`pygeohydro.pygeohydro`#

Accessing data from the supported databases through their APIs.

Module Contents#

class pygeohydro.pygeohydro.EHydro(data_type='points')#

Access USACE Hydrographic Surveys (eHydro).

Notes

For more info visit: https://navigation.usace.army.mil/Survey/Hydro

bygeom(geom, geo_crs=4326, sql_clause='', distance=None, return_m=False, return_geom=True)#

Get features within a geometry that can be combined with a SQL where clause.

byids(field, fids, return_m=False, return_geom=True)#

Get features by object IDs.

bysql(sql_clause, return_m=False, return_geom=True)#

Get features using a valid SQL 92 WHERE clause.

Parameters:

data_type (str, optional) – Type of the survey data to retrieve, defaults to points. Note that the points data type gets the best available point cloud data, i.e., if SurveyPointHD is available, it will be returned, otherwise SurveyPoint will be returned. Available types are:

points: Point clouds
outlines: Polygons of survey outlines
contours: Depth contours
bathymetry: Bathymetry data

Note that point clouds are not available for all surveys.

property survey_grid: geopandas.GeoDataFrame#

Full survey availability on hexagonal grid cells of 35 km resolution.

class pygeohydro.pygeohydro.NID#

Retrieve data from the National Inventory of Dams web service.

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 feather file.

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 feather file.

Parameters:: fname (str, pathlib.Path, optional) – The path to the file to save the data to, defaults to ./cache/nid_inventory.feather.

pygeohydro.pygeohydro.get_camels()#

Get streaflow and basin attributes of all 671 stations in CAMELS dataset.

Notes

For more info on CAMELS visit: https://ral.ucar.edu/solutions/products/camels

Returns:: tuple of geopandas.GeoDataFrame and xarray.Dataset – The first is basin attributes as a geopandas.GeoDataFrame and the second is streamflow data and basin attributes as an xarray.Dataset.
Return type:: tuple[geopandas.GeoDataFrame, xarray.Dataset]

pygeohydro.pygeohydro.soil_gnatsgo(layers, geometry, crs=4326)#

Get US soil data from the gNATSGO dataset.

Notes

This function uses Microsoft’s Planetary Computer service to get the data. The dataset’s description and its supported soil properties can be found at: https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters

Parameters:

layers (list of str or str) – Target layer(s). Available layers can be found at the dataset’s website here.
geometry (Polygon, MultiPolygon, or tuple of length 4) – Geometry or bounding box of the region of interest.
crs (int, str, or pyproj.CRS, optional) – The input geometry CRS, defaults to epsg:4326.

Returns:

xarray.Dataset – Requested soil properties.

Return type:

xarray.Dataset

pygeohydro.pygeohydro.soil_properties(properties='*', soil_dir='cache')#

Get soil properties dataset in the United States from ScienceBase.

Notes

This function downloads the source zip files from ScienceBase , extracts the included .tif files, and return them as an xarray.Dataset.

Parameters:

properties (list of str or str, optional) – Soil properties to extract, default to “*”, i.e., all the properties. Available properties are awc for available water capacity, fc for field capacity, and por for porosity.
soil_dir (str or pathlib.Pathlib.Path) – Directory to store zip files or if exists read from them, defaults to ./cache.

pygeohydro.pygeohydro.ssebopeta_bycoords(coords, dates, crs=4326)#

Daily actual ET for a dataframe of coords from SSEBop database in mm/day.

Parameters:

coords (pandas.DataFrame) – A dataframe with id, x, y columns.
dates (tuple or list, optional) – Start and end dates as a tuple (start, end) or a list of years [2001, 2010, …].
crs (str, int, or pyproj.CRS, optional) – The CRS of the input coordinates, defaults to epsg:4326.

Returns:

xarray.Dataset – Daily actual ET in mm/day as a dataset with time and location_id dimensions. The location_id dimension is the same as the id column in the input dataframe.

Return type:

xarray.Dataset

pygeohydro.pygeohydro.ssebopeta_bygeom(geometry, dates, geo_crs=4326)#

Get daily actual ET for a region from SSEBop database.

Notes

Since there’s still no web service available for subsetting SSEBop, the data first needs to be downloaded for the requested period then it is masked by the region of interest locally. Therefore, it’s not as fast as other functions and the bottleneck could be the download speed.

Parameters:

geometry (shapely.Polygon or tuple) – The geometry for downloading clipping the data. For a tuple bbox, the order should be (west, south, east, north).
dates (tuple or list, optional) – Start and end dates as a tuple (start, end) or a list of years [2001, 2010, …].
geo_crs (str, int, or pyproj.CRS, optional) – The CRS of the input geometry, defaults to epsg:4326.

Returns:

xarray.DataArray – Daily actual ET within a geometry in mm/day at 1 km resolution

Return type:

xarray.DataArray

pygeohydro.pygeohydro#

Module Contents#

`pygeohydro.pygeohydro`#