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updated tutorial hyperlinks
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a-charbon committed Dec 4, 2023
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4 changes: 2 additions & 2 deletions docs/tutorials/Snow/base_tutorial.jl
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# structurally-enforced constraints. The following steps through a
# basic use-case of the system on an already-cleaned dataset, though
# exploration of optional keyword arguments in the developed code and
# additional tools for scraping data (explained in the [data tutorial](../data_tutorial.jl))
# additional tools for scraping data (explained in the [data tutorial](../data_tutorial/))
# provide for a richer set of functionality.

# The updates of the neural snow model follow the equation
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# ClimaLSM.Snow.DataTools) to scrape SNOTEL data directly. We also set the
# unit timestep seen in this data (daily, so 1 day) to be used for
# setting the network's constraints as well as generating timeseries during usage.
# To see the code that generated this data file, check out the [data tutorial](../data_tutorial.jl).
# To see the code that generated this data file, check out the [data tutorial](../data_tutorial/).
data_download_link = "https://caltech.box.com/shared/static/n59m3iqcgr60gllp65rsrd3k0mtnsfmg.csv"
data = CSV.read(HTTP.get(data_download_link).body, DataFrame)
Δt = Second(86400);
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2 changes: 1 addition & 1 deletion docs/tutorials/Snow/data_tutorial.jl
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Expand Up @@ -49,7 +49,7 @@ scales = Dict{Symbol, Real}(
# refers to 2023-01-01 or the last available date. Most of these stations
# are commented out for the sake of speed and readability in generating
# the tutorial, but can be uncommented to yield the full dataset found in
# `cleandata.csv` used in the [base tutorial](../base_tutorial.jl). Stations were
# `cleandata.csv` used in the [base tutorial](../base_tutorial/). Stations were
# selected based upon their availability of the features utilized in
# creating the model used in the paper.

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