python基于站点经纬度绘制降水空间散点分布图

2744557306

导入库并读取数据

​​import numpy as npimport pandas as pdimport cartopy.crs as ccrsimport cartopy.feature as cfeatureimport matplotlib.pyplot as pltfrom matplotlib import colors, cmimport cmapsimport geocat.viz as gvfrom cartopy.io.shapereader import Readerdata=pd.read_csv('20190722.csv',dtype=np.float64,header=None,delimiter=',',encoding='gbk')lat=np.array(data[1])lon=np.array(data[2])rain=np.array(data[3])

设置colorbar刻度及区间色调

scales = [0.1, 10, 25, 50, 75, 100]cmap = cmaps.rainbowboundaries = [0, 0.1, 10, 25, 50, 75, 100, 150]norm = colors.BoundaryNorm(boundaries, cmap.N)mappable = cm.ScalarMappable(norm=norm, cmap=cmap)

. L# M) Q* p1 I1 H+ L设置散点标记的颜色区间

marker_colors = mappable.to_rgba(boundaries)sizes = np.geomspace(10, 250, len(boundaries))plt.figure(figsize=(9, 6))projection = ccrs.PlateCarree()ax = plt.axes(projection=projection)ax.set_extent([97, 109, 26, 34], crs=projection)

- Z2 j0 j. v9 I3 _! ]6 t添加四川地图

shap=Reader('SCmap.shp').geometries()sichuan = cfeature.ShapelyFeature(shap,crs=ccrs.PlateCarree(),edgecolor='k', facecolor='none')ax.add_feature(sichuan)

6 x( W9 Z/ M* H' ~3 |) x9 X设置x、y轴经纬度刻度

​

​gv.set_axes_limits_and_ticks(ax,xticks=np.linspace(97, 109, 5),yticks=np.linspace(26, 34, 5))gv.add_lat_lon_ticklabels(ax)gv.add_major_minor_ticks(ax,x_minor_per_major=1,y_minor_per_major=1,labelsize=12)# Remove ticks on the top and right sides of the plotax.tick_params(axis='both', which='both', top=False, right=False)

绘制不同降水区间散点图

masked_lon = np.where(rain < scales[0], lon, np.nan)masked_lat = np.where(rain < scales[0], lat, np.nan)plt.scatter(masked_lon,masked_lat,s=sizes[0],color=marker_colors[0],zorder=1)for x in range(1, len(scales)):    masked_lon = np.where(rain >= scales[x - 1], lon, np.nan)    masked_lon = np.where(rain < scales[x], masked_lon, np.nan)    masked_lat = np.where(rain >= scales[x - 1], lat, np.nan)    masked_lat = np.where(rain < scales[x], masked_lat, np.nan)    plt.scatter(masked_lon,masked_lat,s=sizes[x],color=marker_colors[x],zorder=1)masked_lon = np.where(rain >= scales[-1], lon, np.nan)masked_lat = np.where(rain >= scales[-1], lat, np.nan)plt.scatter(masked_lon,masked_lat,s=sizes[-1],color=marker_colors[-1],zorder=1)

考标记出某一站点

​plt.colorbar(mappable=mappable,ax=ax,orientation='horizontal',label='Rainfall Amount(mm)',             drawedges=True,format='%.2f',ticks=scales)plt.scatter(103.12,30.08,s=20)plt.annotate(r'$mingshan$', xy=(103.12,30.08),xytext=(4,-100),xycoords='data',textcoords='offset points',             fontsize=16,arrowprops=dict(arrowstyle='->',connectionstyle='arc3'))plt.savefig('test.png')
5 k# E9 n  }7 O* C! U# o8 t% i