Source code for harmonica.forward.prisms_layer

# Copyright (c) 2018 The Harmonica Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
#
# This code is part of the Fatiando a Terra project (https://www.fatiando.org)
#
"""
Define a layer of prisms
"""
import warnings
import numpy as np
import xarray as xr
import verde as vd

from .prism import prism_gravity


[docs]def prisms_layer( coordinates, surface, reference, properties=None, ): """ Create a layer of prisms of equal size Build a regular grid of prisms of equal size on the horizontal directions with variable top and bottom boundaries and properties like density, magnetization, etc. The function returns a :class:`xarray.Dataset` containing ``easting``, ``northing``, ``top`` and ``bottom`` coordinates, and all physical properties as ``data_var`` s. The ``easting`` and ``northing`` coordinates correspond to the location of the center of each prism. The ``prisms_layer`` dataset accessor can be used to access special methods and attributes for the layer of prisms, like the horizontal dimensions of the prisms, getting the boundaries of each prisms, etc. See :class:`XarrayAcessorPrismLayer` for the definition of these methods and attributes. Parameters ---------- coordinates : tuple List containing the coordinates of the centers of the prisms in the following order: ``easting``, ``northing``. The arrays must be 1d arrays containing the coordiantes of the centers per axis, or could be 2d arrays as the ones returned by :func:``numpy.meshgrid``. All coordinates should be in meters and should define a regular grid. surface : 2d-array Array used to create the uppermost boundary of the prisms layer. All heights should be in meters. On every point where ``surface`` is below ``reference``, the ``surface`` value will be used to set the ``bottom`` boundary of that prism, while the ``reference`` value will be used to set the ``top`` boundary of the prism. reference : float or 2d-array Reference surface used to create the lowermost boundary of the prisms layer. It can be either a plane or an irregular surface passed as 2d array. Height(s) must be in meters. properties : dict or None Dictionary containing the physical properties of the prisms. The keys must be strings that will be used to name the corresponding ``data_var`` inside the :class:`xarray.Dataset`, while the values must be 2d-arrays. All physical properties must be passed in SI units. If None, no ``data_var`` will be added to the :class:`xarray.Dataset`. Default is None. Returns ------- dataset : :class:`xarray.Dataset` Dataset containing the coordinates of the center of each prism, the height of its top and bottom boundaries and its corresponding physical properties. See also -------- harmonica.PrismsLayer Examples -------- >>> # Create a synthetic relief >>> import numpy as np >>> easting = np.linspace(0, 10, 5) >>> northing = np.linspace(2, 8, 4) >>> surface = np.arange(20, dtype=float).reshape((4, 5)) >>> density = 2670.0 * np.ones_like(surface) >>> # Define a layer of prisms >>> prisms = prisms_layer( ... (easting, northing), ... surface, ... reference=0, ... properties={"density": density}, ... ) >>> print(prisms) <xarray.Dataset> Dimensions: (easting: 5, northing: 4) Coordinates: * easting (easting) float64 0.0 2.5 5.0 7.5 10.0 * northing (northing) float64 2.0 4.0 6.0 8.0 top (northing, easting) float64 0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0 bottom (northing, easting) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 Data variables: density (northing, easting) float64 2.67e+03 2.67e+03 ... 2.67e+03 Attributes: coords_units: meters properties_units: SI >>> # Get the boundaries of the layer (will exceed the region) >>> print(prisms.prisms_layer.boundaries) (-1.25, 11.25, 1.0, 9.0) >>> # Get the boundaries of one of the prisms >>> print(prisms.prisms_layer.get_prism((0, 2))) (3.75, 6.25, 1.0, 3.0, 0.0, 2.0) """ # noqa: W505 dims = ("northing", "easting") # Initialize data and data_names as None data, data_names = None, None # If properties were passed, then replace data_names and data for its keys # and values, respectively if properties: data_names = tuple(p for p in properties.keys()) data = tuple(p for p in properties.values()) # Create xr.Dataset for prisms prisms = vd.make_xarray_grid( coordinates, data=data, data_names=data_names, dims=dims ) _check_regular_grid(prisms.easting.values, prisms.northing.values) # Append some attributes to the xr.Dataset attrs = {"coords_units": "meters", "properties_units": "SI"} prisms.attrs = attrs # Create the top and bottom coordinates of the prisms prisms.prisms_layer.update_top_bottom(surface, reference) return prisms
def _check_regular_grid(easting, northing): """ Check if the easting and northing coordinates define a regular grid .. note: This function should live inside Verde in the future """ if not np.allclose(easting[1] - easting[0], easting[1:] - easting[:-1]): raise ValueError("Passed easting coordiantes are not evenly spaced.") if not np.allclose(northing[1] - northing[0], northing[1:] - northing[:-1]): raise ValueError("Passed northing coordiantes are not evenly spaced.")
[docs]@xr.register_dataset_accessor("prisms_layer") class DatasetAccessorPrismsLayer: """ Defines dataset accessor for layer of prisms .. warning:: This class is not intended to be initialized. Use the `prisms_layer` accessor for accessing the methods and attributes of this class. See also -------- harmonica.prisms_layer """ def __init__(self, xarray_obj): self._obj = xarray_obj @property def dims(self): """ Return the dims tuple of the prism layer The tuple follows the xarray order: ``"northing"``, ``"easting"``. """ return ("northing", "easting") @property def spacing(self): """ Spacing between center of prisms Returns ------- s_north : float Spacing between center of prisms on the South-North direction. s_east : float Spacing between center of prisms on the West-East direction. """ easting, northing = self._obj.easting.values, self._obj.northing.values _check_regular_grid(easting, northing) s_north, s_east = northing[1] - northing[0], easting[1] - easting[0] return s_north, s_east @property def boundaries(self): """ Boundaries of the layer Returns ------- boundaries : tuple Boundaries of the layer of prisms in the following order: ``west``, ``east``, ``south``, ``north``. """ s_north, s_east = self.spacing west = self._obj.easting.values.min() - s_east / 2 east = self._obj.easting.values.max() + s_east / 2 south = self._obj.northing.values.min() - s_north / 2 north = self._obj.northing.values.max() + s_north / 2 return west, east, south, north @property def size(self): """ Return the total number of prisms on the layer Returns ------- size : int Total number of prisms in the layer. """ return self._obj.northing.size * self._obj.easting.size @property def shape(self): """ Return the number of prisms on each direction Returns ------- n_north : int Number of prisms on the South-North direction. n_east : int Number of prisms on the West-East direction. """ return (self._obj.northing.size, self._obj.easting.size) def _get_prism_horizontal_boundaries(self, easting, northing): """ Compute the horizontal boundaries of the prism Parameters ---------- easting : float or array Easting coordinate of the center of the prism northing : float or array Northing coordinate of the center of the prism """ spacing = self.spacing west = easting - spacing[1] / 2 east = easting + spacing[1] / 2 south = northing - spacing[0] / 2 north = northing + spacing[0] / 2 return west, east, south, north
[docs] def update_top_bottom(self, surface, reference): """ Update top and bottom boundaries of the layer Change the values of the ``top`` and ``bottom`` coordiantes based on the passed ``surface`` and ``reference``. The ``top`` and ``bottom`` boundaries of every prism will be equal to the corresponding ``surface`` and ``reference`` values, respectively, if ``surface`` is above the ``reference`` on that point. Otherwise the ``top`` and ``bottom`` boundaries of the prism will be equal to its corresponding ``reference`` and ``surface``, respectively. Parameters ---------- surface : 2d-array Array used to create the uppermost boundary of the prisms layer. All heights should be in meters. On every point where ``surface`` is below ``reference``, the ``surface`` value will be used to set the ``bottom`` boundary of that prism, while the ``reference`` value will be used to set the ``top`` boundary of the prism. reference : float or 2d-array Reference surface used to create the lowermost boundary of the prisms layer. It can be either a plane or an irregular surface passed as 2d array. Height(s) must be in meters. """ if surface.shape != self.shape: raise ValueError( f"Invalid surface array with shape '{surface.shape}'. " + "Its shape should be compatible with the coordinates " + "of the layer of prisms." ) if isinstance(reference, np.ndarray): if reference.shape != self.shape: raise ValueError( f"Invalid reference array with shape '{reference.shape}'. " + "Its shape should be compatible with the coordinates " + "of the layer of prisms." ) else: reference *= np.ones(self.shape) top = surface.copy() bottom = reference.copy() reverse = surface < reference top[reverse] = reference[reverse] bottom[reverse] = surface[reverse] self._obj.coords["top"] = (self.dims, top) self._obj.coords["bottom"] = (self.dims, bottom)
[docs] def gravity(self, coordinates, field, density_name="density", **kwargs): """ Computes the gravity generated by the layer of prisms Uses :func:`harmonica.prism_gravity` for computing the gravity field generated by the prisms of the layer. The density of the prisms will be assigned from the ``data_var`` chosen through the ``density_name`` argument. Ignores the prisms which ``top`` or ``bottom`` boundaries are ``np.nan``s. All ``kwargs`` will be passed to :func:`harmonica.prism_gravity`. Parameters ---------- coordinates : list or 1d-array List or array containing ``easting``, ``northing`` and ``upward`` of the computation points defined on a Cartesian coordinate system. All coordinates should be in meters. field : str Gravitational field that wants to be computed. The available fields are: - Gravitational potential: ``potential`` - Downward acceleration: ``g_z`` density_name : str (optional) Name of the property layer (or ``data_var`` of the :class:`xarray.Dataset`) that will be used for the density of each prism in the layer. Default to ``"density"`` Returns ------- result : array Gravitational field generated by the prisms on the computation points in mGal. See also -------- harmonica.prism_gravity """ # Get boundaries and density of the prisms boundaries = self._to_prisms() density = self._obj[density_name].values # Get the mask for selecting only the prisms whose top boundary, bottom # boundary and density have no nans mask = self._get_nonans_mask(property_name=density_name) # Select only the boundaries and density elements for masked prisms boundaries = boundaries[mask.ravel()] density = density[mask] # Return gravity field of prisms return prism_gravity( coordinates, prisms=boundaries, density=density, field=field, **kwargs, )
def _get_nonans_mask(self, property_name=None): """ Build a mask for prisms with no nans on top, bottom or a property Parameters ---------- property_name : str (optional) Name of the property layer (or ``data_var`` of the :class:`xarray.Dataset`) that will be used for masking the prisms in the layer. Returns ------- mask : 2d-array Array of bools that can be used as a mask for selecting prisms with no nans on top boundaries, bottom boundaries and the passed property. """ # Mask the prisms that contains no nans on top and bottom boundaries mask = np.logical_and( np.logical_not(np.isnan(self._obj.top.values)), np.logical_not(np.isnan(self._obj.bottom.values)), ) # Mask the prisms that contains nans on the selected property if property_name is not None: mask_property = np.logical_not(np.isnan(self._obj[property_name].values)) # Warn if a nan is found within the masked property if not mask_property[mask].all(): warnings.warn( 'Found missing values in "{}" property '.format(property_name) + "of the prisms layer. " + "The prisms with a nan as " + '"{}" will be ignored.'.format(property_name) ) mask = np.logical_and(mask, mask_property) return mask def _to_prisms(self): """ Return the boundaries of each prism of the layer Returns ------- prisms : 2d-array Array containing the boundaries of each prism of the layer. Each row contains the boundaries of each prism in the following order: ``west``, ``east``, ``south``, ``north``, ``bottom``, ``top``. """ easting, northing = np.meshgrid( self._obj.easting.values, self._obj.northing.values ) west, east, south, north = self._get_prism_horizontal_boundaries( easting.ravel(), northing.ravel() ) bottom = self._obj.bottom.values.ravel() top = self._obj.top.values.ravel() prisms = np.vstack((west, east, south, north, bottom, top)).T return prisms
[docs] def get_prism(self, indices): """ Return the boundaries of the chosen prism Parameters ---------- indices : tuple Indices of the desired prism of the layer in the following order: ``(index_northing, index_easting)``. Returns ------- prism : tuple Boundaries of the prisms in the following order: ``west``, ``east``, ``south``, ``north``, ``bottom``, ``top``. """ # Get the center of the prism center_easting = self._obj.easting.values[indices[1]] center_northing = self._obj.northing.values[indices[0]] # Calculate the boundaries of the prism west, east, south, north = self._get_prism_horizontal_boundaries( center_easting, center_northing ) bottom = self._obj.bottom.values[indices] top = self._obj.top.values[indices] return west, east, south, north, bottom, top