Source code for karta.raster.grid

Raster grid representations
import copy
import math
import numbers
import warnings
import numpy as np
from . import _gdal
from . import crfuncs
from .band import SimpleBand, CompressedBand, BandIndexer
from .coordgen import CoordinateGenerator
from .. import errors
from import Cartesian

CRS_DEFAULT = Cartesian

[docs]class Grid(object): """ Grid base class """ @property def nodata(self): return self._nodata
[docs] def max(self): """ Return the maximum non-nan in """ tmp = self[self.data_mask] if len(tmp) != 0: return tmp.max() else: return np.nan
[docs] def min(self): """ Return the minimum non-nan in """ tmp = self[self.data_mask] if len(tmp) != 0: return tmp.min() else: return np.nan
[docs] def minmax(self): """ Return the minimum and maximum value of data array """ tmp = self[self.data_mask_full] if len(tmp) != 0: return (tmp.min(), tmp.max()) else: return (np.nan, np.nan)
[docs] def copy(self): """ Return a deep copy """ return copy.deepcopy(self)
[docs] def apply(self, func, inplace=False, band=None): """ Apply a vector function to grid values. Parameters ---------- func : callable function that takes a vector of values and returns an equally-sized vector of values with the same type inplace : boolean, optional whether to perform operation in place rather than returning a new grid instance (default False) band : int, optional if provided, only apply *func* to a specific band Returns ------- instance of type(self) """ msk = self.data_mask_full if band is not None: if not isinstance(band, int) or not (0 <= band < self.nbands): raise ValueError("band must be an integer in [0, nbands)") msk *= np.array([i == band for i in range(self.nbands)]) if inplace: self[msk] = func(self[msk]) return self else: newgrid = self.copy() newgrid[msk] = func(self[msk]) return newgrid
[docs]class RegularGrid(Grid): """ Regular structured grid class. A RegularGrid contains a fixed number of rows and columns with a constant spacing and a list of bands representing scalar fields. Positions on a RegularGrid are referenced to their array indices (i,j) using an affine transform *T* such that :: T = (a, b, c, d, e, f) x = a + j*c + i*e y = b + i*d + j*f or as a matrix transformation, :: |e c a| |i| |x| | | * |j| = | | |d f b| |1| |y| where (x,y) are the coordinates of the lower left corner of each pixel. Meaning: - (a,b) defines the lower-left grid cell corner - (c,d) defines the resolution in the horizontal and vertical directions - (e,f) can be used to define a rotation In the common case of a "east-right, north-up" grid, e = f = 0. """ def __init__(self, transform, values=None, bands=None, crs=None, nodata_value=None, bandclass=None): """ Create a RegularGrid instance. Parameters ---------- transform : 6-tuple of floats a six element list, tuple, or dictionary defining the geotransform of the grid: ``[xllcorner, yllcorner, dx, dy, sx, sy]`` values : ndarray (nrows x ncols), optional grid data from which raster bands will be formed. A two-dimensional array will be transformed into a single band, while a three-dimension MxNxP array will become P MxN bands. The structure used to store band data internally can be controlled by the *bandclass* keyword argument. bands : list of band types, optional list of allocated Band-like objects. If provided, they are assumed to be of a single type, which overrides *bandclass*. crs : subclass, optional Grid coordinate reference system. default CRS_DEFAULT nodata_value : number, optional specifies a value to represent NoData or null pixels. The default is chosen depending on the input type of *values* or *bands*. If neither is provided, the default is NaN. bandclass : class, optional indicates the band class used to represent grid data. default BAND_CLASS_DEFAULT """ if hasattr(transform, "keys"): self._transform = tuple([float(transform[f]) for f in ("xllcorner", "yllcorner", "dx", "dy", "sx", "sy")]) elif len(transform) == 6: self._transform = tuple(float(a) for a in transform) else: raise errors.GridError("RegularGrid must be initialized with a " "transform iterable or dictionary") if bands is not None: self._bndcls = type(bands[0]) elif bandclass is None: self._bndcls = BAND_CLASS_DEFAULT else: self._bndcls = bandclass if bands is not None: if any(bands[0].size != b.size for b in bands[1:]): size_str = " ".join(set(str(b.size) for b in bands)) raise ValueError("bands have mismatching size: {0}".format(size_str)) self.bands = bands else: self.bands = [] if bands is None and (values is not None): if values.ndim == 2: band = self._bndcls(values.shape, values.dtype.type) band.setblock(0, 0, values) self.bands.append(band) elif values.ndim == 3: for iband in range(values.shape[2]): band = self._bndcls(values.shape[:2], values.dtype.type) band.setblock(0, 0, values[:,:,iband]) self.bands.append(band) else: raise ValueError("`values` must have two or three dimensions") self._bandindexer = BandIndexer(self.bands) if crs is None: = CRS_DEFAULT else: = crs if nodata_value is None: if len(self.bands) == 0: self._nodata = np.nan else: self._nodata = get_nodata(self.bands[0].dtype) else: self._nodata = nodata_value return def __add__(self, other): if self._equivalent_structure(other): return RegularGrid(copy.copy(self.transform), values=self[:,:]+other[:,:],, nodata_value=self.nodata) else: raise ValueError(self, other) def __sub__(self, other): if self._equivalent_structure(other): return RegularGrid(copy.copy(self.transform), values=self[:,:]-other[:,:],, nodata_value=self.nodata) else: raise ValueError(self, other) def __getitem__(self, key): return self._bandindexer[key] def __setitem__(self, key, value): self._bandindexer[key] = value return def _equivalent_structure(self, other): return (self._transform == other._transform) and \ (self.size == other.size) @property def transform(self): return self._transform @property def values(self): return self._bandindexer @property def size(self): return self.bands[0].size @property def nbands(self): return len(self.bands) @property def nodata_value(self): return self._nodata
[docs] def set_nodata_value(self, val): """ Redefine value used to indicate nodata. Parameters ---------- val : number """ self[:,:,:] = np.where(self.data_mask_full, self[:,:,:], val) self._nodata = val return
[docs] def center_llref(self): """ Return the 'lower-left' reference in terms of a center coordinate. """ T = self._transform xllcenter = self._transform[0] + 0.5 * (T[2] + T[4]) yllcenter = self._transform[1] + 0.5 * (T[3] + T[5]) return xllcenter, yllcenter
[docs] def corner_llref(self): """ Return the 'lower-left' reference in terms of a center coordinate. """ return self._transform[:2]
[docs] def coordmesh(self, *args, **kwargs): """ Alias for center_coords() """ return self.center_coords(*args, **kwargs)
[docs] def coordinates(self, crs=None): """ Return a CoordinateGenerator instance for computing the coordinates of indices in *crs*. This behaves like the array returned by `center_coords()`, but computes coordinates on demand rather than at once. Parameters ---------- crs : CRS, optional Coordinate system of output coordinates. If None (default), output coordinates are not transformed from **. Returns ------- CoordinateGenerator Notes ----- This is an experimental replacement for coordmesh() and center_coords(). """ if crs is None: crs = return CoordinateGenerator(self.transform, self.size,, crs)
[docs] def center_coords(self): """ Return the coordinates of cell centers. """ t = self._transform xcoords = np.empty(self.size) ycoords = np.empty(self.size) icol = np.arange(self.size[1]) for i in range(self.size[0]): xcoords[i,:] = t[0] + (icol+0.5)*t[2] + (i+0.5)*t[4] ycoords[i,:] = (t[1] + (i+0.5)*t[3] + (icol+0.5)*t[5]) return xcoords, ycoords
[docs] def vertex_coords(self): """ Return the coordinates of cell vertices. """ t = self._transform ny, nx = self.size xcoords = np.empty((ny+1, nx+1)) ycoords = np.empty((ny+1, nx+1)) icol = np.arange(0, self.size[1]+1) for i in range(self.size[0]): xcoords[i,:] = t[0] + icol*t[2] + i*t[4] ycoords[i,:] = (t[1] + i*t[3] + icol*t[5]) return xcoords, ycoords
@property def origin(self): return self.transform[:2] @property def resolution(self): return self.transform[2:4] @property def skew(self): return self.transform[4:] @property def bbox(self): extent = self.get_extent(reference="edge") return extent[0], extent[2], extent[1], extent[3] @property def data_bbox(self): extent = self.get_data_extent(reference="edge") return extent[0], extent[2], extent[1], extent[3] @property def extent(self): return self.get_extent() def get_bbox(self, crs=None): a, b, c, d = self.get_extent(reference="edge", crs=crs) return a, c, b, d
[docs] def get_extent(self, reference='center', crs=None): """ Return the region characteristics as a tuple (xmin, xmax, ymin, ymax). Parameters ---------- reference : string either 'center' or 'edge'. crs : subclass coordinate system of output """ if reference == 'center': x0, y0 = self.center_llref() n = -1 elif reference == 'edge': x0, y0 = self.corner_llref() n = 0 else: raise errors.GridError("`reference` must be 'center' or 'edge'") ny, nx = self.size dx, dy = self._transform[2:4] sx, sy = self._transform[4:] sgn = lambda a: 0 if a==0 else a/abs(a) if sgn(dx) == sgn(sx): x1 = x0 + dx*(nx+n) + sx*(ny+n) else: x1 = x0 + dx*(nx+n) - sx*(ny+n) if sgn(dy) == sgn(sy): y1 = y0 + dy*(ny+n) + sy*(nx+n) else: y1 = y0 + dy*(ny+n) - sy*(nx+n) if crs is None or (crs == (a, b, c, d) = min(x0, x1), max(x0, x1), min(y0, y1), max(y0, y1) else: xg0, yg0 =, x0, y0) xg1, yg1 =, x0, y1) xg2, yg2 =, x1, y0) xg3, yg3 =, x1, y1) a = min(xg0, xg1, xg2, xg3) b = max(xg0, xg1, xg2, xg3) c = min(yg0, yg1, yg2, yg3) d = max(yg0, yg1, yg2, yg3) return a, b, c, d
[docs] def get_data_extent(self, reference='center', nodata=None, crs=None): """ Return the region characteristics as a tuple (xmin, xmax, ymin, ymax). Parameters ---------- reference : string either 'center' or 'edge'. crs : subclass coordinate system of output """ if nodata is None: nodata = self.nodata if np.isnan(nodata): isdata = lambda a: ~np.isnan(a) else: def isdata(a): return a != nodata dx, dy = self.transform[2:4] sx, sy = self.transform[4:6] x0 = self.transform[0] + 0.5*dx + 0.5*sx y0 = self.transform[1] + 0.5*dy + 0.5*sy ny, nx = self.size # Reading a row is fast, so process from bottom to top bx = x0 by = y0 + ny*dy tx = x0 ty = y0 lx = x0 + nx*dx ly = y0 rx = y0 ry = y0 jvec = np.arange(nx) for i, row in enumerate(self[:,:]): x = (x0 + jvec*dx + i*sx)[isdata(row.squeeze())] y = (y0 + i*dy + jvec*sy)[isdata(row.squeeze())] if len(x) != 0: xmax, xmin = x.max(), x.min() ymax, ymin = y.max(), y.min() if ymin < by: by = ymin bx = x[y==ymin] if ymax > ty: ty = ymax tx = x[y==ymax] if xmin < lx: lx = xmin ly = y[x==xmin] if xmax > rx: rx = xmax ry = y[x==xmax] if reference == 'center': pass elif reference == 'edge': lx -= 0.5*dx - 0.5*sx rx += 0.5*dx + 0.5*sx by -= 0.5*dy - 0.5*sy ty += 0.5*dy + 0.5*sy else: raise errors.GridError("`reference` must be 'center' or 'edge'") if (crs is not None) and (crs != lx, ly =, lx, ly) rx, ry =, rx, ry) bx, by =, bx, by) tx, ty =, tx, ty) return (lx, rx, by, ty)
@property def data_mask_full(self): """ 8-bit mask of valid data cells by band """ if np.isnan(self.nodata): isdata = lambda a: ~np.isnan(a) else: isdata = lambda a: a != self.nodata return np.dstack([isdata(self[:,:,i]) for i in range(len(self.bands))]) @property def data_mask(self): """ 8-bit mask of valid data cells, collapsed across bands """ return np.all(self.data_mask_full, axis=-1)
[docs] def aschunks(self, size=(-1, -1), overlap=(0, 0), copy=True): """ Generator for grid chunks. This may be useful for parallel or memory-controlled grid processing. Parameters ---------- size : tuple of two integers, optional size of the chunks to return (default approximately one-quarter of each dimension) overlap : tuple of two integers, optional number of pixels of overlap (default (0, 0)) copy : bool whether to force returned grids to be copies warning: output may be a copy regardless, depending on the band class Yields ------ RegularGrid smaller grids """ ny, nx = self.size if size == (-1, -1): size = (nx//4, ny//4) i0 = 0 j0 = 0 T0 = self.transform while 1: if j0 >= nx: j0 = 0 i0 += size[1]-overlap[1] if i0 >= ny: break T = [self.transform[0] + j0*T0[2] + i0*T0[4], self.transform[1] + i0*T0[3] + j0*T0[5], T0[2], T0[3], T0[4], T0[5]] if copy: v = self[i0:i0+size[1], j0:j0+size[0]].copy() else: v = self[i0:i0+size[1], j0:j0+size[0]] yield RegularGrid(T, values=v,, nodata_value=self.nodata) j0 += size[0]-overlap[0]
[docs] def clip(self, xmin, xmax, ymin, ymax, crs=None): """ Return a clipped version of grid with cell centers constrained to a bounding box. Parameters ---------- xmin : float xmax : float ymin : float ymax : float crs : subclass, optional """ if crs is not None: x, y = crs.transform(, [xmin, xmin, xmax, xmax], [ymin, ymax, ymin, ymax]) xmin = min(x) xmax = max(x) ymin = min(y) ymax = max(y) # Convert to center coords t = self.transform ll = self.get_positions(xmin, ymin) lr = self.get_positions(xmax, ymin) ul = self.get_positions(xmin, ymax) ur = self.get_positions(xmax, ymax) i0 = int(np.ceil(min(ll[0], lr[0], ul[0], ur[0]))) i1 = int(np.floor(max(ll[0], lr[0], ul[0], ur[0]))) + 1 j0 = int(np.ceil(min(ll[1], lr[1], ul[1], ur[1]))) j1 = int(np.floor(max(ll[1], lr[1], ul[1], ur[1]))) + 1 values = self[i0:i1,j0:j1].copy() x0 = t[0] + j0*t[2] + i0*t[4] y0 = t[1] + i0*t[3] + j0*t[5] tnew = (x0, y0, t[2], t[3], t[4], t[5]) return RegularGrid(tnew, values,, nodata_value=self.nodata)
[docs] def resize(self, bboxnew): """ Return a new grid grid with outer edges given by a bounding box. If the grid origin shifts by a non-integer factor of the grid resolution, nearest neighbour values are selected. Parameters ---------- bboxnew : 4-tuple of floats (xmin, ymin, xmax, ymax). """ # Define own rounding function so that half-values are treated consistently def _round(a): r = a%1 if r <= 0.5: return a//1 else: return a//1+1 bb = self.bbox bbnew = list(bboxnew) dx, dy, sx, sy = self.transform[2:] # Redefine output box dimensions to be an integer factor of dx, dy bbnew[2] = bbnew[0] + dx*math.ceil((bbnew[2]-bbnew[0])/dx) bbnew[3] = bbnew[1] + dy*math.ceil((bbnew[3]-bbnew[1])/dy) ny, nx = self.size nxnew = int(_round((bbnew[2]-bbnew[0])/dx)) nynew = int(_round((bbnew[3]-bbnew[1])/dy)) Tnew = [bbnew[0], bbnew[1], dx, dy, sx, sy] # determine the indices of existing data on the new grid j0new = max(0, int(_round((bb[0]-bbnew[0])/dx))) j1new = min(nxnew, int(_round((bb[2]-bbnew[0])/dx))) i0new = max(0, int(_round((bb[1]-bbnew[1])/dy))) i1new = min(nynew, int(_round((bb[3]-bbnew[1])/dy))) # determine the indices of the data to copy on the old grid j0 = max(0, int(_round((bbnew[0]-bb[0])/dx))) j1 = min(nx, int(_round((bbnew[2]-bb[0])/dx))) i0 = max(0, int(_round((bbnew[1]-bb[1])/dy))) i1 = min(ny, int(_round((bbnew[3]-bb[1])/dy))) newbands = [] for band in self.bands: newband = self._bndcls((nynew, nxnew), dtype=band.dtype, initval=self.nodata) newband.setblock(i0new, j0new, band.getblock(i0, j0, i1-i0, j1-j0)) newbands.append(newband) gridnew = RegularGrid(Tnew, bands=newbands,, nodata_value=self.nodata) return gridnew
[docs] def mask_by_poly(self, polys, inplace=False): """ Return a grid with all elements outside the bounds of a polygon masked as nodata. Parameters ---------- polys : Polygon, Multipolygon or list of Polygon instances region(s) defining masking boundary inplace : bool, optional if True, perform masking in-place (default False) """ if getattr(polys, "_geotype", "") == "Polygon": polys = [polys] def unpack_multipolygons(L): out = [] for item in L: if getattr(item, "_geotype", "") == "Multipolygon": out.extend(item[i] for i in range(len(item))) elif getattr(item, "_geotype", "") == "Polygon": out.append(item) elif isinstance(item, (list, tuple)): out.extend(unpack_multipolygons(a for a in item)) return out polys = unpack_multipolygons(polys) ny, nx = self.size msk = np.zeros([ny, nx], dtype=np.bool) for poly in polys: x, y = poly.get_coordinate_lists([:2] if not poly.isclockwise(): x = x[::-1] y = y[::-1] _msk = mask_poly(x, y, nx, ny, self.transform) msk = (msk | _msk) if inplace: for i in range(len(self.bands)): data = self[:,:,i] data[~msk] = self.nodata self[:,:,i] = data return self else: msk = np.broadcast_to(np.atleast_3d(msk), [ny, nx, self.nbands]) return RegularGrid(self.transform, values=np.where(msk, self[:,:,:], self.nodata),, nodata_value=self.nodata)
def _resample_transform(self, transform, method='nearest'): """ Resample grid to match a new transform. Arguments --------- transform : iterable method : str, optional default 'nearest' Returns ------- RegularGrid """ cg = CoordinateGenerator(transform, self.size,, X, Y = cg[:,:] if method == 'nearest': values = self.sample_nearest(X, Y) elif method == 'linear': values = self.sample_bilinear(X, Y) else: raise NotImplementedError('method "{0}" unavailable'.format(method)) if values.ndim == 3: values = values.transpose(1, 2, 0) return RegularGrid(transform, values=values,, nodata_value=self.nodata) def _align_origin(self, x, y, method='nearest'): """ Shift the grid so that the transform anchor is an integer multiple of x, y from the coordinate system origin at (0, 0). For example, if a grid has transform [3.2, 3.2, 1, 1, 0, 0] (raster pixels of dimension 1x1, with a lower left outer corner at 3.2, 3.2), calling `align_origin(1, 1)` will resample the grid to have transform [3, 3, 1, 1, 0, 0], while `align_origin(5, 5)` with resample the grid to [5, 5, 1, 1, 0, 0]. In both cases, the grid reference has been shifted. Arguments --------- x, y : float method : str, optional Returns ------- RegularGrid """ t = [x * round(self._transform[0] / x), y * round(self._transform[1] / y), self._transform[2], self._transform[3], self._transform[4], self._transform[5]] return self._resample_transform(t, method=method)
[docs] def resample(self, dx, dy, method='nearest'): """ Resample array to have spacing *dx*, *dy*. The grid origin remains in the same position. Parameters ---------- dx : float cell dimension 1 dy : float cell dimension 2 method : str, optional interpolation method, currently 'nearest' and 'linear' supported """ if dx <= 0 or dy <= 0: raise ValueError("resolution must be positive " "(got {0}, {1})".format(dx, dy)) xmin, xmax, ymin, ymax = self.get_extent() ny = int((ymax - ymin) // dy) + 1 nx = int((xmax - xmin) // dx) + 1 t = self._transform tnew = (xmin-0.5*dx-0.5*t[4], ymin-0.5*dy-0.5*t[5], dx, dy, t[4], t[5]) cg = CoordinateGenerator(tnew, (ny, nx),, X, Y = cg[:,:] if method == 'nearest': values = self.sample_nearest(X, Y) elif method == 'linear': values = self.sample_bilinear(X, Y) else: raise NotImplementedError('method "{0}" unavailable'.format(method)) if values.ndim == 3: values = values.transpose(1, 2, 0) return RegularGrid(tnew, values=values,, nodata_value=self.nodata)
[docs] def get_positions(self, x, y): """ Return the float row and column indices for the point nearest geographical coordinates. Parameters ---------- x, y : float or vector vertices of points to compute indices for Raises ------ ValueError if inputs have unequal size """ if hasattr(x, "__iter__"): x = np.array(x, dtype=np.float64) y = np.array(y, dtype=np.float64) if len(x) != len(y): raise ValueError("inputs must have equal size") return crfuncs.get_positions_vec(self.transform, x, y) else: x = np.array([x], dtype=np.float64) y = np.array([y], dtype=np.float64) I, J = crfuncs.get_positions_vec(self.transform, x, y) return I[0], J[0]
def _get_indices(self, x, y): """ Return the integer row and column indices for the point nearest geographical coordinates. Unlike `get_positions()`, this method raises and exception when points are out of range. Parameters ---------- x, y : float or vector vertices of points to compute indices for """ i, j = self.get_positions(x, y) i = np.round(i).astype(np.int32) j = np.round(j).astype(np.int32) return i,j
[docs] def get_indices(self, x, y): """ Return the integer row and column indices for the point nearest geographical coordinates. Unlike `get_positions()`, this method raises and exception when points are out of range. Parameters ---------- x, y : float or vector vertices of points to compute indices for Raises ------ IndexError points outside of Grid bbox """ ny, nx = self.size i, j = self._get_indices(x, y) if hasattr(i, "__iter__"): if i.min() < 0 or i.max() > ny-1 or j.min() < 0 or j.max() > nx-1: raise IndexError("coordinates outside grid region " "({0})".format(self.bbox)) else: if i < 0 or i > ny-1 or j < 0 or j > nx-1: raise IndexError("coordinate outside grid region " "({0})".format(self.bbox)) return i,j
[docs] def sample_nearest(self, x, y): """ Return the value nearest to coordinates using a nearest grid center sampling scheme. Parameters ---------- x, y : float or vector vertices of points to compute indices for Returns ------- float or vector the size of the output has one more dimension than the input. if the input are scalar, the output is a vector length p representing band values. if the input is a vector length n, the ouput is p x n. if the input is an m x n array, the output is p x m x n. Raises ------ IndexError points outside of Grid bbox """ m, n = self.size if not hasattr(x, "__iter__"): i, j = self._get_indices(x, y) if (0 <= i < m) and (0 <= j < n): return np.atleast_1d(self[i,j]) else: return self.nodata_value if not isinstance(x, np.ndarray): x = np.array(x) y = np.array(y) I, J = self._get_indices(x.ravel(), y.ravel()) out_of_bounds_mask = (I < 0) | (I >= m) | (J < 0) | (J >= n) I = I[~out_of_bounds_mask] J = J[~out_of_bounds_mask] Imn = int(np.floor(I.min())-1) Imx = int(np.ceil(I.max()+1)) Jmn = int(np.floor(J.min())-1) Jmx = int(np.ceil(J.max()+1)) Imn = max(Imn, 0) Jmn = max(Jmn, 0) Imx = min(Imx, self.size[0]) Jmx = min(Jmx, self.size[1]) I -= Imn J -= Jmn data = np.empty((out_of_bounds_mask.shape[0], self.nbands), dtype=self.bands[0].dtype) data[out_of_bounds_mask, :] = self.nodata_value data[~out_of_bounds_mask, :] = self[Imn:Imx,Jmn:Jmx,:][I,J].reshape(sum(~out_of_bounds_mask), self.nbands) if x.ndim == 1: return data.reshape(self.nbands, x.shape[0]) else: return data.reshape(self.nbands, x.shape[0], x.shape[1])
[docs] def sample_bilinear(self, x, y): """ Return the value nearest to coordinates using a bi-linear sampling scheme. Parameters ---------- x, y : float or vector vertices of points to compute indices for Returns ------- float or vector the size of the output has one more dimension than the input. if the input are scalar, the output is a vector length p representing band values. if the input is a vector length n, the ouput is p x n. if the input is an m x n array, the output is p x m x n. Raises ------ IndexError points outside of Grid bbox """ if not hasattr(x, "__iter__"): dim = 0 x = np.array([x]) y = np.array([y]) elif not isinstance(x, np.ndarray): x = np.array(x) y = np.array(y) dim = x.ndim else: dim = x.ndim I, J = self.get_positions(x.ravel(), y.ravel()) # If the grid is large, decompressing everthing is expensive, so # compute only the region necessary if len(I) != 1: Imn = int(np.floor(I.min())-1) Imx = int(np.ceil(I.max()+1)) Jmn = int(np.floor(J.min())-1) Jmx = int(np.ceil(J.max()+1)) Imn = max(Imn, 0) Jmn = max(Jmn, 0) Imx = min(Imx, self.size[0]) Jmx = min(Jmx, self.size[1]) I -= Imn J -= Jmn else: Imn = None Imx = None Jmn = None Jmx = None data = [] for i, band in enumerate(self.bands): v = self[Imn:Imx,Jmn:Jmx,i].squeeze() if band.dtype in (np.float32, np.float64): data.append(crfuncs.sample_bilinear_double(I, J, v.astype(np.float64), self.nodata_value)) elif band.dtype in (np.int16, np.int32, np.int64): data.append(crfuncs.sample_bilinear_int(I, J, v.astype(np.int32), self.nodata_value)) elif band.dtype in (np.uint8, np.uint16, np.uint32): data.append(crfuncs.sample_bilinear_uint(I, J, v.astype(np.uint16), self.nodata_value)) else: raise NotImplementedError("no sample_bilinear method for dtype:" " {0}".format(band.dtype)) if dim == 0: return np.array([d[0] for d in data]) elif dim == 1: return np.atleast_2d(data) elif dim == 2: m, n = x.shape return np.concatenate([d.reshape((1, m, n)) for d in data], axis=0)
[docs] def sample(self, *args, **kwargs): """ Return the values nearest positions. Positions may be: - a karta.Point instance - a karta.Multipoint instance, or - a pair of x, y coordinate arrays Parameters ---------- positions : karta.Point, karta.Multipoint, or two lists of floats see above crs :, optional used when coordinate lists are provided, otherwise the coordinate system is taken from the crs attribute of the geometry method : string, optional may be one of 'nearest', 'bilinear' (default). Returns ------- float or vector The size of the output has one more dimension than the input. If the input are scalar, the output is a p-vector representing band values. If the input is a n-vector, the ouput is p x n. If the input is an m x n array, the output is p x m x n. Raises ------ IndexError points outside of Grid bbox """ crs = kwargs.get("crs", None) method = kwargs.get("method", "bilinear") argerror = TypeError("'sample' method takes a Point, a Multipoint, or x and y coordinate arrays") if hasattr(args[0], "_geotype"): crs = args[0].crs if args[0]._geotype == "Point": x, y = args[0].get_vertex([:2] elif args[0]._geotype == "Multipoint": x, y = args[0].get_coordinate_lists( else: raise argerror else: try: x = args[0] y = args[1] if crs is None: crs = else: x, y = crs.transform(, x, y) except IndexError: raise argerror if len(x) != len(y): raise argerror if method == "nearest": v = self.sample_nearest(x, y) elif method == "bilinear": v = self.sample_bilinear(x, y) else: raise ValueError("method '{0}' not available".format(method)) return v
[docs] def profile(self, line, resolution=None, **kw): """ Sample along a *Line* at a specified interval. Parameters ---------- line : karta.Line defines the sampling path resolution : float, optional sample spacing, taken to be the minimum grid resolution by default Additional keyword arguments passed to `RegularGrid.sample` (e.g. to specify sampling method) Returns ------- Multipoint sample points ndarray grid value at sample points """ if resolution is None: resolution = min(self.transform[2:4]) points = line.to_points(resolution) z = self.sample(*points.coordinates, **kw) if len(self.bands) != 1: for i in range(self.nbands):"band_{0}".format(i), z[i]) else:"band_0", z[0]) return points, z
[docs] def to_geotiff(self, fnm, compress="PACKBITS", tiled=False, **kw): """ Write data to a GeoTiff file using GDAL. Parameters ---------- fnm : str output file name compress: str or None, optional "PACKBITS" (default), "DEFLATE", "LZW", "LZMA", or None """ return _gdal.write(fnm, self, compress=compress, **kw)
[docs] def to_gtiff(self, *args, **kwargs): """ Alias for to_geotiff """ warnings.warn("method `to_gtiff` has been renamed `to_geotiff`", FutureWarning) return self.to_geotiff(*args, **kwargs)
[docs] def to_aai(self, f, reference='corner', nodata_value=-9999): """ Save internal data as an ASCII grid. Based on the ESRI standard, only isometric grids (i.e. `hdr['dx'] == hdr['dy']` can be saved. Parameters ---------- f : str a file-like object or a filename reference : str specify a header reference ('center' | 'corner') nodata_value : number specify how NaNs should be represented Raises ------ GridError input grid is not isometric and can therefore not be saved """ if reference not in ('center', 'corner'): raise errors.GridError("reference in AAIGrid.tofile() must be 'center' or " "'corner'") dx, dy = self.resolution if dx != dy: raise errors.GridError("ESRI ASCII grids require isometric grid cells") sx, sy = self.skew if sx != sy != 0.0: raise errors.GridError("skewed grids cannot be written as ESRI ASCII") ny, nx = self.bands[0].size if not hasattr(f, 'read'): f = open(f, "w") try: data_a = self[:,:].copy() data_a[np.isnan(data_a)] = nodata_value f.write("NCOLS {0}\n".format(nx)) f.write("NROWS {0}\n".format(ny)) if reference == 'center': xll, yll = self.center_llref() f.write("XLLCENTER {0}\n".format(xll)) f.write("YLLCENTER {0}\n".format(yll)) elif reference == 'corner': xll, yll = self.corner_llref() f.write("XLLCORNER {0}\n".format(xll)) f.write("YLLCORNER {0}\n".format(yll)) f.write("CELLSIZE {0}\n".format(dx)) f.write("NODATA_VALUE {0}\n".format(nodata_value)) f.writelines([str(row).replace(',','')[1:-1] + '\n' for row in data_a.tolist()]) finally: f.close() return
def merge(grids, weights=None): """ Construct a grid mosiac by averaging multiple grids. Currently limited to grids whose sampling is an integer translation from each other. Parameters ---------- grids : iterable of Grid objects grids to combine weights : iterable of floats, optional weighting factors for computing grid averages Raises ------ GridError, ValueError input grids have inconsistent transform properties """ # Check grid class if not all(isinstance(grid, RegularGrid) for grid in grids): raise TypeError("all grids must be type RegularGrid") T = grids[0].transform # Check grid stretch and skew for i, grid in enumerate(grids[1:]): if grid.transform[2:6] != T[2:6]: raise errors.GridError("grid %d transform stretch and skew " "does not match grid 1" % (i+2,)) # Check grid offset excmsg = "grid %d not an integer translation from grid 1" for i, grid in enumerate(grids[1:]): if ((grid.transform[0]-T[0]) / float(T[2])) % 1 > 1e-15: raise ValueError(excmsg % (i+2,)) if ((grid.transform[1]-T[1]) / float(T[3])) % 1 > 1e-15: raise ValueError(excmsg % (i+2,)) # Check number of grid bands if len(set([len(grid.bands) for grid in grids])) != 1: raise ValueError("all grids to mosaic must have the same number of bands") # Compute weighting coefficients by normalizing *weights* if weights is None: weights = np.ones(len(grids)) else: weights = np.asarray(weights, dtype=np.float32) normalizedweights = weights * len(weights) / weights.sum() # Compute final grid extent xmin, xmax, ymin, ymax = grids[0].get_extent(reference='edge') for grid in grids[1:]: _xmin, _xmax, _ymin, _ymax = grid.get_extent(reference='edge') xmin = min(xmin, _xmin) xmax = max(xmax, _xmax) ymin = min(ymin, _ymin) ymax = max(ymax, _ymax) nx = int(round((xmax-xmin) / T[2])) ny = int(round((ymax-ymin) / T[3])) # Allocate data array and copy each grid's data typ = grids[0].bands[0].dtype outbands = [] for iband in range(len(grids[0].bands)): values = np.zeros([ny, nx], dtype=typ) counts = np.zeros([ny, nx], dtype=np.float32) for grid, weight in zip(grids, normalizedweights): _xmin, _xmax, _ymin, _ymax = grid.get_extent(reference='edge') offx = int((_xmin-xmin) / T[2]) offy = int((_ymin-ymin) / T[3]) _ny, _nx = grid.size mask = grid.data_mask counts[offy:offy+_ny,offx:offx+_nx][mask] += weight _bnd = grid.bands[iband] values[offy:offy+_ny,offx:offx+_nx][mask] += typ(_bnd.getblock(0, 0, *_bnd.size)[mask]) * weight del mask validcountmask = (counts!=0.0) values[validcountmask] = values[validcountmask] / counts[validcountmask] values[~validcountmask] = grids[0].nodata band = CompressedBand((ny, nx), typ, initval=grids[0].nodata) band.setblock(0, 0, values) outbands.append(band) Tmerge = [xmin, ymin] + list(T[2:]) return RegularGrid(Tmerge, bands=outbands, crs=grids[0].crs, nodata_value=grids[0].nodata) def get_nodata(T): """ Return a default value for NODATA given a type For unsigned integer types, returns largest representable value. For signed integer types, returns smallest negative representable value. For floating point types (incl. complex), returns NaN. Parameters ---------- T : type numeric type to choose NODATA for Raises ------ ValueError input is not an integer, real, or complex numeric type """ if T in (np.uint8, np.uint16, np.uint32, np.uint64): return np.iinfo(T).max elif issubclass(T, numbers.Integral): return np.iinfo(T).min elif issubclass(T, (numbers.Real, numbers.Complex)): return np.nan else: raise ValueError("No default NODATA value for type {0}".format(T)) def gridpoints(x, y, z, transform, crs): """ Return a grid computed by averaging point data over cells. Parameters ---------- x : iterable point data x coordinates y : iterable point data y coordinates z : iterable point data values transform : 6-tuple of floats geotransform: ``[xllcorner, yllcorner, xres, yres, xskew, yskew]`` crs : subclass coordinate reference system object """ ny = int((np.max(y) - transform[1]) // transform[3]) + 1 nx = int((np.max(x) - transform[0]) // transform[2]) + 1 grid = RegularGrid(transform, values=np.empty([ny, nx]), crs=crs, nodata_value=np.nan) array = np.zeros([ny, nx]) (I, J) = grid.get_indices(x, y) try: err = crfuncs.fillarray_double(array, I.astype(np.int32), J.astype(np.int32), z, grid.nodata) if err != 0: raise RuntimeError("failure in fillarray_double") except ValueError: # Fast version works when *z* is of type double (np.float64). # Python fallback for other types counts = np.zeros([ny, nx], dtype=np.int16) for (i,j,z_) in zip(I, J, z): array[i,j] += z_ counts[i,j] += 1 m = counts!=0 array[m] = array[m] / counts[m] array[~m] = np.nan grid[:,:,0] = array return grid def mask_poly(xpoly, ypoly, nx, ny, transform): """ Create a grid mask based on a clockwise-oriented polygon. Parameters ---------- xpoly, ypoly : lists of floats sequences of points representing polygon nx : int ny : int size of grid transform : list[float] affine transformation describing grid layout and origin ``T == [x0, y0, dx, dy, sx, sy]`` Returns ------- ndarray """ mask = np.zeros((ny, nx), dtype=np.int8) # find left southernmost index (will start and end on this point) x0, y0 = xpoly[0], ypoly[0] i_bot = 0 for i in range(1, len(ypoly)): if ypoly[i] < y0 or (ypoly[i] == y0 and xpoly[i] < x0): x0 = xpoly[i] y0 = ypoly[i] i_bot = i x0 = xpoly[i_bot] y0 = ypoly[i_bot] # compute the grid indices of the starting point ta, tb, tc, td, te, tf = transform # normalize grid transform so increasing i, j corresponds to north, east if tc < 0: ta = ta + nx*tc tc = -tc if td < 0: tb = tb + ny*td td = -td i0 = int(round((y0-tb - tf/tc*(x0-ta)) / (td - tf*te/tc))) j0 = int(round((x0-ta - te/td*(y0-tb)) / (tc - te*tf/td))) for el in range(1, len(xpoly)+1): idx = (el + i_bot) % len(xpoly) x1 = xpoly[idx] y1 = ypoly[idx] # (Unbounded) grid indices of the segment end points i1 = int(round((y1-tb - tf/tc*(x1-ta)) / (td - tf*te/tc))) j1 = int(round((x1-ta - te/td*(y1-tb)) / (tc - te*tf/td))) # If segment is horizontal or off-grid, ignore if ((0 <= i0 < ny) and (0 <= i1 < ny)) or (y1 != y0): if y1 > y0: # upward - mark grid cells to the right for i in range(i0, i1): if (0 <= i < ny): j = int(round((i-i0) * (x1-x0)/(y1-y0) + j0)) if j < nx: mask[i, max(0, j):] += 1 else: # downward - unmark grid cells to the right for i in range(i1, i0): if (0 <= i < ny): j = int(round((i-i1) * (x1-x0)/(y1-y0) + j1)) if j < nx: mask[i, max(0, j):] -= 1 x0 = x1 y0 = y1 i0 = i1 j0 = j1 return mask.astype(np.bool)