import numpy as np
from yt.utilities.lib.pixelization_routines import pixelize_cartesian, pixelize_cylinder
from .coordinate_handler import (
CoordinateHandler,
_get_coord_fields,
_setup_dummy_cartesian_coords_and_widths,
)
[docs]
class GeographicCoordinateHandler(CoordinateHandler):
radial_axis = "altitude"
name = "geographic"
def __init__(self, ds, ordering=None):
if ordering is None:
ordering = ("latitude", "longitude", self.radial_axis)
super().__init__(ds, ordering)
self.image_units = {}
self.image_units[self.axis_id["latitude"]] = (None, None)
self.image_units[self.axis_id["longitude"]] = (None, None)
self.image_units[self.axis_id[self.radial_axis]] = ("deg", "deg")
[docs]
def setup_fields(self, registry):
# Missing implementation for x, y and z coordinates.
_setup_dummy_cartesian_coords_and_widths(registry, axes=("x", "y", "z"))
f1, f2 = _get_coord_fields(self.axis_id["latitude"], "")
registry.add_field(
("index", "dlatitude"),
sampling_type="cell",
function=f1,
display_field=False,
units="",
)
registry.add_field(
("index", "latitude"),
sampling_type="cell",
function=f2,
display_field=False,
units="",
)
f1, f2 = _get_coord_fields(self.axis_id["longitude"], "")
registry.add_field(
("index", "dlongitude"),
sampling_type="cell",
function=f1,
display_field=False,
units="",
)
registry.add_field(
("index", "longitude"),
sampling_type="cell",
function=f2,
display_field=False,
units="",
)
f1, f2 = _get_coord_fields(self.axis_id[self.radial_axis])
registry.add_field(
("index", f"d{self.radial_axis}"),
sampling_type="cell",
function=f1,
display_field=False,
units="code_length",
)
registry.add_field(
("index", self.radial_axis),
sampling_type="cell",
function=f2,
display_field=False,
units="code_length",
)
def _SphericalVolume(field, data):
# We can use the transformed coordinates here.
# Here we compute the spherical volume element exactly
r = data["index", "r"]
dr = data["index", "dr"]
theta = data["index", "theta"]
dtheta = data["index", "dtheta"]
vol = ((r + 0.5 * dr) ** 3 - (r - 0.5 * dr) ** 3) / 3.0
vol *= np.cos(theta - 0.5 * dtheta) - np.cos(theta + 0.5 * dtheta)
vol *= data["index", "dphi"]
return vol
registry.add_field(
("index", "cell_volume"),
sampling_type="cell",
function=_SphericalVolume,
units="code_length**3",
)
registry.alias(("index", "volume"), ("index", "cell_volume"))
def _path_radial_axis(field, data):
return data["index", f"d{self.radial_axis}"]
registry.add_field(
("index", f"path_element_{self.radial_axis}"),
sampling_type="cell",
function=_path_radial_axis,
units="code_length",
)
def _path_latitude(field, data):
# We use r here explicitly
return data["index", "r"] * data["index", "dlatitude"] * np.pi / 180.0
registry.add_field(
("index", "path_element_latitude"),
sampling_type="cell",
function=_path_latitude,
units="code_length",
)
def _path_longitude(field, data):
# We use r here explicitly
return (
data["index", "r"]
* data["index", "dlongitude"]
* np.pi
/ 180.0
* np.sin((data["index", "latitude"] + 90.0) * np.pi / 180.0)
)
registry.add_field(
("index", "path_element_longitude"),
sampling_type="cell",
function=_path_longitude,
units="code_length",
)
def _latitude_to_theta(field, data):
# latitude runs from -90 to 90
return (data[("index", "latitude")] + 90) * np.pi / 180.0
registry.add_field(
("index", "theta"),
sampling_type="cell",
function=_latitude_to_theta,
units="",
)
def _dlatitude_to_dtheta(field, data):
return data[("index", "dlatitude")] * np.pi / 180.0
registry.add_field(
("index", "dtheta"),
sampling_type="cell",
function=_dlatitude_to_dtheta,
units="",
)
def _longitude_to_phi(field, data):
# longitude runs from -180 to 180
return (data[("index", "longitude")] + 180) * np.pi / 180.0
registry.add_field(
("index", "phi"), sampling_type="cell", function=_longitude_to_phi, units=""
)
def _dlongitude_to_dphi(field, data):
return data[("index", "dlongitude")] * np.pi / 180.0
registry.add_field(
("index", "dphi"),
sampling_type="cell",
function=_dlongitude_to_dphi,
units="",
)
self._setup_radial_fields(registry)
def _setup_radial_fields(self, registry):
# This stays here because we don't want to risk the field detector not
# properly getting the data_source, etc.
def _altitude_to_radius(field, data):
surface_height = data.get_field_parameter("surface_height")
if surface_height is None:
if hasattr(data.ds, "surface_height"):
surface_height = data.ds.surface_height
else:
surface_height = data.ds.quan(0.0, "code_length")
return data[("index", "altitude")] + surface_height
registry.add_field(
("index", "r"),
sampling_type="cell",
function=_altitude_to_radius,
units="code_length",
)
registry.alias(("index", "dr"), ("index", "daltitude"))
def _retrieve_radial_offset(self, data_source=None):
# This returns the factor by which the radial field is multiplied and
# the scalar its offset by. Typically the "factor" will *only* be
# either 1.0 or -1.0. The order will be factor * r + offset.
# Altitude is the radius from the central zone minus the radius of the
# surface. Depth to radius is negative value of depth plus the
# outermost radius.
surface_height = None
if data_source is not None:
surface_height = data_source.get_field_parameter("surface_height")
if surface_height is None:
if hasattr(self.ds, "surface_height"):
surface_height = self.ds.surface_height
else:
surface_height = self.ds.quan(0.0, "code_length")
return surface_height, 1.0
[docs]
def pixelize(
self,
dimension,
data_source,
field,
bounds,
size,
antialias=True,
periodic=True,
*,
return_mask=False,
):
if self.axis_name[dimension] in ("latitude", "longitude"):
buff, mask = self._cyl_pixelize(
data_source, field, bounds, size, antialias, dimension
)
elif self.axis_name[dimension] == self.radial_axis:
buff, mask = self._ortho_pixelize(
data_source, field, bounds, size, antialias, dimension, periodic
)
else:
raise NotImplementedError
if return_mask:
assert mask is None or mask.dtype == bool
return buff, mask
else:
return buff
[docs]
def pixelize_line(self, field, start_point, end_point, npoints):
raise NotImplementedError
def _ortho_pixelize(
self, data_source, field, bounds, size, antialias, dimension, periodic
):
period = self.period[:2].copy()
period[0] = self.period[self.x_axis[dimension]]
period[1] = self.period[self.y_axis[dimension]]
if hasattr(period, "in_units"):
period = period.in_units("code_length").d
# For a radial axis, px will correspond to longitude and py will
# correspond to latitude.
px = data_source["px"]
pdx = data_source["pdx"]
py = data_source["py"]
pdy = data_source["pdy"]
buff = np.full((size[1], size[0]), np.nan, dtype="float64")
mask = pixelize_cartesian(
buff,
px,
py,
pdx,
pdy,
data_source[field],
bounds,
int(antialias),
period,
int(periodic),
)
return buff, mask
def _cyl_pixelize(self, data_source, field, bounds, size, antialias, dimension):
offset, factor = self._retrieve_radial_offset(data_source)
r = factor * data_source["py"] + offset
# Because of the axis-ordering, dimensions 0 and 1 both have r as py
# and the angular coordinate as px. But we need to figure out how to
# convert our coordinate back to an actual angle, based on which
# dimension we're in.
pdx = data_source["pdx"].d * np.pi / 180
if self.axis_name[self.x_axis[dimension]] == "latitude":
px = (data_source["px"].d + 90) * np.pi / 180
do_transpose = True
elif self.axis_name[self.x_axis[dimension]] == "longitude":
px = (data_source["px"].d + 180) * np.pi / 180
do_transpose = False
else:
# We should never get here!
raise NotImplementedError
buff = np.full((size[1], size[0]), np.nan, dtype="f8")
mask = pixelize_cylinder(
buff,
r,
data_source["pdy"],
px,
pdx,
data_source[field],
bounds,
return_mask=True,
)
if do_transpose:
buff = buff.transpose()
mask = mask.transpose()
return buff, mask
[docs]
def convert_from_cartesian(self, coord):
raise NotImplementedError
[docs]
def convert_to_cartesian(self, coord):
offset, factor = self._retrieve_radial_offset()
if isinstance(coord, np.ndarray) and len(coord.shape) > 1:
rad = self.axis_id[self.radial_axis]
lon = self.axis_id["longitude"]
lat = self.axis_id["latitude"]
r = factor * coord[:, rad] + offset
theta = coord[:, lon] * np.pi / 180
phi = coord[:, lat] * np.pi / 180
nc = np.zeros_like(coord)
# r, theta, phi
nc[:, lat] = np.cos(phi) * np.sin(theta) * r
nc[:, lon] = np.sin(phi) * np.sin(theta) * r
nc[:, rad] = np.cos(theta) * r
else:
a, b, c = coord
theta = b * np.pi / 180
phi = a * np.pi / 180
r = factor * c + offset
nc = (
np.cos(phi) * np.sin(theta) * r,
np.sin(phi) * np.sin(theta) * r,
np.cos(theta) * r,
)
return nc
[docs]
def convert_to_cylindrical(self, coord):
raise NotImplementedError
[docs]
def convert_from_cylindrical(self, coord):
raise NotImplementedError
[docs]
def convert_to_spherical(self, coord):
raise NotImplementedError
[docs]
def convert_from_spherical(self, coord):
raise NotImplementedError
_image_axis_name = None
@property
def image_axis_name(self):
if self._image_axis_name is not None:
return self._image_axis_name
# This is the x and y axes labels that get displayed. For
# non-Cartesian coordinates, we usually want to override these for
# Cartesian coordinates, since we transform them.
rv = {
self.axis_id["latitude"]: (
"x / \\sin(\\mathrm{latitude})",
"y / \\sin(\\mathrm{latitude})",
),
self.axis_id["longitude"]: ("R", "z"),
self.axis_id[self.radial_axis]: ("longitude", "latitude"),
}
for i in list(rv.keys()):
rv[self.axis_name[i]] = rv[i]
rv[self.axis_name[i].capitalize()] = rv[i]
self._image_axis_name = rv
return rv
_x_pairs = (
("latitude", "longitude"),
("longitude", "latitude"),
("altitude", "longitude"),
)
_y_pairs = (
("latitude", "altitude"),
("longitude", "altitude"),
("altitude", "latitude"),
)
_data_projection = None
@property
def data_projection(self):
# this will control the default projection to use when displaying data
if self._data_projection is not None:
return self._data_projection
dpj = {}
for ax in self.axis_order:
if ax == self.radial_axis:
dpj[ax] = "Mollweide"
else:
dpj[ax] = None
self._data_projection = dpj
return dpj
_data_transform = None
@property
def data_transform(self):
# this is the coordinate system on which the data is defined (the crs).
if self._data_transform is not None:
return self._data_transform
dtx = {}
for ax in self.axis_order:
if ax == self.radial_axis:
dtx[ax] = "PlateCarree"
else:
dtx[ax] = None
self._data_transform = dtx
return dtx
@property
def period(self):
return self.ds.domain_width
[docs]
def sanitize_center(self, center, axis):
center, display_center = super().sanitize_center(center, axis)
name = self.axis_name[axis]
if name == self.radial_axis:
display_center = center
elif name == "latitude":
display_center = (
0.0 * display_center[0],
0.0 * display_center[1],
0.0 * display_center[2],
)
elif name == "longitude":
ri = self.axis_id[self.radial_axis]
c = (self.ds.domain_right_edge[ri] + self.ds.domain_left_edge[ri]) / 2.0
display_center = [
0.0 * display_center[0],
0.0 * display_center[1],
0.0 * display_center[2],
]
display_center[self.axis_id["latitude"]] = c
return center, display_center
[docs]
def sanitize_width(self, axis, width, depth):
name = self.axis_name[axis]
if width is not None:
width = super().sanitize_width(axis, width, depth)
elif name == self.radial_axis:
rax = self.radial_axis
width = [
self.ds.domain_width[self.x_axis[rax]],
self.ds.domain_width[self.y_axis[rax]],
]
elif name == "latitude":
ri = self.axis_id[self.radial_axis]
# Remember, in spherical coordinates when we cut in theta,
# we create a conic section
width = [2.0 * self.ds.domain_width[ri], 2.0 * self.ds.domain_width[ri]]
elif name == "longitude":
ri = self.axis_id[self.radial_axis]
width = [self.ds.domain_width[ri], 2.0 * self.ds.domain_width[ri]]
return width
[docs]
class InternalGeographicCoordinateHandler(GeographicCoordinateHandler):
radial_axis = "depth"
name = "internal_geographic"
def _setup_radial_fields(self, registry):
# Altitude is the radius from the central zone minus the radius of the
# surface.
def _depth_to_radius(field, data):
outer_radius = data.get_field_parameter("outer_radius")
if outer_radius is None:
if hasattr(data.ds, "outer_radius"):
outer_radius = data.ds.outer_radius
else:
# Otherwise, we assume that the depth goes to full depth,
# so we can look at the domain right edge in depth.
rax = self.axis_id[self.radial_axis]
outer_radius = data.ds.domain_right_edge[rax]
return -1.0 * data[("index", "depth")] + outer_radius
registry.add_field(
("index", "r"),
sampling_type="cell",
function=_depth_to_radius,
units="code_length",
)
registry.alias(("index", "dr"), ("index", "ddepth"))
def _retrieve_radial_offset(self, data_source=None):
# Depth means switching sign and adding to full radius
outer_radius = None
if data_source is not None:
outer_radius = data_source.get_field_parameter("outer_radius")
if outer_radius is None:
if hasattr(self.ds, "outer_radius"):
outer_radius = self.ds.outer_radius
else:
# Otherwise, we assume that the depth goes to full depth,
# so we can look at the domain right edge in depth.
rax = self.axis_id[self.radial_axis]
outer_radius = self.ds.domain_right_edge[rax]
return outer_radius, -1.0
_x_pairs = (
("latitude", "longitude"),
("longitude", "latitude"),
("depth", "longitude"),
)
_y_pairs = (("latitude", "depth"), ("longitude", "depth"), ("depth", "latitude"))
[docs]
def sanitize_center(self, center, axis):
center, display_center = super(
GeographicCoordinateHandler, self
).sanitize_center(center, axis)
name = self.axis_name[axis]
if name == self.radial_axis:
display_center = center
elif name == "latitude":
display_center = (
0.0 * display_center[0],
0.0 * display_center[1],
0.0 * display_center[2],
)
elif name == "longitude":
ri = self.axis_id[self.radial_axis]
offset, factor = self._retrieve_radial_offset()
outermost = factor * self.ds.domain_left_edge[ri] + offset
display_center = [
0.0 * display_center[0],
0.0 * display_center[1],
0.0 * display_center[2],
]
display_center[self.axis_id["latitude"]] = outermost / 2.0
return center, display_center
[docs]
def sanitize_width(self, axis, width, depth):
name = self.axis_name[axis]
if width is not None:
width = super(GeographicCoordinateHandler, self).sanitize_width(
axis, width, depth
)
elif name == self.radial_axis:
rax = self.radial_axis
width = [
self.ds.domain_width[self.x_axis[rax]],
self.ds.domain_width[self.y_axis[rax]],
]
elif name == "latitude":
ri = self.axis_id[self.radial_axis]
# Remember, in spherical coordinates when we cut in theta,
# we create a conic section
offset, factor = self._retrieve_radial_offset()
outermost = factor * self.ds.domain_left_edge[ri] + offset
width = [2.0 * outermost, 2.0 * outermost]
elif name == "longitude":
ri = self.axis_id[self.radial_axis]
offset, factor = self._retrieve_radial_offset()
outermost = factor * self.ds.domain_left_edge[ri] + offset
width = [outermost, 2.0 * outermost]
return width