How to find the time nearest to local 2 pm for every day?

[zxdawn]

I have a 3-hour data and wanna get the data nearest to 2 pm.

Here's the example:

import numpy as np
import pandas as pd
import xarray as xr

# create data
lon = np.arange(-180, 180, 10)
lat = np.arange(-90, 90.5, 0.5)
time = pd.date_range('2019-08-10 00:00', '2019-08-11 21:00', freq='3h')
data = np.random.rand(len(time), len(lat), len(lon))
data = xr.DataArray(data, dims=['time', 'lat', 'lon'], coords={'time': time, 'lat': lat, 'lon': lon})
hour_offset = (lon+7.5)//15

output = np.full((len(time), len(lat), len(lon)), np.nan)
time = np.unique(data.time.dt.strftime('%Y-%m-%d'))

# iterate the day
for index_t,t in enumerate(time):
    # iterate longitude to pick data nearest to 2 pm
    for index_lon, lon in enumerate(data['lon']):
        time = pd.to_datetime(f'{t} 14:00') + pd.Timedelta(hour_offset[index_lon], unit='h')
        output[index_t, :, index_lon] = data.sel(time=time, lon=lon, method='nearest')

I have to iterate the day list and converted time for every grid. Is it possible to use something like apply_ufunc to accelerate?

[zxdawn]

Find out the solution:

import numpy as np
import pandas as pd
import xarray as xr

# create data
lon = np.arange(-180, 180, 10)
lat = np.arange(-90, 90.5, 0.5)
time = pd.date_range('2019-08-10 00:00', '2019-08-10 21:00', freq='3h')
data = np.random.rand(len(time), len(lat), len(lon))
data = xr.DataArray(data, dims=['time', 'lat', 'lon'], coords={'time': time, 'lat': lat, 'lon': lon})
hour_offset = (lon+7.5)//15

time_3d = np.broadcast_to(time.hour.values[:, np.newaxis, np.newaxis], (len(time), len(lat), len(lon)))
timedelta_3d = np.broadcast_to(hour_offset[np.newaxis, np.newaxis, :], (len(time), len(lat), len(lon)))
min_index = abs(time_3d + timedelta_3d - 14).argmin(axis=0).astype('int')
output = data.isel(time=xr.DataArray(min_index, dims=['lat', 'lon'], coords={'lat': lat, 'lon': lon}))