| import xarray as xr | |
| def detrend_dim(da, dim, deg=1): | |
| # detrend along a single dimension | |
| p = da.polyfit(dim=dim, deg=deg) | |
| fit = xr.polyval(dim, p.polyfit_coefficients) | |
| return da - fit | |
| def detrend(da, dims, deg=1): | |
| # detrend along multiple dimensions |
| import xarray as xr | |
| url = "https://www.ncei.noaa.gov/thredds-ocean/dodsC/woa23/DATA/temperature/netcdf/decav/1.00/woa23_decav_t16_01.nc" | |
| ds = xr.open_dataset(url, decode_times=False) | |
| t_diff = ds.t_an - ds.t_an.isel(depth=0) | |
| t_diff.sel(lat=0, method="nearest").plot(x="lon", ylim=(5000, 0), vmax=0, vmin=-25) | |
| t_diff.sel(lat=0, method="nearest").squeeze().plot.contour(levels=[-22], ylim=(5000, 0), colors='w') |
| import xarray as xr | |
| import numpy as np | |
| # create an example dataset | |
| da = xr.DataArray(np.random.rand(10,30,40), dims=['time', 'lat', 'lon']) | |
| # define a function to compute a linear trend of a timeseries | |
| def linear_trend(x): | |
| pf = np.polyfit(x.time, x, 1) | |
| # we need to return a dataarray or else xarray's groupby won't be happy |
This is a rough guide to using OSN with Pangeo tools.
Open Storage Network (OSN) is a distributed data service to support active data sharing and transfer between academic institutions, leveraging existing NSF-funded resources. It is funded by NSF and the Schmidt Futures Foundation.