Add waterdata demo #208
Add waterdata demo #208
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…t contain geometry
jzemmels
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jzemmels
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This is great! I appreciate the consistent throughline example. I've just a few comments that you might consider, but I'm only requesting changes on the errors I ran into (the double monitoring_location_id error + the GeoPandas-dependent code).
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| "# Using the `waterdata` module to pull data from the USGS Water Data APIs\n", | ||
| "The `waterdata` module will eventually replace the `nwis` module for accessing USGS water data. It leverages the [Water Data APIs](https://api.waterdata.usgs.gov/) to download metadata, daily values, and instantaneous values. \n", |
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Partially saves you from updating verb tenses once nwis is officially gone.
| "The `waterdata` module will eventually replace the `nwis` module for accessing USGS water data. It leverages the [Water Data APIs](https://api.waterdata.usgs.gov/) to download metadata, daily values, and instantaneous values. \n", | |
| "The `waterdata` module replaces the `nwis` module for accessing USGS water data. It leverages the [Water Data APIs](https://api.waterdata.usgs.gov/) to download metadata, daily values, and instantaneous values. \n", |
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| "## Lay of the Land\n", |
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Smart to start high-level!
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| "## Lay of the Land\n", | ||
| "Now that your API key is configured, it's time to take a 10,000-ft view of the functions in the `waterdata` module.\n", |
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Might be good to link to the specific OGC API homepage somewhere -- maybe here? https://api.waterdata.usgs.gov/ogcapi
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| "### A few key tips\n", |
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Maybe link to the docs where there are more general tips? https://api.waterdata.usgs.gov/docs/ogcapi/
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| "In the dataframe above, we are looking at 5 timeseries returned, ordered by monitoring location. You can also see that the first two rows show two different kinds of discharge for the same monitoring location: a mean daily discharge timeseries (with statistic id 00003, which represents \"mean\") and an instantaneous discharge timeseries (with statistic id 00011, which represents \"points\" or \"instantaneous\" values). Look closely and you may also notice that the `parent_timeseries_id` column for daily mean discharge matches the `time_series_id` for the instantaneous discharge. This is because once instantaneous measurements began at the site, they were used to calculate the daily mean." |
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Maybe link to the statistic-codes reference table: https://api.waterdata.usgs.gov/ogcapi/v0/collections/statistic-codes/items
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| "latest_instantaneous['datetime'] = latest_instantaneous['time'].astype(str)\n", | ||
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| "latest_instantaneous[['geometry', 'monitoring_location_id', 'datetime', 'value', 'unit_of_measure']].set_crs(crs=\"WGS84\").explore(column='value', cmap='YlOrRd', scheme=None, legend=True)" |
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Assumes you have GeoPandas installed. I'd either tell the reader that the remaining code requires GeoPandas or find a non-GeoPandas workaround.
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| "Currently, users may only request 3 years or less of continuous data in one pull. For this example, let's pull the last 1 year of daily mean values and instantaneous values for these Missouri River sites. We'll skip pulling geometry in the `waterdata.get_daily()` function; the `waterdata.get_continuous()` function does not return geometry at all." |
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Mention why skipping geometry might be desired (reducing response size/repeated information)
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Add an explanation for this code and/or interpretation of the output
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Honest question from not knowing matplotlib well: is there a way to save the plot above in an object (or something similar) so you can add on to it here, rather than re-writing all of the code again? Analogous to "plt + geom_point(...)"
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End the vignette with a list of links/resources to learn more
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Built off the PR that adds reference table functions.
Adds a demo of the
waterdatafunctions todrpy. Addresses: #200Also adds a few query parameters to the
get_time_series_metadata()function.