pygeohydro.nid#

Accessing data from the supported databases through their APIs.

Module Contents#

class pygeohydro.nid.NID#

Retrieve data from the National Inventory of Dams web service.

get_byfilter(query_list)#

Query dams by filters from the National Inventory of Dams web service.

Parameters:

query_list (list of dict) – List of dictionary of query parameters. For an exhaustive list of the parameters, use the advanced fields dataframe that can be accessed via NID().fields_meta. Some filter require min/max values such as damHeight and drainageArea. For such filters, the min/max values should be passed like so: {filter_key: ["[min1 max1]", "[min2 max2]"]}.

Returns:

list of geopandas.GeoDataFrame – Query results in the same order as the input query list.

Return type:

list[geopandas.GeoDataFrame]

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> query_list = [
...    {"drainageArea": ["[200 500]"]},
...    {"nidId": ["CA01222"]},
... ]
>>> dam_dfs = nid.get_byfilter(query_list)
get_bygeom(geometry, geo_crs)#

Retrieve NID data within a geometry.

Parameters:

  • geometry (Polygon, MultiPolygon, or tuple of length 4) – Geometry or bounding box (west, south, east, north) for extracting the data.

  • geo_crs (list of str) – The CRS of the input geometry.

Returns:

geopandas.GeoDataFrame – GeoDataFrame of NID data

Return type:

geopandas.GeoDataFrame

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> dams = nid.get_bygeom((-69.77, 45.07, -69.31, 45.45), 4326)
get_suggestions(text, context_key=None)#

Get suggestions from the National Inventory of Dams web service.

Notes

This function is useful for exploring and/or narrowing down the filter fields that are needed to query the dams using get_byfilter.

Parameters:

  • text (str) – Text to query for suggestions.

  • context_key (str, optional) – Suggestion context, defaults to empty string, i.e., all context keys. For a list of valid context keys, see NID().fields_meta.

Returns:

tuple of pandas.DataFrame – The suggestions for the requested text as two DataFrames: First, is suggestions found in the dams properties and second, those found in the query fields such as states, huc6, etc.

Return type:

tuple[pandas.DataFrame, pandas.DataFrame]

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> dams, contexts = nid.get_suggestions("houston", "city")
inventory_byid(federal_ids)#

Get extra attributes for dams based on their dam ID.

Notes

This function is meant to be used for getting extra attributes for dams. For example, first you need to use either get_bygeom or get_byfilter to get basic attributes of the target dams. Then you can use this function to get extra attributes using the id column of the GeoDataFrame that get_bygeom or get_byfilter returns.

Parameters:

federal_ids (list of str) – List of the target dam Federal IDs.

Returns:

pandas.DataFrame – Dams with extra attributes in addition to the standard NID fields that other NID methods return.

Return type:

geopandas.GeoDataFrame

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> dams = nid.inventory_byid(['KY01232', 'GA02400', 'NE04081', 'IL55070', 'TN05345'])
stage_nid_inventory(fname=None)#

Download the entire NID inventory data and save to a parquet file.

Parameters:

fname (str, pathlib.Path, optional) – The path to the file to save the data to, defaults to ./cache/full_nid_inventory.parquet.

property df#

Entire NID inventory (csv version) as a pandas.DataFrame.

property gdf#

Entire NID inventory (gpkg version) as a geopandas.GeoDataFrame.

property nid_inventory_path: pathlib.Path#

Path to the NID inventory parquet file.