import numpy as np
from scipy.spatial.distance import cdist
[docs]class RegularDiscretizationGrid:
"""
A lightweight regular grid in n dimensions,
without points storage.
Attributes
----------
origin: numpy.ndarray
The origin point, shape: (n_dims,)
deltas: numpy.ndarray
The step sizes, shape: (n_dims,)
n_steps: numpy.ndarray
The number of steps, shape: (n_dims,)
interpolation: str
The interpolation method: None, nearest, linear
tol: numpy.ndarray
The tolerances for grid bounds, shape: (n_dims,),
or None
digits: int
The grid point precision
:group: utils
"""
INT_INF = -999999
[docs] def __init__(
self,
origin,
deltas,
n_steps,
interpolation=None,
tol=None,
digits=12,
):
"""
Constructor
Parameters
----------
origin: array-like
The origin point, len: n_dims
deltas: array-like
The step sizes, len: n_dims.
n_steps: array-like
The number of steps, len: n_dims. Use
INT_INF for infinite.
interpolation: str
The interpolation method: None, nearest, linear
tol: list of float, optional
The tolerances for grid bounds, default is 0,
shape: (n_dims,)
digits: int
The grid point precision
"""
self.origin = np.array(origin, dtype=np.float64)
self.n_steps = np.array(n_steps, dtype=np.int32)
self.deltas = np.array(deltas, dtype=np.float64)
self.digits = digits
self.interpolation = interpolation
if tol is not None:
self.tol = np.zeros(self.n_dims, dtype=np.float64)
self.tol[:] = tol
else:
self.tol = None
self._opts = None
@property
def n_points(self):
"""
The number of points in each dimension
Returns
-------
numpy.ndarray :
The number of points in each dimension,
shape: (n_dims,)
"""
n = self.n_steps + 1
n[n == self.INT_INF + 1] = self.INT_INF
return n
@property
def n_dims(self):
"""
The number of dimensions
Returns
-------
int :
The number of dimensions
"""
return len(self.origin)
@property
def p_min(self):
"""
The minimal grid point values
Returns
-------
numpy.ndarray:
The minimal grid point values,
shape: (n_dims,)
"""
m = self.origin.copy()
m[(self.n_steps == self.INT_INF) & (self.deltas < 0)] = -np.inf
return m
@property
def p_max(self):
"""
The maximal grid point values
Returns
-------
numpy.ndarray:
The maximal grid point values,
shape: (n_dims,)
"""
m = self.origin + self.n_steps * self.deltas
m[(self.n_steps == self.INT_INF) & (self.deltas > 0)] = np.inf
return np.round(m, self.digits)
[docs] def print_info(self, spaces=0):
"""
Prints basic information
Parameters
----------
spaces: int
The prepending spaces
"""
s = "" if spaces == 0 else " " * spaces
print(f"{s}n_dims :", self.n_dims)
print(f"{s}deltas :", self.deltas.tolist())
print(f"{s}n_steps :", self.n_steps)
print(f"{s}n_points:", self.n_points)
print(f"{s}p_min :", self.p_min)
print(f"{s}p_max :", self.p_max)
def _error_info(self, p, for_ocell=False):
"""
Helper for printing information at interpolation error
"""
print("GDIM:", self.n_points.tolist())
print("GMIN:", self.p_min.tolist())
print("GMAX:", self.p_max.tolist())
print("GTOL:", self.tol.tolist())
if for_ocell:
cmin = self._ocell[:, 0]
cmax = self._ocell[:, 1]
print("CMIN:", cmin.tolist())
print("CMAX:", cmax.tolist())
print("Q :", p)
else:
print("P :", p)
def _error_infos(self, pts, for_ocell=False):
"""
Helper for printing information at interpolation error
"""
print("GDIM:", self.n_points.tolist())
print("GMIN:", self.p_min.tolist())
print("GMAX:", self.p_max.tolist())
print("GTOL:", self.tol.tolist())
if for_ocell:
cmin = self._ocell[:, 0]
cmax = self._ocell[:, 1]
print("CMIN:", cmin.tolist())
print("CMAX:", cmax.tolist())
print("VMIN:", np.min(pts, axis=0).tolist())
print("VMAX:", np.max(pts, axis=0).tolist())
sel = np.any(pts < cmin[None, :], axis=1)
if np.any(sel):
s = np.argwhere(sel)[0][0]
print(
f"Found {np.sum(sel)} coords blow lower bounds, e.g. coord {s}: q = {pts[s]}"
)
sel = np.any(pts > cmax[None, :], axis=1)
if np.any(sel):
s = np.argwhere(sel)[0][0]
print(
f"Found {np.sum(sel)} coords above higher bounds, e.g. coord {s}: q = {pts[s]}"
)
else:
print("VMIN:", np.min(pts, axis=0))
print("VMAX:", np.max(pts, axis=0))
sel = np.any(pts < self.p_min[None, :], axis=1)
if np.any(sel):
s = np.argwhere(sel)[0][0]
print(
f"Found {np.sum(sel)} points blow lower bounds, e.g. point {s}: p = {pts[s]}"
)
sel = np.any(pts > self.p_max[None, :], axis=1)
if np.any(sel):
s = np.argwhere(sel)[0][0]
print(
f"Found {np.sum(sel)} points above higher bounds, e.g. point {s}: p = {pts[s]}"
)
[docs] def is_gridi(self, inds):
"""
Checks if grid indices are valid
Parameters
----------
inds: int
The grid point indices, shape: (n_dims,)
Returns
-------
bool :
True if on grid
"""
sel0 = ~(self.n_steps == self.INT_INF)
sel = (inds < 0) | (sel0 & (inds >= self.n_points))
return not np.any(sel)
[docs] def i2gp(self, inds, error=True):
"""
Translates grid point indices to grid point.
Parameters
----------
inds: int
The grid point indices, shape: (n_dims,)
error: bool
Flag for throwing error if off-grid, else
return None in that case
Returns
-------
gp: numpy.ndarray
The grid point, shape: (n_dims,)
"""
if not self.is_gridi(inds):
if error:
self.print_info()
raise ValueError(f"Grind indices {inds} are not on grid")
return np.round(self.origin + inds * self.deltas, self.digits)
[docs] def find_grid_inds(self, inds):
"""
Finds indices that are on grid
Parameters
----------
inds: numpy.ndarray
The grid point index candidates,
shape: (n_inds, n_dims)
Returns
-------
sel_grid: numpy.ndarray of bool
Subset selection of on-grid indices,
shape: (n_inds, n_dims)
"""
sel0 = ~(self.n_steps == self.INT_INF)
sel = (inds < 0) | (sel0[None, :] & (inds >= self.n_points[None, :]))
return ~sel
[docs] def inds2gpts(self, inds):
"""
Translates grid point indices to grid points.
Parameters
----------
inds: array-like
The integer grid point indices, shape:
(n_gpts, dims)
Returns
-------
gpts: numpy.ndarray
The grid points, shape: (n_gpts, n_dims)
"""
selg = np.all(self.find_grid_inds(inds), axis=1)
if not np.all(selg):
selg = np.where(selg)[0]
raise ValueError(
f"Found {len(selg)} indices outside grid, e.g. index {selg[0]}: {inds[selg[0]]}"
)
o = self.origin[None, :]
d = self.deltas[None, :]
return np.round(o + inds * d, self.digits)
def _gp2i(self, gp, allow_outer=True, lower_left=False):
"""
Helper function for indices calculation
"""
if lower_left:
inds = np.round((gp - self.origin) / self.deltas, self.digits).astype(
np.int32
)
else:
inds = np.round((gp - self.origin) / self.deltas).astype(np.int32)
if not allow_outer:
sel0 = ~(self.n_steps == self.INT_INF)
sel = sel0 & (inds == self.n_points - 1)
inds[sel] -= 1
return inds
def _gpts2inds(self, gpts, allow_outer=True, lower_left=False):
"""
Helper function for index calculation
"""
o = self.origin[None, :]
d = self.deltas[None, :]
if lower_left:
inds = np.round((gpts - o) / d, self.digits).astype(np.int32)
else:
inds = np.round((gpts - o) / d).astype(np.int32)
sel0 = ~(self.n_steps == self.INT_INF)
if not allow_outer:
sel = sel0[None, :] & (inds == self.n_points[None, :] - 1)
inds[sel] -= 1
return inds
[docs] def apply_tol(self, p):
"""
Get tolerance corrected point
Parameters
----------
p: numpy.ndarray
The point, shape: (n_dims,)
Returns
-------
q: numpy.ndarray
The corrected point, shape: (n_dims,)
"""
if self.tol is not None:
q = p.copy()
dlo = q - self.p_min
sel = (dlo < 0.0) & (dlo >= -self.tol)
q[sel] = self.p_min[sel]
dhi = q - self.p_max
sel = (dhi > 0.0) & (dhi <= self.tol)
q[sel] = self.p_max[sel]
return q
return p
[docs] def apply_tols(self, pts):
"""
Get tolerance corrected points
Parameters
----------
pts: numpy.ndarray
The points, shape: (n_pts, n_dims)
Returns
-------
q: numpy.ndarray
The corrected points, shape: (n_pts, n_dims)
"""
if self.tol is not None:
qts = pts.copy()
dlo = qts - self.p_min[None, :]
sel = (dlo < 0.0) & (dlo >= -self.tol[None, :])
if np.any(sel):
pmin = np.zeros_like(qts)
pmin[:] = self.p_min[None, :]
qts[sel] = pmin[sel]
dhi = qts - self.p_max[None, :]
sel = (dhi > 0.0) & (dhi <= self.tol[None, :])
if np.any(sel):
pmax = np.zeros_like(qts)
pmax[:] = self.p_max[None, :]
qts[sel] = pmax[sel]
return qts
return pts
[docs] def is_gridpoint(self, p, allow_outer=True, ret_inds=False):
"""
Checks if a point is on grid.
Parameters
----------
p: numpy.ndarray
The point, shape: (n_dims,)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
ret_inds: bool
Additionally return indices
Returns
-------
bool :
True if on grid
inds: numpy.ndarray, optional
The grid point indices, shape: (n_dims,)
"""
p = self.apply_tol(p)
inds = self._gp2i(p, allow_outer)
if not self.is_gridi(inds):
if ret_inds:
return False, inds
return False
p0 = np.round(self.origin + inds * self.deltas, self.digits)
if ret_inds:
return np.all(p0 == p), inds
return np.all(p0 == p)
[docs] def find_gridpoints(self, pts, allow_outer=True, ret_inds=False):
"""
Finds points that are on grid.
Parameters
----------
pts: numpy.ndarray
The points, shape: (n_pts, n_dims)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
ret_inds: bool
Additionally return indices
Returns
-------
sel_grid: numpy.ndarray of bool
Subset selection of points that are on grid,
shape: (n_pts, n_dims)
inds: numpy.ndarray, optional
The grid point indices, shape: (n_gpts, n_dims)
"""
pts = self.apply_tols(pts)
inds = self._gpts2inds(pts, allow_outer)
sel = self.find_grid_inds(inds)
if np.any(sel):
o = self.origin[None, :]
d = self.deltas[None, :]
p0 = np.round(o + inds * d, self.digits)
sel = sel & (p0 == pts)
if ret_inds:
return sel, inds[np.all(sel, axis=1)]
return sel
[docs] def all_gridpoints(self, pts, allow_outer=True):
"""
Checks if all points are on grid.
Parameters
----------
pts: numpy.ndarray
The points space, shape: (n_pts, n_dims)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
Returns
-------
bool :
True if all points on grid
"""
selg = self.find_gridpoints(pts, allow_outer)
return np.all(selg)
[docs] def in_grid(self, p):
"""
Checks if a point is located within the grid.
Parameters
----------
p: numpy.ndarray
The point, shape: (n_dims,)
Returns
-------
bool :
True if within grid
"""
p = self.apply_tol(p)
return np.all((p >= self.p_min) & (p <= self.p_max))
[docs] def find_ingrid(self, pts):
"""
Finds points that are on grid.
Parameters
----------
pts: numpy.ndarray
The points, shape: (n_pts, n_dims)
Returns
-------
sel_grid: numpy.ndarray of bool
Subset selection of points that are in grid,
shape: (n_pts, n_dims)
"""
pts = self.apply_tols(pts)
return (pts >= self.p_min[None, :]) & (pts <= self.p_max[None, :])
[docs] def gp2i(self, gp, allow_outer=True, error=True):
"""
Get grid index of a grid point
Parameters
----------
gp: numpy.ndarray
The point, shape: (n_dims,)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
error: bool
Flag for throwing error if off-grid, else
return None in that case
Returns
-------
inds: numpy.ndarray
The lower-left grid corner point indices, shape: (n_dims,)
"""
isgp, inds = self.is_gridpoint(gp, allow_outer, ret_inds=True)
if isgp:
if error:
self._error_info(gp)
raise KeyError(f"Point gp = {gp} is not on grid")
return None
if not self.is_gridi(inds):
if error:
self._error_info(gp)
raise ValueError(f"Point {gp} out of grid")
return None
return inds
[docs] def gpts2inds(self, gpts, allow_outer=True, error=True):
"""
Get grid indices of grid points.
Parameters
----------
gpts: numpy.ndarray
The grid points, shape: (n_gpts, n_dims)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
error: bool
Flag for throwing error if off-grid, else
return None in that case
Returns
-------
inds: numpy.ndarray
The lower-left grid corner indices,
shape: (n_gpts, n_dims)
"""
selg, inds = self.find_gridpoints(gpts, allow_outer, ret_inds=True)
if not np.all(selg):
if error:
self._error_infos(gpts)
sel = np.where(np.any(~selg, axis=1))[0]
raise KeyError(
f"Found {len(sel)} points not on grid, e.g. point {sel[0]}: {gpts[0]}"
)
return None
return inds
[docs] def get_corner(self, p, allow_outer=True):
"""
Get the lower-left grid corner of a point.
Parameters
----------
p: numpy.ndarray
The point, shape: (n_dims,)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
Returns
-------
p0: numpy.ndarray
The lower-left grid corner point, shape: (n_dims,)
"""
p = self.apply_tol(p)
if not self.in_grid(p):
self.print_info()
raise ValueError(f"Point {p} not in grid")
inds = self._gp2i(p, allow_outer, lower_left=True)
if not self.is_gridi(inds):
self.print_info()
raise KeyError(f"Grind indices {inds} are not on grid")
return np.round(self.origin + inds * self.deltas, self.digits)
[docs] def get_corners(self, pts, allow_outer=True):
"""
Get the lower-left grid corners of points.
Parameters
----------
pts: numpy.ndarray
The points space, shape: (n_pts, n_dims)
allow_outer: bool
Allow outermost point indices, else
reduce those to lower-left cell corner
Returns
-------
p0: numpy.ndarray
The lower-left grid corner points, shape: (n_pts, n_dims)
"""
pts = self.apply_tols(pts)
selg = self.find_ingrid(pts)
if not np.all(selg):
self._error_infos(pts)
selg = np.where(np.any(~selg), axis=1)[0]
raise ValueError(
f"Found {len(selg)} points out of grid, e.g. point {selg[0]}: {pts[selg[0]]}"
)
o = self.origin[None, :]
d = self.deltas[None, :]
inds = self._gpts2inds(pts, allow_outer, lower_left=True)
selg = self.find_grid_inds(inds)
if not np.all(selg):
self._error_infos(pts)
selg = np.where(np.any(~selg), axis=1)[0]
raise ValueError(
f"Found {len(selg)} indices not on grid, e.g. indices {selg[0]}: {inds[selg[0]]}"
)
return np.round(o + inds * d, self.digits)
[docs] def get_cell(self, p):
"""
Get the grid cell that contains a point.
Parameters
----------
p: numpy.ndarray
The point, shape: (n_dims,)
Returns
-------
cell: numpy.ndarray
The min and max values of each dimension. Shape:
(n_dims, 2)
"""
cell = np.zeros((self.n_dims, 2), dtype=np.float64)
cell[:] = self.get_corner(p, allow_outer=False)[:, None]
cell[:, 1] += self.deltas
return np.round(cell, self.digits)
[docs] def get_cells(self, pts):
"""
Get the grid cells that contain the given points,
one cell per point.
Parameters
----------
pts: numpy.ndarray
The points, shape: (n_pts, n_dims)
Returns
-------
cells: numpy.ndarray
The min and max values of each dimension. Shape:
(n_pts, n_dims, 2)
"""
n_pts = pts.shape[0]
cells = np.zeros((n_pts, self.n_dims, 2), dtype=np.float64)
cells[:] = self.get_corners(pts, allow_outer=False)[:, :, None]
cells[:, :, 1] += self.deltas[None, :]
return np.round(cells, self.digits)
def _get_opts(self):
"""
Helper function that returns unit origin cell points
"""
if self._opts is None:
ocell = np.zeros((self.n_dims, 2), dtype=np.int8)
ocell[:, 1] += 1
self._opts = np.stack(np.meshgrid(*ocell, indexing="ij"), axis=-1)
self._opts = self._opts.reshape(2**self.n_dims, self.n_dims)
del ocell
return self._opts
def _interpolate_ocell(self, qts):
"""
Helper function for interpolation weights in
unit hypercube.
Classic volume weighting, see e.g. Fig. 2 and Eq. (4) in
http://dx.doi.org/10.1088/0004-6256/139/2/342
"""
assert (
qts >= 0.0
).all(), f"Found coordinates below 0: {qts[np.any(qts<0., axis=-1)].tolist()}"
assert (
qts <= 1.0
).all(), f"Found coordinates above 1: {qts[np.any(qts>1., axis=-1)].tolist()}"
opts = self._get_opts()
return np.prod(1 - np.abs(qts[:, None] - opts[None, :]), axis=-1)
[docs] def interpolation_coeffs_point(self, p):
"""
Get the interpolation coefficients for
a point.
Example
-------
>>> g = RegularDiscretizationGrid(...)
>>> p = ...
>>> gpts, c = g.interpolation_coeffs_point(p)
>>> ratg = ... calc results at gpts, shape (n_gpts, x) ...
>>> ires = np.einsum('gx,g->x', ratg, c)
Parameters
----------
p: numpy.ndarray
The point, shape: (n_dims,)
Returns
-------
gpts: numpy.ndarray
The grid points relevant for coeffs,
shape: (n_gpts, n_dims)
coeffs: numpy.ndarray
The interpolation coefficients, shape: (n_gpts,)
"""
p = self.apply_tol(p)
cell = self.get_cell(p)
p0 = cell[:, 0]
if self.interpolation is None:
return p0[None, :], np.ones(1, dtype=np.float64)
elif self.interpolation == "nearest":
gpts = np.stack(np.meshgrid(*cell, indexing="ij"), axis=-1)
gpts = np.round(gpts, self.digits).reshape(2**self.n_dims, self.n_dims)
dist = np.linalg.norm(gpts - p[None, :], axis=-1)
return gpts[None, np.argmin(dist)], np.ones(1, dtype=np.float64)
elif self.interpolation == "linear":
qts = np.round(
(p[None, :] - p0[None, :]) / self.deltas[None, :], self.digits
)
try:
coeffs = self._interpolate_ocell(qts)[0]
except AssertionError as e:
self._error_info(p, for_ocell=True)
raise e
gpts = np.stack(np.meshgrid(*cell, indexing="ij"), axis=-1)
gpts = np.round(gpts, self.digits).reshape(2**self.n_dims, self.n_dims)
else:
raise ValueError(
f"Unknown interpolation '{self.interpolation}'. Please choose: snap, nearest, linear"
)
sel = np.abs(coeffs) < 1.0e-14
if np.any(sel):
gpts = gpts[~sel]
coeffs = coeffs[~sel]
return gpts, coeffs
[docs] def interpolation_coeffs_points(self, pts, ret_pmap=False):
"""
Get the interpolation coefficients for a set of points.
Example
-------
>>> g = RegularDiscretizationGrid(...)
>>> pts = ...
>>> gpts, c = g.interpolation_coeffs_points(pts)
>>> ratg = ... calc results at gpts, shape (n_gpts, x) ...
>>> ires = np.einsum('gx,pg->px', ratg, c)
Parameters
----------
pts: numpy.ndarray
The points, shape: (n_pts, n_dims)
ret_pmap: bool
Additionally return the map from pts to
gpts
Returns
-------
gpts: numpy.ndarray
The grid points relevant for coeffs,
shape: (n_gpts, n_dims)
coeffs: numpy.ndarray
The interpolation coefficients, shape:
(n_pts, n_gpts)
pmap: numpy.ndarray, optional
The map from pts to gpts, shape: (n_pts, n_gp)
"""
pts = self.apply_tols(pts)
n_pts = len(pts)
if self.interpolation is None:
p0 = self.get_corners(pts, allow_outer=True)
coeffs = np.ones((n_pts, 1), dtype=np.float64)
gpts = p0[:, None, :]
elif self.interpolation == "nearest":
p0 = self.get_corners(pts, allow_outer=True)
qts = np.round((pts - p0) / self.deltas[None, :], self.digits)
opts = self._get_opts()
inds = np.argmin(cdist(qts, opts), axis=-1)
gpts = np.round(
p0
+ np.take_along_axis(opts, inds[:, None], axis=0)
* self.deltas[None, :],
self.digits,
)[:, None, :]
coeffs = np.ones((n_pts, 1), dtype=np.float64)
del qts, opts, inds
elif self.interpolation == "linear":
p0 = self.get_corners(pts, allow_outer=False)
qts = np.round((pts - p0) / self.deltas[None, :], self.digits)
try:
coeffs = self._interpolate_ocell(qts) # shape: (n_pts, n_gp)
except AssertionError as e:
self._error_infos(qts, for_ocell=True)
raise e
opts = self._get_opts()
gpts = np.round(
p0[:, None] + opts[None, :] * self.deltas[None, :], self.digits
) # shape: (n_pts, n_gp, n_dims)
del p0, qts, opts
# remove points with zero weights:
sel = np.all(np.abs(coeffs) < 1.0e-14, axis=0)
if np.any(sel):
coeffs = coeffs[:, ~sel]
gpts = gpts[:, ~sel]
# reorganize data to single grid point array:
n_pts, n_gp = coeffs.shape
n_apts = n_pts * n_gp
gpts, amap = np.unique(
gpts.reshape(n_apts, self.n_dims), axis=0, return_inverse=True
)
n_gpts = len(gpts)
amap = amap.reshape(n_pts, n_gp)
temp = coeffs
coeffs = np.zeros((n_pts, n_gpts), dtype=np.float64)
np.put_along_axis(coeffs, amap, temp, axis=1)
if ret_pmap:
return gpts, coeffs, amap
return gpts, coeffs
[docs] def deriv_coeffs_gridpoints(self, inds, var, order=2, orderb=1):
"""
Calculates the derivative coefficients at grid points.
Parameters
----------
inds: numpy.ndarray
The integer grid point indices, shape:
(n_inds, n_dims)
var: int
The dimension representing the variable
wrt which to differentiate
order: int
The finite difference order,
1 = forward, -1 = backward, 2 = centre
orderb: int
The finite difference order at boundary points
Returns
-------
gpts: numpy.ndarray
The grid points relevant for coeffs,
shape: (n_gpts, n_dims)
coeffs: numpy.ndarray
The gradient coefficients, shape:
(n_inds, n_gpts)
"""
# check indices:
if var < 0 or var > self.n_dims:
raise ValueError(
f"Variable choice '{var}' exceeds dimensions, n_dims = {self.n_dims}"
)
ipts = self.inds2gpts(inds)
n_inds = len(inds)
# find number of finite difference points n_gpts:
sel_bleft = inds[:, var] == 0
sel_bright = inds[:, var] == self.n_points[var] - 1
n_gpts = 2
if np.any(sel_bleft | sel_bright):
if orderb == 2:
n_gpts = 3
s_centre = ~(sel_bleft | sel_bright)
else:
s_centre = np.s_[:]
# initialize output:
gpts = np.zeros((n_inds, n_gpts, self.n_dims), dtype=np.float64)
coeffs = np.zeros((n_inds, n_gpts), dtype=np.float64)
gpts[:] = self.origin[None, None, :]
# coeffs for left boundary points:
if np.any(sel_bleft):
if orderb == 1:
gpts[:, 0][sel_bleft] = ipts[sel_bleft]
coeffs[:, 0][sel_bleft] = -1.0
gpts[:, 1][sel_bleft] = ipts[sel_bleft]
gpts[:, 1, var][sel_bleft] += self.deltas[var]
coeffs[:, 1][sel_bleft] = 1.0
elif orderb == 2:
gpts[:, 0][sel_bleft] = ipts[sel_bleft]
coeffs[:, 0][sel_bleft] = -1.5
gpts[:, 1][sel_bleft] = ipts[sel_bleft]
gpts[:, 1, var][sel_bleft] += self.deltas[var]
coeffs[:, 1][sel_bleft] = 2.0
gpts[:, 2][sel_bleft] = ipts[sel_bleft]
gpts[:, 2, var][sel_bleft] += 2 * self.deltas[var]
coeffs[:, 2][sel_bleft] = -0.5
else:
raise NotImplementedError(
f"Choice 'orderb = {orderb}' is not supported, please choose: 1 or 2"
)
# coeffs for right boundary points:
if np.any(sel_bright):
if orderb == 1:
gpts[:, 0][sel_bright] = ipts[sel_bright]
coeffs[:, 0][sel_bright] = 1.0
gpts[:, 1][sel_bright] = ipts[sel_bright]
gpts[:, 1, var][sel_bright] -= self.deltas[var]
coeffs[:, 1][sel_bright] = -1.0
elif orderb == 2:
gpts[:, 0][sel_bright] = ipts[sel_bright]
coeffs[:, 0][sel_bright] = 1.5
gpts[:, 1][sel_bright] = ipts[sel_bright]
gpts[:, 1, var][sel_bright] -= self.deltas[var]
coeffs[:, 1][sel_bright] = -2.0
gpts[:, 2][sel_bright] = ipts[sel_bright]
gpts[:, 2, var][sel_bright] -= 2 * self.deltas[var]
coeffs[:, 2][sel_bright] = 0.5
else:
raise NotImplementedError(
f"Choice 'orderb = {orderb}' is not supported, please choose: 1 or 2"
)
# coeffs for central points:
if not np.all(sel_bleft | sel_bright):
if order == 1:
gpts[:, 0][s_centre] = ipts[s_centre]
gpts[:, 0, var][s_centre] += self.deltas[var]
coeffs[:, 0][s_centre] = 1.0
gpts[:, 1][s_centre] = ipts[s_centre]
coeffs[:, 1][s_centre] = -1.0
elif order == -1:
gpts[:, 0][s_centre] = ipts[s_centre]
coeffs[:, 0][s_centre] = 1.0
gpts[:, 1][s_centre] = ipts[s_centre]
gpts[:, 1, var][s_centre] -= self.deltas[var]
coeffs[:, 1][s_centre] = -1.0
elif order == 2:
gpts[:, 0][s_centre] = ipts[s_centre]
gpts[:, 0, var][s_centre] += self.deltas[var]
coeffs[:, 0][s_centre] = 0.5
gpts[:, 1][s_centre] = ipts[s_centre]
gpts[:, 1, var][s_centre] -= self.deltas[var]
coeffs[:, 1][s_centre] = -0.5
else:
raise NotImplementedError(
f"Choice 'order = {order}' is not supported, please choose: -1 (backward), 1 (forward), 2 (centre)"
)
# reorganize data to single grid point array:
n_pts, n_gp = coeffs.shape
n_apts = n_pts * n_gp
gpts = np.round(gpts, self.digits)
gpts, amap = np.unique(
gpts.reshape(n_apts, self.n_dims), axis=0, return_inverse=True
)
n_gpts = len(gpts)
amap = amap.reshape(n_pts, n_gp)
temp = coeffs
coeffs = np.zeros((n_pts, n_gpts), dtype=np.float64)
np.put_along_axis(coeffs, amap, temp, axis=1)
return gpts, coeffs / self.deltas[var]
[docs] def deriv_coeffs(self, pts, var, order=2, orderb=1):
"""
Calculates the derivative coefficients at points.
Parameters
----------
pts: numpy.ndarray
The evaluation points, shape: (n_pts, n_dims)
var: int
The dimension representing the variable
wrt which to differentiate
order: int
The finite difference order,
1 = forward, -1 = backward, 2 = centre
orderb: int
The finite difference order at boundary points
Returns
-------
gpts: numpy.ndarray
The grid points relevant for coeffs,
shape: (n_gpts, n_dims)
coeffs: numpy.ndarray
The gradient coefficients, shape:
(n_pts, n_gpts)
"""
gpts0, coeffs0, pmap = self.interpolation_coeffs_points(pts, ret_pmap=True)
inds0 = self.gpts2inds(gpts0, allow_outer=True)
gpts, coeffs1 = self.deriv_coeffs_gridpoints(inds0, var, order, orderb)
n_pts = len(pts)
n_inds0 = len(inds0)
pmat = np.zeros((n_pts, n_inds0), dtype=np.int8)
np.put_along_axis(pmat, pmap, 1.0, axis=1)
coeffs = np.einsum("pi,pi,ig->pg", coeffs0, pmat, coeffs1)
return gpts, coeffs
[docs] def grad_coeffs_gridpoints(self, inds, vars, order=2, orderb=1):
"""
Calculates the gradient coefficients at grid points.
Parameters
----------
inds: numpy.ndarray
The integer grid point indices, shape:
(n_inds, n_dims)
vars: list of int, optional
The dimensions representing the variables
wrt which to differentiate, shape: (n_vars,).
Default is all dimensions
order: int or list of int
The finite difference order,
1 = forward, -1 = backward, 2 = centre
orderb: int list of int
The finite difference order at boundary points
Returns
-------
gpts: numpy.ndarray
The grid points relevant for coeffs,
shape: (n_gpts, n_dims)
coeffs: numpy.ndarray
The gradient coefficients,
shape: (n_inds, n_vars, n_gpts)
"""
if vars is None:
vars = np.arange(self.n_dims)
n_vars = len(vars)
n_inds = len(inds)
gpts = None
cfs = None
sizes = []
for vi, v in enumerate(vars):
o = order if isinstance(order, int) else order[vi]
ob = orderb if isinstance(orderb, int) else orderb[vi]
hg, hc = self.deriv_coeffs_gridpoints(inds, v, o, ob)
if gpts is None:
gpts = hg
cfs = hc
else:
gpts = np.append(gpts, hg, axis=0)
cfs = np.append(cfs, hc, axis=1)
sizes.append(len(hg))
del hg, hc
gpts, gmap = np.unique(gpts, axis=0, return_inverse=True)
n_gpts = len(gpts)
coeffs = np.zeros((n_inds, n_vars, n_gpts), dtype=np.float64)
i0 = 0
for vi, s in enumerate(sizes):
i1 = i0 + s
np.put_along_axis(coeffs[:, vi], gmap[None, i0:i1], cfs[:, i0:i1], axis=1)
i0 = i1
return gpts, coeffs
[docs] def grad_coeffs(self, pts, vars, order=2, orderb=1):
"""
Calculates the gradient coefficients at grid points.
Parameters
----------
pts: numpy.ndarray
The evaluation points, shape: (n_pts, n_dims)
vars: list of int, optional
The dimensions representing the variables
wrt which to differentiate, shape: (n_vars,).
Default is all dimensions
order: int list of int
The finite difference order,
1 = forward, -1 = backward, 2 = centre
orderb: int list of int
The finite difference order at boundary points
Returns
-------
gpts: numpy.ndarray
The grid points relevant for coeffs,
shape: (n_gpts, n_dims)
coeffs: numpy.ndarray
The gradient coefficients,
shape: (n_pts, n_vars, n_gpts)
"""
gpts0, coeffs0, pmap = self.interpolation_coeffs_points(pts, ret_pmap=True)
inds0 = self.gpts2inds(gpts0, allow_outer=True)
gpts, coeffs1 = self.grad_coeffs_gridpoints(inds0, vars, order, orderb)
n_pts = len(pts)
n_inds0 = len(inds0)
pmat = np.zeros((n_pts, n_inds0), dtype=np.int8)
np.put_along_axis(pmat, pmap, 1.0, axis=1)
coeffs = np.einsum("pi,pi,ivg->pvg", coeffs0, pmat, coeffs1)
return gpts, coeffs