Python气象数据处理与绘图：克里金(Kriging)插值与可视化
1、安装模块
1!pip install PyKrige
2!pip install plotnine
3!pip install openpyxl
2、导入模块
1import pandas as pd 2from pykrige.ok import OrdinaryKriging 3import plotnine 4from plotnine import * 5import geopandas as gpd 6import shapefile 7import matplotlib.pyplot as plt 8import cartopy.crs as ccrs 9import cartopy.io.shapereader as shpreader10import cmaps11from matplotlib.path import Path12from matplotlib.patches import PathPatch3、读取数据
1df = pd.read_excel('/home/mw/input/meiyu6520/meiyu_sh_2020.xlsx')
2df
1# 读取站点经度2lons = df['lon']3# 读取站点纬度4lats = df['lat']5# 读取梅雨量数据6data = df['meiyu']7# 生成经纬度网格点8grid_lon = np.linspace(120.8, 122.1,1300)9grid_lat = np.linspace(30.6, 31.9,1300)4、克里金(Kriging)插值
1OK = OrdinaryKriging(lons, lats, data, variogram_model='gaussian',nlags=6)
2z1, ss1 = OK.execute('grid', grid_lon, grid_lat)
3z1.shape
1(1300, 1300)转换成网格
1xgrid, ygrid = np.meshgrid(grid_lon, grid_lat)
将插值网格数据整理
1df_grid = pd.DataFrame(dict(long=xgrid.flatten(),lat=ygrid.flatten()))添加插值结果
1df_grid['Krig_gaussian'] = z1.flatten()
2df_grid
输出：
1long lat Krig_gaussian 20 121.000000 30.5 541.076134 31 121.020408 30.5 540.997891 42 121.040816 30.5 540.770766 53 121.061224 30.5 540.381399 64 121.081633 30.5 539.818047 7... ... ... ... 82495 121.918367 31.5 567.391506 92496 121.938776 31.5 562.730899102497 121.959184 31.5 558.418169112498 121.979592 31.5 554.460195122499 122.000000 31.5 550.857796132500 rows × 3 columns5、读取上海的行政区划
1sh = gpd.read_file('/home/mw/input/meiyu6520/Shanghai.shp')
2sh
输出：
1City District Province Code geometry 20 上海市 普陀区 上海市 310107 POLYGON ((121.35622 31.23362, 121.35418 31.237... 31 上海市 宝山区 上海市 230506 POLYGON ((121.48552 31.31156, 121.48541 31.311... 42 上海市 崇明区 上海市 310151 MULTIPOLYGON (((121.87022 31.29554, 121.86596 ... 53 上海市 奉贤区 上海市 310120 POLYGON ((121.56443 30.99643, 121.57047 30.998... 64 上海市 虹口区 上海市 310109 POLYGON ((121.46828 31.31520, 121.46831 31.316... 75 上海市 黄浦区 上海市 310101 POLYGON ((121.48781 31.24419, 121.49483 31.242... 86 上海市 嘉定区 上海市 310114 POLYGON ((121.33689 31.29506, 121.33650 31.294... 97 上海市 金山区 上海市 310116 POLYGON ((121.00206 30.95104, 121.00764 30.947...108 上海市 静安区 上海市 310106 POLYGON ((121.46808 31.32032, 121.46809 31.320...119 上海市 闵行区 上海市 310112 POLYGON ((121.33689 31.23674, 121.33835 31.237...1210 上海市 浦东新区 上海市 310115 MULTIPOLYGON (((121.97077 31.15756, 121.96568 ...1311 上海市 青浦区 上海市 310118 POLYGON ((121.31900 31.15867, 121.31953 31.157...1412 上海市 松江区 上海市 310117 POLYGON ((121.02906 30.94388, 121.02804 30.943...1513 上海市 徐汇区 上海市 310104 POLYGON ((121.45800 31.21929, 121.45807 31.219...1614 上海市 杨浦区 上海市 310110 POLYGON ((121.52288 31.34289, 121.52549 31.346...1715 上海市 长宁区 上海市 310105 POLYGON ((121.43938 31.21444, 121.43946 31.214...绘图：
1sh.plot()
6、插值结果可视化
1fig = plt.figure(figsize=(12, 8)) 2ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) 3ax.set_extent([120.8, 122.1, 30.6, 31.9], crs=ccrs.PlateCarree()) 4 5province = shpreader.Reader('/home/mw/input/meiyu6520/Shanghai.shp') 6ax.add_geometries(province.geometries(), crs=ccrs.PlateCarree(), linewidths=0.5,edgecolor='k',facecolor='none') 7 8cf = ax.contourf(xgrid, ygrid, z1, levels=np.linspace(0,800,21), cmap=cmaps.MPL_rainbow, transform=ccrs.PlateCarree()) 910def shp2clip(originfig, ax, shpfile):11 sf = shapefile.Reader(shpfile)12 vertices = []13 codes = []14 for shape_rec in sf.shapeRecords():15 pts = shape_rec.shape.points16 prt = list(shape_rec.shape.parts) + [len(pts)]17 for i in range(len(prt) - 1):18 for j in range(prt[i], prt[i + 1]):19 vertices.append((pts[j][0], pts[j][1]))20 codes += [Path.MOVETO]21 codes += [Path.LINETO] * (prt[i + 1] - prt[i] - 2)22 codes += [Path.CLOSEPOLY]23 clip = Path(vertices, codes)24 clip = PathPatch(clip, transform=ax.transData)25 for contour in originfig.collections:26 contour.set_clip_path(clip)27 return contour2829shp2clip(cf, ax, '/home/mw/input/meiyu6520/Shanghai.shp')3031cb = plt.colorbar(cf)32cb.set_label('Rainfall(mm)',fontsize=15)3334plt.show() 赞 (0)