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from datetime import datetime, timedelta
from siphon.catalog import TDSCatalog
date = datetime.utcnow() - timedelta(days=1)
cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/nexrad/level3/'
f'N0Q/LRX/{date:%Y%m%d}/catalog.xml')
We could manually figure out what dataset we're looking for and generate that name (or index). Siphon provides some helpers to simplify this process, provided the names of the dataset follow a pattern with the timestamp in the name:
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request_time = date.replace(hour=18, minute=30, second=0, microsecond=0)
ds = cat.datasets.filter_time_nearest(request_time)
ds
We can also find the list of datasets within a time range:
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datasets = cat.datasets.filter_time_range(request_time, request_time + timedelta(hours=1))
print(datasets)
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# YOUR CODE GOES HERE
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# %load solutions/datasets.py
Accessing catalogs is only part of the story; Siphon is much more useful if you're trying to access/download datasets.
For instance, using our data that we just retrieved:
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ds = datasets[0]
We can ask Siphon to download the file locally:
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ds.download()
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import os; os.listdir()
Or better yet, get a file-like object that lets us read from the file as if it were local:
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fobj = ds.remote_open()
data = fobj.read()
print(len(data))
This is handy if you have Python code to read a particular format.
It's also possible to get access to the file through services that provide netCDF4-like access, but for the remote file. This access allows downloading information only for variables of interest, or for (index-based) subsets of that data:
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nc = ds.remote_access()
By default this uses CDMRemote (if available), but it's also possible to ask for OPeNDAP (using netCDF4-python).
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print(list(nc.variables))