Issue
I want to extract the measured wind from a station inside a moving ship, which I have the latitude, longitude and time values and the wind value for each time step in space. I can extract a fixed point in space for all time steps but I would like to extract for example the wind at time step x to a date longitude and latitude as the ship moves. How can I do this from the code below?
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)
Reading the observation station names:
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))
My code to extract a fixed variable at a given point in space is like this, but I would like to extract during the ship's movement, for example at time 11/12/2017 00:00, latitude -5.775115 and longitude -35.206590 I have a value of the wind, and in the next time step for another latitude x longitude I have another value. How can I adapt my code for this?
Solution
This is another perfect use case for xarray's advanced indexing! I feel like this part of the user guide needs a cape and a theme song :)
I'll use a made up dataset and set of stations which are similar (I think) to yours. First step is to reset your Date-Time index, so you can use it in pulling the nearest time value from the xarray.Dataset, since you want a common index for the time, lat, and lon:
In [14]: stations = stations.reset_index(drop=False)
...: stations
Out[14]:
Date-Time Latitude Longitude
0 2017-11-16 00:00:00 0.219547 -38.247914
1 2017-11-16 06:00:00 0.861717 -38.188858
2 2017-11-16 12:00:00 1.529534 -38.131039
3 2017-11-16 18:00:00 2.243760 -38.067467
4 2017-11-17 00:00:00 2.961202 -38.009050
5 2017-12-10 00:00:00 -5.775127 -35.206581
6 2017-12-10 06:00:00 -5.775120 -35.206598
7 2017-12-10 12:00:00 -5.775119 -35.206583
8 2017-12-10 18:00:00 -5.775122 -35.206584
9 2017-12-11 00:00:00 -5.775115 -35.206590
In [15]: ds
Out[15]:
<xarray.Dataset>
Dimensions: (lat: 40, lon: 40, time: 241)
Coordinates:
* lat (lat) float64 -9.75 -9.25 -8.75 -8.25 -7.75 ... 8.25 8.75 9.25 9.75
* lon (lon) float64 -44.75 -44.25 -43.75 -43.25 ... -26.25 -25.75 -25.25
* time (time) datetime64[ns] 2017-11-01 2017-11-01T06:00:00 ... 2017-12-31
Data variables:
temp (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
tempdw (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
sst (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
ws (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
ws100 (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
wdir (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
wdir100 (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
Using the advanced indexing rules, if we select from the dataset using DataArrays as indexers, the result will be reshaped to match the indexer. What this means is that we can take your stations dataframe, which has the values time, lat, and lon, and pull the nearest indices from the xarray dataset:
In [16]: ds_over_observations = ds.sel(
...: time=stations["Date-Time"].to_xarray(),
...: lat=stations["Latitude"].to_xarray(),
...: lon=stations["Longitude"].to_xarray(),
...: method="nearest",
...: )
Now, our data has the same index as your dataframe!
In [17]: ds_over_observations
Out[17]:
<xarray.Dataset>
Dimensions: (index: 10)
Coordinates:
lat (index) float64 0.25 0.75 1.75 2.25 ... -5.75 -5.75 -5.75 -5.75
lon (index) float64 -38.25 -38.25 -38.25 ... -35.25 -35.25 -35.25
time (index) datetime64[ns] 2017-11-16 ... 2017-12-11
* index (index) int64 0 1 2 3 4 5 6 7 8 9
Data variables:
temp (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
tempdw (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
sst (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
ws (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
ws100 (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
wdir (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
wdir100 (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
You can dump this into pandas with .to_dataframe:
In [18]: df = ds_over_observations.to_dataframe()
In [19]: df
Out[19]:
lat lon time temp tempdw sst ws ws100 wdir wdir100
index
0 0.25 -38.25 2017-11-16 00:00:00 0.188724 0.188724 0.188724 0.188724 0.188724 0.188724 0.188724
1 0.75 -38.25 2017-11-16 06:00:00 0.222025 0.222025 0.222025 0.222025 0.222025 0.222025 0.222025
2 1.75 -38.25 2017-11-16 12:00:00 0.675417 0.675417 0.675417 0.675417 0.675417 0.675417 0.675417
3 2.25 -38.25 2017-11-16 18:00:00 0.919019 0.919019 0.919019 0.919019 0.919019 0.919019 0.919019
4 2.75 -38.25 2017-11-17 00:00:00 0.566266 0.566266 0.566266 0.566266 0.566266 0.566266 0.566266
5 -5.75 -35.25 2017-12-10 00:00:00 0.652490 0.652490 0.652490 0.652490 0.652490 0.652490 0.652490
6 -5.75 -35.25 2017-12-10 06:00:00 0.429541 0.429541 0.429541 0.429541 0.429541 0.429541 0.429541
7 -5.75 -35.25 2017-12-10 12:00:00 0.113352 0.113352 0.113352 0.113352 0.113352 0.113352 0.113352
8 -5.75 -35.25 2017-12-10 18:00:00 0.923058 0.923058 0.923058 0.923058 0.923058 0.923058 0.923058
9 -5.75 -35.25 2017-12-11 00:00:00 0.609493 0.609493 0.609493 0.609493 0.609493 0.609493 0.609493
The index in the result is the same one as the stations data. If you'd like, you could merge in the original values using pd.concat([stations, df], axis=1).set_index("Date-Time") to get your original index back, alongside all the weather data:
In [20]: pd.concat([stations, df], axis=1).set_index("Date-Time")
Out[20]:
Latitude Longitude lat lon time temp tempdw sst ws ws100 wdir wdir100
Date-Time
2017-11-16 00:00:00 0.219547 -38.247914 0.25 -38.25 2017-11-16 00:00:00 0.188724 0.188724 0.188724 0.188724 0.188724 0.188724 0.188724
2017-11-16 06:00:00 0.861717 -38.188858 0.75 -38.25 2017-11-16 06:00:00 0.222025 0.222025 0.222025 0.222025 0.222025 0.222025 0.222025
2017-11-16 12:00:00 1.529534 -38.131039 1.75 -38.25 2017-11-16 12:00:00 0.675417 0.675417 0.675417 0.675417 0.675417 0.675417 0.675417
2017-11-16 18:00:00 2.243760 -38.067467 2.25 -38.25 2017-11-16 18:00:00 0.919019 0.919019 0.919019 0.919019 0.919019 0.919019 0.919019
2017-11-17 00:00:00 2.961202 -38.009050 2.75 -38.25 2017-11-17 00:00:00 0.566266 0.566266 0.566266 0.566266 0.566266 0.566266 0.566266
2017-12-10 00:00:00 -5.775127 -35.206581 -5.75 -35.25 2017-12-10 00:00:00 0.652490 0.652490 0.652490 0.652490 0.652490 0.652490 0.652490
2017-12-10 06:00:00 -5.775120 -35.206598 -5.75 -35.25 2017-12-10 06:00:00 0.429541 0.429541 0.429541 0.429541 0.429541 0.429541 0.429541
2017-12-10 12:00:00 -5.775119 -35.206583 -5.75 -35.25 2017-12-10 12:00:00 0.113352 0.113352 0.113352 0.113352 0.113352 0.113352 0.113352
2017-12-10 18:00:00 -5.775122 -35.206584 -5.75 -35.25 2017-12-10 18:00:00 0.923058 0.923058 0.923058 0.923058 0.923058 0.923058 0.923058
2017-12-11 00:00:00 -5.775115 -35.206590 -5.75 -35.25 2017-12-11 00:00:00 0.609493 0.609493 0.609493 0.609493 0.609493 0.609493 0.609493
Answered By - Michael Delgado
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