mpl_toolkits.basemap.Basemap.drawmapscale
m.drawmapscale(-119-6, 37-7.2, -119-6, 37-7.2, 500, barstyle='fancy', yoffset=20000)

# make image bigger:
plt.gcf().set_size_inches(15,15)

plt.show()

底图中的倾斜网格网络绘图

2 个答案: