def test_center_1():
"""Change the center to [0, 3, 0]
Every point will be shifted by 3 in the y-domain
With this, we should not be able to see any of the y-axis particles
(0, 1, 0) -> (0, -2)
(0, 2, 0) -> (0, -1)
(0, 0, 1) -> (0, -3)
(0, 0, 2) -> (0, -3)
(0, 0, 3) -> (0, -3)
(0, 0, 0) -> (0, -3)
(1, 0, 0) -> (1, -3)
"""
expected_maxima = ([0.0, 0.0, 0.0, 1.0], [-2.0, -1.0, -3.0, -3.0])
normal_vector = [0.0, 0.0, 1.0]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
# center = [(left_edge[0] + right_edge[0])/2,
# left_edge[1],
# (left_edge[2] + right_edge[2])/2]
center = [0.0, 3.0, 0.0]
width = right_edge - left_edge
buf1 = OffAP.off_axis_projection(
ds, center, normal_vector, width, resolution, ("gas", "density")
)
find_compare_maxima(expected_maxima, buf1, resolution, width)
def test_basic_rotation_1():
"""All particles on Z-axis should now be on the negative Y-Axis
fake_sph_orientation has three z-axis particles,
so there should be three y-axis particles after rotation
(0, 0, 1) -> (0, -1)
(0, 0, 2) -> (0, -2)
(0, 0, 3) -> (0, -3)
In addition, we should find a local maxima at (0, 0) due to:
(0, 0, 0) -> (0, 0)
(0, 1, 0) -> (0, 0)
(0, 2, 0) -> (0, 0)
and the one particle on the x-axis should not change its position:
(1, 0, 0) -> (1, 0)
"""
expected_maxima = ([0.0, 0.0, 0.0, 0.0, 1.0], [0.0, -1.0, -2.0, -3.0, 0.0])
normal_vector = [0.0, 1.0, 0.0]
north_vector = [0.0, 0.0, -1.0]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = (left_edge + right_edge) / 2
width = right_edge - left_edge
buf1 = OffAP.off_axis_projection(
ds,
center,
normal_vector,
width,
resolution,
("gas", "density"),
north_vector=north_vector,
)
find_compare_maxima(expected_maxima, buf1, resolution, width)
def test_no_rotation():
"""Determines if a projection processed through
off_axis_projection with no rotation will give the same
image buffer if processed directly through
pixelize_sph_kernel_projection
"""
normal_vector = [0.0, 0.0, 1.0]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
ad = ds.all_data()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = (left_edge + right_edge) / 2
width = right_edge - left_edge
px = ad[("all", "particle_position_x")]
py = ad[("all", "particle_position_y")]
hsml = ad[("all", "smoothing_length")]
quantity_to_smooth = ad[("gas", "density")]
density = ad[("io", "density")]
mass = ad[("io", "particle_mass")]
bounds = [-4, 4, -4, 4, -4, 4]
buf2 = np.zeros(resolution)
buf1 = OffAP.off_axis_projection(
ds, center, normal_vector, width, resolution, ("gas", "density")
)
pixelize_sph_kernel_projection(
buf2, px, py, hsml, mass, density, quantity_to_smooth, bounds
)
assert_almost_equal(buf1.ndarray_view(), buf2)
def test_basic_rotation_3():
"""Rotation of z-axis onto negative z-axis.
All fake particles on z-axis should now be of the negative z-Axis.
fake_sph_orientation has three z-axis particles,
so we should have a local maxima at (0, 0)
(0, 0, 1) -> (0, 0)
(0, 0, 2) -> (0, 0)
(0, 0, 3) -> (0, 0)
In addition, (0, 0, 0) should also contribute to the local maxima at (0, 0):
(0, 0, 0) -> (0, 0)
x-axis particles should be rotated as such:
(1, 0, 0) -> (0, -1)
and same goes for y-axis particles:
(0, 1, 0) -> (-1, 0)
(0, 2, 0) -> (-2, 0)
"""
expected_maxima = ([0.0, 0.0, -1.0, -2.0], [0.0, -1.0, 0.0, 0.0])
normal_vector = [0.0, 0.0, -1.0]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = (left_edge + right_edge) / 2
width = right_edge - left_edge
buf1 = OffAP.off_axis_projection(
ds, center, normal_vector, width, resolution, ("gas", "density")
)
find_compare_maxima(expected_maxima, buf1, resolution, width)
def __init__(self, ds, normal, fields, center='c', width=(1.0, 'unitary'),
weight_field=None, image_res=512, data_source=None,
north_vector=None, depth=(1.0, "unitary"),
method='integrate', length_unit=None):
fields = ensure_list(fields)
center, dcenter = ds.coordinates.sanitize_center(center, 4)
buf = {}
width = ds.coordinates.sanitize_width(normal, width, depth)
wd = tuple(el.in_units('code_length').v for el in width)
if not iterable(image_res):
image_res = (image_res, image_res)
res = (image_res[0], image_res[1])
if data_source is None:
source = ds
else:
source = data_source
for field in fields:
buf[field] = off_axis_projection(source, center, normal, wd,
res, field, north_vector=north_vector,
method=method, weight=weight_field).swapaxes(0,1)
center = ds.arr([0.0]*2, 'code_length')
w, not_an_frb, lunit = construct_image(ds, normal, buf, center,
image_res, width, length_unit)
super(FITSOffAxisProjection, self).__init__(buf, fields=fields, wcs=w,
length_unit=lunit, ds=ds)
def test_basic_rotation_2():
""" Rotation of x-axis onto z-axis. All particles on z-axis should now be on the negative x-Axis
fake_sph_orientation has three z-axis particles, so there should be three x-axis particles
after rotation
(0, 0, 1) -> (-1, 0)
(0, 0, 2) -> (-2, 0)
(0, 0, 3) -> (-3, 0)
In addition, we should find a local maxima at (0, 0) due to:
(0, 0, 0) -> (0, 0)
(1, 0, 0) -> (0, 0)
and the two particles on the y-axis should not change its position:
(0, 1, 0) -> (0, 1)
(0, 2, 0) -> (0, 2)
"""
expected_maxima = ([-1., -2., -3., 0., 0., 0.], [0., 0., 0., 0., 1., 2.])
normal_vector = [1., 0., 0.]
north_vector = [0., 1., 0.]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = (left_edge + right_edge) / 2
width = (right_edge - left_edge)
buf1 = OffAP.off_axis_projection(ds,
center,
normal_vector,
width,
resolution, ('gas', 'density'),
north_vector=north_vector)
find_compare_maxima(expected_maxima, buf1, resolution, width)
def test_basic_rotation_4():
""" Rotation of x-axis to z-axis and original z-axis to y-axis with the use
of the north_vector. All fake particles on z-axis should now be on the
y-Axis. All fake particles on the x-axis should now be on the z-axis, and
all fake particles on the y-axis should now be on the x-axis.
(0, 0, 1) -> (0, 1)
(0, 0, 2) -> (0, 2)
(0, 0, 3) -> (0, 3)
In addition, (0, 0, 0) should contribute to the local maxima at (0, 0):
(0, 0, 0) -> (0, 0)
x-axis particles should be rotated and contribute to the local maxima at (0, 0):
(1, 0, 0) -> (0, 0)
and the y-axis particles shift into the positive x direction:
(0, 1, 0) -> (1, 0)
(0, 2, 0) -> (2, 0)
"""
expected_maxima = ([0., 0., 0., 0., 1., 2.], [1., 2., 3., 0., 0., 0.])
normal_vector = [1., 0., 0.]
north_vector = [0., 0., 1.]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = (left_edge + right_edge) / 2
width = (right_edge - left_edge)
buf1 = OffAP.off_axis_projection(ds,
center,
normal_vector,
width,
resolution, ('gas', 'density'),
north_vector=north_vector)
find_compare_maxima(expected_maxima, buf1, resolution, width)
def test_center_3():
""" Change the center to the left edge, or [0, -8, 0]
Every point will be shifted by 8 in the y-domain
With this, we should not be able to see anything !
"""
expected_maxima = ([], [])
normal_vector = [0., 0., 1.]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = [0., -1., 0.]
width = [(right_edge[0] - left_edge[0]), left_edge[1],
(right_edge[2] - left_edge[2])]
buf1 = OffAP.off_axis_projection(ds, center, normal_vector, width,
resolution, ('gas', 'density'))
find_compare_maxima(expected_maxima, buf1, resolution, width)
def __init__(
self,
ds,
normal,
fields,
center="c",
width=(1.0, "unitary"),
weight_field=None,
image_res=512,
data_source=None,
north_vector=None,
depth=(1.0, "unitary"),
method="integrate",
length_unit=None,
):
fields = list(iter_fields(fields))
center, dcenter = ds.coordinates.sanitize_center(center, 4)
buf = {}
width = ds.coordinates.sanitize_width(normal, width, depth)
wd = tuple(el.in_units("code_length").v for el in width)
if not is_sequence(image_res):
image_res = (image_res, image_res)
res = (image_res[0], image_res[1])
if data_source is None:
source = ds
else:
source = data_source
for field in fields:
buf[field] = off_axis_projection(
source,
center,
normal,
wd,
res,
field,
north_vector=north_vector,
method=method,
weight=weight_field,
).swapaxes(0, 1)
center = ds.arr([0.0] * 2, "code_length")
w, not_an_frb, lunit = construct_image(
ds, normal, buf, center, image_res, width, length_unit
)
super().__init__(buf, fields=fields, wcs=w, length_unit=lunit, ds=ds)
def test_center_2():
""" Change the center to [0, -1, 0]
Every point will be shifted by 1 in the y-domain
With this, we should not be able to see any of the y-axis particles
(0, 1, 0) -> (0, 2)
(0, 2, 0) -> (0, 3)
(0, 0, 1) -> (0, 1)
(0, 0, 2) -> (0, 1)
(0, 0, 3) -> (0, 1)
(0, 0, 0) -> (0, 1)
(1, 0, 0) -> (1, 1)
"""
expected_maxima = ([0., 0., 0., 1.], [2., 3., 1., 1.])
normal_vector = [0., 0., 1.]
resolution = (64, 64)
ds = fake_sph_orientation_ds()
left_edge = ds.domain_left_edge
right_edge = ds.domain_right_edge
center = [0., -1., 0.]
width = (right_edge - left_edge)
buf1 = OffAP.off_axis_projection(ds, center, normal_vector, width,
resolution, ('gas', 'density'))
find_compare_maxima(expected_maxima, buf1, resolution, width)
if proj_axis in ['x', 'y', 'z']:
# On-Axis Projections
prj = ds_sync.proj(stokes.I, proj_axis)
buf = prj.to_frb(width[0], res_obs, height=width[1])
else:
# Off-Axis Projections
buf = {}
width = ds_sync.coordinates.sanitize_width(proj_axis, width,
(1.0, 'unitary'))
wd = tuple(w.in_units('code_length').v for w in width)
for field in fields:
buf[field] = off_axis_projection(ds_sync, [0, 0, 0],
proj_axis,
wd,
res_obs,
field,
north_vector=[1, 0, 0],
num_threads=0).swapaxes(0, 1)
fits_image = FITSImageData(buf, fields=fields, wcs=w)
for nu in nus:
stokes = StokesFieldName(ptype, nu, proj_axis, field_type='flash')
field = stokes.I[1]
fits_image[field].data.units.registry.add(
'beam',
float(beam_area.in_units('rad**2').v),
dimensions=yt.units.dimensions.solid_angle,
tex_repr='beam')
fits_image.set_unit(field, 'Jy/beam')
nu = yt.YTQuantity(*nu)
header_dict.update({
def test_fits_image():
curdir = os.getcwd()
tmpdir = tempfile.mkdtemp()
os.chdir(tmpdir)
fields = ("density", "temperature")
units = (
"g/cm**3",
"K",
)
ds = fake_random_ds(64, fields=fields, units=units, nprocs=16, length_unit=100.0)
prj = ds.proj("density", 2)
prj_frb = prj.to_frb((0.5, "unitary"), 128)
fid1 = prj_frb.to_fits_data(
fields=[("gas", "density"), ("gas", "temperature")], length_unit="cm"
)
fits_prj = FITSProjection(
ds,
"z",
[ds.fields.gas.density, "temperature"],
image_res=128,
width=(0.5, "unitary"),
)
assert_equal(fid1["density"].data, fits_prj["density"].data)
assert_equal(fid1["temperature"].data, fits_prj["temperature"].data)
fid1.writeto("fid1.fits", overwrite=True)
new_fid1 = FITSImageData.from_file("fid1.fits")
assert_equal(fid1["density"].data, new_fid1["density"].data)
assert_equal(fid1["temperature"].data, new_fid1["temperature"].data)
assert_equal(fid1.length_unit, new_fid1.length_unit)
assert_equal(fid1.time_unit, new_fid1.time_unit)
assert_equal(fid1.mass_unit, new_fid1.mass_unit)
assert_equal(fid1.velocity_unit, new_fid1.velocity_unit)
assert_equal(fid1.magnetic_unit, new_fid1.magnetic_unit)
assert_equal(fid1.current_time, new_fid1.current_time)
ds2 = load("fid1.fits")
ds2.index
assert ("fits", "density") in ds2.field_list
assert ("fits", "temperature") in ds2.field_list
dw_cm = ds2.domain_width.in_units("cm")
assert dw_cm[0].v == 50.0
assert dw_cm[1].v == 50.0
slc = ds.slice(2, 0.5)
slc_frb = slc.to_frb((0.5, "unitary"), 128)
fid2 = slc_frb.to_fits_data(
fields=[("gas", "density"), ("gas", "temperature")], length_unit="cm"
)
fits_slc = FITSSlice(
ds,
"z",
[("gas", "density"), ("gas", "temperature")],
image_res=128,
width=(0.5, "unitary"),
)
assert_equal(fid2["density"].data, fits_slc["density"].data)
assert_equal(fid2["temperature"].data, fits_slc["temperature"].data)
dens_img = fid2.pop("density")
temp_img = fid2.pop("temperature")
combined_fid = FITSImageData.from_images([dens_img, temp_img])
assert_equal(combined_fid.length_unit, dens_img.length_unit)
assert_equal(combined_fid.time_unit, dens_img.time_unit)
assert_equal(combined_fid.mass_unit, dens_img.mass_unit)
assert_equal(combined_fid.velocity_unit, dens_img.velocity_unit)
assert_equal(combined_fid.magnetic_unit, dens_img.magnetic_unit)
assert_equal(combined_fid.current_time, dens_img.current_time)
cut = ds.cutting([0.1, 0.2, -0.9], [0.5, 0.42, 0.6])
cut_frb = cut.to_frb((0.5, "unitary"), 128)
fid3 = cut_frb.to_fits_data(
fields=[("gas", "density"), ds.fields.gas.temperature], length_unit="cm"
)
fits_cut = FITSOffAxisSlice(
ds,
[0.1, 0.2, -0.9],
["density", "temperature"],
image_res=128,
center=[0.5, 0.42, 0.6],
width=(0.5, "unitary"),
)
assert_equal(fid3["density"].data, fits_cut["density"].data)
assert_equal(fid3["temperature"].data, fits_cut["temperature"].data)
fid3.create_sky_wcs([30.0, 45.0], (1.0, "arcsec/kpc"))
fid3.writeto("fid3.fits", overwrite=True)
new_fid3 = FITSImageData.from_file("fid3.fits")
assert_same_wcs(fid3.wcs, new_fid3.wcs)
assert new_fid3.wcs.wcs.cunit[0] == "deg"
assert new_fid3.wcs.wcs.cunit[1] == "deg"
assert new_fid3.wcs.wcs.ctype[0] == "RA---TAN"
assert new_fid3.wcs.wcs.ctype[1] == "DEC--TAN"
buf = off_axis_projection(
ds, ds.domain_center, [0.1, 0.2, -0.9], 0.5, 128, "density"
).swapaxes(0, 1)
fid4 = FITSImageData(buf, fields="density", width=100.0)
fits_oap = FITSOffAxisProjection(
ds,
[0.1, 0.2, -0.9],
"density",
width=(0.5, "unitary"),
image_res=128,
depth=(0.5, "unitary"),
)
assert_equal(fid4["density"].data, fits_oap["density"].data)
fid4.create_sky_wcs([30.0, 45.0], (1.0, "arcsec/kpc"), replace_old_wcs=False)
assert fid4.wcs.wcs.cunit[0] == "cm"
assert fid4.wcs.wcs.cunit[1] == "cm"
assert fid4.wcs.wcs.ctype[0] == "linear"
assert fid4.wcs.wcs.ctype[1] == "linear"
assert fid4.wcsa.wcs.cunit[0] == "deg"
assert fid4.wcsa.wcs.cunit[1] == "deg"
assert fid4.wcsa.wcs.ctype[0] == "RA---TAN"
assert fid4.wcsa.wcs.ctype[1] == "DEC--TAN"
cvg = ds.covering_grid(
ds.index.max_level,
[0.25, 0.25, 0.25],
[32, 32, 32],
fields=["density", "temperature"],
)
fid5 = cvg.to_fits_data(fields=["density", "temperature"])
assert fid5.dimensionality == 3
fid5.update_header("density", "time", 0.1)
fid5.update_header("all", "units", "cgs")
assert fid5["density"].header["time"] == 0.1
assert fid5["temperature"].header["units"] == "cgs"
assert fid5["density"].header["units"] == "cgs"
fid6 = FITSImageData.from_images(fid5)
fid5.change_image_name("density", "mass_per_volume")
assert fid5["mass_per_volume"].name == "mass_per_volume"
assert fid5["mass_per_volume"].header["BTYPE"] == "mass_per_volume"
assert "mass_per_volume" in fid5.fields
assert "mass_per_volume" in fid5.field_units
assert "density" not in fid5.fields
assert "density" not in fid5.field_units
assert "density" in fid6.fields
assert_equal(fid6["density"].data, fid5["mass_per_volume"].data)
fid7 = FITSImageData.from_images(fid4)
fid7.convolve("density", (3.0, "cm"))
sigma = 3.0 / fid7.wcs.wcs.cdelt[0]
kernel = _astropy.conv.Gaussian2DKernel(x_stddev=sigma)
data_conv = _astropy.conv.convolve(fid4["density"].data.d, kernel)
assert_allclose(data_conv, fid7["density"].data.d)
# We need to manually close all the file descriptors so
# that windows can delete the folder that contains them.
ds2.close()
for fid in (fid1, fid2, fid3, fid4, fid5, fid6, fid7, new_fid1, new_fid3):
fid.close()
os.chdir(curdir)
shutil.rmtree(tmpdir)
def test_fits_image():
tmpdir = tempfile.mkdtemp()
curdir = os.getcwd()
os.chdir(tmpdir)
fields = ("density", "temperature")
units = (
'g/cm**3',
'K',
)
ds = fake_random_ds(64,
fields=fields,
units=units,
nprocs=16,
length_unit=100.0)
prj = ds.proj("density", 2)
prj_frb = prj.to_frb((0.5, "unitary"), 128)
fid1 = FITSImageData(prj_frb,
fields=["density", "temperature"],
units="cm")
fits_prj = FITSProjection(ds,
"z", ["density", "temperature"],
image_res=128,
width=(0.5, "unitary"))
assert_equal(fid1.get_data("density"), fits_prj.get_data("density"))
assert_equal(fid1.get_data("temperature"),
fits_prj.get_data("temperature"))
fid1.writeto("fid1.fits", clobber=True)
new_fid1 = FITSImageData.from_file("fid1.fits")
assert_equal(fid1.get_data("density"), new_fid1.get_data("density"))
assert_equal(fid1.get_data("temperature"),
new_fid1.get_data("temperature"))
ds2 = load("fid1.fits")
ds2.index
assert ("fits", "density") in ds2.field_list
assert ("fits", "temperature") in ds2.field_list
dw_cm = ds2.domain_width.in_units("cm")
assert dw_cm[0].v == 50.
assert dw_cm[1].v == 50.
slc = ds.slice(2, 0.5)
slc_frb = slc.to_frb((0.5, "unitary"), 128)
fid2 = FITSImageData(slc_frb,
fields=["density", "temperature"],
units="cm")
fits_slc = FITSSlice(ds,
"z", ["density", "temperature"],
image_res=128,
width=(0.5, "unitary"))
assert_equal(fid2.get_data("density"), fits_slc.get_data("density"))
assert_equal(fid2.get_data("temperature"),
fits_slc.get_data("temperature"))
dens_img = fid2.pop("density")
temp_img = fid2.pop("temperature")
# This already has some assertions in it, so we don't need to do anything
# with it other than just make one
FITSImageData.from_images([dens_img, temp_img])
cut = ds.cutting([0.1, 0.2, -0.9], [0.5, 0.42, 0.6])
cut_frb = cut.to_frb((0.5, "unitary"), 128)
fid3 = FITSImageData(cut_frb,
fields=["density", "temperature"],
units="cm")
fits_cut = FITSOffAxisSlice(ds, [0.1, 0.2, -0.9],
["density", "temperature"],
image_res=128,
center=[0.5, 0.42, 0.6],
width=(0.5, "unitary"))
assert_equal(fid3.get_data("density"), fits_cut.get_data("density"))
assert_equal(fid3.get_data("temperature"),
fits_cut.get_data("temperature"))
fid3.create_sky_wcs([30., 45.], (1.0, "arcsec/kpc"))
fid3.writeto("fid3.fits", clobber=True)
new_fid3 = FITSImageData.from_file("fid3.fits")
assert_same_wcs(fid3.wcs, new_fid3.wcs)
assert new_fid3.wcs.wcs.cunit[0] == "deg"
assert new_fid3.wcs.wcs.cunit[1] == "deg"
assert new_fid3.wcs.wcs.ctype[0] == "RA---TAN"
assert new_fid3.wcs.wcs.ctype[1] == "DEC--TAN"
buf = off_axis_projection(ds, ds.domain_center, [0.1, 0.2, -0.9], 0.5, 128,
"density").swapaxes(0, 1)
fid4 = FITSImageData(buf, fields="density", width=100.0)
fits_oap = FITSOffAxisProjection(ds, [0.1, 0.2, -0.9],
"density",
width=(0.5, "unitary"),
image_res=128,
depth_res=128,
depth=(0.5, "unitary"))
assert_equal(fid4.get_data("density"), fits_oap.get_data("density"))
fid4.create_sky_wcs([30., 45.], (1.0, "arcsec/kpc"), replace_old_wcs=False)
assert fid4.wcs.wcs.cunit[0] == "cm"
assert fid4.wcs.wcs.cunit[1] == "cm"
assert fid4.wcs.wcs.ctype[0] == "linear"
assert fid4.wcs.wcs.ctype[1] == "linear"
assert fid4.wcsa.wcs.cunit[0] == "deg"
assert fid4.wcsa.wcs.cunit[1] == "deg"
assert fid4.wcsa.wcs.ctype[0] == "RA---TAN"
assert fid4.wcsa.wcs.ctype[1] == "DEC--TAN"
cvg = ds.covering_grid(ds.index.max_level, [0.25, 0.25, 0.25],
[32, 32, 32],
fields=["density", "temperature"])
fid5 = FITSImageData(cvg, fields=["density", "temperature"])
assert fid5.dimensionality == 3
fid5.update_header("density", "time", 0.1)
fid5.update_header("all", "units", "cgs")
assert fid5["density"].header["time"] == 0.1
assert fid5["temperature"].header["units"] == "cgs"
assert fid5["density"].header["units"] == "cgs"
os.chdir(curdir)
shutil.rmtree(tmpdir)
def compare_vector_conversions(data_source):
prng = np.random.RandomState(8675309)
normals = [[1, 0, 0], [0, 1, 0], [0, 0, 1]] + [
random_unit_vector(prng) for i in range(2)
]
bulk_velocities = [random_velocity_vector(prng) for i in range(2)]
for bv in bulk_velocities:
bulk_velocity = bv * cm / s
data_source.set_field_parameter("bulk_velocity", bulk_velocity)
data_source.clear_data()
vmag = data_source["velocity_magnitude"]
vrad = data_source["velocity_spherical_radius"]
for normal in normals:
data_source.set_field_parameter("normal", normal)
data_source.clear_data()
assert_allclose_units(vrad, data_source["velocity_spherical_radius"])
vmag_new = data_source["velocity_magnitude"]
assert_allclose_units(vmag, vmag_new)
vmag_cart = np.sqrt(
(data_source["velocity_x"] - bulk_velocity[0]) ** 2
+ (data_source["velocity_y"] - bulk_velocity[1]) ** 2
+ (data_source["velocity_z"] - bulk_velocity[2]) ** 2
)
assert_allclose_units(vmag, vmag_cart)
vmag_cyl = np.sqrt(
data_source["velocity_cylindrical_radius"] ** 2
+ data_source["velocity_cylindrical_theta"] ** 2
+ data_source["velocity_cylindrical_z"] ** 2
)
assert_allclose_units(vmag, vmag_cyl)
vmag_sph = np.sqrt(
data_source["velocity_spherical_radius"] ** 2
+ data_source["velocity_spherical_theta"] ** 2
+ data_source["velocity_spherical_phi"] ** 2
)
assert_allclose_units(vmag, vmag_sph)
for i, d in enumerate("xyz"):
assert_allclose_units(
data_source[f"velocity_{d}"] - bulk_velocity[i],
data_source[f"relative_velocity_{d}"],
)
for i, ax in enumerate("xyz"):
data_source.set_field_parameter("axis", i)
data_source.clear_data()
assert_allclose_units(
data_source["velocity_los"], data_source[f"relative_velocity_{ax}"]
)
for i, ax in enumerate("xyz"):
prj = data_source.ds.proj("velocity_los", i, weight_field="density")
assert_allclose_units(prj["velocity_los"], prj[f"velocity_{ax}"])
data_source.clear_data()
ax = [0.1, 0.2, -0.3]
data_source.set_field_parameter("axis", ax)
ax /= np.sqrt(np.dot(ax, ax))
vlos = data_source["relative_velocity_x"] * ax[0]
vlos += data_source["relative_velocity_y"] * ax[1]
vlos += data_source["relative_velocity_z"] * ax[2]
assert_allclose_units(data_source["velocity_los"], vlos)
buf_los = off_axis_projection(
data_source,
data_source.ds.domain_center,
ax,
0.5,
128,
("gas", "velocity_los"),
weight=("gas", "density"),
)
buf_x = off_axis_projection(
data_source,
data_source.ds.domain_center,
ax,
0.5,
128,
("gas", "relative_velocity_x"),
weight=("gas", "density"),
)
buf_y = off_axis_projection(
data_source,
data_source.ds.domain_center,
ax,
0.5,
128,
("gas", "relative_velocity_y"),
weight=("gas", "density"),
)
buf_z = off_axis_projection(
data_source,
data_source.ds.domain_center,
ax,
0.5,
128,
("gas", "relative_velocity_z"),
weight=("gas", "density"),
)
vlos = buf_x * ax[0] + buf_y * ax[1] + buf_z * ax[2]
assert_allclose_units(buf_los, vlos, rtol=1.0e-6)
def __init__(self,
ds,
normal,
field,
velocity_bounds,
center="c",
width=(1.0, "unitary"),
dims=100,
thermal_broad=False,
atomic_weight=56.,
depth=(1.0, "unitary"),
depth_res=256,
method="integrate",
weight_field=None,
no_shifting=False,
north_vector=None,
no_ghost=True,
data_source=None):
r""" Initialize a PPVCube object.
Parameters
----------
ds : dataset
The dataset.
normal : array_like or string
The normal vector along with to make the projections. If an array, it
will be normalized. If a string, it will be assumed to be along one of the
principal axes of the domain ("x", "y", or "z").
field : string
The field to project.
velocity_bounds : tuple
A 4-tuple of (vmin, vmax, nbins, units) for the velocity bounds to
integrate over.
center : A sequence of floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : float, tuple, or YTQuantity.
The width of the projection. A float will assume the width is in code units.
A (value, unit) tuple or YTQuantity allows for the units of the width to be
specified. Implies width = height, e.g. the aspect ratio of the PPVCube's
spatial dimensions is 1.
dims : integer, optional
The spatial resolution of the cube. Implies nx = ny, e.g. the
aspect ratio of the PPVCube's spatial dimensions is 1.
thermal_broad : boolean, optional
Whether or not to broaden the line using the gas temperature. Default: False.
atomic_weight : float, optional
Set this value to the atomic weight of the particle that is emitting the line
if *thermal_broad* is True. Defaults to 56 (Fe).
depth : A tuple or a float, optional
A tuple containing the depth to project through and the string
key of the unit: (width, 'unit'). If set to a float, code units
are assumed. Only for off-axis cubes.
depth_res : integer, optional
Deprecated, this is still in the function signature for API
compatibility
method : string, optional
Set the projection method to be used.
"integrate" : line of sight integration over the line element.
"sum" : straight summation over the line of sight.
weight_field : string, optional
The name of the weighting field. Set to None for no weight.
no_shifting : boolean, optional
If set, no shifting due to velocity will occur but only thermal broadening.
Should not be set when *thermal_broad* is False, otherwise nothing happens!
north_vector : a sequence of floats
A vector defining the 'up' direction. This option sets the orientation of
the plane of projection. If not set, an arbitrary grid-aligned north_vector
is chosen. Ignored in the case of on-axis cubes.
no_ghost: bool, optional
Optimization option for off-axis cases. 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
data_source : yt.data_objects.data_containers.YTSelectionContainer, optional
If specified, this will be the data source used for selecting regions to project.
Examples
--------
>>> i = 60*np.pi/180.
>>> L = [0.0,np.sin(i),np.cos(i)]
>>> cube = PPVCube(ds, L, "density", (-5.,4.,100,"km/s"), width=(10.,"kpc"))
"""
self.ds = ds
self.field = field
self.width = width
self.particle_mass = atomic_weight * mh
self.thermal_broad = thermal_broad
self.no_shifting = no_shifting
if not isinstance(normal, str):
width = ds.coordinates.sanitize_width(normal, width, depth)
width = tuple(el.in_units('code_length').v for el in width)
if not hasattr(ds.fields.gas, "temperature") and thermal_broad:
raise RuntimeError("thermal_broad cannot be True if there is "
"no 'temperature' field!")
if no_shifting and not thermal_broad:
raise RuntimeError(
"no_shifting cannot be True when thermal_broad is False!")
self.center = ds.coordinates.sanitize_center(center, normal)[0]
self.nx = dims
self.ny = dims
self.nv = velocity_bounds[2]
if method not in ["integrate", "sum"]:
raise RuntimeError("Only the 'integrate' and 'sum' projection +"
"methods are supported in PPVCube.")
dd = ds.all_data()
fd = dd._determine_fields(field)[0]
self.field_units = ds._get_field_info(fd).units
self.vbins = ds.arr(
np.linspace(velocity_bounds[0], velocity_bounds[1],
velocity_bounds[2] + 1), velocity_bounds[3])
self._vbins = self.vbins.copy()
self.vmid = 0.5 * (self.vbins[1:] + self.vbins[:-1])
self.vmid_cgs = self.vmid.in_cgs().v
self.dv = self.vbins[1] - self.vbins[0]
self.dv_cgs = self.dv.in_cgs().v
self.current_v = 0.0
_vlos = create_vlos(normal, self.no_shifting)
self.ds.add_field(("gas", "v_los"),
function=_vlos,
units="cm/s",
sampling_type='cell')
_intensity = self._create_intensity()
self.ds.add_field(("gas", "intensity"),
function=_intensity,
units=self.field_units,
sampling_type='cell')
if method == "integrate" and weight_field is None:
self.proj_units = str(ds.quan(1.0, self.field_units + "*cm").units)
elif method == "sum":
self.proj_units = self.field_units
storage = {}
pbar = get_pbar("Generating cube.", self.nv)
for sto, i in parallel_objects(range(self.nv), storage=storage):
self.current_v = self.vmid_cgs[i]
if isinstance(normal, str):
prj = ds.proj("intensity",
ds.coordinates.axis_id[normal],
method=method,
weight_field=weight_field,
data_source=data_source)
buf = prj.to_frb(width, self.nx,
center=self.center)["intensity"]
else:
if data_source is None:
source = ds
else:
source = data_source
buf = off_axis_projection(source,
self.center,
normal,
width, (self.nx, self.ny),
"intensity",
north_vector=north_vector,
no_ghost=no_ghost,
method=method,
weight=weight_field)
sto.result_id = i
sto.result = buf.swapaxes(0, 1)
pbar.update(i)
pbar.finish()
self.data = ds.arr(np.zeros((self.nx, self.ny, self.nv)),
self.proj_units)
if is_root():
for i, buf in sorted(storage.items()):
self.data[:, :, i] = buf.transpose()
self.axis_type = "velocity"
# Now fix the width
if is_sequence(self.width):
self.width = ds.quan(self.width[0], self.width[1])
elif not isinstance(self.width, YTQuantity):
self.width = ds.quan(self.width, "code_length")
self.ds.field_info.pop(("gas", "intensity"))
self.ds.field_info.pop(("gas", "v_los"))
def off_axis(self,
L,
center="c",
width=(1.0, "unitary"),
depth=(1.0, "unitary"),
nx=800,
nz=800,
north_vector=None,
no_ghost=False,
source=None):
r""" Make an off-axis projection of the SZ signal.
Parameters
----------
L : array_like
The normal vector of the projection.
center : A sequence of floats, a string, or a tuple.
The coordinate of the center of the image. If set to 'c', 'center' or
left blank, the plot is centered on the middle of the domain. If set to
'max' or 'm', the center will be located at the maximum of the
('gas', 'density') field. Centering on the max or min of a specific
field is supported by providing a tuple such as ("min","temperature") or
("max","dark_matter_density"). Units can be specified by passing in *center*
as a tuple containing a coordinate and string unit name or by passing
in a YTArray. If a list or unitless array is supplied, code units are
assumed.
width : tuple or a float.
Width can have four different formats to support windows with variable
x and y widths. They are:
================================== =======================
format example
================================== =======================
(float, string) (10,'kpc')
((float, string), (float, string)) ((10,'kpc'),(15,'kpc'))
float 0.2
(float, float) (0.2, 0.3)
================================== =======================
For example, (10, 'kpc') requests a plot window that is 10 kiloparsecs
wide in the x and y directions, ((10,'kpc'),(15,'kpc')) requests a
window that is 10 kiloparsecs wide along the x axis and 15
kiloparsecs wide along the y axis. In the other two examples, code
units are assumed, for example (0.2, 0.3) requests a plot that has an
x width of 0.2 and a y width of 0.3 in code units. If units are
provided the resulting plot axis labels will use the supplied units.
depth : A tuple or a float
A tuple containing the depth to project through and the string
key of the unit: (width, 'unit'). If set to a float, code units
are assumed
nx : integer, optional
The dimensions on a side of the projection image.
nz : integer, optional
Deprecated, this is still in the function signature for API
compatibility
north_vector : a sequence of floats
A vector defining the 'up' direction in the plot. This
option sets the orientation of the slicing plane. If not
set, an arbitrary grid-aligned north-vector is chosen.
no_ghost: bool, optional
Optimization option for off-axis cases. 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
source : yt.data_objects.data_containers.YTSelectionContainer, optional
If specified, this will be the data source used for selecting regions
to project.
Examples
--------
>>> L = np.array([0.5, 1.0, 0.75])
>>> szprj.off_axis(L, center="c", width=(2.0, "Mpc"))
"""
wd = self.ds.coordinates.sanitize_width(L, width, depth)
w = tuple(el.in_units('code_length').v for el in wd)
ctr, dctr = self.ds.coordinates.sanitize_center(center, L)
res = (nx, nx)
if source is None:
source = self.ds
beta_par = generate_beta_par(L)
self.ds.add_field(("gas", "beta_par"),
function=beta_par,
units="g/cm**3")
setup_sunyaev_zeldovich_fields(self.ds)
dens = off_axis_projection(source,
ctr,
L,
w,
res,
"density",
north_vector=north_vector,
no_ghost=no_ghost)
Te = off_axis_projection(source,
ctr,
L,
w,
res,
"t_sz",
north_vector=north_vector,
no_ghost=no_ghost) / dens
bpar = off_axis_projection(source,
ctr,
L,
w,
res,
"beta_par",
north_vector=north_vector,
no_ghost=no_ghost) / dens
omega1 = off_axis_projection(source,
ctr,
L,
w,
res,
"t_squared",
north_vector=north_vector,
no_ghost=no_ghost) / dens
omega1 = omega1 / (Te * Te) - 1.
if self.high_order:
bperp2 = off_axis_projection(source,
ctr,
L,
w,
res,
"beta_perp_squared",
north_vector=north_vector,
no_ghost=no_ghost) / dens
sigma1 = off_axis_projection(source,
ctr,
L,
w,
res,
"t_beta_par",
north_vector=north_vector,
no_ghost=no_ghost) / dens
sigma1 = sigma1 / Te - bpar
kappa1 = off_axis_projection(source,
ctr,
L,
w,
res,
"beta_par_squared",
north_vector=north_vector,
no_ghost=no_ghost) / dens
kappa1 -= bpar
else:
bperp2 = np.zeros((nx, nx))
sigma1 = np.zeros((nx, nx))
kappa1 = np.zeros((nx, nx))
tau = sigma_thompson * dens * self.mueinv / mh
self.bounds = (-0.5 * wd[0], 0.5 * wd[0], -0.5 * wd[1], 0.5 * wd[1])
self.dx = wd[0] / nx
self.dy = wd[1] / nx
self.nx = nx
self._compute_intensity(np.array(tau), np.array(Te), np.array(bpar),
np.array(omega1), np.array(sigma1),
np.array(kappa1), np.array(bperp2))
self.ds.field_info.pop(("gas", "beta_par"))
