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  • eof

    import eofs
    from eofs.standard import Eof
    import pandas as pd
    import numpy as np
    import glob
    import datetime
    from matplotlib import pyplot as plt
    import copy
    import re
    import time
    from pylab import *
    import matplotlib.dates as mdate
    import matplotlib.patches as patches
    import matplotlib.ticker as ticker
    import xarray as ax
    import copy
    import geopandas as gpd
    from pykrige.ok import OrdinaryKriging
    from pykrige.kriging_tools import write_asc_grid
    import pykrige.kriging_tools as kt
    from matplotlib.colors import LinearSegmentedColormap
    from matplotlib.patches import Path, PathPatch
    from shapely.geometry import LineString
    df = pd.read_csv(r'.6月xq表层.csv')
    fg = gpd.read_file(r'.覆盖.shp')
    fg1 = fg.to_crs(crs = 'EPSG:4326')
    bj = gpd.read_file(r'.xinqiao1.shp')
    bj1 = bj.to_crs(crs = 'EPSG:4326')
    bj2 = bj1.boundary[0]
    bj3 = bj2.coords[:]
    bj4 = list(bj3)
    x = []
    y =[]
    for i in range(len(bj4)):
    g = bj4[i][0]
    c = bj4[i][1]
    x.append(g)
    y.append(c)
    lons = np.array(x)
    lats = np.array(y)
    lons_1 = df['经度']
    lats_1 = df['纬度']
    a = df.iloc[:,9:22].T
    pd.set_option('display.max_rows', 10)
    pd.set_option('display.max_columns', 4)
    # print(a)
    # 预处理
    c = a.mean(axis=1)
    td = a.std(axis=1)
    # print(td)
    for i in range(len(a)):
    for j in range(0,34):
    # print(a.iloc[i,j])
    a.iloc[i,j] = (a.iloc[i,j]-c[i])/td[i] # 列标准化
    # print(a)
    a1 = copy.deepcopy(np.array(a))

    # a2 = pd.DataFrame(a1)
    # print(a2)
    # a2.to_csv(r'.iaozhunc.csv',encoding='utf-8')

    # eofs

    solver = Eof(a1,ddof=0,center=False) # center 意味着要减去所有的平均,TMD
    eofs = solver.eofs(neofs=3, eofscaling=0)
    weights = solver.eigenvalues(3) / sum(solver.eigenvalues()) # 贡献量
    print(weights)
    pt = pd.DataFrame(eofs.T,columns=['fir','sec','thi']) # 模态
    print(pt)
    z1 = pt['fir']
    z2 = pt['sec']
    z3 = pt['thi']
    pcs = solver.pcs(npcs =3)
    # print(pcs)
    tc = pd.DataFrame(pcs,columns=['fir','sec','thi']) # 时间、权重
    tc_fir = tc['fir']
    tc_sec = tc['sec']
    tc_thi = tc['thi']
    grid_space = 0.001
    grid_lon = np.arange(np.amin(lons), np.amax(lons), grid_space)
    grid_lat = np.arange(np.amin(lats-0.0001), np.amax(lats), grid_space)
    OK1 = OrdinaryKriging(lons_1, lats_1,z1, variogram_model='spherical', verbose=False, enable_plotting=False,nlags=20)
    OK2 = OrdinaryKriging(lons_1, lats_1,z2, variogram_model='spherical', verbose=False, enable_plotting=False,nlags=20)
    OK3 = OrdinaryKriging(lons_1, lats_1,z3, variogram_model='spherical', verbose=False, enable_plotting=False,nlags=20)
    z_1,sss1 =OK1.execute('grid', grid_lon, grid_lat)
    z_2,sss2 =OK2.execute('grid', grid_lon, grid_lat)
    z_3,sss3 =OK3.execute('grid', grid_lon, grid_lat)
    X, Y = np.meshgrid(grid_lon, grid_lat)
    fig = plt.figure(figsize=(16,12))
    subplots_adjust(left=0.1,bottom=0.1,top=0.95,right=0.95,hspace=0.15,wspace=0.0005)
    # grid1 =plt.GridSpec(3,2,wspace=0.5,hspace=0.1)
    ax = fig.add_subplot(321)
    ax.plot(tc.index,tc_fir,color = 'black')
    ax1 = fig.add_subplot(322)
    cs =ax1.contourf(X,Y,z_1,200,extend='both',cmap='jet')
    cbar = fig.colorbar(cs,ax =ax1,fraction = 0.09)
    fg1.geometry.plot(facecolor='white',edgecolor='white',alpha=1,ax =ax1)
    ax2 = fig.add_subplot(323)
    ax2.plot(tc.index,tc_sec,color = 'black')
    ax3 = fig.add_subplot(324)
    cs1 =ax3.contourf(X,Y,z_2,200,extend='both',cmap='jet')
    cbar1 = fig.colorbar(cs,ax =ax3,fraction = 0.09)
    fg1.geometry.plot(facecolor='white',edgecolor='white',alpha=1,ax =ax3)
    ax4 = fig.add_subplot(325)
    ax4.plot(tc.index,tc_thi,color = 'black')
    ax5= fig.add_subplot(326)
    cs2 =ax5.contourf(X,Y,z_3,200,extend='both',cmap='jet')
    cbar2 = fig.colorbar(cs,ax =ax5,fraction = 0.09)
    fg1.geometry.plot(facecolor='white',edgecolor='white',alpha=1,ax =ax5)

    plt.show()

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  • 原文地址:https://www.cnblogs.com/chenyun-delft3d/p/14274234.html
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