Source code for yt.visualization.volume_rendering.off_axis_projection

Volume rendering


# Copyright (c) 2014, yt Development Team.
# Distributed under the terms of the Modified BSD License.
# The full license is in the file COPYING.txt, distributed with this software.

from .scene import Scene
from .render_source import VolumeSource
from .transfer_functions import ProjectionTransferFunction
from .utils import data_source_or_all
from yt.funcs import mylog, iterable
from yt.utilities.lib.partitioned_grid import \
from yt.data_objects.api import ImageArray
import numpy as np

[docs]def off_axis_projection(data_source, center, normal_vector, width, resolution, item, weight=None, volume=None, no_ghost=False, interpolated=False, north_vector=None, num_threads=1, method='integrate'): r"""Project through a dataset, off-axis, and return the image plane. This function will accept the necessary items to integrate through a volume at an arbitrary angle and return the integrated field of view to the user. Note that if a weight is supplied, it will multiply the pre-interpolated values together, then create cell-centered values, then interpolate within the cell to conduct the integration. Parameters ---------- data_source : ~yt.data_objects.static_output.Dataset or ~yt.data_objects.data_containers.YTSelectionDataContainer This is the dataset or data object to volume render. center : array_like The current 'center' of the view port -- the focal point for the camera. normal_vector : array_like The vector between the camera position and the center. width : float or list of floats The current width of the image. If a single float, the volume is cubical, but if not, it is left/right, top/bottom, front/back resolution : int or list of ints The number of pixels in each direction. item: string The field to project through the volume weight : optional, default None If supplied, the field will be pre-multiplied by this, then divided by the integrated value of this field. This returns an average rather than a sum. volume : `yt.extensions.volume_rendering.AMRKDTree`, optional The volume to ray cast through. Can be specified for finer-grained control, but otherwise will be automatically generated. no_ghost: bool, optional Optimization option. If True, homogenized bricks will extrapolate out from grid instead of interpolating from ghost zones that have to first be calculated. This can lead to large speed improvements, but at a loss of accuracy/smoothness in resulting image. The effects are less notable when the transfer function is smooth and broad. Default: True interpolated : optional, default False If True, the data is first interpolated to vertex-centered data, then tri-linearly interpolated along the ray. Not suggested for quantitative studies. north_vector : optional, array_like, default None A vector that, if specified, restricts the orientation such that the north vector dotted into the image plane points "up". Useful for rotations num_threads: integer, optional, default 1 Use this many OpenMP threads during projection. method : string The method of projection. Valid methods are: "integrate" with no weight_field specified : integrate the requested field along the line of sight. "integrate" with a weight_field specified : weight the requested field by the weighting field and integrate along the line of sight. "sum" : This method is the same as integrate, except that it does not multiply by a path length when performing the integration, and is just a straight summation of the field along the given axis. WARNING: This should only be used for uniform resolution grid datasets, as other datasets may result in unphysical images. or camera movements. Returns ------- image : array An (N,N) array of the final integrated values, in float64 form. Examples -------- >>> image = off_axis_projection(ds, [0.5, 0.5, 0.5], [0.2,0.3,0.4], ... 0.2, N, "temperature", "density") >>> write_image(np.log10(image), "offaxis.png") """ if method not in ['integrate','sum']: raise NotImplementedError("Only 'integrate' or 'sum' methods are valid for off-axis-projections") if interpolated is True: raise NotImplementedError("Only interpolated=False methods are currently implemented for off-axis-projections") data_source = data_source_or_all(data_source) # Sanitize units if not hasattr(center, "units"): center = data_source.ds.arr(center, 'code_length') if not hasattr(width, "units"): width = data_source.ds.arr(width, 'code_length') sc = Scene() data_source.ds.index if item is None: field = data_source.ds.field_list[0]'Setting default field to %s' % field.__repr__()) funits = data_source.ds._get_field_info(item).units vol = VolumeSource(data_source, item) if weight is None: vol.set_field(item) else: # This is a temporary field, which we will remove at the end. weightfield = ("index", "temp_weightfield") def _make_wf(f, w): def temp_weightfield(a, b): tr = b[f].astype("float64") * b[w] return b.apply_units(tr, a.units) return temp_weightfield data_source.ds.field_info.add_field(weightfield, sampling_type="cell", function=_make_wf(item, weight)) # Now we have to tell the dataset to add it and to calculate # its dependencies.. deps, _ = data_source.ds.field_info.check_derived_fields([weightfield]) data_source.ds.field_dependencies.update(deps) vol.set_field(weightfield) vol.set_weight_field(weight) ptf = ProjectionTransferFunction() vol.set_transfer_function(ptf) camera = sc.add_camera(data_source) camera.set_width(width) if not iterable(resolution): resolution = [resolution]*2 camera.resolution = resolution if not iterable(width): width = data_source.ds.arr([width]*3) normal = np.array(normal_vector) normal = normal / np.linalg.norm(normal) camera.position = center - width[2]*normal camera.focus = center # If north_vector is None, we set the default here. # This is chosen so that if normal_vector is one of the # cartesian coordinate axes, the projection will match # the corresponding on-axis projection. if north_vector is None: vecs = np.identity(3) t = np.cross(vecs, normal).sum(axis=1) ax = t.argmax() east_vector = np.cross(vecs[ax, :], normal).ravel() north = np.cross(normal, east_vector).ravel() else: north = np.array(north_vector) north = north / np.linalg.norm(north) camera.switch_orientation(normal, north) sc.add_source(vol) vol.set_sampler(camera, interpolated=False) assert (vol.sampler is not None) fields = [vol.field] if vol.weight_field is not None: fields.append(vol.weight_field) mylog.debug("Casting rays") for i, (grid, mask) in enumerate(data_source.blocks): data = [(grid[f] * mask).astype("float64") for f in fields] pg = PartitionedGrid(, data, mask.astype('uint8'), grid.LeftEdge, grid.RightEdge, grid.ActiveDimensions.astype("int64")) grid.clear_data() vol.sampler(pg, num_threads = num_threads) image = vol.finalize_image(camera, vol.sampler.aimage) image = ImageArray(image, funits, registry=data_source.ds.unit_registry, if weight is not None: data_source.ds.field_info.pop(("index", "temp_weightfield")) if method == "integrate": if weight is None: dl = width[2].in_units(data_source.ds.unit_system["length"]) image *= dl else: mask = image[:,:,1] == 0 image[:,:,0] /= image[:,:,1] image[mask] = 0 return image[:,:,0]