Python插值未测量高度的天气数据

import pandas as pd import numpy as np import scipy.interpolate df = pd.read_fwf(data, widths=[7] * 11) df = df.drop([0, 1]) df = df.drop(columns=['DWPT', 'RELH', 'MIXR', 'THTA', 'THTE', 'THTV']) #Not needed data, only: pressure, temperature, windspeed and wind direction are needed. HGHT = np.array(df['HGHT']) #Height PRES = np.array(df['PRES']) #Pressure TEMP = np.array(df['TEMP']) #Temperature DRCT = np.array(df['DRCT']) #Wind direction SKNT = np.array(df['SKNT']) #Wind speed Pressure = scipy.interpolate.interp1d(HGHT, PRES) #I only did pressure for the test. >>> Pressure(203) #Insert height as argument. array(1000.)

data = ''' PRES HGHT TEMP DWPT RELH MIXR DRCT SKNT THTA THTE THTV 1022.0 11 26.6 14.6 48 10.32 95 8 297.9 328.2 299.7 1014.0 81 23.6 12.6 50 9.11 111 9 295.6 322.1 297.2 1012.0 98 23.5 12.5 50 9.05 115 10 295.6 322.0 297.2 1000.0 203 22.6 11.6 50 8.65 120 14 295.8 321.0 297.3 982.0 361 21.4 10.4 49 8.12 134 16 296.1 319.9 297.5 979.0 387 21.8 8.8 43 7.31 136 16 296.7 318.3 298.1 968.0 485 21.3 7.4 41 6.70 145 17 297.2 317.1 298.4 956.0 593 20.8 5.8 38 6.08 146 16 297.8 315.9 298.9 925.0 876 18.4 5.4 42 6.11 150 14 298.1 316.4 299.2 850.0 1593 11.8 3.8 58 5.95 145 14 298.5 316.3 299.6 832.0 1772 10.1 3.6 64 6.01 140 14 298.5 316.5 299.6 827.0 1822 9.6 3.6 66 6.03 141 13 298.5 316.6 299.6 814.0 1953 8.8 -0.2 53 4.65 145 12 299.0 313.2 299.9 802.0 2076 9.8 -13.2 18 1.73 148 12 301.4 307.0 301.7 775.0 2359 8.7 -18.5 13 1.15 155 10 303.1 306.9 303.3 751.0 2619 7.6 -23.4 9 0.78 178 10 304.7 307.4 304.8 728.0 2873 6.1 -19.6 14 1.12 200 10 305.8 309.5 306.0 718.0 2986 5.4 -17.9 17 1.31 190 10 306.2 310.6 306.5 700.0 3194 4.2 -14.8 24 1.74 180 8 307.1 312.9 307.4 657.0 3704 1.6 -16.9 24 1.55 210 12 309.8 315.0 310.1 616.0 4223 -1.1 -19.1 24 1.38 197 10 312.4 317.2 312.7 594.0 4510 -3.5 -17.5 33 1.64 190 10 312.9 318.5 313.2 573.0 4795 -5.9 -15.9 45 1.94 203 10 313.3 319.9 313.7 561.0 4960 -7.3 -18.9 39 1.54 210 10 313.6 318.8 313.8 558.0 5002 -7.7 -19.7 38 1.45 209 10 313.6 318.6 313.9 551.0 5100 -7.3 -35.3 9 0.35 207 10 315.2 316.5 315.3 550.0 5114 -7.3 -34.3 10 0.38 207 10 315.4 316.8 315.4 529.0 5416 -8.6 -37.5 8 0.29 200 10 317.3 318.4 317.4 528.0 5431 -8.7 -37.7 8 0.28 200 10 317.4 318.5 317.4 500.0 5850 -12.1 -36.1 12 0.35 210 12 318.2 319.6 318.3 489.0 6019 -13.3 -37.3 11 0.32 215 12 318.8 320.0 318.8 479.0 6175 -14.4 -38.4 11 0.29 205 10 319.3 320.4 319.4 456.0 6548 -17.1 -41.1 11 0.23 240 10 320.5 321.4 320.5 449.0 6665 -17.9 -41.9 10 0.22 245 8 320.9 321.7 320.9 443.0 6766 -17.9 -30.9 31 0.66 249 7 322.1 324.6 322.2 427.0 7038 -20.6 -31.0 39 0.68 260 4 322.1 324.6 322.2 400.0 7520 -25.3 -31.3 57 0.70 260 6 322.0 324.6 322.2 391.0 7685 -26.9 -31.9 63 0.68 272 7 322.0 324.5 322.2 390.0 7703 -27.1 -32.1 62 0.67 273 7 322.0 324.5 322.1 384.0 7815 -27.3 -42.3 23 0.24 281 8 323.2 324.1 323.2 366.0 8158 -29.3 -47.3 16 0.15 305 10 325.0 325.6 325.1 355.0 8376 -30.5 -50.5 12 0.11 311 9 326.2 326.6 326.2 300.0 9550 -39.1 -51.1 27 0.12 345 8 330.1 330.6 330.2 262.0 10456 -47.0 -56.8 32 0.07 325 10 331.6 331.9 331.6 250.0 10770 -49.7 -58.7 34 0.06 335 10 332.1 332.3 332.1 238.0 11089 -52.3 -63.3 25 0.03 328 9 332.8 333.0 332.8 220.0 11592 -56.5 -62.5 47 0.04 318 7 333.9 334.1 333.9 208.0 11944 -59.1 -65.1 46 0.03 310 6 335.3 335.4 335.3 200.0 12190 -60.9 -66.9 45 0.02 290 10 336.2 336.3 336.2 197.0 12283 -61.5 -67.3 46 0.02 285 12 336.6 336.7 336.6 188.0 12570 -63.5 -68.4 51 0.02 295 16 338.0 338.1 338.0 181.0 12805 -61.0 -72.4 21 0.01 290 19 345.6 345.7 345.6 176.0 12978 -59.2 -75.3 11 0.01 300 10 351.4 351.4 351.4 174.0 13048 -58.5 -76.5 8 0.01 273 6 353.8 353.8 353.8 173.0 13085 -58.4 -76.7 8 0.01 260 4 354.5 354.5 354.5 169.0 13231 -58.2 -77.6 7 0.01 240 10 357.3 357.3 357.3 162.0 13497 -57.7 -79.2 5 0.00 260 19 362.4 362.4 362.4 159.0 13614 -57.5 -79.9 4 0.00 270 17 364.7 364.7 364.7 156.0 13734 -57.3 -80.7 4 0.00 250 10 367.1 367.1 367.1 151.0 13938 -56.9 -81.9 3 0.00 254 11 371.1 371.2 371.1 150.0 13980 -56.9 -81.9 3 0.00 255 12 371.8 371.9 371.8 142.0 14329 -56.7 -82.3 3 0.00 240 16 378.0 378.0 378.0 135.0 14650 -56.6 -82.6 2 0.00 280 21 383.8 383.8 383.8 132.0 14793 -56.5 -82.8 2 0.00 280 16 386.4 386.4 386.4 129.0 14940 -56.5 -82.9 2 0.00 245 16 389.0 389.0 389.0 127.0 15039 -56.4 -83.0 2 0.00 245 21 390.8 390.9 390.8 122.0 15295 -56.3 -83.3 2 0.00 275 27 395.6 395.6 395.6 119.0 15453 -56.2 -83.5 2 0.00 295 21 398.5 398.6 398.5 117.0 15561 -56.2 -83.6 2 0.00 295 14 400.6 400.6 400.6 115.0 15671 -56.1 -83.7 2 0.00 255 8 402.6 402.6 402.6 114.0 15726 -56.1 -83.8 2 0.00 240 10 403.7 403.7 403.7 109.0 16012 -56.0 -84.1 2 0.00 240 12 409.1 409.2 409.1 107.0 16129 -55.9 -84.2 2 0.00 200 10 411.4 411.4 411.4 100.0 16560 -55.7 -84.7 2 0.00 230 17 419.8 419.9 419.8 98.0 16689 -55.6 -84.7 2 0.00 250 16 422.4 422.4 422.4 95.0 16888 -55.5 -84.7 2 0.00 235 19 426.4 426.4 426.4 92.0 17093 -55.4 -84.7 1 0.00 250 23 430.5 430.5 430.5 87.0 17450 -55.2 -84.6 1 0.00 270 14 437.8 437.8 437.8 85.0 17599 -55.1 -84.6 1 0.00 245 6 440.9 440.9 440.9 82.0 17829 -55.0 -84.6 1 0.00 250 10 445.7 445.7 445.7 78.0 18148 -54.9 -84.6 1 0.00 210 10 452.4 452.5 452.4 75.0 18399 -54.7 -84.5 1 0.00 240 17 457.8 457.9 457.8 74.0 18485 -54.7 -84.5 1 0.00 265 12 459.7 459.7 459.7 70.0 18840 -54.5 -84.5 1 0.00 255 10 467.4 467.5 467.4 69.0 18932 -54.0 -84.3 1 0.00 265 10 470.4 470.4 470.4 66.0 19217 -52.5 -83.5 1 0.01 0 0 479.6 479.6 479.6 65.9 19227 -52.5 -83.5 1 0.01 358 0 479.9 480.0 479.9 59.0 19937 -53.1 -83.7 1 0.01 185 10 493.9 493.9 493.9 57.0 20159 -53.3 -83.8 1 0.01 205 17 498.3 498.4 498.3 55.0 20388 -53.5 -83.9 1 0.01 240 12 503.0 503.0 503.0 52.0 20748 -53.9 -84.0 1 0.01 200 4 510.3 510.4 510.3 51.0 20873 -54.0 -84.1 1 0.01 200 10 512.9 513.0 512.9 50.0 21000 -54.1 -84.1 1 0.01 215 12 515.5 515.6 515.5 42.6 22025 -53.9 -84.9 1 0.01 100 10 540.2 540.2 540.2 40.0 22430 -52.1 -83.8 1 0.01 55 10 554.4 554.5 554.4 39.0 22593 -51.4 -83.4 1 0.01 105 12 560.2 560.3 560.2 38.8 22626 -51.3 -83.3 1 0.01 109 12 561.4 561.5 561.4 38.0 22761 -51.4 -83.3 1 0.01 125 14 564.6 564.7 564.6 37.0 22934 -51.4 -83.4 1 0.01 155 12 568.8 568.9 568.8 36.0 23111 -51.5 -83.5 1 0.01 165 10 573.1 573.2 573.1 33.0 23674 -51.7 -83.7 1 0.01 120 10 587.0 587.0 587.0 32.0 23873 -51.8 -83.8 1 0.01 120 10 591.9 592.0 592.0 30.0 24290 -51.9 -83.9 1 0.01 5 2 602.5 602.7 602.5 28.0 24737 -52.0 -83.8 1 0.01 45 10 614.3 614.4 614.3 27.0 24973 -52.0 -83.7 1 0.01 85 12 620.6 620.7 620.6 26.0 25217 -52.1 -83.6 1 0.01 100 14 627.1 627.3 627.2 23.0 26012 -52.2 -83.4 1 0.02 115 10 649.0 649.2 649.0 22.2 26241 -52.3 -83.3 1 0.02 99 7 655.5 655.7 655.5 21.0 26603 -51.1 -83.2 1 0.02 75 4 669.5 669.7 669.5 20.0 26920 -50.1 -83.1 1 0.02 75 6 682.1 682.3 682.1 19.0 27262 -49.4 -82.8 1 0.02 95 10 694.3 694.6 694.3 18.0 27622 -48.6 -82.4 1 0.03 125 10 707.5 707.8 707.5 17.0 28003 -47.9 -82.0 1 0.03 60 10 721.7 722.0 721.7 16.0 28407 -47.0 -81.6 1 0.03 100 14 737.0 737.4 737.0 13.0 29791 -44.1 -80.2 1 0.05 65 10 792.0 792.6 792.0 12.0 30325 -43.0 -79.7 1 0.06 115 14 814.3 815.0 814.3 11.0 30905 -41.8 -79.1 1 0.07 110 2 839.1 840.0 839.2 10.0 31540 -40.5 -78.5 1 0.08 80 8 867.2 868.3 867.3 9.0 32262 -39.1 -78.1 1 0.10 70 10 899.1 900.4 899.2 8.1 32984 -37.7 -77.7 1 0.12 151 11 932.1 933.8 932.2 8.0 160 12 '''

2条回答

网友

1楼 · 编辑于 2024-06-16 13:43:52

如果只需要线性插值，可以使用np.interp

网友

2楼 · 编辑于 2024-06-16 13:43:52

请尝试一下，看看是否适合您

ind= np.linspace(0,500,500).astype(int)
df.set_index(['HGHT'], inplace=True)
df1=df.reindex(ind)
df1.interpolate(method= 'linear',limit_direction ='both', inplace=True)
df1.reset_index(inplace=True)

细节

ind= np.linspace(0,500,500).astype(int)

我们生成一个数字数组。在本例中，我将0生成到500，要生成的总数是500，因此每一步都是一个。对于你的数据集，你将不得不做ti到25000。所以，np.linspace(0,25000,25000)

df.set_index(['HGHT'], inplace=True)

我们将df的索引设置为生成的数组必须确认的列。你知道吗

df1=df.reindex(ind)

我们重新索引df&输出是一个新的数据帧，您也可以将它设置为相同的数据帧。你知道吗

df1.interpolate(method= 'linear',limit_direction ='both', inplace=True)
df1.reset_index(inplace=True)

我们使用dataframe.interpolate()对其余的值进行交错处理，最后将索引重置为与以前一样的数据帧。你知道吗

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