PYTHON: Rewrite interpolations.
Signed-off-by: Grzegorz Kowal <grzegorz@amuncode.org>
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@ -22,14 +22,10 @@
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================================================================================
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module: AMUN
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module: INTERPOLATION
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Python module with subroutines to read AMUN code HDF5 files.
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The only requirements for this package are:
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- numpy
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- scipy (optional, for data interpolation)
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Support module for Amun snapshots for the block prolongation with different
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types of interpolation.
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--------------------------------------------------------------------------------
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"""
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@ -43,154 +39,129 @@ except ImportError:
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def rebin(a, newshape):
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'''
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Subroutine changes the size of the input array to to new shape,
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by copying cells or averaging them.
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'''
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assert len(a.shape) == len(newshape)
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'''
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Subroutine changes the size of the input array to to new shape,
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by copying cells or averaging them.
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'''
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assert len(a.shape) == len(newshape)
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m = a.ndim - 1
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if (a.shape[m] > newshape[m]):
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if a.ndim == 3:
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nn = [newshape[0], int(a.shape[0] / newshape[0]),
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newshape[1], int(a.shape[1] / newshape[1]),
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newshape[2], int(a.shape[2] / newshape[2])]
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return a.reshape(nn).mean(5).mean(3).mean(1)
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m = a.ndim - 1
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if (a.shape[m] > newshape[m]):
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if a.ndim == 3:
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nn = [newshape[0], a.shape[0] // newshape[0], \
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newshape[1], a.shape[1] // newshape[1], \
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newshape[2], a.shape[2] // newshape[2]]
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return a.reshape(nn).mean(5).mean(3).mean(1)
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else:
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nn = [newshape[0], a.shape[0] // newshape[0], \
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newshape[1], a.shape[1] // newshape[1]]
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return a.reshape(nn).mean(3).mean(1)
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else:
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nn = [newshape[0], int(a.shape[0] / newshape[0]),
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newshape[1], int(a.shape[1] / newshape[1])]
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return a.reshape(nn).mean(3).mean(1)
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else:
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for n in range(a.ndim):
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a = np.repeat(a, int(newshape[n] / a.shape[n]), axis=n)
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return(a)
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for n in range(a.ndim):
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a = np.repeat(a, newshape[n] // a.shape[n], axis=n)
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return(a)
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def interpolate(a, newshape, nghosts, method='rebin', order=3):
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'''
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Subroutine rescales the block by interpolating its values.
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'''
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if (method == 'rebin' or not scipy_available):
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def interpolate(a, newshape, nghosts=0, method=None, order=1):
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'''
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Subroutine rescales the block by interpolating its values.
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'''
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if method == None or method == 'rebin' or not scipy_available:
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# calculate the indices in order to remove the ghost zones
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#
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if a.ndim == 3:
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ib = nghosts
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jb = nghosts
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kb = nghosts
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ie = a.shape[2] - nghosts
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je = a.shape[1] - nghosts
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ke = a.shape[0] - nghosts
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if (a.shape[0] == 1):
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kb = 0
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ke = 1
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ng = nghosts
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if a.ndim == 3:
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return rebin(a[ng:-ng,ng:-ng,ng:-ng], newshape)
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else:
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return rebin(a[ng:-ng,ng:-ng], newshape)
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elif method == 'zoom':
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zf = (newshape[1] // (a.shape[1] - 2 * nghosts))
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ng = zf * nghosts
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if a.ndim == 3:
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return zoom(a, zf, order=order, grid_mode=True, mode='nearest')[ng:-ng,ng:-ng,ng:-ng]
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else:
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return zoom(a, zf, order=order, grid_mode=True, mode='nearest')[ng:-ng,ng:-ng]
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elif method in [ 'monotonic', 'pchip' ]:
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dims = np.arange(a.ndim)
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q = a
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for n in dims:
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d2 = np.roll(q,-1, axis=0) + np.roll(q, 1, axis=0) - 2.0 * q
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q = q - d2 / 24.0
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d = np.array(q.shape)
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xo = (np.arange(0.5, a.shape[n]) - nghosts) / (a.shape[n] - 2 * nghosts)
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xn = np.arange(0.5, newshape[n]) / newshape[n]
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u = q.reshape([d[0], q.size // d[0]])
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f = np.zeros([newshape[n], q.size // d[0]])
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for i in range(q.size // d[0]):
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f[:,i] = pchip_interpolate(xo, u[:,i], xn)
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d[0] = newshape[n]
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f = f.reshape(d)
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q = f.transpose(np.roll(dims, -1))
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return q
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elif method == 'spline':
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dims = np.arange(a.ndim)
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q = a
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for n in dims:
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d2 = np.roll(q,-1, axis=0) + np.roll(q, 1, axis=0) - 2.0 * q
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q = q - d2 / 24.0
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d = np.array(q.shape)
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xo = (np.arange(0.5, a.shape[n]) - nghosts) / (a.shape[n] - 2 * nghosts)
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xn = np.arange(0.5, newshape[n]) / newshape[n]
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u = q.reshape([d[0], q.size // d[0]])
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f = np.zeros([newshape[n], q.size // d[0]])
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for i in range(q.size // d[0]):
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t = splrep(xo, u[:,i], k=5, s=0.0)
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f[:,i] = splev(xn, t)
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d[0] = newshape[n]
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f = f.reshape(d)
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q = f.transpose(np.roll(dims, -1))
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return q
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return rebin(a[kb:ke,jb:je,ib:ie], newshape)
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else:
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ib = nghosts
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jb = nghosts
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ie = a.shape[1] - nghosts
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je = a.shape[0] - nghosts
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return rebin(a[jb:je,ib:ie], newshape)
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dims = np.arange(a.ndim)
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q = a
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elif (method == 'zoom'):
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for n in dims:
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fc = int(newshape[1] / (a.shape[1] - 2 * nghosts))
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ib, ie = fc * nghosts, - fc * nghosts
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jb, je = fc * nghosts, - fc * nghosts
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if a.ndim == 3:
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kb, ke = fc * nghosts, - fc * nghosts
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if (a.shape[0] == 1):
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kb = 0
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ke = 1
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d2 = np.roll(q,-1, axis=0) + np.roll(q, 1, axis=0) - 2.0 * q
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q = q - d2 / 24.0
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return zoom(a, fc, order=order, grid_mode=True, mode='nearest')[kb:ke,jb:je,ib:ie]
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else:
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return zoom(a, fc, order=order, grid_mode=True, mode='nearest')[jb:je,ib:ie]
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d = np.array(q.shape)
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elif (method == 'monotonic' or method == 'pchip'):
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xo = (np.arange(0.5, a.shape[n]) - nghosts) / (a.shape[n] - 2 * nghosts)
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xn = np.arange(0.5, newshape[n]) / newshape[n]
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dims = np.arange(a.ndim)
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u = q.reshape([d[0], q.size // d[0]])
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f = np.zeros([newshape[n], q.size // d[0]])
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for i in range(q.size // d[0]):
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t = interp1d(xo, u[:,i], kind=method)
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f[:,i] = t(xn)
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q = a
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d[0] = newshape[n]
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f = f.reshape(d)
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for n in dims:
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q = f.transpose(np.roll(dims, -1))
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d2 = np.roll(q,-1, axis=0) + np.roll(q, 1, axis=0) - 2.0 * q
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q = q - d2 / 24.0
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d = np.array(q.shape)
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xo = (np.arange(0.5, a.shape[n]) - nghosts) / (a.shape[n] - 2 * nghosts)
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xn = np.arange(0.5, newshape[n]) / newshape[n]
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u = q.reshape([d[0], int(q.size / d[0])])
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f = np.zeros([newshape[n], int(q.size / d[0])])
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for i in range(int(q.size / d[0])):
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f[:,i] = pchip_interpolate(xo, u[:,i], xn)
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d[0] = newshape[n]
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f = f.reshape(d)
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q = f.transpose(np.roll(dims, -1))
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return q
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elif (method == 'spline'):
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dims = np.arange(a.ndim)
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q = a
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for n in dims:
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d2 = np.roll(q,-1, axis=0) + np.roll(q, 1, axis=0) - 2.0 * q
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q = q - d2 / 24.0
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d = np.array(q.shape)
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xo = (np.arange(0.5, a.shape[n]) - nghosts) / (a.shape[n] - 2 * nghosts)
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xn = np.arange(0.5, newshape[n]) / newshape[n]
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u = q.reshape([d[0], int(q.size / d[0])])
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f = np.zeros([newshape[n], int(q.size / d[0])])
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for i in range(int(q.size / d[0])):
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t = splrep(xo, u[:,i], k=5, s=0.0)
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f[:,i] = splev(xn, t)
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d[0] = newshape[n]
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f = f.reshape(d)
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q = f.transpose(np.roll(dims, -1))
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return q
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else:
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dims = np.arange(a.ndim)
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q = a
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for n in dims:
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d2 = np.roll(q,-1, axis=0) + np.roll(q, 1, axis=0) - 2.0 * q
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q = q - d2 / 24.0
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d = np.array(q.shape)
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xo = (np.arange(0.5, a.shape[n]) - nghosts) / (a.shape[n] - 2 * nghosts)
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xn = np.arange(0.5, newshape[n]) / newshape[n]
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u = q.reshape([d[0], int(q.size / d[0])])
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f = np.zeros([newshape[n], int(q.size / d[0])])
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for i in range(int(q.size / d[0])):
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t = interp1d(xo, u[:,i], kind=method)
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f[:,i] = t(xn)
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d[0] = newshape[n]
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f = f.reshape(d)
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q = f.transpose(np.roll(dims, -1))
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return q
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return q
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