Thanks for the great introduction to working with netcdf files. I really enjoyed this series so far! Do you have any recommendations on what packages to install if I'm learning climate data analyses (e.g. xclim) and comparing the performance of forecast models (e.g. climpred)? Any video resources I should try studying from?
Hi, friend, would you give an example of how to manipulate the sequence during looping through a THREDDS data server? I mean opening and appending the dataset in a giving order, thank you very much.
This really depends on which order you want to open your files. You could manually create a list of filenames if you have a specific order in mind and do a for loop through that list.
You can use the `interp` method in xarray to interpolate the data from 1 degree to 0.01 degrees. Here's how you can do it: ```python import xarray as xr # Load your xarray dataset # For example, assuming your dataset is named 'data' Let's imagine you have an xarray object called 'data' data_interp = data.interp(lat=range(data.lat.min(), data.lat.max(), 0.01), lon=range(data.lon.min(), data.lon.max(), 0.01)) However, one problem is that you also need to interpolate the data between 359 degrees and 1 degrees. Luckily for you, I have a function you can use for this. In the below function the data are sampled at 2.5 degree intervals and I am interpolating to 0.5 degrees. Method should be one of "linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial" def interpolate_data(ds, method): ds_90_to_270 = ds.sel(lon=slice(87.5, 272.5)) ds_90_to_270_interp = ds_90_to_270.interp(lat=np.arange(-90, 90, 0.5), lon=np.arange(87.5, 272.5, 0.5), method=method) ds_90_to_270_interp = ds_90_to_270_interp.sel(lon=slice(90, 270)) ds_90_to_neg90 = ds_90_to_270_interp.assign_coords(lon=(ds_90_to_270_interp.lon + 180) % 360 - 180) # Combine the interpolated parts ds_0_to_90 = ds.sel(lon=slice(0, 92.5)) ds_270_to_360 = ds.sel(lon=slice(267.5, 360)) ds_combined = xr.concat([ds_0_to_90, ds_270_to_360], dim='lon') ds_neg90_to_90 = ds_combined.assign_coords(lon=(ds_combined.lon + 180) % 360 - 180) ds_neg90_to_90_interp = ds_neg90_to_90.interp(lat=np.arange(-90, 90, 0.5), lon=np.arange(-92.5, 92.5, 0.5), method=method) ds_neg90_to_90_interp = ds_neg90_to_90_interp.sel(lon=slice(-90, 90)) interpolated_ds = xr.concat([ds_neg90_to_90_interp, ds_90_to_neg90], dim='lon') interpolated_ds = interpolated_ds.sortby('lon') return interpolated_ds
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Thanks for the great introduction to working with netcdf files. I really enjoyed this series so far! Do you have any recommendations on what packages to install if I'm learning climate data analyses (e.g. xclim) and comparing the performance of forecast models (e.g. climpred)? Any video resources I should try studying from?
Thanks for the comment. Your question regarding data analysis is a bit outside of my area, but good luck!
Great video Luke. Thanks😙
My pleasure!
Hi, friend, would you give an example of how to manipulate the sequence during looping through a THREDDS data server? I mean opening and appending the dataset in a giving order, thank you very much.
This really depends on which order you want to open your files. You could manually create a list of filenames if you have a specific order in mind and do a for loop through that list.
ok，thank you very much
How to resample the .nc grided data from 1degree to 0.01degree grid? Give me the code for multiple files
You can use the `interp` method in xarray to interpolate the data from 1 degree to 0.01 degrees. Here's how you can do it:
```python
import xarray as xr
# Load your xarray dataset
# For example, assuming your dataset is named 'data'
Let's imagine you have an xarray object called 'data'
data_interp = data.interp(lat=range(data.lat.min(), data.lat.max(), 0.01), lon=range(data.lon.min(), data.lon.max(), 0.01))
However, one problem is that you also need to interpolate the data between 359 degrees and 1 degrees. Luckily for you, I have a function you can use for this. In the below function the data are sampled at 2.5 degree intervals and I am interpolating to 0.5 degrees. Method should be one of "linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"
def interpolate_data(ds, method):
ds_90_to_270 = ds.sel(lon=slice(87.5, 272.5))
ds_90_to_270_interp = ds_90_to_270.interp(lat=np.arange(-90, 90, 0.5), lon=np.arange(87.5, 272.5, 0.5), method=method)
ds_90_to_270_interp = ds_90_to_270_interp.sel(lon=slice(90, 270))
ds_90_to_neg90 = ds_90_to_270_interp.assign_coords(lon=(ds_90_to_270_interp.lon + 180) % 360 - 180)
# Combine the interpolated parts
ds_0_to_90 = ds.sel(lon=slice(0, 92.5))
ds_270_to_360 = ds.sel(lon=slice(267.5, 360))
ds_combined = xr.concat([ds_0_to_90, ds_270_to_360], dim='lon')
ds_neg90_to_90 = ds_combined.assign_coords(lon=(ds_combined.lon + 180) % 360 - 180)
ds_neg90_to_90_interp = ds_neg90_to_90.interp(lat=np.arange(-90, 90, 0.5), lon=np.arange(-92.5, 92.5, 0.5), method=method)
ds_neg90_to_90_interp = ds_neg90_to_90_interp.sel(lon=slice(-90, 90))
interpolated_ds = xr.concat([ds_neg90_to_90_interp, ds_90_to_neg90], dim='lon')
interpolated_ds = interpolated_ds.sortby('lon')
return interpolated_ds
For multiple files just include that within your for loop. The execution of the function I mean. Defining the function can go at the top of your code.