def interpolate_hmat(mesh, hmat, method='spline', kx=1, ky=1, **kwargs):
assert isinstance(mesh, jigsaw_msh_t)
assert isinstance(hmat, jigsaw_msh_t)
assert method in ['spline', 'linear', 'nearest']
kwargs.update({'kx': kx, 'ky': ky})
if method == 'spline':
values = RectBivariateSpline(hmat.xgrid, hmat.ygrid, hmat.value.T,
**kwargs).ev(mesh.vert2['coord'][:, 0],
mesh.vert2['coord'][:, 1])
mesh.value = np.array(values.reshape((values.size, 1)),
dtype=jigsaw_msh_t.REALS_t)
else:
raise NotImplementedError("Only 'spline' method is available")
def interp_cross(x_in, y_in, data, x0, x1, y0, y1, rint=None, mode='linear'):
u"""
(x0,y0)-(x1,y1)に沿った直線上の点に内挿する
"""
if np.ndim(x_in) == 2: x_in = x_in[0,:]
if np.ndim(y_in) == 2: y_in = y_in[:,0]
ndim = np.ndim(data)
if ndim > 2:
oldshape = data.shape
data = data.reshape(-1, oldshape[-2], oldshape[-1])
elif ndim < 2:
raise ValueError, "input data dimension size must be larger than 2. input ndim is {}".format(ndim)
# make output grid
if rint is None:
rint = max(np.diff(x_in).max(), np.diff(y_in).max())
rmax = np.hypot(x1-x0, y1-y0)
nr = rmax//rint + 1
theta = np.arctan2(y1-y0, x1-x0)
r_out = np.linspace(0, rmax, nr)
x_out = r_out*np.cos(theta) + x0
y_out = r_out*np.sin(theta) + y0
xrev = yrev = 1
if x0 > x1:
x_out = x_out[::-1]
xrev = -1
if y0 > y1:
y_out = y_out[::-1]
yrev = -1
# interpolate
if ndim == 2:
if mode == 'linear':
out = RectBivariateSpline(y_in, x_in, data, kx=1, ky=1)(y_out, x_out)[::xrev,::yrev].diagonal()
elif mode == 'cubic':
out = RectBivariateSpline(y_in, x_in, data, kx=3, ky=3)(y_out, x_out)[::xrev,::yrev].diagonal()
else:
zn, yn, xn = data.shape
out = np.ma.empty((zn,nr), dtype=np.float32)
for z in range(zn):
if mode == 'linear':
out[z,:] = RectBivariateSpline(y_in, x_in, data[z,:,:], kx=1, ky=1)(y_out, x_out)[::xrev,::yrev].diagonal()
elif mode == 'cubic':
out[z,:] = RectBivariateSpline(y_in, x_in, data[z,:,:], kx=3, ky=3)(y_out, x_out)[::xrev,::yrev].diagonal()
out = out.reshape(oldshape[:-2] + (-1,))
return x_out, y_out, r_out, out
def interp_center(x, y, data, cx, cy, rmax_domain, rint=None, mode='linear'):
u"""
(cx,cy)を中心とした相対座標に内挿する
:Arguments:
**x, y** : array_like
x,y座標
**data** : ndarray
データ配列
**cx,cy** : float
内挿の中心座標
**:rmax_domain**
出力する座標の範囲. x=[cx-rmax_domain,cx+rmax_domain],
y=[cy-rmax_domain,cy+rmax_domain]が出力範囲になる．
**rlint** : float, optional
出力する座標の間隔．指定しない場合は入力座標に応じて決定．
**mode** : {'linear','cubic'}, optional
内挿方法
:Returns:
**xs_out, ys_out** : 2darray
出力座標．(cx,cy)からの相対座標．
**out** : ndarray
内挿したデータ配列
"""
if np.ndim(x) == 2: x = x[0,:]
if np.ndim(y) == 2: y = y[:,0]
ndim = np.ndim(data)
if ndim > 2:
oldshape = data.shape
data = data.reshape(-1, oldshape[-2], oldshape[-1])
elif ndim < 2:
raise ValueError, "input data dimension size must be larger than 2. input ndim is {}".format(ndim)
# make output grid
if rint is None:
rint = max(np.diff(x).max(), np.diff(y).max())
rmax = rmax_domain - rmax_domain%rint
x_out = np.arange(-rmax, rmax+rint/2., rint)
y_out = np.arange(-rmax, rmax+rint/2., rint)
x_in = x - cx
y_in = y - cy
# interpolate
if ndim == 2:
if mode == 'linear':
out = RectBivariateSpline(y_in, x_in, data, kx=1, ky=1)(y_out, x_out)
elif mode == 'cubic':
out = RectBivariateSpline(y_in, x_in, data, kx=3, ky=3)(y_out, x_out)
else:
zn, _, _ = data.shape
xn, yn = len(x_out), len(y_out)
out = np.ma.empty((zn,yn,xn), dtype=np.float32)
for z in range(zn):
if mode == 'linear':
out[z,:,:] = RectBivariateSpline(y_in, x_in, data[z,:,:], kx=1, ky=1)(y_out, x_out)
elif mode == 'cubic':
out[z,:,:] = RectBivariateSpline(y_in, x_in, data[z,:,:], kx=3, ky=3)(y_out, x_out)
out = out.reshape(oldshape[:-2] + (yn,xn))
xs_out, ys_out = np.meshgrid(x_out, y_out)
return xs_out, ys_out, out
