def make_point_sources(area, exp_time, positions, sky_center,
spectra, prng=None):
r"""
Create a new :class:`~pyxsim.event_list.EventList` which contains
point sources.
Parameters
----------
area : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The collecting area to determine the number of events. If units are
not specified, it is assumed to be in cm^2.
exp_time : float, (value, unit) tuple, :class:`~yt.units.yt_array.YTQuantity`, or :class:`~astropy.units.Quantity`
The exposure time to determine the number of events. If units are
not specified, it is assumed to be in seconds.
positions : array of source positions, shape 2xN
The positions of the point sources in RA, Dec, where N is the
number of point sources. Coordinates should be in degrees.
sky_center : array-like
Center RA, Dec of the events in degrees.
spectra : list (size N) of :class:`~soxs.spectra.Spectrum` objects
The spectra for the point sources, where N is the number
of point sources. Assumed to be in the observer frame.
prng : integer or :class:`~numpy.random.RandomState` object
A pseudo-random number generator. Typically will only be specified
if you have a reason to generate the same set of random numbers, such as for a
test. Default is to use the :mod:`numpy.random` module.
"""
prng = parse_prng(prng)
spectra = ensure_list(spectra)
positions = ensure_list(positions)
area = parse_value(area, "cm**2")
exp_time = parse_value(exp_time, "s")
t_exp = exp_time.value/comm.size
x = []
y = []
e = []
for pos, spectrum in zip(positions, spectra):
eobs = spectrum.generate_energies(t_exp, area.value, prng=prng)
ne = eobs.size
x.append(YTArray([pos[0]] * ne, "deg"))
y.append(YTArray([pos[1]] * ne, "deg"))
e.append(YTArray.from_astropy(eobs))
parameters = {"sky_center": YTArray(sky_center, "degree"),
"exp_time": exp_time,
"area": area}
events = {}
events["xsky"] = uconcatenate(x)
events["ysky"] = uconcatenate(y)
events["eobs"] = uconcatenate(e)
return EventList(events, parameters)
def __init__(self, field):
"""
This validator ensures that the output file has a given data field stored
in it.
"""
FieldValidator.__init__(self)
self.fields = ensure_list(field)
def save_field(ds, fields, field_parameters=None):
"""
Write a single field associated with the dataset ds to the
backup file.
Parameters
----------
ds : Dataset object
The yt dataset that the field is associated with.
fields : field of list of fields
The name(s) of the field(s) to save.
field_parameters : dictionary
A dictionary of field parameters to set.
"""
fields = ensure_list(fields)
for field_name in fields:
if isinstance(field_name, tuple):
field_name = field_name[1]
field_obj = ds._get_field_info(field_name)
if field_obj.particle_type:
print("Saving particle fields currently not supported.")
return
with _get_backup_file(ds) as f:
# now save the field
_write_fields_to_gdf(ds, f, fields,
particle_type_name="dark_matter",
field_parameters=field_parameters)
def __init__(self, data_source, x_field, y_fields,
weight_field="cell_mass", n_bins=64,
accumulation=False, fractional=False,
label=None, plot_spec=None,
x_log=None, y_log=None):
if x_log is None:
logs = None
else:
logs = {x_field:x_log}
profiles = [create_profile(data_source, [x_field],
n_bins=[n_bins],
fields=ensure_list(y_fields),
weight_field=weight_field,
accumulation=accumulation,
fractional=fractional,
logs=logs)]
if plot_spec is None:
plot_spec = [dict() for p in profiles]
if not isinstance(plot_spec, list):
plot_spec = [plot_spec.copy() for p in profiles]
ProfilePlot._initialize_instance(self, profiles, label, plot_spec, y_log)
def __init__(self, name, function, units=None,
take_log=True, validators=None,
particle_type=False, vector_field=False, display_field=True,
not_in_all=False, display_name=None, output_units = None):
self.name = name
self.take_log = take_log
self.display_name = display_name
self.not_in_all = not_in_all
self.display_field = display_field
self.particle_type = particle_type
self.vector_field = vector_field
if output_units is None: output_units = units
self.output_units = output_units
self._function = function
if validators:
self.validators = ensure_list(validators)
else:
self.validators = []
# handle units
if units is None:
self.units = ''
elif isinstance(units, str):
if units.lower() == 'auto':
self.units = None
else:
self.units = units
elif isinstance(units, Unit):
self.units = str(units)
else:
raise FieldUnitsError("Cannot handle units '%s' (type %s)." \
"Please provide a string or Unit " \
"object." % (units, type(units)) )
def __init__(self, ds, axis, fields, center="c", width=None, image_res=None, **kwargs):
fields = ensure_list(fields)
axis = fix_axis(axis, ds)
center, dcenter = ds.coordinates.sanitize_center(center, axis)
slc = ds.slice(axis, center[axis], **kwargs)
w, frb = construct_image(ds, axis, slc, dcenter, width=width, image_res=image_res)
super(FITSSlice, self).__init__(frb, fields=fields, wcs=w)
def __init__(self, ds, normal, fields, center='c', width=None, image_res=512,
north_vector=None):
fields = ensure_list(fields)
center, dcenter = ds.coordinates.sanitize_center(center, 4)
cut = ds.cutting(normal, center, north_vector=north_vector)
center = ds.arr([0.0] * 2, 'code_length')
w, frb = construct_image(ds, normal, cut, center, width=width, image_res=image_res)
super(FITSOffAxisSlice, self).__init__(frb, fields=fields, wcs=w)
def get_dependencies(self, fields):
deps = []
fi = self.ds.field_info
for field in fields:
if any(getattr(v,"ghost_zones", 0) > 0 for v in
fi[field].validators): continue
deps += ensure_list(fi[field].get_dependencies(ds=self.ds).requested)
return list(set(deps))
def __init__(self, ds, axis, fields, center="c", width=None,
weight_field=None, image_res=None, **kwargs):
fields = ensure_list(fields)
axis = fix_axis(axis, ds)
center, dcenter = ds.coordinates.sanitize_center(center, axis)
prj = ds.proj(fields[0], axis, weight_field=weight_field, **kwargs)
w, frb = construct_image(ds, axis, prj, dcenter, width=width, image_res=image_res)
super(FITSProjection, self).__init__(frb, fields=fields, wcs=w)
def write_to_gdf(ds, gdf_path, fields=None,
data_author=None, data_comment=None,
dataset_units=None, particle_type_name="dark_matter",
clobber=False):
"""
Write a dataset to the given path in the Grid Data Format.
Parameters
----------
ds : Dataset object
The yt data to write out.
gdf_path : string
The path of the file to output.
fields : field or list of fields
The fields(s) to write out. If None, defaults to
ds.field_list.
data_author : string, optional
The name of the author who wrote the data. Default: None.
data_comment : string, optional
A descriptive comment. Default: None.
dataset_units : dictionary, optional
A dictionary of (value, unit) tuples to set the default units
of the dataset. Keys can be:
"length_unit"
"time_unit"
"mass_unit"
"velocity_unit"
"magnetic_unit"
If not specified, these will carry over from the parent
dataset.
particle_type_name : string, optional
The particle type of the particles in the dataset. Default: "dark_matter"
clobber : boolean, optional
Whether or not to clobber an already existing file. If False, attempting
to overwrite an existing file will result in an exception.
Examples
--------
>>> dataset_units = {"length_unit":(1.0,"Mpc"),
... "time_unit":(1.0,"Myr")}
>>> write_to_gdf(ds, "clumps.h5", data_author="John ZuHone",
... dataset_units=dataset_units,
... data_comment="My Really Cool Dataset", clobber=True)
"""
if fields is None:
fields = ds.field_list
fields = ensure_list(fields)
with _create_new_gdf(ds, gdf_path, data_author,
data_comment,
dataset_units=dataset_units,
particle_type_name=particle_type_name,
clobber=clobber) as f:
# now add the fields one-by-one
_write_fields_to_gdf(ds, f, fields, particle_type_name)
def from_sizes(cls, sizes):
sizes = ensure_list(sizes)
pool = cls()
rank = pool.comm.rank
for i,size in enumerate(sizes):
if iterable(size):
size, name = size
else:
name = "workgroup_%02i" % i
pool.add_workgroup(size, name = name)
for wg in pool.workgroups:
if rank in wg.ranks: workgroup = wg
return pool, workgroup
def sanitize_label(label, nprofiles):
label = ensure_list(label)
if len(label) == 1:
label = label * nprofiles
if len(label) != nprofiles:
raise RuntimeError("Number of labels must match number of profiles")
for l in label:
if l is not None and not isinstance(l, string_types):
raise RuntimeError("All labels must be None or a string")
return label
def __init__(self, data_source, x_field, y_field, z_fields,
weight_field="cell_mass", x_bins=128, y_bins=128,
accumulation=False, fractional=False,
fontsize=18, figure_size=8.0):
profile = create_profile(
data_source,
[x_field, y_field],
ensure_list(z_fields),
n_bins=[x_bins, y_bins],
weight_field=weight_field,
accumulation=accumulation,
fractional=fractional)
type(self)._initialize_instance(self, data_source, profile, fontsize,
figure_size)
def _initialize_instance(cls, obj, profiles, labels, plot_specs, y_log):
obj.profiles = ensure_list(profiles)
obj.x_log = None
obj.y_log = {}
if y_log is not None:
for field, log in y_log.items():
field, = obj.profiles[0].data_source._determine_fields([field])
obj.y_log[field] = log
obj.y_title = {}
obj.label = sanitize_label(labels, len(obj.profiles))
if plot_specs is None:
plot_specs = [dict() for p in obj.profiles]
obj.plot_spec = plot_specs
obj.figures = FigureContainer()
obj.axes = AxesContainer(obj.figures)
obj._setup_plots()
return obj
def set_zlim(self, field, zmin, zmax, dynamic_range=None):
"""set the scale of the colormap
Parameters
----------
field : string
the field to set a colormap scale
if field == 'all', applies to all plots.
zmin : float
the new minimum of the colormap scale. If 'min', will
set to the minimum value in the current view.
zmax : float
the new maximum of the colormap scale. If 'max', will
set to the maximum value in the current view.
Other Parameters
----------------
dynamic_range : float (default: None)
The dynamic range of the image.
If zmin == None, will set zmin = zmax / dynamic_range
If zmax == None, will set zmax = zmin * dynamic_range
When dynamic_range is specified, defaults to setting
zmin = zmax / dynamic_range.
"""
if field is 'all':
fields = list(self.plots.keys())
else:
fields = ensure_list(field)
for field in self.data_source._determine_fields(fields):
myzmin = zmin
myzmax = zmax
if zmin == 'min':
myzmin = self.plots[field].image._A.min()
if zmax == 'max':
myzmax = self.plots[field].image._A.max()
if dynamic_range is not None:
if zmax is None:
myzmax = myzmin * dynamic_range
else:
myzmin = myzmax / dynamic_range
self.plots[field].zmin = myzmin
self.plots[field].zmax = myzmax
return self
def __init__(self, ds, normal, fields, center='c', width=(1.0, 'unitary'),
weight_field=None, image_res=512, depth_res=256,
north_vector=None, depth=(1.0,"unitary"), no_ghost=False, method='integrate'):
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], depth_res)
for field in fields:
buf[field] = off_axis_projection(ds, center, normal, wd, res, field,
no_ghost=no_ghost, north_vector=north_vector,
method=method, weight=weight_field).swapaxes(0, 1)
center = ds.arr([0.0] * 2, 'code_length')
w, not_an_frb = construct_image(ds, normal, buf, center, width=width, image_res=image_res)
super(FITSOffAxisProjection, self).__init__(buf, fields=fields, wcs=w)
def __init__(self,
ds,
axis,
fields,
center="c",
width=None,
image_res=None,
**kwargs):
fields = ensure_list(fields)
axis = fix_axis(axis, ds)
center, dcenter = ds.coordinates.sanitize_center(center, axis)
slc = ds.slice(axis, center[axis], **kwargs)
w, frb = construct_image(ds,
axis,
slc,
dcenter,
width=width,
image_res=image_res)
super(FITSSlice, self).__init__(frb, fields=fields, wcs=w)
def __init__(self,
ds,
normal,
fields,
image_res=512,
center='c',
width=None,
north_vector=None,
length_unit=None):
fields = ensure_list(fields)
center, dcenter = ds.coordinates.sanitize_center(center, 4)
cut = ds.cutting(normal, center, north_vector=north_vector)
center = ds.arr([0.0] * 2, 'code_length')
w, frb, lunit = construct_image(ds, normal, cut, center, image_res,
width, length_unit)
super(FITSOffAxisSlice, self).__init__(frb,
fields=fields,
length_unit=lunit,
wcs=w)
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'):
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 = construct_image(ds,
normal,
buf,
center,
width=width,
image_res=image_res)
super(FITSOffAxisProjection, self).__init__(buf, fields=fields, wcs=w)
def get_log(self, field):
"""get the transform type of a field.
Parameters
----------
field : string
the field to get a transform
if field == 'all', applies to all plots.
"""
# devnote : accepts_all_fields decorator is not applicable here because the return variable isn't self
log = {}
if field == "all":
fields = list(self.plots.keys())
else:
fields = ensure_list(field)
for field in self.data_source._determine_fields(fields):
log[field] = self._field_transform[field] == log_transform
return log
def __init__(self, data_source, x_field, y_field, z_fields,
weight_field="cell_mass", x_bins=128, y_bins=128,
accumulation=False, fractional=False,
fontsize=18, figure_size=8.0):
if isinstance(data_source.ds, YTProfileDataset):
profile = data_source.ds.profile
else:
profile = create_profile(
data_source,
[x_field, y_field],
ensure_list(z_fields),
n_bins=[x_bins, y_bins],
weight_field=weight_field,
accumulation=accumulation,
fractional=fractional)
type(self)._initialize_instance(self, data_source, profile, fontsize,
figure_size)
def __init__(self, data_source, x_field, y_field, z_fields=None,
color='b', x_bins=800, y_bins=800, weight_field=None,
deposition='ngp', fontsize=18, figure_size=8.0):
# if no z_fields are passed in, use a constant color
if z_fields is None:
self.use_cbar = False
self.splat_color = color
z_fields = ['particle_ones']
profile = create_profile(
data_source,
[x_field, y_field],
ensure_list(z_fields),
n_bins=[x_bins, y_bins],
weight_field=weight_field,
deposition=deposition)
type(self)._initialize_instance(self, data_source, profile, fontsize,
figure_size)
def __init__(self,
ds,
axis,
fields,
center="c",
width=None,
weight_field=None,
image_res=None,
**kwargs):
fields = ensure_list(fields)
axis = fix_axis(axis, ds)
center, dcenter = ds.coordinates.sanitize_center(center, axis)
prj = ds.proj(fields[0], axis, weight_field=weight_field, **kwargs)
w, frb = construct_image(ds,
axis,
prj,
dcenter,
width=width,
image_res=image_res)
super(FITSProjection, self).__init__(frb, fields=fields, wcs=w)
def __init__(self,
data_source,
conditionals,
ds=None,
field_parameters=None,
base_object=None):
if base_object is not None:
# passing base_object explicitly has been deprecated,
# but we handle it here for backward compatibility
if data_source is not None:
raise RuntimeError(
"Cannot use both base_object and data_source")
data_source = base_object
super(YTCutRegion, self).__init__(data_source.center,
ds,
field_parameters,
data_source=data_source)
self.conditionals = ensure_list(conditionals)
self.base_object = data_source
self._selector = None
def __init__(self, data_source, x_field, y_field, z_fields=None,
color='b', x_bins=800, y_bins=800, weight_field=None,
deposition='ngp', fontsize=18, figure_size=8.0):
# if no z_fields are passed in, use a constant color
if z_fields is None:
self.use_cbar = False
self.splat_color = color
z_fields = ['particle_ones']
profile = create_profile(
data_source,
[x_field, y_field],
ensure_list(z_fields),
n_bins=[x_bins, y_bins],
weight_field=weight_field,
deposition=deposition)
type(self)._initialize_instance(self, data_source, profile, fontsize,
figure_size)
def export_fits(self, filename, fields=None, overwrite=False,
other_keys=None, units="cm", **kwargs):
r"""Export a set of pixelized fields to a FITS file.
This will export a set of FITS images of either the fields specified
or all the fields already in the object.
Parameters
----------
filename : string
The name of the FITS file to be written.
fields : list of strings
These fields will be pixelized and output. If "None", the keys of the
FRB will be used.
overwrite : boolean
If the file exists, this governs whether we will overwrite.
other_keys : dictionary, optional
A set of header keys and values to write into the FITS header.
units : string, optional
the length units that the coordinates are written in, default 'cm'.
"""
from yt.visualization.fits_image import FITSImageData
if fields is None:
fields = list(self.data.keys())
else:
fields = ensure_list(fields)
if len(fields) == 0:
raise RuntimeError(
"No fields to export. Either pass a field or list of fields to "
"export_fits or access a field from the fixed resolution buffer "
"object."
)
fib = FITSImageData(self, fields=fields, units=units)
if other_keys is not None:
for k,v in other_keys.items():
fib.update_all_headers(k,v)
fib.writeto(filename, overwrite=overwrite, **kwargs)
def __init__(
self,
data_source,
conditionals,
ds=None,
field_parameters=None,
base_object=None,
locals=None,
):
if locals is None:
locals = {}
validate_object(data_source, YTSelectionContainer)
validate_iterable(conditionals)
for condition in conditionals:
validate_object(condition, str)
validate_object(ds, Dataset)
validate_object(field_parameters, dict)
validate_object(base_object, YTSelectionContainer)
if base_object is not None:
# passing base_object explicitly has been deprecated,
# but we handle it here for backward compatibility
if data_source is not None:
raise RuntimeError(
"Cannot use both base_object and data_source")
data_source = base_object
self.conditionals = ensure_list(conditionals)
if isinstance(data_source, YTCutRegion):
# If the source is also a cut region, add its conditionals
# and set the source to be its source.
# Preserve order of conditionals.
self.conditionals = data_source.conditionals + self.conditionals
data_source = data_source.base_object
super(YTCutRegion, self).__init__(data_source.center,
ds,
field_parameters,
data_source=data_source)
self.base_object = data_source
self.locals = locals
self._selector = None
def _initialize_instance(cls, obj, profiles, labels, plot_specs, y_log):
from matplotlib.font_manager import FontProperties
obj._font_properties = FontProperties(family='stixgeneral', size=18)
obj._font_color = None
obj.profiles = ensure_list(profiles)
obj.x_log = None
obj.y_log = {}
if y_log is not None:
for field, log in y_log.items():
field, = obj.profiles[0].data_source._determine_fields([field])
obj.y_log[field] = log
obj.y_title = {}
obj.label = sanitize_label(labels, len(obj.profiles))
if plot_specs is None:
plot_specs = [dict() for p in obj.profiles]
obj.plot_spec = plot_specs
obj.plots = PlotContainer()
obj.figures = FigureContainer(obj.plots)
obj.axes = AxesContainer(obj.plots)
obj._setup_plots()
return obj
def __init__(self, data_source, conditionals, ds=None,
field_parameters=None, base_object=None):
validate_object(data_source, YTSelectionContainer)
validate_iterable(conditionals)
for condition in conditionals:
validate_object(condition, string_types)
validate_object(ds, Dataset)
validate_object(field_parameters, dict)
validate_object(base_object, YTSelectionContainer)
if base_object is not None:
# passing base_object explicitly has been deprecated,
# but we handle it here for backward compatibility
if data_source is not None:
raise RuntimeError(
"Cannot use both base_object and data_source")
data_source=base_object
super(YTCutRegion, self).__init__(
data_source.center, ds, field_parameters, data_source=data_source)
self.conditionals = ensure_list(conditionals)
self.base_object = data_source
self._selector = None
def __init__(self,
name,
function,
units=None,
take_log=True,
validators=None,
particle_type=False,
vector_field=False,
display_field=True,
not_in_all=False,
display_name=None,
output_units=None):
self.name = name
self.take_log = take_log
self.display_name = display_name
self.not_in_all = not_in_all
self.display_field = display_field
self.particle_type = particle_type
self.vector_field = vector_field
if output_units is None: output_units = units
self.output_units = output_units
self._function = function
if validators:
self.validators = ensure_list(validators)
else:
self.validators = []
# handle units
if units is None:
self.units = ""
elif isinstance(units, str):
self.units = units
elif isinstance(units, Unit):
self.units = str(units)
else:
raise FieldUnitsError("Cannot handle units '%s' (type %s)." \
"Please provide a string or Unit " \
"object." % (units, type(units)) )
def set_ylim(self, field, ymin=None, ymax=None):
"""Sets the plot limits for the specified field we are binning.
Parameters
----------
field : string or field tuple
The field that we want to adjust the plot limits for.
ymin : float or None
The new y minimum. Defaults to None, which leaves the ymin
unchanged.
ymax : float or None
The new y maximum. Defaults to None, which leaves the ymax
unchanged.
Examples
--------
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> pp = yt.ProfilePlot(ds.all_data(), 'density', ['temperature', 'x-velocity'])
>>> pp.set_ylim('temperature', 1e4, 1e6)
>>> pp.save()
"""
if field is 'all':
fields = list(self.axes.keys())
else:
fields = ensure_list(field)
for profile in self.profiles:
for field in profile.data_source._determine_fields(fields):
if field in profile.field_map:
field = profile.field_map[field]
self.axes.ylim[field] = (ymin, ymax)
# Continue on to the next profile.
break
return self
def get_data(self, fields):
mylog.info("Getting %s using ParticleIO" % str(fields))
fields = ensure_list(fields)
if not self.ds.index.io._particle_reader:
mylog.info("not self.ds.index.io._particle_reader")
return self.source.get_data(fields)
rtype, args = self._get_args()
count_list, grid_list = [], []
for grid in self.source._grids:
if grid.NumberOfParticles == 0: continue
grid_list.append(grid)
if self.source._is_fully_enclosed(grid):
count_list.append(grid.NumberOfParticles)
else:
count_list.append(-1)
# region type, left_edge, right_edge, periodic, grid_list
fields_to_read = []
conv_factors = []
for field in fields:
f = self.ds.field_info[field]
to_add = f.get_dependencies(ds=self.ds).requested
to_add = list(np.unique(to_add))
if len(to_add) != 1: raise KeyError
fields_to_read += to_add
if f._particle_convert_function is None:
func = f._convert_function
else:
func = f.particle_convert
func = particle_converter(func)
conv_factors.append(
np.fromiter((func(g) for g in grid_list),
count=len(grid_list),
dtype='float64'))
conv_factors = np.array(conv_factors).transpose()
self.conv_factors = conv_factors
rvs = self.ds.index.io._read_particles(fields_to_read, rtype, args,
grid_list, count_list,
conv_factors)
for [n, v] in zip(fields, rvs):
self.source.field_data[n] = v
def hide_colorbar(self, field=None):
"""
Hides the colorbar for a plot and updates the size of the
plot accordingly. Defaults to operating on all fields for a
PlotContainer object.
Parameters
----------
field : string, field tuple, or list of strings or field tuples (optional)
The name of the field(s) that we want to hide the colorbar. If None
is provided, will default to using all fields available for this
object.
Examples
--------
This will save an image with no colorbar.
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> s.hide_colorbar()
>>> s.save()
This will save an image with no axis or colorbar.
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> s = SlicePlot(ds, 2, 'density', 'c', (20, 'kpc'))
>>> s.hide_axes()
>>> s.hide_colorbar()
>>> s.save()
"""
if field is None:
field = self.fields
field = ensure_list(field)
for f in field:
self.plots[f].hide_colorbar()
return self
def set_ylim(self, field, ymin=None, ymax=None):
"""Sets the plot limits for the specified field we are binning.
Parameters
----------
field : string or field tuple
The field that we want to adjust the plot limits for.
ymin : float or None
The new y minimum. Defaults to None, which leaves the ymin
unchanged.
ymax : float or None
The new y maximum. Defaults to None, which leaves the ymax
unchanged.
Examples
--------
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>> pp = yt.ProfilePlot(ds.all_data(), 'density', ['temperature', 'x-velocity'])
>>> pp.set_ylim('temperature', 1e4, 1e6)
>>> pp.save()
"""
if field is 'all':
fields = self.axes.keys()
else:
fields = ensure_list(field)
for profile in self.profiles:
for field in profile.data_source._determine_fields(fields):
if field in profile.field_map:
field = profile.field_map[field]
self.axes.ylim[field] = (ymin, ymax)
# Continue on to the next profile.
break
return self
def eval(self, tasks, obj=None):
tasks = ensure_list(tasks)
return_values = {}
for store, ds in self.piter(return_values):
store.result = []
for task in tasks:
try:
style = inspect.getargspec(task.eval)[0][1]
if style == 'ds':
arg = ds
elif style == 'data_object':
if obj is None:
obj = DatasetSeriesObject(self, "all_data")
arg = obj.get(ds)
rv = task.eval(arg)
# We catch and store YT-originating exceptions
# This fixes the standard problem of having a sphere that's too
# small.
except YTException:
pass
store.result.append(rv)
return [v for k, v in sorted(return_values.items())]
def __init__(self,
data_source,
x_field,
y_fields,
weight_field="cell_mass",
n_bins=64,
accumulation=False,
fractional=False,
label=None,
plot_spec=None,
x_log=None,
y_log=None):
if x_log is None:
logs = None
else:
logs = {x_field: x_log}
if isinstance(data_source.ds, YTProfileDataset):
profiles = [data_source.ds.profile]
else:
profiles = [
create_profile(data_source, [x_field],
n_bins=[n_bins],
fields=ensure_list(y_fields),
weight_field=weight_field,
accumulation=accumulation,
fractional=fractional,
logs=logs)
]
if plot_spec is None:
plot_spec = [dict() for p in profiles]
if not isinstance(plot_spec, list):
plot_spec = [plot_spec.copy() for p in profiles]
ProfilePlot._initialize_instance(self, profiles, label, plot_spec,
y_log)
def _find_field_values_at_points(self, fields, coords):
r"""Find the value of fields at a set of coordinates.
Returns the values [field1, field2,...] of the fields at the given
(x, y, z) points. Returns a numpy array of field values cross coords
"""
coords = self.ds.arr(ensure_numpy_array(coords), 'code_length')
grids = self._find_points(coords[:, 0], coords[:, 1], coords[:, 2])[0]
fields = ensure_list(fields)
mark = np.zeros(3, dtype=np.int)
out = []
# create point -> grid mapping
grid_index = {}
for coord_index, grid in enumerate(grids):
if grid not in grid_index:
grid_index[grid] = []
grid_index[grid].append(coord_index)
out = []
for field in fields:
funit = self.ds._get_field_info(field).units
out.append(self.ds.arr(np.empty((len(coords))), funit))
for grid in grid_index:
cellwidth = (grid.RightEdge -
grid.LeftEdge) / grid.ActiveDimensions
for field_index, field in enumerate(fields):
for coord_index in grid_index[grid]:
mark = ((coords[coord_index, :] - grid.LeftEdge) /
cellwidth)
mark = np.array(mark, dtype='int64')
out[field_index][coord_index] = \
grid[field][mark[0], mark[1], mark[2]]
if len(fields) == 1:
return out[0]
return out
def to_pw(self, fields=None, center="c", width=None, axes_unit=None):
r"""Create a :class:`~yt.visualization.plot_window.PWViewerMPL` from this
object.
This is a bare-bones mechanism of creating a plot window from this
object, which can then be moved around, zoomed, and on and on. All
behavior of the plot window is relegated to that routine.
"""
normal = self.normal
center = self.center
self.fields = ensure_list(fields) + [
k for k in self.field_data.keys() if k not in self._key_fields
]
from yt.visualization.fixed_resolution import FixedResolutionBuffer
from yt.visualization.plot_window import (
PWViewerMPL,
get_oblique_window_parameters,
)
(bounds, center_rot) = get_oblique_window_parameters(
normal, center, width, self.ds
)
pw = PWViewerMPL(
self,
bounds,
fields=self.fields,
origin="center-window",
periodic=False,
oblique=True,
frb_generator=FixedResolutionBuffer,
plot_type="OffAxisSlice",
)
if axes_unit is not None:
pw.set_axes_unit(axes_unit)
pw._setup_plots()
return pw
def __init__(self, name, sub_types):
self.name = name
self.sub_types = ensure_list(sub_types)
def __init__(
self,
name,
sampling_type,
function,
units=None,
take_log=True,
validators=None,
particle_type=None,
vector_field=False,
display_field=True,
not_in_all=False,
display_name=None,
output_units=None,
dimensions=None,
ds=None,
nodal_flag=None,
):
self.name = name
self.take_log = take_log
self.display_name = display_name
self.not_in_all = not_in_all
self.display_field = display_field
if particle_type:
warnings.warn(
"particle_type for derived fields "
"has been replaced with sampling_type = 'particle'",
DeprecationWarning,
)
sampling_type = "particle"
self.sampling_type = sampling_type
self.vector_field = vector_field
self.ds = ds
if self.ds is not None:
self._ionization_label_format = self.ds._ionization_label_format
else:
self._ionization_label_format = "roman_numeral"
if nodal_flag is None:
self.nodal_flag = [0, 0, 0]
else:
self.nodal_flag = nodal_flag
self._function = function
if validators:
self.validators = ensure_list(validators)
else:
self.validators = []
# handle units
if units is None:
self.units = ""
elif isinstance(units, str):
if units.lower() == "auto":
if dimensions is None:
raise RuntimeError(
"To set units='auto', please specify the dimensions "
"of the field with dimensions=<dimensions of field>!")
self.units = None
else:
self.units = units
elif isinstance(units, Unit):
self.units = str(units)
elif isinstance(units, bytes):
self.units = units.decode("utf-8")
else:
raise FieldUnitsError("Cannot handle units '%s' (type %s)."
"Please provide a string or Unit "
"object." % (units, type(units)))
if output_units is None:
output_units = self.units
self.output_units = output_units
if isinstance(dimensions, str):
dimensions = getattr(ytdims, dimensions)
self.dimensions = dimensions
def __init__(self, prop):
"""
This validator ensures that the data object has a given python attribute.
"""
FieldValidator.__init__(self)
self.prop = ensure_list(prop)
def __init__(self, name, sub_types):
self.name = name
self.sub_types = ensure_list(sub_types)
def run(self):
self.result = [[p.id for p in ensure_list(g.Parent) \
if g.Parent is not None]
for g in self.ds.index.grids]
def __call__(self, fields, non_zero=False):
fields = ensure_list(fields)
rv = super(Extrema, self).__call__(fields, non_zero)
if len(rv) == 1: rv = rv[0]
return rv
def write_to_gdf(ds,
gdf_path,
fields=None,
data_author=None,
data_comment=None,
dataset_units=None,
particle_type_name="dark_matter",
clobber=False):
"""
Write a dataset to the given path in the Grid Data Format.
Parameters
----------
ds : Dataset object
The yt data to write out.
gdf_path : string
The path of the file to output.
fields : field or list of fields
The fields(s) to write out. If None, defaults to
ds.field_list.
data_author : string, optional
The name of the author who wrote the data. Default: None.
data_comment : string, optional
A descriptive comment. Default: None.
dataset_units : dictionary, optional
A dictionary of (value, unit) tuples to set the default units
of the dataset. Keys can be:
* "length_unit"
* "time_unit"
* "mass_unit"
* "velocity_unit"
* "magnetic_unit"
If not specified, these will carry over from the parent
dataset.
particle_type_name : string, optional
The particle type of the particles in the dataset. Default: "dark_matter"
clobber : boolean, optional
Whether or not to clobber an already existing file. If False, attempting
to overwrite an existing file will result in an exception.
Examples
--------
>>> dataset_units = {"length_unit":(1.0,"Mpc"),
... "time_unit":(1.0,"Myr")}
>>> write_to_gdf(ds, "clumps.h5", data_author="John ZuHone",
... dataset_units=dataset_units,
... data_comment="My Really Cool Dataset", clobber=True)
"""
if fields is None:
fields = ds.field_list
fields = ensure_list(fields)
with _create_new_gdf(ds,
gdf_path,
data_author,
data_comment,
dataset_units=dataset_units,
particle_type_name=particle_type_name,
clobber=clobber) as f:
# now add the fields one-by-one
_write_fields_to_gdf(ds, f, fields, particle_type_name)
def __call__(self, fields, weight):
fields = ensure_list(fields)
rv = super(WeightedAverageQuantity, self).__call__(fields, weight)
if len(rv) == 1: rv = rv[0]
return rv
def __init__(self,
filename,
dataset_type='fits',
auxiliary_files=[],
nprocs=None,
storage_filename=None,
nan_mask=None,
suppress_astropy_warnings=True,
parameters=None,
units_override=None,
unit_system="cgs"):
if parameters is None:
parameters = {}
parameters["nprocs"] = nprocs
self.specified_parameters = parameters
if suppress_astropy_warnings:
warnings.filterwarnings('ignore', module="astropy", append=True)
auxiliary_files = ensure_list(auxiliary_files)
self.filenames = [filename] + auxiliary_files
self.num_files = len(self.filenames)
self.fluid_types += ("fits", )
if nan_mask is None:
self.nan_mask = {}
elif isinstance(nan_mask, float):
self.nan_mask = {"all": nan_mask}
elif isinstance(nan_mask, dict):
self.nan_mask = nan_mask
self._handle = FITSFileHandler(self.filenames[0])
if (isinstance(self.filenames[0],
_astropy.pyfits.hdu.image._ImageBaseHDU)
or isinstance(self.filenames[0], _astropy.pyfits.HDUList)):
fn = "InMemoryFITSFile_%s" % uuid.uuid4().hex
else:
fn = self.filenames[0]
self._handle._fits_files.append(self._handle)
if self.num_files > 1:
for fits_file in auxiliary_files:
if isinstance(fits_file,
_astropy.pyfits.hdu.image._ImageBaseHDU):
f = _astropy.pyfits.HDUList([fits_file])
elif isinstance(fits_file, _astropy.pyfits.HDUList):
f = fits_file
else:
if os.path.exists(fits_file):
fn = fits_file
else:
fn = os.path.join(ytcfg.get("yt", "test_data_dir"),
fits_file)
f = _astropy.pyfits.open(fn,
memmap=True,
do_not_scale_image_data=True,
ignore_blank=True)
self._handle._fits_files.append(f)
self.refine_by = 2
Dataset.__init__(self,
fn,
dataset_type,
units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
def __init__(
self,
data,
fields=None,
length_unit=None,
width=None,
img_ctr=None,
wcs=None,
current_time=None,
time_unit=None,
mass_unit=None,
velocity_unit=None,
magnetic_unit=None,
ds=None,
unit_header=None,
**kwargs,
):
r""" Initialize a FITSImageData object.
FITSImageData contains a collection of FITS ImageHDU instances and
WCS information, along with units for each of the images. FITSImageData
instances can be constructed from ImageArrays, NumPy arrays, dicts
of such arrays, FixedResolutionBuffers, and YTCoveringGrids. The latter
two are the most powerful because WCS information can be constructed
automatically from their coordinates.
Parameters
----------
data : FixedResolutionBuffer or a YTCoveringGrid. Or, an
ImageArray, an numpy.ndarray, or dict of such arrays
The data to be made into a FITS image or images.
fields : single string or list of strings, optional
The field names for the data. If *fields* is none and *data* has
keys, it will use these for the fields. If *data* is just a
single array one field name must be specified.
length_unit : string
The units of the WCS coordinates and the length unit of the file.
Defaults to the length unit of the dataset, if there is one, or
"cm" if there is not.
width : float or YTQuantity
The width of the image. Either a single value or iterable of values.
If a float, assumed to be in *units*. Only used if this information
is not already provided by *data*.
img_ctr : array_like or YTArray
The center coordinates of the image. If a list or NumPy array,
it is assumed to be in *units*. Only used if this information
is not already provided by *data*.
wcs : `~astropy.wcs.WCS` instance, optional
Supply an AstroPy WCS instance. Will override automatic WCS
creation from FixedResolutionBuffers and YTCoveringGrids.
current_time : float, tuple, or YTQuantity, optional
The current time of the image(s). If not specified, one will
be set from the dataset if there is one. If a float, it will
be assumed to be in *time_unit* units.
time_unit : string
The default time units of the file. Defaults to "s".
mass_unit : string
The default time units of the file. Defaults to "g".
velocity_unit : string
The default velocity units of the file. Defaults to "cm/s".
magnetic_unit : string
The default magnetic units of the file. Defaults to "gauss".
ds : `~yt.static_output.Dataset` instance, optional
The dataset associated with the image(s), typically used
to transfer metadata to the header(s). Does not need to be
specified if *data* has a dataset as an attribute.
Examples
--------
>>> # This example uses a FRB.
>>> ds = load("sloshing_nomag2_hdf5_plt_cnt_0150")
>>> prj = ds.proj(2, "kT", weight_field="density")
>>> frb = prj.to_frb((0.5, "Mpc"), 800)
>>> # This example just uses the FRB and puts the coords in kpc.
>>> f_kpc = FITSImageData(frb, fields="kT", length_unit="kpc",
... time_unit=(1.0, "Gyr"))
>>> # This example specifies a specific WCS.
>>> from astropy.wcs import WCS
>>> w = WCS(naxis=self.dimensionality)
>>> w.wcs.crval = [30., 45.] # RA, Dec in degrees
>>> w.wcs.cunit = ["deg"]*2
>>> nx, ny = 800, 800
>>> w.wcs.crpix = [0.5*(nx+1), 0.5*(ny+1)]
>>> w.wcs.ctype = ["RA---TAN","DEC--TAN"]
>>> scale = 1./3600. # One arcsec per pixel
>>> w.wcs.cdelt = [-scale, scale]
>>> f_deg = FITSImageData(frb, fields="kT", wcs=w)
>>> f_deg.writeto("temp.fits")
"""
if fields is not None:
fields = ensure_list(fields)
if "units" in kwargs:
issue_deprecation_warning(
"The 'units' keyword argument has been replaced "
"by the 'length_unit' keyword argument and the "
"former has been deprecated. Setting 'length_unit' "
"to 'units'.")
length_unit = kwargs.pop("units")
if ds is None:
ds = getattr(data, "ds", None)
self.fields = []
self.field_units = {}
if unit_header is None:
self._set_units(ds, [
length_unit, mass_unit, time_unit, velocity_unit, magnetic_unit
])
else:
self._set_units_from_header(unit_header)
wcs_unit = str(self.length_unit.units)
self._fix_current_time(ds, current_time)
if width is None:
width = 1.0
if isinstance(width, tuple):
if ds is None:
width = YTQuantity(width[0], width[1])
else:
width = ds.quan(width[0], width[1])
if img_ctr is None:
img_ctr = np.zeros(3)
exclude_fields = [
"x",
"y",
"z",
"px",
"py",
"pz",
"pdx",
"pdy",
"pdz",
"weight_field",
]
if isinstance(data, _astropy.pyfits.PrimaryHDU):
data = _astropy.pyfits.HDUList([data])
if isinstance(data, _astropy.pyfits.HDUList):
self.hdulist = data
for hdu in data:
self.fields.append(hdu.header["btype"])
self.field_units[hdu.header["btype"]] = hdu.header["bunit"]
self.shape = self.hdulist[0].shape
self.dimensionality = len(self.shape)
wcs_names = [
key for key in self.hdulist[0].header if "WCSNAME" in key
]
for name in wcs_names:
if name == "WCSNAME":
key = " "
else:
key = name[-1]
w = _astropy.pywcs.WCS(header=self.hdulist[0].header,
key=key,
naxis=self.dimensionality)
setattr(self, "wcs" + key.strip().lower(), w)
return
self.hdulist = _astropy.pyfits.HDUList()
if hasattr(data, "keys"):
img_data = data
if fields is None:
fields = list(img_data.keys())
elif isinstance(data, np.ndarray):
if fields is None:
mylog.warning("No field name given for this array. "
"Calling it 'image_data'.")
fn = "image_data"
fields = [fn]
else:
fn = fields[0]
img_data = {fn: data}
for fd in fields:
if isinstance(fd, tuple):
self.fields.append(fd[1])
elif isinstance(fd, DerivedField):
self.fields.append(fd.name[1])
else:
self.fields.append(fd)
# Sanity checking names
s = set()
duplicates = set(f for f in self.fields if f in s or s.add(f))
if len(duplicates) > 0:
for i, fd in enumerate(self.fields):
if fd in duplicates:
if isinstance(fields[i], tuple):
ftype, fname = fields[i]
elif isinstance(fields[i], DerivedField):
ftype, fname = fields[i].name
else:
raise RuntimeError("Cannot distinguish between fields "
"with same name %s!" % fd)
self.fields[i] = "%s_%s" % (ftype, fname)
first = True
for i, name, field in zip(count(), self.fields, fields):
if name not in exclude_fields:
this_img = img_data[field]
if hasattr(img_data[field], "units"):
if this_img.units.is_code_unit:
mylog.warning("Cannot generate an image with code "
"units. Converting to units in CGS.")
funits = this_img.units.get_base_equivalent("cgs")
else:
funits = this_img.units
self.field_units[name] = str(funits)
else:
self.field_units[name] = "dimensionless"
mylog.info("Making a FITS image of field %s", name)
if isinstance(this_img, ImageArray):
if i == 0:
self.shape = this_img.shape[::-1]
this_img = np.asarray(this_img)
else:
if i == 0:
self.shape = this_img.shape
this_img = np.asarray(this_img.T)
if first:
hdu = _astropy.pyfits.PrimaryHDU(this_img)
first = False
else:
hdu = _astropy.pyfits.ImageHDU(this_img)
hdu.name = name
hdu.header["btype"] = name
hdu.header["bunit"] = re.sub("()", "", self.field_units[name])
for unit in ("length", "time", "mass", "velocity", "magnetic"):
if unit == "magnetic":
short_unit = "bf"
else:
short_unit = unit[0]
key = "{}unit".format(short_unit)
value = getattr(self, "{}_unit".format(unit))
if value is not None:
hdu.header[key] = float(value.value)
hdu.header.comments[key] = "[%s]" % value.units
hdu.header["time"] = float(self.current_time.value)
self.hdulist.append(hdu)
self.dimensionality = len(self.shape)
if wcs is None:
w = _astropy.pywcs.WCS(header=self.hdulist[0].header,
naxis=self.dimensionality)
# FRBs and covering grids are special cases where
# we have coordinate information, so we take advantage
# of this and construct the WCS object
if isinstance(img_data, FixedResolutionBuffer):
dx = (img_data.bounds[1] -
img_data.bounds[0]).to_value(wcs_unit)
dy = (img_data.bounds[3] -
img_data.bounds[2]).to_value(wcs_unit)
dx /= self.shape[0]
dy /= self.shape[1]
xctr = 0.5 * (img_data.bounds[1] +
img_data.bounds[0]).to_value(wcs_unit)
yctr = 0.5 * (img_data.bounds[3] +
img_data.bounds[2]).to_value(wcs_unit)
center = [xctr, yctr]
cdelt = [dx, dy]
elif isinstance(img_data, YTCoveringGrid):
cdelt = img_data.dds.to_value(wcs_unit)
center = 0.5 * (img_data.left_edge +
img_data.right_edge).to_value(wcs_unit)
else:
# If img_data is just an array we use the width and img_ctr
# parameters to determine the cell widths
if not iterable(width):
width = [width] * self.dimensionality
if isinstance(width[0], YTQuantity):
cdelt = [
wh.to_value(wcs_unit) / n
for wh, n in zip(width, self.shape)
]
else:
cdelt = [float(wh) / n for wh, n in zip(width, self.shape)]
center = img_ctr[:self.dimensionality]
w.wcs.crpix = 0.5 * (np.array(self.shape) + 1)
w.wcs.crval = center
w.wcs.cdelt = cdelt
w.wcs.ctype = ["linear"] * self.dimensionality
w.wcs.cunit = [wcs_unit] * self.dimensionality
self.set_wcs(w)
else:
self.set_wcs(wcs)
def __init__(self, parameters):
"""
This validator ensures that the dataset has a given parameter.
"""
FieldValidator.__init__(self)
self.parameters = ensure_list(parameters)
def __init__(self, prop):
"""
This validator ensures that the data object has a given python attribute.
"""
FieldValidator.__init__(self)
self.prop = ensure_list(prop)
def __call__(self, fields):
fields = ensure_list(fields)
rv = super(TotalQuantity, self).__call__(fields)
if len(rv) == 1: rv = rv[0]
return rv
def get_data(self, fields):
fields = ensure_list(fields)
self.source.get_data(fields, force_particle_read=True)
rvs = [self.source[field] for field in fields]
if len(fields) == 1: return rvs[0]
return rvs
def run(self):
self.result = \
all(g in ensure_list(c.Parent) for g in self.ds.index.grids
for c in g.Children)
