Source code for dea_tools.spatial

## dea_spatialtools.py
"""
Tools for spatially manipulating Digital Earth Australia data.

License: The code in this notebook is licensed under the Apache License, 
Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0). Digital Earth 
Australia data is licensed under the Creative Commons by Attribution 4.0 
license (https://creativecommons.org/licenses/by/4.0/).

Contact: If you need assistance, please post a question on the Open Data 
Cube Slack channel (http://slack.opendatacube.org/) or on the GIS Stack 
Exchange (https://gis.stackexchange.com/questions/ask?tags=open-data-cube) 
using the `open-data-cube` tag (you can view previously asked questions 
here: https://gis.stackexchange.com/questions/tagged/open-data-cube). 

If you would like to report an issue with this script, file one on 
Github: https://github.com/GeoscienceAustralia/dea-notebooks/issues/new

Last modified: August 2023

"""

# Import required packages
import dask
import fiona
import warnings
import collections
import odc.geo.xr
import numpy as np
import pandas as pd
import xarray as xr
import geopandas as gpd
import rasterio.features
import scipy.interpolate
import multiprocessing as mp
from odc.geo.geom import Geometry
from odc.geo.crs import CRS
from scipy import ndimage as nd
from skimage.measure import label
from rasterstats import zonal_stats
from skimage.measure import find_contours
from geopy.geocoders import Nominatim
from geopy.exc import GeocoderUnavailable, GeocoderServiceError

from datacube.utils.cog import write_cog
from shapely.geometry import LineString, MultiLineString, shape, mapping


[docs] def points_on_line(gdf, index, distance=30): """ Generates evenly-spaced point features along a specific line feature in a `geopandas.GeoDataFrame`. Parameters: ----------- gdf : geopandas.GeoDataFrame A `geopandas.GeoDataFrame` containing line features with an index and CRS. index : string or int An value giving the index of the line to generate points along distance : integer or float, optional A number giving the interval at which to generate points along the line feature. Defaults to 30, which will generate a point at every 30 metres along the line. Returns: -------- points_gdf : geopandas.GeoDataFrame A `geopandas.GeoDataFrame` containing point features at every `distance` along the selected line. """ # Select individual line to generate points along line_feature = gdf.loc[[index]].geometry # If multiple features are returned, take unary union if line_feature.shape[0] > 0: line_feature = line_feature.unary_union else: line_feature = line_feature.iloc[0] # Generate points along line and convert to geopandas.GeoDataFrame points_line = [ line_feature.interpolate(i) for i in range(0, int(line_feature.length), distance) ] points_gdf = gpd.GeoDataFrame(geometry=points_line, crs=gdf.crs) return points_gdf
[docs] def add_geobox(ds, crs=None): """ Ensure that an xarray DataArray has a GeoBox and .odc.* accessor using `odc.geo`. If `ds` is missing a Coordinate Reference System (CRS), this can be supplied using the `crs` param. Parameters ---------- ds : xarray.Dataset or xarray.DataArray Input xarray object that needs to be checked for spatial information. crs : str, optional Coordinate Reference System (CRS) information for the input `ds` array. If `ds` already has a CRS, then `crs` is not required. Default is None. Returns ------- xarray.Dataset or xarray.DataArray The input xarray object with added `.odc.x` attributes to access spatial information. """ # Import the odc-geo package to add `.odc.x` attributes # to our input xarray object import odc.geo.xr # If a CRS is not found, use custom provided CRS if ds.odc.crs is None and crs is not None: ds = ds.odc.assign_crs(crs) elif ds.odc.crs is None and crs is None: raise ValueError( "Unable to determine `ds`'s coordinate " "reference system (CRS). Please provide a " "CRS using the `crs` parameter " "(e.g. `crs='EPSG:3577'`)." ) return ds
[docs] def xr_vectorize( da, attribute_col=None, crs=None, dtype="float32", output_path=None, verbose=True, **rasterio_kwargs, ): """ Vectorises a raster ``xarray.DataArray`` into a vector ``geopandas.GeoDataFrame``. Parameters ---------- da : xarray.DataArray The input ``xarray.DataArray`` data to vectorise. attribute_col : str, optional Name of the attribute column in the resulting ``geopandas.GeoDataFrame``. Values from ``da`` converted to polygons will be assigned to this column. If None, the column name will default to 'attribute'. crs : str or CRS object, optional If ``da``'s coordinate reference system (CRS) cannot be determined, provide a CRS using this parameter. (e.g. 'EPSG:3577'). dtype : str, optional Data type of must be one of int16, int32, uint8, uint16, or float32 output_path : string, optional Provide an optional string file path to export the vectorised data to file. Supports any vector file formats supported by ``geopandas.GeoDataFrame.to_file()``. verbose : bool, optional Print debugging messages. Default True. **rasterio_kwargs : A set of keyword arguments to ``rasterio.features.shapes``. Can include `mask` and `connectivity`. Returns ------- gdf : geopandas.GeoDataFrame """ # Add GeoBox and odc.* accessor to array using `odc-geo` da = add_geobox(da, crs) # Run the vectorizing function vectors = rasterio.features.shapes( source=da.data.astype(dtype), transform=da.odc.transform, **rasterio_kwargs ) # Convert the generator into a list vectors = list(vectors) # Extract the polygon coordinates and values from the list polygons = [polygon for polygon, value in vectors] values = [value for polygon, value in vectors] # Convert polygon coordinates into polygon shapes polygons = [shape(polygon) for polygon in polygons] # Create a geopandas dataframe populated with the polygon shapes attribute_name = attribute_col if attribute_col is not None else "attribute" gdf = gpd.GeoDataFrame( data={attribute_name: values}, geometry=polygons, crs=da.odc.crs ) # If a file path is supplied, export to file if output_path is not None: if verbose: print(f"Exporting vector data to {output_path}") gdf.to_file(output_path) return gdf
[docs] def xr_rasterize( gdf, da, attribute_col=None, crs=None, name=None, output_path=None, verbose=True, **rasterio_kwargs, ): """ Rasterizes a vector ``geopandas.GeoDataFrame`` into a raster ``xarray.DataArray``. Parameters ---------- gdf : geopandas.GeoDataFrame A ``geopandas.GeoDataFrame`` object containing the vector data you want to rasterise. da : xarray.DataArray or xarray.Dataset The shape, coordinates, dimensions, and transform of this object are used to define the array that ``gdf`` is rasterized into. It effectively provides a spatial template. attribute_col : string, optional Name of the attribute column in ``gdf`` containing values for each vector feature that will be rasterized. If None, the output will be a boolean array of 1's and 0's. crs : str or CRS object, optional If ``da``'s coordinate reference system (CRS) cannot be determined, provide a CRS using this parameter. (e.g. 'EPSG:3577'). name : str, optional An optional name used for the output ``xarray.DataArray`. output_path : string, optional Provide an optional string file path to export the rasterized data as a GeoTIFF file. verbose : bool, optional Print debugging messages. Default True. **rasterio_kwargs : A set of keyword arguments to ``rasterio.features.rasterize``. Can include: 'all_touched', 'merge_alg', 'dtype'. Returns ------- da_rasterized : xarray.DataArray The rasterized vector data. """ # Add GeoBox and odc.* accessor to array using `odc-geo` da = add_geobox(da, crs) # Reproject vector data to raster's CRS gdf_reproj = gdf.to_crs(crs=da.odc.crs) # If an attribute column is specified, rasterise using vector # attribute values. Otherwise, rasterise into a boolean array if attribute_col is not None: # Use the geometry and attributes from `gdf` to create an iterable shapes = zip(gdf_reproj.geometry, gdf_reproj[attribute_col]) else: # Use geometry directly (will produce a boolean numpy array) shapes = gdf_reproj.geometry # Rasterise shapes into a numpy array im = rasterio.features.rasterize( shapes=shapes, out_shape=da.odc.geobox.shape, transform=da.odc.geobox.transform, **rasterio_kwargs, ) # Convert numpy array to a full xarray.DataArray # and set array name if supplied da_rasterized = odc.geo.xr.wrap_xr(im=im, gbox=da.odc.geobox) da_rasterized = da_rasterized.rename(name) # If a file path is supplied, export to file if output_path is not None: if verbose: print(f"Exporting raster data to {output_path}") write_cog(da_rasterized, output_path, overwrite=True) return da_rasterized
[docs] def subpixel_contours( da, z_values=[0.0], crs=None, attribute_df=None, output_path=None, min_vertices=2, dim="time", time_format="%Y-%m-%d", errors="ignore", verbose=True, ): """ Uses `skimage.measure.find_contours` to extract multiple z-value contour lines from a two-dimensional array (e.g. multiple elevations from a single DEM), or one z-value for each array along a specified dimension of a multi-dimensional array (e.g. to map waterlines across time by extracting a 0 NDWI contour from each individual timestep in an xarray timeseries). Contours are returned as a geopandas.GeoDataFrame with one row per z-value or one row per array along a specified dimension. The `attribute_df` parameter can be used to pass custom attributes to the output contour features. Last modified: May 2023 Parameters ---------- da : xarray DataArray A two-dimensional or multi-dimensional array from which contours are extracted. If a two-dimensional array is provided, the analysis will run in 'single array, multiple z-values' mode which allows you to specify multiple `z_values` to be extracted. If a multi-dimensional array is provided, the analysis will run in 'single z-value, multiple arrays' mode allowing you to extract contours for each array along the dimension specified by the `dim` parameter. z_values : int, float or list of ints, floats An individual z-value or list of multiple z-values to extract from the array. If operating in 'single z-value, multiple arrays' mode specify only a single z-value. crs : string or CRS object, optional If ``da``'s coordinate reference system (CRS) cannot be determined, provide a CRS using this parameter. (e.g. 'EPSG:3577'). output_path : string, optional The path and filename for the output shapefile. attribute_df : pandas.Dataframe, optional A pandas.Dataframe containing attributes to pass to the output contour features. The dataframe must contain either the same number of rows as supplied `z_values` (in 'multiple z-value, single array' mode), or the same number of rows as the number of arrays along the `dim` dimension ('single z-value, multiple arrays mode'). min_vertices : int, optional The minimum number of vertices required for a contour to be extracted. The default (and minimum) value is 2, which is the smallest number required to produce a contour line (i.e. a start and end point). Higher values remove smaller contours, potentially removing noise from the output dataset. dim : string, optional The name of the dimension along which to extract contours when operating in 'single z-value, multiple arrays' mode. The default is 'time', which extracts contours for each array along the time dimension. time_format : string, optional The format used to convert `numpy.datetime64` values to strings if applied to data with a "time" dimension. Defaults to "%Y-%m-%d". errors : string, optional If 'raise', then any failed contours will raise an exception. If 'ignore' (the default), a list of failed contours will be printed. If no contours are returned, an exception will always be raised. verbose : bool, optional Print debugging messages. Default is True. Returns ------- output_gdf : geopandas geodataframe A geopandas geodataframe object with one feature per z-value ('single array, multiple z-values' mode), or one row per array along the dimension specified by the `dim` parameter ('single z-value, multiple arrays' mode). If `attribute_df` was provided, these values will be included in the shapefile's attribute table. """ def _contours_to_multiline(da_i, z_value, min_vertices=2): """ Helper function to apply marching squares contour extraction to an array and return a data as a shapely MultiLineString. The `min_vertices` parameter allows you to drop small contours with less than X vertices. """ # Extracts contours from array, and converts each discrete # contour into a Shapely LineString feature. If the function # returns a KeyError, this may be due to an unresolved issue in # scikit-image: https://github.com/scikit-image/scikit-image/issues/4830 # A temporary workaround is to peturb the z-value by a tiny # amount (1e-12) before using it to extract the contour. try: line_features = [ LineString(i[:, [1, 0]]) for i in find_contours(da_i.data, z_value) if i.shape[0] >= min_vertices ] except KeyError: line_features = [ LineString(i[:, [1, 0]]) for i in find_contours(da_i.data, z_value + 1e-12) if i.shape[0] >= min_vertices ] # Output resulting lines into a single combined MultiLineString return MultiLineString(line_features) def _time_format(i, time_format): """ Converts numpy.datetime64 into formatted strings; otherwise returns data as-is. """ if isinstance(i, np.datetime64): ts = pd.to_datetime(str(i)) i = ts.strftime(time_format) return i # Verify input data is a xr.DataArray if not isinstance(da, xr.DataArray): raise ValueError( "The input `da` is not an xarray.DataArray. " "If you supplied an xarray.Dataset, pass in one " "of its data variables using the syntax " "`da=ds.<variable name>`." ) # Add GeoBox and odc.* accessor to array using `odc-geo` da = add_geobox(da, crs) # If z_values is supplied is not a list, convert to list: z_values = ( z_values if (isinstance(z_values, list) or isinstance(z_values, np.ndarray)) else [z_values] ) # If dask collection, load into memory if dask.is_dask_collection(da): if verbose: print(f"Loading data into memory using Dask") da = da.compute() # Test number of dimensions in supplied data array if len(da.shape) == 2: if verbose: print(f"Operating in multiple z-value, single array mode") dim = "z_value" contour_arrays = { _time_format(i, time_format): _contours_to_multiline(da, i, min_vertices) for i in z_values } else: # Test if only a single z-value is given when operating in # single z-value, multiple arrays mode if verbose: print(f"Operating in single z-value, multiple arrays mode") if len(z_values) > 1: raise ValueError( "Please provide a single z-value when operating " "in single z-value, multiple arrays mode" ) contour_arrays = { _time_format(i, time_format): _contours_to_multiline( da_i, z_values[0], min_vertices ) for i, da_i in da.groupby(dim) } # If attributes are provided, add the contour keys to that dataframe if attribute_df is not None: try: attribute_df.insert(0, dim, contour_arrays.keys()) # If this fails, it is due to the applied attribute table not # matching the structure of the loaded data except ValueError: if len(da.shape) == 2: raise ValueError( f"The provided `attribute_df` contains a different " f"number of rows ({len(attribute_df.index)}) " f"than the number of supplied `z_values` " f"({len(z_values)})." ) else: raise ValueError( f"The provided `attribute_df` contains a different " f"number of rows ({len(attribute_df.index)}) " f"than the number of arrays along the '{dim}' " f"dimension ({len(da[dim])})." ) # Otherwise, use the contour keys as the only main attributes else: attribute_df = list(contour_arrays.keys()) # Convert output contours to a geopandas.GeoDataFrame contours_gdf = gpd.GeoDataFrame( data=attribute_df, geometry=list(contour_arrays.values()), crs=da.odc.crs ) # Define affine and use to convert array coords to geographic coords. # We need to add 0.5 x pixel size to the x and y to obtain the centre # point of our pixels, rather than the top-left corner affine = da.odc.geobox.transform shapely_affine = [ affine.a, affine.b, affine.d, affine.e, affine.xoff + affine.a / 2.0, affine.yoff + affine.e / 2.0, ] contours_gdf["geometry"] = contours_gdf.affine_transform(shapely_affine) # Rename the data column to match the dimension contours_gdf = contours_gdf.rename({0: dim}, axis=1) # Drop empty timesteps empty_contours = contours_gdf.geometry.is_empty failed = ", ".join(map(str, contours_gdf[empty_contours][dim].to_list())) contours_gdf = contours_gdf[~empty_contours] # Raise exception if no data is returned, or if any contours fail # when `errors='raise'. Otherwise, print failed contours if empty_contours.all() and errors == "raise": raise ValueError( "Failed to generate any valid contours; verify that " "values passed to `z_values` are valid and present " "in `da`" ) elif empty_contours.all() and errors == "ignore": if verbose: print( "Failed to generate any valid contours; verify that " "values passed to `z_values` are valid and present " "in `da`" ) elif empty_contours.any() and errors == "raise": raise Exception(f"Failed to generate contours: {failed}") elif empty_contours.any() and errors == "ignore": if verbose: print(f"Failed to generate contours: {failed}") # If asked to write out file, test if GeoJSON or ESRI Shapefile. If # GeoJSON, convert to EPSG:4326 before exporting. if output_path and output_path.endswith(".geojson"): if verbose: print(f"Writing contours to {output_path}") contours_gdf.to_crs("EPSG:4326").to_file(filename=output_path) if output_path and output_path.endswith(".shp"): if verbose: print(f"Writing contours to {output_path}") contours_gdf.to_file(filename=output_path) return contours_gdf
[docs] def interpolate_2d( ds, x_coords, y_coords, z_coords, method="linear", factor=1, verbose=False, **kwargs ): """ This function takes points with X, Y and Z coordinates, and interpolates Z-values across the extent of an existing xarray dataset. This can be useful for producing smooth surfaces from point data that can be compared directly against satellite data derived from an OpenDataCube query. Supported interpolation methods include 'linear', 'nearest' and 'cubic (using `scipy.interpolate.griddata`), and 'rbf' (using `scipy.interpolate.Rbf`). Last modified: February 2020 Parameters ---------- ds : xarray DataArray or Dataset A two-dimensional or multi-dimensional array from which x and y dimensions will be copied and used for the area in which to interpolate point data. x_coords, y_coords : numpy array Arrays containing X and Y coordinates for all points (e.g. longitudes and latitudes). z_coords : numpy array An array containing Z coordinates for all points (e.g. elevations). These are the values you wish to interpolate between. method : string, optional The method used to interpolate between point values. This string is either passed to `scipy.interpolate.griddata` (for 'linear', 'nearest' and 'cubic' methods), or used to specify Radial Basis Function interpolation using `scipy.interpolate.Rbf` ('rbf'). Defaults to 'linear'. factor : int, optional An optional integer that can be used to subsample the spatial interpolation extent to obtain faster interpolation times, then up-sample this array back to the original dimensions of the data as a final step. For example, setting `factor=10` will interpolate data into a grid that has one tenth of the resolution of `ds`. This approach will be significantly faster than interpolating at full resolution, but will potentially produce less accurate or reliable results. verbose : bool, optional Print debugging messages. Default False. **kwargs : Optional keyword arguments to pass to either `scipy.interpolate.griddata` (if `method` is 'linear', 'nearest' or 'cubic'), or `scipy.interpolate.Rbf` (is `method` is 'rbf'). Returns ------- interp_2d_array : xarray DataArray An xarray DataArray containing with x and y coordinates copied from `ds_array`, and Z-values interpolated from the points data. """ # Extract xy and elev points points_xy = np.vstack([x_coords, y_coords]).T # Extract x and y coordinates to interpolate into. # If `factor` is greater than 1, the coordinates will be subsampled # for faster run-times. If the last x or y value in the subsampled # grid aren't the same as the last x or y values in the original # full resolution grid, add the final full resolution grid value to # ensure data is interpolated up to the very edge of the array if ds.x[::factor][-1].item() == ds.x[-1].item(): x_grid_coords = ds.x[::factor].values else: x_grid_coords = ds.x[::factor].values.tolist() + [ds.x[-1].item()] if ds.y[::factor][-1].item() == ds.y[-1].item(): y_grid_coords = ds.y[::factor].values else: y_grid_coords = ds.y[::factor].values.tolist() + [ds.y[-1].item()] # Create grid to interpolate into grid_y, grid_x = np.meshgrid(x_grid_coords, y_grid_coords) # Apply scipy.interpolate.griddata interpolation methods if method in ("linear", "nearest", "cubic"): # Interpolate x, y and z values interp_2d = scipy.interpolate.griddata( points=points_xy, values=z_coords, xi=(grid_y, grid_x), method=method, **kwargs, ) # Apply Radial Basis Function interpolation elif method == "rbf": # Interpolate x, y and z values rbf = scipy.interpolate.Rbf(x_coords, y_coords, z_coords, **kwargs) interp_2d = rbf(grid_y, grid_x) # Create xarray dataarray from the data and resample to ds coords interp_2d_da = xr.DataArray( interp_2d, coords=[y_grid_coords, x_grid_coords], dims=["y", "x"] ) # If factor is greater than 1, resample the interpolated array to # match the input `ds` array if factor > 1: interp_2d_da = interp_2d_da.interp_like(ds) return interp_2d_da
[docs] def contours_to_arrays(gdf, col): """ This function converts a polyline shapefile into an array with three columns giving the X, Y and Z coordinates of each vertex. This data can then be used as an input to interpolation procedures (e.g. using a function like `interpolate_2d`. Last modified: October 2021 Parameters ---------- gdf : Geopandas GeoDataFrame A GeoPandas GeoDataFrame of lines to convert into point coordinates. col : str A string giving the name of the GeoDataFrame field to use as Z-values. Returns ------- A numpy array with three columns giving the X, Y and Z coordinates of each vertex in the input GeoDataFrame. """ coords_zvals = [] for i in range(0, len(gdf)): val = gdf.iloc[i][col] try: coords = np.concatenate( [np.vstack(x.coords.xy).T for x in gdf.iloc[i].geometry.geoms] ) except: coords = np.vstack(gdf.iloc[i].geometry.coords.xy).T coords_zvals.append( np.column_stack((coords, np.full(np.shape(coords)[0], fill_value=val))) ) return np.concatenate(coords_zvals)
[docs] def largest_region(bool_array, **kwargs): """ Takes a boolean array and identifies the largest contiguous region of connected True values. This is returned as a new array with cells in the largest region marked as True, and all other cells marked as False. Parameters ---------- bool_array : boolean array A boolean array (numpy or xarray.DataArray) with True values for the areas that will be inspected to find the largest group of connected cells **kwargs : Optional keyword arguments to pass to `measure.label` Returns ------- largest_region : boolean array A boolean array with cells in the largest region marked as True, and all other cells marked as False. """ # First, break boolean array into unique, discrete regions/blobs blobs_labels = label(bool_array, background=0, **kwargs) # Count the size of each blob, excluding the background class (0) ids, counts = np.unique(blobs_labels[blobs_labels > 0], return_counts=True) # Identify the region ID of the largest blob largest_region_id = ids[np.argmax(counts)] # Produce a boolean array where 1 == the largest region largest_region = blobs_labels == largest_region_id return largest_region
[docs] def transform_geojson_wgs_to_epsg(geojson, EPSG): """ Takes a geojson dictionary and converts it from WGS84 (EPSG:4326) to desired EPSG Parameters ---------- geojson: dict a geojson dictionary containing a 'geometry' key, in WGS84 coordinates EPSG: int numeric code for the EPSG coordinate referecnce system to transform into Returns ------- transformed_geojson: dict a geojson dictionary containing a 'coordinates' key, in the desired CRS """ gg = Geometry(geojson["geometry"], CRS("epsg:4326")) gg = gg.to_crs(CRS(f"epsg:{EPSG}")) return gg.__geo_interface__
[docs] def zonal_stats_parallel(shp, raster, statistics, out_shp, ncpus, **kwargs): """ Summarizing raster datasets based on vector geometries in parallel. Each cpu recieves an equal chunk of the dataset. Utilizes the perrygeo/rasterstats package. Parameters ---------- shp : str Path to shapefile that contains polygons over which zonal statistics are calculated raster: str Path to the raster from which the statistics are calculated. This can be a virtual raster (.vrt). statistics: list list of statistics to calculate. e.g. ['min', 'max', 'median', 'majority', 'sum'] out_shp: str Path to export shapefile containing zonal statistics. ncpus: int number of cores to parallelize the operations over. kwargs: Any other keyword arguments to rasterstats.zonal_stats() See https://github.com/perrygeo/python-rasterstats for all options Returns ------- Exports a shapefile to disk containing the zonal statistics requested """ # yields n sized chunks from list l (used for splitting task to multiple processes) def chunks(l, n): for i in range(0, len(l), n): yield l[i : i + n] # calculates zonal stats and adds results to a dictionary def worker(z, raster, d): z_stats = zonal_stats(z, raster, stats=statistics, **kwargs) for i in range(0, len(z_stats)): d[z[i]["id"]] = z_stats[i] # write output polygon def write_output(zones, out_shp, d): # copy schema and crs from input and add new fields for each statistic schema = zones.schema.copy() crs = zones.crs for stat in statistics: schema["properties"][stat] = "float" with fiona.open(out_shp, "w", "ESRI Shapefile", schema, crs) as output: for elem in zones: for stat in statistics: elem["properties"][stat] = d[elem["id"]][stat] output.write( { "properties": elem["properties"], "geometry": mapping(shape(elem["geometry"])), } ) with fiona.open(shp) as zones: jobs = [] # create manager dictionary (polygon ids=keys, stats=entries) # where multiple processes can write without conflicts man = mp.Manager() d = man.dict() # split zone polygons into 'ncpus' chunks for parallel processing # and call worker() for each split = chunks(zones, len(zones) // ncpus) for z in split: p = mp.Process(target=worker, args=(z, raster, d)) p.start() jobs.append(p) # wait that all chunks are finished [j.join() for j in jobs] write_output(zones, out_shp, d)
[docs] def reverse_geocode(coords, site_classes=None, state_classes=None): """ Takes a latitude and longitude coordinate, and performs a reverse geocode to return a plain-text description of the location in the form: Site, State E.g.: `reverse_geocode(coords=(-35.282163, 149.128835))` 'Canberra, Australian Capital Territory' Parameters ---------- coords : tuple of floats A tuple of (latitude, longitude) coordinates used to perform the reverse geocode. site_classes : list of strings, optional A list of strings used to define the site part of the plain text location description. Because the contents of the geocoded address can vary greatly depending on location, these strings are tested against the address one by one until a match is made. Defaults to: `['city', 'town', 'village', 'suburb', 'hamlet', 'county', 'municipality']`. state_classes : list of strings, optional A list of strings used to define the state part of the plain text location description. These strings are tested against the address one by one until a match is made. Defaults to: `['state', 'territory']`. Returns ------- If a valid geocoded address is found, a plain text location description will be returned: 'Site, State' If no valid address is found, formatted coordinates will be returned instead: 'XX.XX S, XX.XX E' """ # Run reverse geocode using coordinates geocoder = Nominatim(user_agent="Digital Earth Australia") # Create plain text-coords as fall-back lat = f"{-coords[0]:.2f} S" if coords[0] < 0 else f"{coords[0]:.2f} N" lon = f"{-coords[1]:.2f} W" if coords[1] < 0 else f"{coords[1]:.2f} E" try: # Get address from geocoded data out = geocoder.reverse(coords) address = out.raw["address"] # Use site and state classes if supplied; else use defaults default_site_classes = [ "city", "town", "village", "suburb", "hamlet", "county", "municipality", ] default_state_classes = ["state", "territory"] site_classes = site_classes if site_classes else default_site_classes state_classes = state_classes if state_classes else default_state_classes # Return the first site or state class that exists in address dict site = next((address[k] for k in site_classes if k in address), None) state = next((address[k] for k in state_classes if k in address), None) # If site and state exist in the data, return this. # Otherwise, return N/E/S/W coordinates. if site and state: # Return as site, state formatted string return f"{site}, {state}" else: # If no geocoding result, return N/E/S/W coordinates print("No valid geocoded location; returning coordinates instead") return f"{lat}, {lon}" except (KeyError, AttributeError, GeocoderUnavailable, GeocoderServiceError): # If no geocoding result, return N/E/S/W coordinates print("No valid geocoded location; returning coordinates instead") return f"{lat}, {lon}"
[docs] def hillshade(dem, elevation, azimuth, vert_exag=1, dx=30, dy=30): """ Calculate hillshade from an input Digital Elevation Model (DEM) array and a sun elevation and azimith. Parameters: ----------- dem : numpy.array A 2D Digital Elevation Model array. elevation : int or float Sun elevation (0-90, degrees up from horizontal). azimith : int or float Sun azimuth (0-360, degrees clockwise from north). vert_exag : int or float, optional The amount to exaggerate the elevation values by when calculating illumination. This can be used either to correct for differences in units between the x-y coordinate system and the elevation coordinate system (e.g. decimal degrees vs. meters) or to exaggerate or de-emphasize topographic effects. dx : int or float, optional The x-spacing (columns) of the input DEM. This is typically the spatial resolution of the DEM. dy : int or float, optional The y-spacing (rows) of the input input DEM. This is typically the spatial resolution of the DEM. Returns: -------- hs : numpy.array A 2D hillshade array with values between 0-1, where 0 is completely in shadow and 1 is completely illuminated. """ from matplotlib.colors import LightSource hs = LightSource(azdeg=azimuth, altdeg=elevation).hillshade( dem, vert_exag=vert_exag, dx=dx, dy=dy ) return hs
[docs] def sun_angles(dc, query): """ For a given spatiotemporal query, calculate mean sun azimuth and elevation for each satellite observation, and return these as a new `xarray.Dataset` with 'sun_elevation' and 'sun_azimuth' variables. Parameters: ----------- dc : datacube.Datacube object Datacube instance used to load data. query : dict A dictionary containing query parameters used to identify satellite observations and load metadata. Returns: -------- sun_angles_ds : xarray.Dataset An `xarray.set` containing a 'sun_elevation' and 'sun_azimuth' variables. """ from datacube.api.query import query_group_by from datacube.model.utils import xr_apply # Identify satellite datasets and group outputs using the # same approach used to group satellite imagery (i.e. solar day) gb = query_group_by(**query) datasets = dc.find_datasets(**query) dataset_array = dc.group_datasets(datasets, gb) # Load and take the mean of metadata from each product sun_azimuth = xr_apply( dataset_array, lambda t, dd: np.mean([d.metadata.eo_sun_azimuth for d in dd]), dtype=float, ) sun_elevation = xr_apply( dataset_array, lambda t, dd: np.mean([d.metadata.eo_sun_elevation for d in dd]), dtype=float, ) # Combine into new xarray.Dataset sun_angles_ds = xr.merge( [sun_elevation.rename("sun_elevation"), sun_azimuth.rename("sun_azimuth")] ) return sun_angles_ds