Source code for imod.select.points

import numpy as np
import xarray as xr

import imod


[docs]def points_in_bounds(da, **points): """ Returns whether points specified by keyword arguments fall within the bounds of ``da``. Parameters ---------- da : xr.DataArray points : keyword arguments of coordinate=values keyword arguments specifying coordinate and values. Please refer to the examples. Returns ------- in_bounds : np.array of bools Examples -------- Create the DataArray, then use the keyword arguments to define along which coordinate to check whether the points are within bounds. >>> nrow, ncol = 3, 4 >>> data = np.arange(12.0).reshape(nrow, ncol) >>> coords = {"x": [0.5, 1.5, 2.5, 3.5], "y": [2.5, 1.5, 0.5]} >>> dims = ("y", "x") >>> da = xr.DataArray(data, coords, dims) >>> x = [0.4, 2.6] >>> points_in_bounds(da, x=x) This works for an arbitrary number of coordinates: >>> y = [1.3, 2.7] >>> points_in_bounds(da, x=x, y=y) """ # check sizes shapes = {} for coord, value in points.items(): arr = np.atleast_1d(value) points[coord] = arr shape = arr.shape if not len(shape) == 1: raise ValueError( f"Coordinate {coord} is not one-dimensional, but has shape: {shape}" ) shapes[coord] = shape if not len(set(shapes.values())) == 1: msg = "\n".join([f"{coord}: {shape}" for coord, shape in shapes.items()]) raise ValueError(f"Shapes of coordinates do match each other:\n{msg}") # Re-use shape state from loop above in_bounds = np.full(shape, True) for key, x in points.items(): da_x = da.coords[key] _, xmin, xmax = imod.util.coord_reference(da_x) # Inplace bitwise operator in_bounds &= (x >= xmin) & (x < xmax) return in_bounds
def _get_indices_1d(da, coordname, x): x = np.atleast_1d(x) x_decreasing = da.indexes[coordname].is_monotonic_decreasing dx, xmin, _ = imod.util.coord_reference(da.coords[coordname]) ncell = da[coordname].size # Compute edges xs = np.full(ncell + 1, xmin) # Turn dx into array if isinstance(dx, float): dx = np.full(ncell, dx) # Always increasing if x_decreasing: xs[1:] += np.abs(dx[::-1]).cumsum() else: xs[1:] += np.abs(dx).cumsum() # From np.searchsorted docstring: # Find the indices into a sorted array a such that, if the corresponding # elements in v were inserted before the indices, the order of a would # be preserved. ixs = np.searchsorted(xs, x, side="right") # Take care of decreasing coordinates if x_decreasing: ixs = ncell - ixs else: ixs = ixs - 1 return ixs
[docs]def points_indices(da, **points): """ Get the indices for points as defined by the arrays x and y. This function will raise a ValueError if the points fall outside of the bounds of the DataArray to avoid undefined behavior. Use the ``imod.select.points_in_bounds`` function to detect these points. Parameters ---------- da : xr.DataArray points : keyword arguments of coordinates and values Returns ------- indices : dict of {coordinate: xr.DataArray with indices} Examples -------- To extract values: >>> x = [1.0, 2.2, 3.0] >>> y = [4.0, 5.6, 7.0] >>> indices = imod.select.points_indices(da, x=x, y=y) >>> ind_y = indices["y"] >>> ind_x = indices["x"] >>> selection = da.isel(x=ind_x, y=ind_y) Or shorter, using dictionary unpacking: >>> indices = imod.select.points_indices(da, x=x, y=y) >>> selection = da.isel(**indices) To set values (in a new array), the following will do the trick: >>> empty = xr.full_like(da, np.nan) >>> empty.data[indices["y"].values, indices["x"].values] = values_to_set Unfortunately, at the time of writing, xarray's .sel method does not support setting values yet. The method here works for both numpy and dask arrays, but you'll have to manage dimensions yourself! The ``imod.select.points_set_values()`` function will take care of the dimensions. """ inside = points_in_bounds(da, **points) # Error handling if not inside.all(): raise ValueError(f"Not all points are within the bounds of the DataArray") indices = {} for coordname, value in points.items(): ind_da = xr.DataArray(_get_indices_1d(da, coordname, value), dims=["index"]) ind_da["index"] = np.arange(ind_da.size) indices[coordname] = ind_da return indices
[docs]def points_values(da, **points): """ Get values from specified points. This function will raise a ValueError if the points fall outside of the bounds of the DataArray to avoid undefined behavior. Use the ``imod.select.points_in_bounds`` function to detect these points. Parameters ---------- da : xr.DataArray points : keyword arguments of coordinate=values keyword arguments specifying coordinate and values. Returns ------- selection : xr.DataArray Examples -------- >>> x = [1.0, 2.2, 3.0] >>> y = [4.0, 5.6, 7.0] >>> selection = imod.select.points_values(da, x=x, y=y) """ for coordname, value in points.items(): if coordname not in da.coords: raise ValueError(f'DataArray has no coordinate "{coordname}"') # contents must be iterable points[coordname] = np.atleast_1d(value) indices = points_indices(da, **points) selection = da.isel(**indices) # Fetch a value from the dictionary, and get its size. sample_size = len(next(iter(points.values()))) selection.coords["index"] = np.arange(sample_size) return selection
[docs]def points_set_values(da, values, **points): """ Set values at specified points. This function will raise a ValueError if the points fall outside of the bounds of the DataArray to avoid undefined behavior. Use the ``imod.select.points_in_bounds`` function to detect these points. Parameters ---------- da : xr.DataArray values : (int, float) or array of (int, float) points : keyword arguments of coordinate=values keyword arguments specifying coordinate and values. Returns ------- None : mutates da. Examples -------- >>> x = [1.0, 2.2, 3.0] >>> y = [4.0, 5.6, 7.0] >>> values = [10.0, 11.0, 12.0] >>> selection = imod.select.points_values(da, values, x=x, y=y) """ inside = points_in_bounds(da, **points) # Error handling if not inside.all(): raise ValueError(f"Not all points are within the bounds of the DataArray") if not isinstance(values, (int, float)): # then it might be an array if len(values) != len(inside): raise ValueError( "Shape of ``values`` does not match shape of coordinates." f"Shape of values: {values.shape}; shape of coordinates: {inside.shape}." ) sel_indices = {} for coordname in points.keys(): if coordname not in da.coords: raise ValueError(f'DataArray has no coordinate "{coordname}"') underlying_dims = da.coords[coordname].dims if len(underlying_dims) != 1: raise ValueError(f"Coordinate {coordname} is not one-dimensional") # Use the first and only element of underlying_dims sel_indices[underlying_dims[0]] = _get_indices_1d( da, coordname, points[coordname] ) # Collect indices in the right order indices = [] for dim in da.dims: indices.append(sel_indices.get(dim, slice(None, None))) # Set values in dask or numpy array da.data[tuple(indices)] = values