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Gravitational potential calculation (#60)
* Rename file * add velocity field * Add velcoity field * Move smoothening * Add verbosity flags * remove blank * Simplify paths * Add potential calculation * Update paths * Add potential field * Add potential calculation * Move away sky matching * Move interpolation functions * Update nbs
This commit is contained in:
Richard Stiskalek
GitHub
parent
26ef1661a4
commit
b3fd14b81f
10 changed files with 12509 additions and 162 deletions
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@ -17,6 +17,8 @@ from warnings import warn
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try:
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import MAS_library as MASL # noqa
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from .density import DensityField # noqa
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from .density import DensityField, PotentialField, VelocityField # noqa
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from .interp import evaluate_cartesian, evaluate_sky, make_sky # noqa
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from .utils import smoothen_field # noqa
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except ImportError:
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warn("MAS_library not found, `DensityField` will not be available", UserWarning) # noqa
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@ -14,16 +14,18 @@
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""
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Density field and cross-correlation calculations.
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TODO:
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- [ ] Project the velocity field along the line of sight.
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"""
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from abc import ABC
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import MAS_library as MASL
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import numpy
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import smoothing_library as SL
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from tqdm import trange
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from ..read.utils import real2redshift
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from .utils import force_single_precision
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from ..read.utils import radec_to_cartesian, real2redshift
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class BaseField(ABC):
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@ -80,101 +82,6 @@ class BaseField(ABC):
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assert MAS in ["NGP", "CIC", "TSC", "PCS"]
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self._MAS = MAS
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def evaluate_cartesian(self, *fields, pos):
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"""
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Evaluate a scalar field at Cartesian coordinates using CIC
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interpolation.
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Parameters
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----------
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field : (list of) 3-dimensional array of shape `(grid, grid, grid)`
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Fields to be interpolated.
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pos : 2-dimensional array of shape `(n_samples, 3)`
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Positions to evaluate the density field. Assumed to be in box
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units.
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Returns
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-------
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interp_fields : (list of) 1-dimensional array of shape `(n_samples,).
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"""
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pos = force_single_precision(pos, "pos")
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nsamples = pos.shape[0]
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interp_fields = [numpy.full(nsamples, numpy.nan, dtype=numpy.float32)
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for __ in range(len(fields))]
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for i, field in enumerate(fields):
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MASL.CIC_interp(field, self.boxsize, pos, interp_fields[i])
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if len(fields) == 1:
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return interp_fields[0]
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return interp_fields
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def evaluate_sky(self, *fields, pos, isdeg=True):
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"""
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Evaluate the scalar fields at given distance, right ascension and
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declination. Assumes an observed in the centre of the box, with
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distance being in :math:`Mpc`. Uses CIC interpolation.
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Parameters
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----------
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fields : (list of) 3-dimensional array of shape `(grid, grid, grid)`
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Field to be interpolated.
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pos : 2-dimensional array of shape `(n_samples, 3)`
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Spherical coordinates to evaluate the field. Columns are distance,
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right ascension, declination, respectively.
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isdeg : bool, optional
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Whether `ra` and `dec` are in degres. By default `True`.
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Returns
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-------
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interp_fields : (list of) 1-dimensional array of shape `(n_samples,).
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"""
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pos = force_single_precision(pos, "pos")
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# We first calculate convert the distance to box coordinates and then
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# convert to Cartesian coordinates.
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X = numpy.copy(pos)
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X[:, 0] = self.box.mpc2box(X[:, 0])
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X = radec_to_cartesian(pos, isdeg)
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# Then we move the origin to match the box coordinates
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X -= 0.5
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return self.evaluate_field(*fields, pos=X)
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def make_sky(self, field, angpos, dist, verbose=True):
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r"""
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Make a sky map of a scalar field. The observer is in the centre of the
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box the field is evaluated along directions `angpos`. Along each
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direction, the field is evaluated distances `dist_marg` and summed.
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Uses CIC interpolation.
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Parameters
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----------
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field : 3-dimensional array of shape `(grid, grid, grid)`
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Field to be interpolated
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angpos : 2-dimensional arrays of shape `(ndir, 2)`
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Directions to evaluate the field. Assumed to be RA
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:math:`\in [0, 360]` and dec :math:`\in [-90, 90]` degrees,
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respectively.
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dist : 1-dimensional array
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Radial distances to evaluate the field.
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verbose : bool, optional
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Verbosity flag.
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Returns
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-------
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interp_field : 1-dimensional array of shape `(n_pos, )`.
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"""
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assert angpos.ndim == 2 and dist.ndim == 1
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# We loop over the angular directions, at each step evaluating a vector
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# of distances. We pre-allocate arrays for speed.
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dir_loop = numpy.full((dist.size, 3), numpy.nan, dtype=numpy.float32)
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ndir = angpos.shape[0]
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out = numpy.zeros(ndir, numpy.nan, dtype=numpy.float32)
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for i in trange(ndir) if verbose else range(ndir):
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dir_loop[1, :] = angpos[i, 0]
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dir_loop[2, :] = angpos[i, 1]
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out[i] = numpy.sum(self.evaluate_sky(field, dir_loop, isdeg=True))
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return out
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###############################################################################
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# Density field calculation #
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@ -183,7 +90,7 @@ class BaseField(ABC):
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class DensityField(BaseField):
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r"""
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Density field calculations. Based primarily on routines of Pylians [1].
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Density field calculation. Based primarily on routines of Pylians [1].
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Parameters
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----------
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@ -203,30 +110,6 @@ class DensityField(BaseField):
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self.box = box
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self.MAS = MAS
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def smoothen(self, field, smooth_scale, threads=1):
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"""
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Smooth a field with a Gaussian filter.
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Parameters
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----------
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field : 3-dimensional array of shape `(grid, grid, grid)`
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Field to be smoothed.
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smooth_scale : float, optional
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Gaussian kernal scale to smoothen the density field, in box units.
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threads : int, optional
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Number of threads. By default 1.
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Returns
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-------
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smoothed_field : 3-dimensional array of shape `(grid, grid, grid)`
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"""
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filter_kind = "Gaussian"
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grid = field.shape[0]
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# FFT of the filter
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W_k = SL.FT_filter(self.boxsize, smooth_scale, grid, filter_kind,
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threads)
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return SL.field_smoothing(field, W_k, threads)
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def overdensity_field(self, delta):
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r"""
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Calculate the overdensity field from the density field.
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@ -310,6 +193,97 @@ class DensityField(BaseField):
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return rho
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###############################################################################
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# Density field calculation #
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###############################################################################
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class VelocityField(BaseField):
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r"""
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Velocity field calculation. Based primarily on routines of Pylians [1].
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Parameters
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----------
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box : :py:class:`csiborgtools.read.BoxUnits`
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The simulation box information and transformations.
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MAS : str
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Mass assignment scheme. Options are Options are: 'NGP' (nearest grid
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point), 'CIC' (cloud-in-cell), 'TSC' (triangular-shape cloud), 'PCS'
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(piecewise cubic spline).
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References
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----------
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[1] https://pylians3.readthedocs.io/
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"""
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def __init__(self, box, MAS):
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self.box = box
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self.MAS = MAS
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def __call__(self, parts, grid, mpart, flip_xz=True, nbatch=30,
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verbose=True):
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"""
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Calculate the velocity field using a Pylians routine [1, 2].
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Iteratively loads the particles into memory, flips their `x` and `z`
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coordinates. Particles are assumed to be in box units.
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Parameters
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----------
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parts : 2-dimensional array of shape `(n_parts, 7)`
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Particle positions, velocities and masses.
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Columns are: `x`, `y`, `z`, `vx`, `vy`, `vz`, `M`.
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grid : int
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Grid size.
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mpart : float
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Particle mass.
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flip_xz : bool, optional
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Whether to flip the `x` and `z` coordinates.
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nbatch : int, optional
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Number of batches to split the particle loading into.
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verbose : bool, optional
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Verbosity flag.
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Returns
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-------
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rho_vel : 3-dimensional array of shape `(3, grid, grid, grid)`.
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Velocity field along each axis.
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References
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----------
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[1] https://pylians3.readthedocs.io/
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[2] https://github.com/franciscovillaescusa/Pylians3/blob/master
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/library/MAS_library/MAS_library.pyx
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"""
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rho_velx = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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rho_vely = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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rho_velz = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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rho_vel = [rho_velx, rho_vely, rho_velz]
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nparts = parts.shape[0]
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batch_size = nparts // nbatch
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start = 0
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for __ in trange(nbatch + 1) if verbose else range(nbatch + 1):
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end = min(start + batch_size, nparts)
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pos = parts[start:end]
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pos, vel, mass = pos[:, :3], pos[:, 3:6], pos[:, 6]
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pos = force_single_precision(pos, "particle_position")
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vel = force_single_precision(vel, "particle_velocity")
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mass = force_single_precision(mass, "particle_mass")
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if flip_xz:
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pos[:, [0, 2]] = pos[:, [2, 0]]
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vel *= mass.reshape(-1, 1) / mpart
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for i in range(3):
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MASL.MA(pos, rho_vel[i], self.boxsize, self.MAS, W=vel[i, :],
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verbose=False)
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if end == nparts:
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break
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start = end
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return numpy.stack(rho_vel)
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###############################################################################
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# Potential field calculation #
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###############################################################################
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@ -377,6 +351,8 @@ class TidalTensorField(BaseField):
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Calculate eigenvalues of the tidal tensor field, sorted in increasing
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order.
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TODO: evaluate this on a grid instead.
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Parameters
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----------
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tidal_tensor : :py:class:`MAS_library.tidal_tensor`
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124
csiborgtools/field/interp.py
Normal file
124
csiborgtools/field/interp.py
Normal file
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@ -0,0 +1,124 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""
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Tools for interpolating 3D fields at arbitrary positions.
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"""
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import MAS_library as MASL
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import numpy
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from tqdm import trange
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from ..read.utils import radec_to_cartesian
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from .utils import force_single_precision
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def evaluate_cartesian(*fields, pos):
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"""
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Evaluate a scalar field at Cartesian coordinates using CIC
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interpolation.
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Parameters
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----------
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field : (list of) 3-dimensional array of shape `(grid, grid, grid)`
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Fields to be interpolated.
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pos : 2-dimensional array of shape `(n_samples, 3)`
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Positions to evaluate the density field. Assumed to be in box
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units.
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Returns
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-------
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interp_fields : (list of) 1-dimensional array of shape `(n_samples,).
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"""
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boxsize = 1.
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pos = force_single_precision(pos, "pos")
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nsamples = pos.shape[0]
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interp_fields = [numpy.full(nsamples, numpy.nan, dtype=numpy.float32)
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for __ in range(len(fields))]
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for i, field in enumerate(fields):
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MASL.CIC_interp(field, boxsize, pos, interp_fields[i])
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if len(fields) == 1:
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return interp_fields[0]
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return interp_fields
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def evaluate_sky(*fields, pos, box, isdeg=True):
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"""
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Evaluate the scalar fields at given distance, right ascension and
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declination. Assumes an observed in the centre of the box, with
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distance being in :math:`Mpc`. Uses CIC interpolation.
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Parameters
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----------
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fields : (list of) 3-dimensional array of shape `(grid, grid, grid)`
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Field to be interpolated.
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pos : 2-dimensional array of shape `(n_samples, 3)`
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Spherical coordinates to evaluate the field. Columns are distance,
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right ascension, declination, respectively.
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box : :py:class:`csiborgtools.read.BoxUnits`
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The simulation box information and transformations.
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isdeg : bool, optional
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Whether `ra` and `dec` are in degres. By default `True`.
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Returns
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-------
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interp_fields : (list of) 1-dimensional array of shape `(n_samples,).
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"""
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pos = force_single_precision(pos, "pos")
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# We first calculate convert the distance to box coordinates and then
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# convert to Cartesian coordinates.
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X = numpy.copy(pos)
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X[:, 0] = box.mpc2box(X[:, 0])
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X = radec_to_cartesian(pos, isdeg)
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# Then we move the origin to match the box coordinates
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X -= 0.5
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return evaluate_cartesian(*fields, pos=X)
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def make_sky(field, angpos, dist, verbose=True):
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r"""
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Make a sky map of a scalar field. The observer is in the centre of the
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box the field is evaluated along directions `angpos`. Along each
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direction, the field is evaluated distances `dist_marg` and summed.
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Uses CIC interpolation.
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Parameters
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----------
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field : 3-dimensional array of shape `(grid, grid, grid)`
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Field to be interpolated
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angpos : 2-dimensional arrays of shape `(ndir, 2)`
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Directions to evaluate the field. Assumed to be RA
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:math:`\in [0, 360]` and dec :math:`\in [-90, 90]` degrees,
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respectively.
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dist : 1-dimensional array
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Radial distances to evaluate the field.
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verbose : bool, optional
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Verbosity flag.
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Returns
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-------
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interp_field : 1-dimensional array of shape `(n_pos, )`.
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"""
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assert angpos.ndim == 2 and dist.ndim == 1
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# We loop over the angular directions, at each step evaluating a vector
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# of distances. We pre-allocate arrays for speed.
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dir_loop = numpy.full((dist.size, 3), numpy.nan, dtype=numpy.float32)
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ndir = angpos.shape[0]
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out = numpy.zeros(ndir, numpy.nan, dtype=numpy.float32)
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for i in trange(ndir) if verbose else range(ndir):
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dir_loop[1, :] = angpos[i, 0]
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dir_loop[2, :] = angpos[i, 1]
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out[i] = numpy.sum(evaluate_sky(field, dir_loop, isdeg=True))
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return out
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@ -18,6 +18,7 @@ Utility functions for the field module.
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from warnings import warn
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import numpy
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import smoothing_library as SL
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def force_single_precision(x, name):
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@ -40,3 +41,27 @@ def force_single_precision(x, name):
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warn(f"Converting `{name}` to float32.", UserWarning, stacklevel=1)
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x = x.astype(numpy.float32)
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return x
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def smoothen_field(field, smooth_scale, boxsize, threads=1):
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"""
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Smooth a field with a Gaussian filter.
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Parameters
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----------
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field : 3-dimensional array of shape `(grid, grid, grid)`
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Field to be smoothed.
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smooth_scale : float, optional
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Gaussian kernal scale to smoothen the density field, in box units.
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boxsize : float
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Size of the box.
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threads : int, optional
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Number of threads. By default 1.
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Returns
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-------
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smoothed_field : 3-dimensional array of shape `(grid, grid, grid)`
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"""
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W_k = SL.FT_filter(boxsize, smooth_scale, field.shape[0], "Gaussian",
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threads)
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return SL.field_smoothing(field, W_k, threads)
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@ -476,15 +476,21 @@ class NPairsOverlap:
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List of cross simulation halo catalogues.
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paths : py:class`csiborgtools.read.CSiBORGPaths`
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CSiBORG paths object.
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verbose : bool, optional
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Verbosity flag for loading the overlap objects.
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"""
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_pairs = None
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def __init__(self, cat0, catxs, paths):
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self._pairs = [PairOverlap(cat0, catx, paths) for catx in catxs]
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def __init__(self, cat0, catxs, paths, verbose=True):
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pairs = [None] * len(catxs)
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for i, catx in enumerate(tqdm(catxs) if verbose else catxs):
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pairs[i] = PairOverlap(cat0, catx, paths)
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|
||||
def summed_overlap(self, from_smoothed, verbose=False):
|
||||
self._pairs = pairs
|
||||
|
||||
def summed_overlap(self, from_smoothed, verbose=True):
|
||||
"""
|
||||
Calcualte summed overlap of each halo in the reference simulation with
|
||||
Calculate summed overlap of each halo in the reference simulation with
|
||||
the cross simulations.
|
||||
|
||||
Parameters
|
||||
|
|
@ -503,7 +509,7 @@ class NPairsOverlap:
|
|||
out[i] = pair.summed_overlap(from_smoothed)
|
||||
return numpy.vstack(out).T
|
||||
|
||||
def prob_nomatch(self, from_smoothed, verbose=False):
|
||||
def prob_nomatch(self, from_smoothed, verbose=True):
|
||||
"""
|
||||
Probability of no match for each halo in the reference simulation with
|
||||
the cross simulation.
|
||||
|
|
@ -526,7 +532,7 @@ class NPairsOverlap:
|
|||
|
||||
def counterpart_mass(self, from_smoothed, overlap_threshold=0.,
|
||||
in_log=False, mass_kind="totpartmass",
|
||||
return_full=True, verbose=False):
|
||||
return_full=False, verbose=True):
|
||||
"""
|
||||
Calculate the expected counterpart mass of each halo in the reference
|
||||
simulation from the crossed simulation.
|
||||
|
|
@ -549,7 +555,7 @@ class NPairsOverlap:
|
|||
Whether to return the full results of matching each pair or
|
||||
calculate summary statistics by Gaussian averaging.
|
||||
verbose : bool, optional
|
||||
Verbosity flag. By default `False`.
|
||||
Verbosity flag.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
|
|||
|
|
@ -322,30 +322,35 @@ class CSiBORGPaths:
|
|||
fname = f"parts_{str(nsim).zfill(5)}.h5"
|
||||
return join(fdir, fname)
|
||||
|
||||
def density_field_path(self, MAS, nsim, in_rsp):
|
||||
def field_path(self, kind, MAS, grid, nsim, in_rsp):
|
||||
"""
|
||||
Path to the files containing the calculated density fields.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
kind : str
|
||||
Field type. Must be one of: `density`, `velocity`, `potential`.
|
||||
MAS : str
|
||||
Mass-assignment scheme. Currently only SPH is supported.
|
||||
Mass-assignment scheme.
|
||||
grid : int
|
||||
Grid size.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
in_rsp : bool
|
||||
Whether the density field is calculated in redshift space.
|
||||
Whether the calculation is performed in redshift space.
|
||||
|
||||
Returns
|
||||
-------
|
||||
path : str
|
||||
"""
|
||||
fdir = join(self.postdir, "environment")
|
||||
assert kind in ["density", "velocity", "potential"]
|
||||
if not isdir(fdir):
|
||||
makedirs(fdir)
|
||||
warn(f"Created directory `{fdir}`.", UserWarning, stacklevel=1)
|
||||
fname = f"density_{MAS}_{str(nsim).zfill(5)}.npy"
|
||||
if in_rsp:
|
||||
fname = fname.replace("density", "density_rsp")
|
||||
kind = kind + "_rsp"
|
||||
fname = f"{kind}_{MAS}_{str(nsim).zfill(5)}_grid{grid}.npy"
|
||||
return join(fdir, fname)
|
||||
|
||||
def knnauto_path(self, run, nsim=None):
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
12025
notebooks/matching.ipynb
Normal file
12025
notebooks/matching.ipynb
Normal file
File diff suppressed because one or more lines are too long
78
scripts/field_derived.py
Normal file
78
scripts/field_derived.py
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
# Copyright (C) 2022 Richard Stiskalek
|
||||
# This program is free software; you can redistribute it and/or modify it
|
||||
# under the terms of the GNU General Public License as published by the
|
||||
# Free Software Foundation; either version 3 of the License, or (at your
|
||||
# option) any later version.
|
||||
#
|
||||
# This program is distributed in the hope that it will be useful, but
|
||||
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
||||
# Public License for more details.
|
||||
#
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
"""
|
||||
MPI script to calculate density field-derived fields in the CSiBORG
|
||||
simulations' final snapshot.
|
||||
"""
|
||||
from argparse import ArgumentParser
|
||||
from datetime import datetime
|
||||
from distutils.util import strtobool
|
||||
|
||||
import numpy
|
||||
from mpi4py import MPI
|
||||
|
||||
try:
|
||||
import csiborgtools
|
||||
except ModuleNotFoundError:
|
||||
import sys
|
||||
sys.path.append("../")
|
||||
import csiborgtools
|
||||
|
||||
comm = MPI.COMM_WORLD
|
||||
rank = comm.Get_rank()
|
||||
nproc = comm.Get_size()
|
||||
verbose = nproc == 1
|
||||
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument("--ics", type=int, nargs="+", default=None,
|
||||
help="IC realisations. If `-1` processes all simulations.")
|
||||
parser.add_argument("--kind", type=str, choices=["potential", "velocity"],
|
||||
help="What derived field to calculate?")
|
||||
parser.add_argument("--MAS", type=str, choices=["NGP", "CIC", "TSC", "PCS"])
|
||||
parser.add_argument("--grid", type=int, help="Grid resolution.")
|
||||
parser.add_argument("--in_rsp", type=lambda x: bool(strtobool(x)),
|
||||
help="Calculate from the RSP density field?")
|
||||
args = parser.parse_args()
|
||||
paths = csiborgtools.read.CSiBORGPaths(**csiborgtools.paths_glamdring)
|
||||
|
||||
if args.ics is None or args.ics[0] == -1:
|
||||
ics = paths.get_ics()
|
||||
else:
|
||||
ics = args.ics
|
||||
|
||||
|
||||
for i in csiborgtools.fits.split_jobs(len(ics), nproc)[rank]:
|
||||
nsim = ics[i]
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: rank {rank} working on simulation {nsim}.",
|
||||
flush=True)
|
||||
nsnap = max(paths.get_snapshots(nsim))
|
||||
box = csiborgtools.read.BoxUnits(nsnap, nsim, paths)
|
||||
density_gen = csiborgtools.field.DensityField(box, args.MAS)
|
||||
|
||||
rho = numpy.load(paths.field_path("density", args.MAS, args.grid, nsim,
|
||||
args.in_rsp))
|
||||
rho = density_gen.overdensity_field(rho)
|
||||
|
||||
if args.kind == "potential":
|
||||
gen = csiborgtools.field.PotentialField(box, args.MAS)
|
||||
else:
|
||||
raise RuntimeError(f"Field {args.kind} is not implemented yet.")
|
||||
|
||||
field = gen(rho)
|
||||
fout = paths.field_path("potential", args.MAS, args.grid, nsim,
|
||||
args.in_rsp)
|
||||
print(f"{datetime.now()}: rank {rank} saving output to `{fout}`.")
|
||||
numpy.save(fout, field)
|
||||
|
|
@ -38,12 +38,16 @@ verbose = nproc == 1
|
|||
parser = ArgumentParser()
|
||||
parser.add_argument("--ics", type=int, nargs="+", default=None,
|
||||
help="IC realisations. If `-1` processes all simulations.")
|
||||
parser.add_argument("--kind", type=str, choices=["density", "velocity"],
|
||||
help="Calculate the density or velocity field?")
|
||||
parser.add_argument("--MAS", type=str, choices=["NGP", "CIC", "TSC", "PCS"],
|
||||
help="Mass assignment scheme.")
|
||||
parser.add_argument("--grid", type=int, help="Grid resolution.")
|
||||
parser.add_argument("--in_rsp", type=lambda x: bool(strtobool(x)),
|
||||
help="Calculate the density field in redshift space?")
|
||||
parser.add_argument("--MAS", type=str, choices=["NGP", "CIC", "TSC", "PCS"])
|
||||
args = parser.parse_args()
|
||||
paths = csiborgtools.read.CSiBORGPaths(**csiborgtools.paths_glamdring)
|
||||
mpart = 1.1641532e-10 # Particle mass in CSiBORG simulations.
|
||||
|
||||
if args.ics is None or args.ics[0] == -1:
|
||||
ics = paths.get_ics()
|
||||
|
|
@ -59,11 +63,14 @@ for i in csiborgtools.fits.split_jobs(len(ics), nproc)[rank]:
|
|||
nsnap = max(paths.get_snapshots(nsim))
|
||||
box = csiborgtools.read.BoxUnits(nsnap, nsim, paths)
|
||||
parts = csiborgtools.read.read_h5(paths.particles_path(nsim))["particles"]
|
||||
density_generator = csiborgtools.field.DensityField(box, args.MAS)
|
||||
|
||||
rho = density_generator(parts, args.grid, in_rsp=args.in_rsp,
|
||||
verbose=verbose)
|
||||
if args.kind == "density":
|
||||
gen = csiborgtools.field.DensityField(box, args.MAS)
|
||||
field = gen(parts, args.grid, in_rsp=args.in_rsp, verbose=verbose)
|
||||
else:
|
||||
gen = csiborgtools.field.VelocityField(box, args.MAS)
|
||||
field = gen(parts, args.grid, mpart, verbose=verbose)
|
||||
|
||||
fout = paths.density_field_path(args.MAS, nsim, args.in_rsp)
|
||||
fout = paths.field_path(args.kind, args.MAS, args.grid, nsim, args.in_rsp)
|
||||
print(f"{datetime.now()}: rank {rank} saving output to `{fout}`.")
|
||||
numpy.save(fout, rho)
|
||||
numpy.save(fout, field)
|
||||
Loading…
Reference in a new issue