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Fixing tidal field calculation (#68)
* Add import * Fix tidal calculation * Add env to paths * Improve plot routines * Add env classification
This commit is contained in:
Richard Stiskalek
GitHub
parent
ccbbbd24b4
commit
cbfd1cbc99
5 changed files with 233 additions and 34 deletions
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@ -17,7 +17,7 @@ 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, PotentialField, VelocityField # noqa
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from .density import DensityField, PotentialField, VelocityField, TidalTensorField # noqa
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from .interp import (evaluate_cartesian, evaluate_sky, field2rsp, # noqa
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make_sky, fill_outside) # noqa
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from .utils import nside2radec, smoothen_field # noqa
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@ -391,17 +391,43 @@ class TidalTensorField(BaseField):
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-------
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eigvals : 3-dimensional array of shape `(grid, grid, grid)`
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"""
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# TODO needs to be checked further
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grid = tidal_tensor.T00.shape[0]
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eigvals = numpy.full((grid, grid, grid, 3), numpy.nan,
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dtype=numpy.float32)
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dummy = numpy.full((3, 3), numpy.nan, dtype=numpy.float32)
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# FILL IN THESER ARGUMENTS
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tidal_tensor_to_eigenvalues(eigvals, dummy, ...)
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dummy_vector = numpy.full(3, numpy.nan, dtype=numpy.float32)
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dummy_tensor = numpy.full((3, 3), numpy.nan, dtype=numpy.float32)
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tidal_tensor_to_eigenvalues(eigvals, dummy_vector, dummy_tensor,
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tidal_tensor.T00, tidal_tensor.T01,
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tidal_tensor.T02, tidal_tensor.T11,
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tidal_tensor.T12, tidal_tensor.T22)
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return eigvals
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@staticmethod
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def eigvals_to_environment(eigvals, threshold=0.0):
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"""
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Calculate the environment of each grid cell based on the eigenvalues
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of the tidal tensor field.
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Parameters
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----------
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eigvals : 4-dimensional array of shape `(grid, grid, grid, 3)`
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The eigenvalues of the tidal tensor field.
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Returns
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-------
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environment : 3-dimensional array of shape `(grid, grid, grid)`
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The environment of each grid cell. Possible values are:
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- 0: void
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- 1: sheet
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- 2: filament
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- 3: knot
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"""
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environment = numpy.full(eigvals.shape[:-1], numpy.nan,
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dtype=numpy.float32)
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eigenvalues_to_environment(environment, eigvals, threshold)
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return environment
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def __call__(self, overdensity_field):
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"""
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Calculate the tidal tensor field.
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@ -422,20 +448,90 @@ class TidalTensorField(BaseField):
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@jit(nopython=True)
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def tidal_tensor_to_eigenvalues(eigvals, dummy, T00, T01, T02, T11, T12, T22):
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def tidal_tensor_to_eigenvalues(eigvals, dummy_vector, dummy_tensor,
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T00, T01, T02, T11, T12, T22):
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"""
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TODO: needs to be checked further.
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Calculate eigenvalues of the tidal tensor field, sorted in decreasing
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absolute value order. JIT implementation to speed up the work.
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Parameters
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----------
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eigvals : 3-dimensional array of shape `(grid, grid, grid)`
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Array to store the eigenvalues.
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dummy_vector : 1-dimensional array of shape `(3,)`
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Dummy vector to store the eigenvalues.
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dummy_tensor : 2-dimensional array of shape `(3, 3)`
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Dummy tensor to store the tidal tensor.
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T00 : 3-dimensional array of shape `(grid, grid, grid)`
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Tidal tensor component :math:`T_{00}`.
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T01 : 3-dimensional array of shape `(grid, grid, grid)`
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Tidal tensor component :math:`T_{01}`.
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T02 : 3-dimensional array of shape `(grid, grid, grid)`
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Tidal tensor component :math:`T_{02}`.
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T11 : 3-dimensional array of shape `(grid, grid, grid)`
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Tidal tensor component :math:`T_{11}`.
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T12 : 3-dimensional array of shape `(grid, grid, grid)`
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Tidal tensor component :math:`T_{12}`.
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T22 : 3-dimensional array of shape `(grid, grid, grid)`
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Tidal tensor component :math:`T_{22}`.
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Returns
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-------
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eigvals : 3-dimensional array of shape `(grid, grid, grid)`
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"""
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grid = T00.shape[0]
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for i in range(grid):
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for j in range(grid):
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for k in range(grid):
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dummy[0, 0] = T00[i, j, k]
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dummy[0, 1] = T01[i, j, k]
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dummy[0, 2] = T02[i, j, k]
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dummy[1, 1] = T11[i, j, k]
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dummy[1, 2] = T12[i, j, k]
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dummy[2, 2] = T22[i, j, k]
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eigvals[i, j, k, :] = numpy.linalg.eigvalsh(dummy, 'U')
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eigvals[i, j, k, :] = numpy.sort(eigvals[i, j, k, :])
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dummy_tensor[0, 0] = T00[i, j, k]
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dummy_tensor[1, 1] = T11[i, j, k]
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dummy_tensor[2, 2] = T22[i, j, k]
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dummy_tensor[0, 1] = T01[i, j, k]
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dummy_tensor[1, 0] = T01[i, j, k]
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dummy_tensor[0, 2] = T02[i, j, k]
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dummy_tensor[2, 0] = T02[i, j, k]
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dummy_tensor[1, 2] = T12[i, j, k]
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dummy_tensor[2, 1] = T12[i, j, k]
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dummy_vector[:] = numpy.linalg.eigvalsh(dummy_tensor)
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eigvals[i, j, k, :] = dummy_vector[
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numpy.argsort(numpy.abs(dummy_vector))][::-1]
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return eigvals
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@jit(nopython=True)
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def eigenvalues_to_environment(environment, eigvals, th):
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"""
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Classify the environment of each grid cell based on the eigenvalues of the
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tidal tensor field.
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Parameters
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----------
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environment : 3-dimensional array of shape `(grid, grid, grid)`
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Array to store the environment.
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eigvals : 4-dimensional array of shape `(grid, grid, grid, 3)`
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The eigenvalues of the tidal tensor field.
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th : float
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Threshold value to classify the environment.
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Returns
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-------
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environment : 3-dimensional array of shape `(grid, grid, grid)`
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"""
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grid = eigvals.shape[0]
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for i in range(grid):
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for j in range(grid):
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for k in range(grid):
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lmbda1, lmbda2, lmbda3 = eigvals[i, j, k, :]
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if lmbda1 < th and lmbda2 < th and lmbda3 < th:
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environment[i, j, k] = 0
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elif lmbda1 < th and lmbda2 < th:
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environment[i, j, k] = 1
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elif lmbda1 < th:
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environment[i, j, k] = 2
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else:
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environment[i, j, k] = 3
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return environment
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@ -364,7 +364,7 @@ class Paths:
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----------
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kind : str
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Field type. Must be one of: `density`, `velocity`, `potential`,
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`radvel`.
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`radvel`, `environment`.
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MAS : str
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Mass-assignment scheme.
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grid : int
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@ -379,7 +379,8 @@ class Paths:
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path : str
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"""
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fdir = join(self.postdir, "environment")
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assert kind in ["density", "velocity", "potential", "radvel"]
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assert kind in ["density", "velocity", "potential", "radvel",
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"environment"]
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if not isdir(fdir):
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makedirs(fdir)
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warn(f"Created directory `{fdir}`.", UserWarning, stacklevel=1)
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