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pygeohydro.pygeohydro

Accessing data from the supported databases through their APIs.

Module Contents

class pygeohydro.pygeohydro.NID(expire_after=EXPIRE, disable_caching=False)

Retrieve data from the National Inventory of Dams web service.

Parameters

  • expire_after (int, optional) – Expiration time for response caching in seconds, defaults to -1 (never expire).

  • disable_caching (bool, optional) – If True, disable caching requests, defaults to False.

get_byfilter(self, 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

geopandas.GeoDataFrame – Query results.

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> query_list = [
...    {"huc6": ["160502", "100500"], "drainageArea": ["[200 500]"]},
...    {"nidId": ["CA01222"]},
... ]
>>> dam_dfs = nid.get_byfilter(query_list)
>>> print(dam_dfs[0].name[0])
Stillwater Point Dam
get_bygeom(self, 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, defaults to epsg:4326.

Returns

geopandas.GeoDataFrame – GeoDataFrame of NID data

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> dams = nid.get_bygeom((-69.77, 45.07, -69.31, 45.45), "epsg:4326")
>>> print(dams.name.iloc[0])
Little Moose
get_suggestions(self, text, context_key='')

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.

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> dams, contexts = nid.get_suggestions("texas", "huc2")
>>> print(contexts.loc["HUC2", "value"])
12
inventory_byid(self, dam_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

dam_ids (list of int or str) – List of the target dam IDs (digists only). Note that the dam IDs are not the same as the NID IDs.

Returns

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

Examples

>>> from pygeohydro import NID
>>> nid = NID()
>>> dams = nid.inventory_byid([514871, 459170, 514868, 463501, 463498])
>>> print(dams.damHeight.max())
120.0
pygeohydro.pygeohydro.cover_statistics(ds)

Percentages of the categorical NLCD cover data.

Parameters

ds (xarray.DataArray) – Cover DataArray from a LULC Dataset from the nlcd function.

Returns

dict – Statistics of NLCD cover data

pygeohydro.pygeohydro.nlcd(geometry, resolution, years=None, region='L48', geo_crs=DEF_CRS, crs=DEF_CRS)

Get data from NLCD database (2019).

Deprecated since version 0.11.5: Use nlcd_bygeom() or nlcd_bycoords() instead.

Parameters

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

  • resolution (float) – The data resolution in meters. The width and height of the output are computed in pixel based on the geometry bounds and the given resolution.

  • years (dict, optional) – The years for NLCD layers as a dictionary, defaults to {'impervious': [2019], 'cover': [2019], 'canopy': [2019], "descriptor": [2019]}. Layers that are not in years are ignored, e.g., {'cover': [2016, 2019]} returns land cover data for 2016 and 2019.

  • region (str, optional) – Region in the US, defaults to L48. Valid values are L48 (for CONUS), HI (for Hawaii), AK (for Alaska), and PR (for Puerto Rico). Both lower and upper cases are acceptable.

  • geo_crs (str, optional) – The CRS of the input geometry, defaults to epsg:4326.

  • crs (str, optional) – The spatial reference system to be used for requesting the data, defaults to epsg:4326.

Returns

xarray.Dataset – NLCD within a geometry

pygeohydro.pygeohydro.nlcd_bycoords(coords, years=None, region='L48', expire_after=EXPIRE, disable_caching=False)

Get data from NLCD database (2019).

Parameters

  • coords (list of tuple) – List of coordinates in the form of (longitude, latitude).

  • years (dict, optional) – The years for NLCD layers as a dictionary, defaults to {'impervious': [2019], 'cover': [2019], 'canopy': [2019], "descriptor": [2019]}. Layers that are not in years are ignored, e.g., {'cover': [2016, 2019]} returns land cover data for 2016 and 2019.

  • region (str, optional) – Region in the US, defaults to L48. Valid values are L48 (for CONUS), HI (for Hawaii), AK (for Alaska), and PR (for Puerto Rico). Both lower and upper cases are acceptable.

  • expire_after (int, optional) – Expiration time for response caching in seconds, defaults to -1 (never expire).

  • disable_caching (bool, optional) – If True, disable caching requests, defaults to False.

Returns

geopandas.GeoDataFrame – A GeoDataFrame with the NLCD data and the coordinates.

pygeohydro.pygeohydro.nlcd_bygeom(geometry, resolution, years=None, region='L48', crs=DEF_CRS, validation=True, expire_after=EXPIRE, disable_caching=False)

Get data from NLCD database (2019).

Parameters

  • geometry (geopandas.GeoDataFrame or geopandas.GeoSeries) – A GeoDataFrame or GeoSeries with the geometry to query. The indices are used as keys in the output dictionary.

  • resolution (float) – The data resolution in meters. The width and height of the output are computed in pixel based on the geometry bounds and the given resolution.

  • years (dict, optional) – The years for NLCD layers as a dictionary, defaults to {'impervious': [2019], 'cover': [2019], 'canopy': [2019], "descriptor": [2019]}. Layers that are not in years are ignored, e.g., {'cover': [2016, 2019]} returns land cover data for 2016 and 2019.

  • region (str, optional) – Region in the US, defaults to L48. Valid values are L48 (for CONUS), HI (for Hawaii), AK (for Alaska), and PR (for Puerto Rico). Both lower and upper cases are acceptable.

  • crs (str, optional) – The spatial reference system to be used for requesting the data, defaults to epsg:4326.

  • validation (bool, optional) – Validate the input arguments from the WMS service, defaults to True. Set this to False if you are sure all the WMS settings such as layer and crs are correct to avoid sending extra requests.

  • expire_after (int, optional) – Expiration time for response caching in seconds, defaults to -1 (never expire).

  • disable_caching (bool, optional) – If True, disable caching requests, defaults to False.

Returns

dict of xarray.Dataset or xarray.Dataset – A single or a dict of NLCD datasets. If dict, the keys are indices of the input GeoDataFrame.

pygeohydro.pygeohydro.ssebopeta_bycoords(coords, dates, crs=DEF_CRS)

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, 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.

pygeohydro.pygeohydro.ssebopeta_bygeom(geometry, dates, geo_crs=DEF_CRS)

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.geometry.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, 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

pygeohydro.pygeohydro.ssebopeta_byloc(coords, dates)

Daily actual ET for a location from SSEBop database in mm/day.

Deprecated since version 0.11.5: Use ssebopeta_bycoords() instead. For now, this function calls ssebopeta_bycoords() but retains the same functionality, i.e., returns a dataframe and accepts only a single coordinate. Whereas the new function returns a xarray.Dataset and accepts a dataframe containing coordinates.

Parameters

  • coords (tuple) – Longitude and latitude of a single location as a tuple (lon, lat)

  • dates (tuple or list, optional) – Start and end dates as a tuple (start, end) or a list of years [2001, 2010, …].

Returns

pandas.Series – Daily actual ET for a location

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