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https://github.com/DifferentiableUniverseInitiative/JaxPM.git
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154 lines
3.8 KiB
Python
154 lines
3.8 KiB
Python
from functools import partial
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import jax.numpy as jnp
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import jax_cosmo as jc
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import numpy as np
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from jax._src import mesh as mesh_lib
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from jax.sharding import PartitionSpec as P
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from jaxpm.distributed import autoshmap
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def fftk(shape, dtype=np.float32):
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"""
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Generate Fourier transform wave numbers for a given mesh.
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Args:
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nc (int): Shape of the mesh grid.
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Returns:
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list: List of wave number arrays for each dimension in
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the order [kx, ky, kz].
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"""
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kx, ky, kz = [jnp.fft.fftfreq(s, dtype=dtype) * 2 * np.pi for s in shape]
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@partial(autoshmap,
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in_specs=(P('x'), P('y'), P(None)),
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out_specs=(P('x'), P(None, 'y'), P(None)))
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def get_kvec(ky, kz, kx):
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return (ky.reshape([-1, 1, 1]),
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kz.reshape([1, -1, 1]),
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kx.reshape([1, 1, -1])) # yapf: disable
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if not mesh_lib.thread_resources.env.physical_mesh.empty:
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ky, kz, kx = get_kvec(ky, kz, kx) # The order corresponds
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else:
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kx, ky, kz = get_kvec(kx, ky, kz) # The order corresponds
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# to the order of dimensions in the transposed FFT
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return kx, ky, kz
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def interpolate_power_spectrum(input, k, pk):
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pk_fn = lambda x: jc.scipy.interpolate.interp(x.reshape(-1), k, pk
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).reshape(x.shape)
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return autoshmap(pk_fn, in_specs=P('x', 'y'), out_specs=P('x', 'y'))(input)
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def gradient_kernel(kvec, direction, order=1):
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"""
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Computes the gradient kernel in the requested direction
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Parameters:
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-----------
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kvec: array
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Array of k values in Fourier space
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direction: int
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Index of the direction in which to take the gradient
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Returns:
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--------
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wts: array
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Complex kernel
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"""
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if order == 0:
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wts = 1j * kvec[direction]
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wts = jnp.squeeze(wts)
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wts[len(wts) // 2] = 0
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wts = wts.reshape(kvec[direction].shape)
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return wts
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else:
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w = kvec[direction]
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a = 1 / 6.0 * (8 * jnp.sin(w) - jnp.sin(2 * w))
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wts = a * 1j
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return wts
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def laplace_kernel(kvec):
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"""
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Compute the Laplace kernel from a given K vector
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Parameters:
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-----------
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kvec: array
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Array of k values in Fourier space
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Returns:
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--------
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wts: array
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Complex kernel
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"""
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kk = sum(ki**2 for ki in kvec)
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wts = jnp.where(kk == 0, 1., 1. / kk)
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return wts
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def longrange_kernel(kvec, r_split):
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"""
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Computes a long range kernel
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Parameters:
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-----------
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kvec: array
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Array of k values in Fourier space
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r_split: float
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TODO: @modichirag add documentation
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Returns:
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--------
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wts: array
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kernel
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"""
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if r_split != 0:
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kk = sum(ki**2 for ki in kvec)
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return np.exp(-kk * r_split**2)
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else:
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return 1.
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def cic_compensation(kvec):
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"""
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Computes cic compensation kernel.
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Adapted from https://github.com/bccp/nbodykit/blob/a387cf429d8cb4a07bb19e3b4325ffdf279a131e/nbodykit/source/mesh/catalog.py#L499
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Itself based on equation 18 (with p=2) of
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`Jing et al 2005 <https://arxiv.org/abs/astro-ph/0409240>`_
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Args:
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kvec: array of k values in Fourier space
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Returns:
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v: array of kernel
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"""
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kwts = [np.sinc(kvec[i] / (2 * np.pi)) for i in range(3)]
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wts = (kwts[0] * kwts[1] * kwts[2])**(-2)
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return wts
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def PGD_kernel(kvec, kl, ks):
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"""
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Computes the PGD kernel
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Parameters:
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-----------
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kvec: array
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Array of k values in Fourier space
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kl: float
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initial long range scale parameter
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ks: float
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initial dhort range scale parameter
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Returns:
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--------
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v: array
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kernel
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"""
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kk = sum(ki**2 for ki in kvec)
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kl2 = kl**2
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ks4 = ks**4
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mask = (kk == 0).nonzero()
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kk[mask] = 1
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v = jnp.exp(-kl2 / kk) * jnp.exp(-kk**2 / ks4)
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imask = (~(kk == 0)).astype(int)
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v *= imask
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return v
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