-
|
In xarray seasonal grouping. I need to group by following group of months: JF,MAM,JJAS,OND and sum the grouped season values to perform trend analysis, whereas default groupby.season months are DJF,MAM,JJA,SON. Code: import xarray as xr
import numpy as np
import pymannkendall as mk
Data = xr.open_dataset(in_file)
print(Data)Data = Data['rain'].groupby('time.season').sum('time')
Data = Data.sel(lon= np.arange(75,81,0.25), lat = np.arange(8, 14,0.25),method ='nearest')
output=[]
for i in np.arange(len(Data.lat.values)):
for j in np.arange(len(Data.lon.values)):
try:
slope_val = mk.original_test(Data[:,i,j]).z
except:
slope_val=-9999
output.append(slope_val)
output = np.copy(output).reshape(Data.lat.size,Data.lon.size)
score = xr.DataArray(output, dims=('lat','lon'), coords={'lat':Data.lat, 'lon':Data.lon}, name='ZScore')
df = score.to_dataframe() |
Beta Was this translation helpful? Give feedback.
Replies: 2 comments 7 replies
-
|
You can pass a DataArray with your self-defined groups to GroupBy . See #6180 on one way how to define the groups (no need to apply the |
Beta Was this translation helpful? Give feedback.
-
|
Ok |
Beta Was this translation helpful? Give feedback.
-
|
Any Solution? |
Beta Was this translation helpful? Give feedback.
-
|
We do not know what does not work for you - so we cannot help. Please see also #5404. |
Beta Was this translation helpful? Give feedback.
-
|
I followed #5134 and tried to regroup the default season pattern DJF,MAM,JJA,SON into JF,MAM,JJAS,OND. import numpy as np
import xarray as xr
import pymannkendall as mk
Data = xr.tutorial.open_dataset("air_temperature")
months = Data.time.dt.month
seasons = xr.full_like(months, fill_value="none", dtype="U4")
seasons.name = "season"
seasons[months.isin([1, 2])] = "JF"
seasons[months.isin([3, 4, 5])] = "MAM"
seasons[months.isin([6, 7, 8,9])] = "JJAS"
seasons[months.isin([10, 11, 12])] = "OND"
x = Data.groupby(seasons).mean().reindex(season =['JF','MAM','JJAS','OND'])
x = x['air'].groupby('season').sum('season')
output =[]
for i in np.arange(len(x.lat.values)):
for j in np.arange(len(x.lon.values)):
try:
slope_val = mk.original_test(x[:,i,j]).trend
except:
slope_val=-9999
output.append(slope_val)
output = np.copy(output).reshape(x.lat.size,x.lon.size)
score = xr.DataArray(output, dims=('lat','lon'), coords={'lat':x.lat, 'lon':x.lon}, name='trend')
df1 = score.to_dataframe() The resultant dataframe is obtained for one season. I need the seasonal sum of each year to perform trend analysis and resultant dataframe for 4 seasons. In the codeline12: I tried to sum for each season ValueError: cannot reduce over dimensions ['season:JF']. expected either '...' to reduce over all dimensions or one or more of ('season', 'lat', 'lon'). |
Beta Was this translation helpful? Give feedback.
-
|
You already reduce the time dimension in line 11 ( However, you loose the the information on the years - do you rather need to use a |
Beta Was this translation helpful? Give feedback.
-
|
On latest Xarray (>2024.04.0) this works: import xarray as xr
from xarray.groupers import SeasonGrouper
ds = xr.tutorial.open_dataset("air_temperature")
ds.groupby(time=SeasonGrouper(["JF", "MAM", "JJAS", "OND"])).mean() |
Beta Was this translation helpful? Give feedback.
On latest Xarray (>2024.04.0) this works:
https://docs.xarray.dev/en/latest/user-guide/time-series.html#handling-seasons