我想要从一艘移动的船内的一个站点提取测量到的风，我有纬度、经度和时间值，以及空间中每个时间步长的风值。我可以提取所有时间步长在空间中的一个固定点，但我想要提取，例如，当船移动时，x时间步长的风是一个日期、经度和纬度。我如何从下面的代码中做到这一点？
data = xr.open_dataset('C:/Users/William Jacondino/Desktop/Dados/ERA5\\ERA5_2017.nc', decode_times=False)
dir_out = 'C:/Users/William Jacondino/Desktop/MovingShip'
if not os.path.exists(dir_out):
os.makedirs(dir_out)
print("\nReading the observation station names:\n")
stations = pd.read_csv(r"C:/Users/William Jacondino/Desktop/MovingShip/Date-TIME.csv",index_col=0, sep='\;')
print(stations)阅读观测站名称：
Latitude Longitude
Date-Time
16/11/2017 00:00 0.219547 -38.247914
16/11/2017 06:00 0.861717 -38.188858
16/11/2017 12:00 1.529534 -38.131039
16/11/2017 18:00 2.243760 -38.067467
17/11/2017 00:00 2.961202 -38.009050
... ... ...
10/12/2017 00:00 -5.775127 -35.206581
10/12/2017 06:00 -5.775120 -35.206598
10/12/2017 12:00 -5.775119 -35.206583
10/12/2017 18:00 -5.775122 -35.206584
11/12/2017 00:00 -5.775115 -35.206590
# variável tempo e unidade
times = data.variables['time'][:]
unit = data.time.units
# variáveis latitude (lat) e longitude (lon)
lon = data.variables['longitude'][:]
lat = data.variables['latitude'][:]
# variável temperatura em 2 metros em celsius
temp = data.variables['t2m'][:]-275.15
# variável temperatura do ponto de orvalho em 2 metros em celsius
tempdw = data.variables['d2m'][:]-275.15
# variável sea surface temperature (sst) em celsius
sst = data.variables['sst'][:]-275.15
# variável Surface sensible heat flux sshf
sshf = data.variables['sshf'][:]
unitsshf = data.sshf.units
# variável Surface latent heat flux
slhf = data.variables['slhf'][:]
unitslhf = data.slhf.units
# variável Mean sea level pressure
msl = data.variables['msl'][:]/100
unitmsl = data.msl.units
# variável Total precipitation em mm/h
tp = data.variables['tp'][:]*1000
# componente zonal do vento em 100 metros
uten100 = data.variables['u100'][:]
unitu100 = data.u100.units
# componente meridional do vento em 100 metros
vten100 = data.variables['v100'][:]
unitv100 = data.v100.units
# componente zonal do vento em 10 metros
uten = data.variables['u10'][:]
unitu = data.u10.units
# componente meridional do vento em 10 metros
vten = data.variables['v10'][:]
unitv = data.v10.units
# calculando a velocidade do vento em 10 metros
ws = (uten**2 + vten**2)**(0.5)
# calculando a velocidade do vento em 100 metros
ws100 = (uten100**2 + vten100**2)**(0.5)
# calculando os ângulos de U e V para obter a direção do vento em 10 metros
wdir = (180 + (np.degrees(np.arctan2(uten, vten)))) % 360
# calculando os ângulos de U e V para obter a direção do vento em 100 metros
wdir100 = (180 + (np.degrees(np.arctan2(uten100, vten100)))) % 360
for key, value in stations.iterrows():
#print(key,value[0], value[1], value[2])
station = value[0]
file_name = "{}{}".format(station+'_1991',".csv")
#print(file_name)
lon_point = value[1]
lat_point = value[2]
########################################
# Encontrando o ponto de Latitude e Longitude mais próximo das estações
# Squared difference of lat and lon
sq_diff_lat = (lat - lat_point)**2
sq_diff_lon = (lon - lon_point)**2
# Identifying the index of the minimum value for lat and lon
min_index_lat = sq_diff_lat.argmin()
min_index_lon = sq_diff_lon.argmin()
print("Generating time series for station {}".format(station))
ref_date = datetime.datetime(int(unit[12:16]),int(unit[17:19]),int(unit[20:22]))
date_range = list()
temp_data = list()
tempdw_data = list()
sst_data = list()
sshf_data = list()
slhf_data = list()
msl_data = list()
tp_data = list()
uten100_data = list()
vten100_data = list()
uten_data = list()
vten_data = list()
ws_data = list()
ws100_data = list()
wdir_data = list()
wdir100_data = list()
for index, time in enumerate(times):
date_time = ref_date+datetime.timedelta(hours=int(time))
date_range.append(date_time)
temp_data.append(temp[index, min_index_lat, min_index_lon].values)
tempdw_data.append(tempdw[index, min_index_lat, min_index_lon].values)
sst_data.append(sst[index, min_index_lat, min_index_lon].values)
sshf_data.append(sshf[index, min_index_lat, min_index_lon].values)
slhf_data.append(slhf[index, min_index_lat, min_index_lon].values)
msl_data.append(msl[index, min_index_lat, min_index_lon].values)
tp_data.append(tp[index, min_index_lat, min_index_lon].values)
uten100_data.append(uten100[index, min_index_lat, min_index_lon].values)
vten100_data.append(vten100[index, min_index_lat, min_index_lon].values)
uten_data.append(uten[index, min_index_lat, min_index_lon].values)
vten_data.append(vten[index, min_index_lat, min_index_lon].values)
ws_data.append(ws[index,min_index_lat,min_index_lon].values)
ws100_data.append(ws100[index,min_index_lat,min_index_lon].values)
wdir_data.append(wdir[index,min_index_lat,min_index_lon].values)
wdir100_data.append(wdir100[index,min_index_lat,min_index_lon].values)
################################################################################
#print(date_range)
df = pd.DataFrame(date_range, columns = ["Date-Time"])
df["Date-Time"] = date_range
df = df.set_index(["Date-Time"])
df["WS10 ({})".format(unitu)] = ws_data
df["WDIR10 ({})".format(units.deg)] = wdir_data
df["WS100 ({})".format(unitu)] = ws100_data
df["WDIR100 ({})".format(units.deg)] = wdir100_data
df["Chuva({})".format(units.mm)] = tp_data
df["MSLP ({})".format(units.hPa)] = msl_data
df["T2M ({})".format(units.degC)] = temp_data
df["Td2M ({})".format(units.degC)] = tempdw_data
df["Surface Sensible Heat Flux ({})".format(unitsshf)] = sshf_data
df["Surface latent heat flux ({})".format(unitslhf)] = slhf_data
df["U10 ({})".format(unitu)] = uten_data
df["V10 ({})".format(unitv)] = vten_data
df["U100 ({})".format(unitu100)] = uten100_data
df["V100 ({})".format(unitv100)] = vten100_data
df["TSM ({})".format(units.degC)] = sst_data
print("The following time series is being saved as .csv files")
df.to_csv(os.path.join(dir_out,file_name), sep=';',encoding="utf-8", index=True)
print("\n! !Successfuly saved all the Time Series the output Directory!!\n{}".format(dir_out))我提取空间中给定点的固定变量的代码是这样的，但我希望在船舶移动期间提取，例如，在11/12/2017 00：00，纬度-5.775115和经度-35.206590我有一个风值，在下一个时间步长中，对于另一个纬度x经度，我有另一个值。我怎样才能使我的代码适应这种情况呢？
1条答案
按热度按时间mqkwyuun1#
这是XARRAY的高级索引的另一个完美用例！我觉得用户指南的这部分需要一个斗篷和一首主题曲：)
我将使用一个虚构的数据集和一组站点，它们与您的相似(我认为)。第一步是重置日期-时间索引，这样您就可以使用它从xarray.Dataset中提取最接近的时间值，因为您需要一个用于时间、经度和经度的公共索引：
使用高级索引规则，如果我们使用DataArray作为索引器从DataSet中进行选择，则结果将被重塑以匹配索引器。这意味着我们可以获取您的站点 Dataframe ，它具有值时间、经度和经度，并从xarray数据集中提取最接近的索引：
现在，我们的数据与您的 Dataframe 具有相同的索引！
您可以使用
.to_dataframe将其转储到Pandas中：结果中的指数与站点数据中的指数相同。如果您愿意，可以使用
pd.concat([stations, df], axis=1).set_index("Date-Time")合并原始值，以恢复原始索引以及所有天气数据：