Comments (5)
Hi @lapidesd!
Thank you for filing this issue! Could you tell me the query that you used to get this error? I'm curious about what went wrong.
I'm guessing it was something like this::
hf.NWIS(stateCd="CA", service="dv", parameterCd="00020")
# "00020" is for air temperature, "00010" is for water temperature (much more commonly collected)
But that didn't bring up an error for me.
Also, if you are interested in submitting a pull request, I'm always looking for contributors! https://hydrofunctions.readthedocs.io/en/latest/contributing.html
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from hydrofunctions.
Excellent!
This query illustrates a problem with how hydrofunctions converts the JSON from the NWIS into a dataframe. It basically takes the data from every single temperature-measuring station in the state of California and resamples it to the frequency of the station with the shortest frequency for the entire length of the query. So if there is a station that recorded temperature every five minutes one day back in 2005, hydrofunctions will output a dataframe with a row for every five minutes starting in 1950 until 2021. And it performs this "service" for you just because you asked for some data.
My plan for the future is to change this so that the data is stored internally in the WaterML format, and a dataframe is created only when you ask for a dataframe. That way the 'upsampling' only occurs if you ask it to occur.
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In the meantime, you can see what stations have collected temperature by just requesting the most recent value:
hf.NWIS(stateCd="CA", service="dv", parameterCd="00010")
You can also use hf.data_catalog(station_id)
or hf.site_file(station_id
to learn more about individual stations.
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closed with 16dd705
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