我的netcdf文件有问题，其中包括平均海平面压力数据。我需要对该数据进行EOF分析，因此需要使用纬度余弦的平方根对数据进行加权。我打开数据集，提取变量，然后将它们乘以权重。
我检查了在加权之前和之后以固定的时间序列和经度打印MSLP值的计算。第二次打印是正确的，并且正是我想要的，但是数据似乎没有保存。实际上，当（关闭数据集后）我重新打开它并重新打印数据时，它们是原始数据，并且没有更改。
Python：
dset = Dataset(newpath,'r+')
lat=dset.variables['lat'][:]
msl=dset.variables['msl'][:]
j=0 #lat counter
print(msl[0,:,0]) #first print check (fixed times series and longitude)
while j<160:
msl[:,j,:]=msl[:,j,:]*math.sqrt(math.fabs(math.cos(lat[j]*0.015708))
j=j+1
print(msl[0,:,0]) #second print check (correct weight in output)
dset.close()
print ('____________________________________________________')
dset = Dataset(newpath)
lat=dset.variables['lat'][:]
msl=dset.variables['msl'][:]
print(msl[0,:,0]) #third print check (original data)
dset.close()
输出：
[101021.06 100858.19 100738.31 100654.19 100615.19 100593.69 100562.56
100511.81 100424.94 100336.31 100258.81 100210.44 100158.69 100104.44
100056.94 100008.06 99980.31 99977.81 99994.44 100021.31 100072.69
100129.69 100195.56 100277.31 100384.06 100517.81 100673.69 100818.69
100937.81 101032.44 101123.56 101225.31 101333.56 101422.81 101508.94
101596.69 101717.69 101827.06 101929.19 102002.31 102085.81 102153.69
102106.94 101980.81 101912.19 101924.94 101964.31 102036.94 102144.56
102230.81 102190.31 102142.06 102129.06 102114.19 102074.81 102042.19
102006.56 101979.81 101939.44 101901.81 101856.69 101799.69 101737.31
101677.81 101612.69 101551.06 101489.06 101424.19 101343.06 101250.44
101160.81 101095.19 101048.06 101033.31 101053.56 101084.81 101087.44
101099.44 101110.44 101126.56 101138.06 101147.31 101155.94 101171.19
101184.44 101209.94 101230.31 101256.69 101282.19 101309.06 101336.06
101372.81 101408.69 101452.81 101490.69 101540.81 101587.69 101642.81
101695.19 101751.56 101803.31 101860.44 101909.06 101956.31 101996.06
102028.94 102050.44 102060.56 102055.81 102040.56 102016.69 101979.31
101924.06 101845.06 101743.94 101633.31 101505.81 101370.44 101216.69
101061.56 100902.94 100739.06 100571.44 100416.94 100282.56 100150.94
100012.94 99876.06 99737.19 99624.56 99533.69 99483.81 99457.81
99473.81 99510.69 99571.06 99627.94 99690.19 99731.69 99777.81
99788.44 99837.69 99868.44 99936.81 99964.44 100129.44 100214.56
100257.94 100333.81 100380.06 100444.94 100485.81 100578.06 100684.31
100783.81 100866.69 100939.44 100999.81 101061.06 101111.31]
[ 41619.645 43605.746 45517.273 47355.477 49134.914 50851.395
52497.957 54074.137 55574.52 57021.02 58424.58 59799.477
61129.76 62418.176 63672.594 64889.86 66086.44 67267.34
68430.6 69571.84 70702.53 71810.86 72900.21 73976.79
75048.58 76118.43 77183.93 78220.58 79216.72 80173.18
81106.555 82028.58 82936.46 83809.59 84661.07 85495.07
86338.734 87154.586 87946.35 88695.16 89435.1 90143.61
90732.586 91231.664 91762.88 92349.1 92941.94 93548.02
94169.59 94755.02 95205.89 95631.93 96073.62 96496.445
96879.016 97250.93 97603.02 97946.78 98260.68 98560.51
98836.43 99084.21 99310.16 99522.38 99712.65 99889.96
100050.59 100192.15 100301.43 100383.13 100451.66 100527.97
100606.76 100701.945 100816.305 100925.96 100991.34 101050.41
101092.79 101124.6 101136.1 101129.66 101106.88 101075.016
101025.43 100972.31 100898.305 100814.47 100713.92 100598.87
100468.02 100330.875 100176.836 100014.86 99830.57 99642.12
99434.164 99217.91 98982.5 98734.42 98465.234 98184.53
97878.83 97554.945 97206.97 96835.48 96436.2 96009.18
95551.17 95066.375 94556.56 94017.25 93444.54 92833.24
92185.11 91511.78 90806.73 90078.13 89316.77 88537.77
87739.2 86919.49 86079.88 85234.65 84389.22 83528.47
82644.56 81743.57 80822.664 79904.28 78983.86 78075.61
77164.734 76264.016 75355.77 74440.77 73497.734 72532.51
71525.42 70494.195 69409.86 68323.33 67193.75 66057.68
64861.55 63718.73 62486.844 61190.336 59873.098 58495.234
57082.75 55608.207 54109.78 52561.516 50947.504 49257.734
47489.68 45635.43 43693.46 41656.83 ]
____________________________________________________
[101021.06 100858.19 100738.31 100654.19 100615.19 100593.69 100562.56
100511.81 100424.94 100336.31 100258.81 100210.44 100158.69 100104.44
100056.94 100008.06 99980.31 99977.81 99994.44 100021.31 100072.69
100129.69 100195.56 100277.31 100384.06 100517.81 100673.69 100818.69
100937.81 101032.44 101123.56 101225.31 101333.56 101422.81 101508.94
101596.69 101717.69 101827.06 101929.19 102002.31 102085.81 102153.69
102106.94 101980.81 101912.19 101924.94 101964.31 102036.94 102144.56
102230.81 102190.31 102142.06 102129.06 102114.19 102074.81 102042.19
102006.56 101979.81 101939.44 101901.81 101856.69 101799.69 101737.31
101677.81 101612.69 101551.06 101489.06 101424.19 101343.06 101250.44
101160.81 101095.19 101048.06 101033.31 101053.56 101084.81 101087.44
101099.44 101110.44 101126.56 101138.06 101147.31 101155.94 101171.19
101184.44 101209.94 101230.31 101256.69 101282.19 101309.06 101336.06
101372.81 101408.69 101452.81 101490.69 101540.81 101587.69 101642.81
101695.19 101751.56 101803.31 101860.44 101909.06 101956.31 101996.06
102028.94 102050.44 102060.56 102055.81 102040.56 102016.69 101979.31
101924.06 101845.06 101743.94 101633.31 101505.81 101370.44 101216.69
101061.56 100902.94 100739.06 100571.44 100416.94 100282.56 100150.94
100012.94 99876.06 99737.19 99624.56 99533.69 99483.81 99457.81
99473.81 99510.69 99571.06 99627.94 99690.19 99731.69 99777.81
99788.44 99837.69 99868.44 99936.81 99964.44 100129.44 100214.56
100257.94 100333.81 100380.06 100444.94 100485.81 100578.06 100684.31
100783.81 100866.69 100939.44 100999.81 101061.06 101111.31]
答案 0 :(得分：1)
这时在脚本的开头：
msl=dset.variables['msl'][:]
msl只是一个Numpy数组，不再与您的NetCDF文件相关，因此您需要将该数组写回到NetCDF文件：
dset.variables['msl'][:] = msl
在第一次关闭NetCDF文件之前。