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
class FITSDataset(Dataset):
_index_class = FITSHierarchy
_field_info_class: Type[FieldInfoContainer] = FITSFieldInfo
_dataset_type = "fits"
_handle = None
def __init__(
self,
filename,
dataset_type="fits",
auxiliary_files=None,
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)
self.filenames = [filename] + list(always_iterable(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 = f"InMemoryFITSFile_{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 _set_code_unit_attributes(self):
"""
Generates the conversion to various physical _units based on the
parameter file
"""
if getattr(self, "length_unit", None) is None:
default_length_units = [
u for u, v in default_unit_symbol_lut.items()
if str(v[1]) == "(length)"
]
more_length_units = []
for unit in default_length_units:
if unit in self.unit_registry.prefixable_units:
more_length_units += [
prefix + unit for prefix in unit_prefixes
]
default_length_units += more_length_units
file_units = []
cunits = [
self.wcs.wcs.cunit[i] for i in range(self.dimensionality)
]
for unit in (_.to_string() for _ in cunits):
if unit in default_length_units:
file_units.append(unit)
if len(set(file_units)) == 1:
length_factor = self.wcs.wcs.cdelt[0]
length_unit = str(file_units[0])
mylog.info("Found length units of %s.", length_unit)
else:
self.no_cgs_equiv_length = True
mylog.warning(
"No length conversion provided. Assuming 1 = 1 cm.")
length_factor = 1.0
length_unit = "cm"
setdefaultattr(self, "length_unit",
self.quan(length_factor, length_unit))
for unit, cgs in [("time", "s"), ("mass", "g")]:
# We set these to cgs for now, but they may have been overridden
if getattr(self, unit + "_unit", None) is not None:
continue
mylog.warning("Assuming 1.0 = 1.0 %s", cgs)
setdefaultattr(self, f"{unit}_unit", self.quan(1.0, cgs))
self.magnetic_unit = np.sqrt(4 * np.pi * self.mass_unit /
(self.time_unit**2 * self.length_unit))
self.magnetic_unit.convert_to_units("gauss")
self.velocity_unit = self.length_unit / self.time_unit
def _parse_parameter_file(self):
self._determine_structure()
self._determine_axes()
if self.parameter_filename.startswith("InMemory"):
self.unique_identifier = time.time()
# Determine dimensionality
self.dimensionality = self.naxis
self.geometry = "cartesian"
# Sometimes a FITS file has a 4D datacube, in which case
# we take the 4th axis and assume it consists of different fields.
if self.dimensionality == 4:
self.dimensionality = 3
self._determine_wcs()
self.current_time = 0.0
self.domain_dimensions = np.array(self.dims)[:self.dimensionality]
if self.dimensionality == 2:
self.domain_dimensions = np.append(self.domain_dimensions,
[int(1)])
self._determine_bbox()
# Get the simulation time
try:
self.current_time = self.parameters["time"]
except Exception:
mylog.warning("Cannot find time")
self.current_time = 0.0
pass
# For now we'll ignore these
self._periodicity = (False, ) * 3
self.current_redshift = 0.0
self.omega_lambda = 0.0
self.omega_matter = 0.0
self.hubble_constant = 0.0
self.cosmological_simulation = 0
self._determine_nprocs()
# Now we can set up some of our parameters for convenience.
for k, v in self.primary_header.items():
self.parameters[k] = v
# Remove potential default keys
self.parameters.pop("", None)
def _determine_nprocs(self):
# If nprocs is None, do some automatic decomposition of the domain
if self.specified_parameters["nprocs"] is None:
nprocs = np.around(
np.prod(self.domain_dimensions) /
32**self.dimensionality).astype("int")
self.parameters["nprocs"] = max(min(nprocs, 512), 1)
else:
self.parameters["nprocs"] = self.specified_parameters["nprocs"]
def _determine_structure(self):
self.primary_header, self.first_image = find_primary_header(
self._handle)
self.naxis = self.primary_header["naxis"]
self.axis_names = [
self.primary_header.get("ctype%d" % (i + 1), "LINEAR")
for i in range(self.naxis)
]
self.dims = [
self.primary_header["naxis%d" % (i + 1)] for i in range(self.naxis)
]
def _determine_wcs(self):
wcs = _astropy.pywcs.WCS(header=self.primary_header)
if self.naxis == 4:
self.wcs = _astropy.pywcs.WCS(naxis=3)
self.wcs.wcs.crpix = wcs.wcs.crpix[:3]
self.wcs.wcs.cdelt = wcs.wcs.cdelt[:3]
self.wcs.wcs.crval = wcs.wcs.crval[:3]
self.wcs.wcs.cunit = [str(unit) for unit in wcs.wcs.cunit][:3]
self.wcs.wcs.ctype = [type for type in wcs.wcs.ctype][:3]
else:
self.wcs = wcs
def _determine_bbox(self):
domain_left_edge = np.array([0.5] * 3)
domain_right_edge = np.array(
[float(dim) + 0.5 for dim in self.domain_dimensions])
if self.dimensionality == 2:
domain_left_edge[-1] = 0.5
domain_right_edge[-1] = 1.5
self.domain_left_edge = domain_left_edge
self.domain_right_edge = domain_right_edge
def _determine_axes(self):
self.lat_axis = 1
self.lon_axis = 0
self.lat_name = "Y"
self.lon_name = "X"
@classmethod
def _is_valid(cls, filename, *args, **kwargs):
fileh = check_fits_valid(filename)
if fileh is None:
return False
else:
fileh.close()
return True
@classmethod
def _guess_candidates(cls, base, directories, files):
candidates = []
for fn, fnl in ((_, _.lower()) for _ in files):
if (fnl.endswith(".fits") or fnl.endswith(".fits.gz")
or fnl.endswith(".fits.fz")):
candidates.append(fn)
# FITS files don't preclude subdirectories
return candidates, True
def close(self):
self._handle.close()
def __init__(self,
filename,
dataset_type='fits',
auxiliary_files=[],
nprocs=None,
storage_filename=None,
nan_mask=None,
spectral_factor=1.0,
z_axis_decomp=False,
suppress_astropy_warnings=True,
parameters=None,
units_override=None,
unit_system="cgs"):
if parameters is None:
parameters = {}
parameters["nprocs"] = nprocs
self.specified_parameters = parameters
self.z_axis_decomp = z_axis_decomp
self.spectral_factor = spectral_factor
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)
if len(self._handle) > 1 and self._handle[1].name == "EVENTS":
self.events_data = True
self.first_image = 1
self.primary_header = self._handle[self.first_image].header
self.naxis = 2
self.wcs = _astropy.pywcs.WCS(naxis=2)
self.events_info = {}
for k, v in self.primary_header.items():
if k.startswith("TTYP"):
if v.lower() in ["x", "y"]:
num = k.strip("TTYPE")
self.events_info[v.lower()] = (
self.primary_header["TLMIN" + num],
self.primary_header["TLMAX" + num],
self.primary_header["TCTYP" + num],
self.primary_header["TCRVL" + num],
self.primary_header["TCDLT" + num],
self.primary_header["TCRPX" + num])
elif v.lower() in ["energy", "time"]:
num = k.strip("TTYPE")
unit = self.primary_header["TUNIT" + num].lower()
if unit.endswith("ev"): unit = unit.replace("ev", "eV")
self.events_info[v.lower()] = unit
self.axis_names = [self.events_info[ax][2] for ax in ["x", "y"]]
self.reblock = 1
if "reblock" in self.specified_parameters:
self.reblock = self.specified_parameters["reblock"]
self.wcs.wcs.cdelt = [
self.events_info["x"][4] * self.reblock,
self.events_info["y"][4] * self.reblock
]
self.wcs.wcs.crpix = [
(self.events_info["x"][5] - 0.5) / self.reblock + 0.5,
(self.events_info["y"][5] - 0.5) / self.reblock + 0.5
]
self.wcs.wcs.ctype = [
self.events_info["x"][2], self.events_info["y"][2]
]
self.wcs.wcs.cunit = ["deg", "deg"]
self.wcs.wcs.crval = [
self.events_info["x"][3], self.events_info["y"][3]
]
self.dims = [
(self.events_info["x"][1] - self.events_info["x"][0]) /
self.reblock,
(self.events_info["y"][1] - self.events_info["y"][0]) /
self.reblock
]
else:
self.events_data = False
# Sometimes the primary hdu doesn't have an image
if len(self._handle) > 1 and self._handle[0].header["naxis"] == 0:
self.first_image = 1
else:
self.first_image = 0
self.primary_header = self._handle[self.first_image].header
self.naxis = self.primary_header["naxis"]
self.axis_names = [
self.primary_header.get("ctype%d" % (i + 1), "LINEAR")
for i in range(self.naxis)
]
self.dims = [
self.primary_header["naxis%d" % (i + 1)]
for i in range(self.naxis)
]
wcs = _astropy.pywcs.WCS(header=self.primary_header)
if self.naxis == 4:
self.wcs = _astropy.pywcs.WCS(naxis=3)
self.wcs.wcs.crpix = wcs.wcs.crpix[:3]
self.wcs.wcs.cdelt = wcs.wcs.cdelt[:3]
self.wcs.wcs.crval = wcs.wcs.crval[:3]
self.wcs.wcs.cunit = [str(unit) for unit in wcs.wcs.cunit][:3]
self.wcs.wcs.ctype = [type for type in wcs.wcs.ctype][:3]
else:
self.wcs = wcs
self.refine_by = 2
Dataset.__init__(self,
fn,
dataset_type,
units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
class FITSDataset(Dataset):
_index_class = FITSHierarchy
_field_info_class = FITSFieldInfo
_dataset_type = "fits"
_handle = None
def __init__(self,
filename,
dataset_type='fits',
auxiliary_files=[],
nprocs=None,
storage_filename=None,
nan_mask=None,
spectral_factor=1.0,
z_axis_decomp=False,
suppress_astropy_warnings=True,
parameters=None,
units_override=None,
unit_system="cgs"):
if parameters is None:
parameters = {}
parameters["nprocs"] = nprocs
self.specified_parameters = parameters
self.z_axis_decomp = z_axis_decomp
self.spectral_factor = spectral_factor
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)
if len(self._handle) > 1 and self._handle[1].name == "EVENTS":
self.events_data = True
self.first_image = 1
self.primary_header = self._handle[self.first_image].header
self.naxis = 2
self.wcs = _astropy.pywcs.WCS(naxis=2)
self.events_info = {}
for k, v in self.primary_header.items():
if k.startswith("TTYP"):
if v.lower() in ["x", "y"]:
num = k.strip("TTYPE")
self.events_info[v.lower()] = (
self.primary_header["TLMIN" + num],
self.primary_header["TLMAX" + num],
self.primary_header["TCTYP" + num],
self.primary_header["TCRVL" + num],
self.primary_header["TCDLT" + num],
self.primary_header["TCRPX" + num])
elif v.lower() in ["energy", "time"]:
num = k.strip("TTYPE")
unit = self.primary_header["TUNIT" + num].lower()
if unit.endswith("ev"): unit = unit.replace("ev", "eV")
self.events_info[v.lower()] = unit
self.axis_names = [self.events_info[ax][2] for ax in ["x", "y"]]
self.reblock = 1
if "reblock" in self.specified_parameters:
self.reblock = self.specified_parameters["reblock"]
self.wcs.wcs.cdelt = [
self.events_info["x"][4] * self.reblock,
self.events_info["y"][4] * self.reblock
]
self.wcs.wcs.crpix = [
(self.events_info["x"][5] - 0.5) / self.reblock + 0.5,
(self.events_info["y"][5] - 0.5) / self.reblock + 0.5
]
self.wcs.wcs.ctype = [
self.events_info["x"][2], self.events_info["y"][2]
]
self.wcs.wcs.cunit = ["deg", "deg"]
self.wcs.wcs.crval = [
self.events_info["x"][3], self.events_info["y"][3]
]
self.dims = [
(self.events_info["x"][1] - self.events_info["x"][0]) /
self.reblock,
(self.events_info["y"][1] - self.events_info["y"][0]) /
self.reblock
]
else:
self.events_data = False
# Sometimes the primary hdu doesn't have an image
if len(self._handle) > 1 and self._handle[0].header["naxis"] == 0:
self.first_image = 1
else:
self.first_image = 0
self.primary_header = self._handle[self.first_image].header
self.naxis = self.primary_header["naxis"]
self.axis_names = [
self.primary_header.get("ctype%d" % (i + 1), "LINEAR")
for i in range(self.naxis)
]
self.dims = [
self.primary_header["naxis%d" % (i + 1)]
for i in range(self.naxis)
]
wcs = _astropy.pywcs.WCS(header=self.primary_header)
if self.naxis == 4:
self.wcs = _astropy.pywcs.WCS(naxis=3)
self.wcs.wcs.crpix = wcs.wcs.crpix[:3]
self.wcs.wcs.cdelt = wcs.wcs.cdelt[:3]
self.wcs.wcs.crval = wcs.wcs.crval[:3]
self.wcs.wcs.cunit = [str(unit) for unit in wcs.wcs.cunit][:3]
self.wcs.wcs.ctype = [type for type in wcs.wcs.ctype][:3]
else:
self.wcs = wcs
self.refine_by = 2
Dataset.__init__(self,
fn,
dataset_type,
units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
def _set_code_unit_attributes(self):
"""
Generates the conversion to various physical _units based on the parameter file
"""
default_length_units = [
u for u, v in default_unit_symbol_lut.items()
if str(v[1]) == "(length)"
]
more_length_units = []
for unit in default_length_units:
if unit in prefixable_units:
more_length_units += [
prefix + unit for prefix in unit_prefixes
]
default_length_units += more_length_units
file_units = []
cunits = [self.wcs.wcs.cunit[i] for i in range(self.dimensionality)]
for unit in (_.to_string() for _ in cunits):
if unit in default_length_units:
file_units.append(unit)
if len(set(file_units)) == 1:
length_factor = self.wcs.wcs.cdelt[0]
length_unit = str(file_units[0])
mylog.info("Found length units of %s." % (length_unit))
else:
self.no_cgs_equiv_length = True
mylog.warning("No length conversion provided. Assuming 1 = 1 cm.")
length_factor = 1.0
length_unit = "cm"
setdefaultattr(self, 'length_unit',
self.quan(length_factor, length_unit))
setdefaultattr(self, 'mass_unit', self.quan(1.0, "g"))
setdefaultattr(self, 'time_unit', self.quan(1.0, "s"))
setdefaultattr(self, 'velocity_unit', self.quan(1.0, "cm/s"))
if "beam_size" in self.specified_parameters:
beam_size = self.specified_parameters["beam_size"]
beam_size = self.quan(beam_size[0], beam_size[1]).in_cgs().value
else:
beam_size = 1.0
self.unit_registry.add("beam",
beam_size,
dimensions=dimensions.solid_angle)
if self.spec_cube:
units = self.wcs_2d.wcs.cunit[0]
if units == "deg": units = "degree"
if units == "rad": units = "radian"
pixel_area = np.prod(np.abs(self.wcs_2d.wcs.cdelt))
pixel_area = self.quan(pixel_area, "%s**2" % (units)).in_cgs()
pixel_dims = pixel_area.units.dimensions
self.unit_registry.add("pixel",
float(pixel_area.value),
dimensions=pixel_dims)
def _parse_parameter_file(self):
if self.parameter_filename.startswith("InMemory"):
self.unique_identifier = time.time()
else:
self.unique_identifier = \
int(os.stat(self.parameter_filename)[stat.ST_CTIME])
# Determine dimensionality
self.dimensionality = self.naxis
self.geometry = "cartesian"
# Sometimes a FITS file has a 4D datacube, in which case
# we take the 4th axis and assume it consists of different fields.
if self.dimensionality == 4: self.dimensionality = 3
self.domain_dimensions = np.array(self.dims)[:self.dimensionality]
if self.dimensionality == 2:
self.domain_dimensions = np.append(self.domain_dimensions,
[int(1)])
domain_left_edge = np.array([0.5] * 3)
domain_right_edge = np.array(
[float(dim) + 0.5 for dim in self.domain_dimensions])
if self.dimensionality == 2:
domain_left_edge[-1] = 0.5
domain_right_edge[-1] = 1.5
self.domain_left_edge = domain_left_edge
self.domain_right_edge = domain_right_edge
# Get the simulation time
try:
self.current_time = self.parameters["time"]
except:
mylog.warning("Cannot find time")
self.current_time = 0.0
pass
# For now we'll ignore these
self.periodicity = (False, ) * 3
self.current_redshift = self.omega_lambda = self.omega_matter = \
self.hubble_constant = self.cosmological_simulation = 0.0
if self.dimensionality == 2 and self.z_axis_decomp:
mylog.warning(
"You asked to decompose along the z-axis, but this is a 2D dataset. "
+ "Ignoring.")
self.z_axis_decomp = False
if self.events_data: self.specified_parameters["nprocs"] = 1
# If nprocs is None, do some automatic decomposition of the domain
if self.specified_parameters["nprocs"] is None:
if self.z_axis_decomp:
nprocs = np.around(self.domain_dimensions[2] / 8).astype("int")
else:
nprocs = np.around(
np.prod(self.domain_dimensions) /
32**self.dimensionality).astype("int")
self.parameters["nprocs"] = max(min(nprocs, 512), 1)
else:
self.parameters["nprocs"] = self.specified_parameters["nprocs"]
# Check to see if this data is in some kind of (Lat,Lon,Vel) format
self.spec_cube = False
self.wcs_2d = None
x = 0
for p in lon_prefixes + lat_prefixes + list(spec_names.keys()):
y = np_char.startswith(self.axis_names[:self.dimensionality], p)
x += np.any(y)
if x == self.dimensionality:
if self.axis_names == ['LINEAR', 'LINEAR']:
self.wcs_2d = self.wcs
self.lat_axis = 1
self.lon_axis = 0
self.lat_name = "Y"
self.lon_name = "X"
else:
self._setup_spec_cube()
# Now we can set up some of our parameters for convenience.
#self.parameters['wcs'] = dict(self.wcs.to_header())
for k, v in self.primary_header.items():
self.parameters[k] = v
# Remove potential default keys
self.parameters.pop('', None)
def _setup_spec_cube(self):
self.spec_cube = True
self.geometry = "spectral_cube"
end = min(self.dimensionality + 1, 4)
if self.events_data:
ctypes = self.axis_names
else:
ctypes = np.array(
[self.primary_header["CTYPE%d" % (i)] for i in range(1, end)])
log_str = "Detected these axes: " + "%s " * len(ctypes)
mylog.info(log_str % tuple([ctype for ctype in ctypes]))
self.lat_axis = np.zeros((end - 1), dtype="bool")
for p in lat_prefixes:
self.lat_axis += np_char.startswith(ctypes, p)
self.lat_axis = np.where(self.lat_axis)[0][0]
self.lat_name = ctypes[self.lat_axis].split("-")[0].lower()
self.lon_axis = np.zeros((end - 1), dtype="bool")
for p in lon_prefixes:
self.lon_axis += np_char.startswith(ctypes, p)
self.lon_axis = np.where(self.lon_axis)[0][0]
self.lon_name = ctypes[self.lon_axis].split("-")[0].lower()
if self.lat_axis == self.lon_axis and self.lat_name == self.lon_name:
self.lat_axis = 1
self.lon_axis = 0
self.lat_name = "Y"
self.lon_name = "X"
if self.wcs.naxis > 2:
self.spec_axis = np.zeros((end - 1), dtype="bool")
for p in spec_names.keys():
self.spec_axis += np_char.startswith(ctypes, p)
self.spec_axis = np.where(self.spec_axis)[0][0]
self.spec_name = spec_names[ctypes[self.spec_axis].split("-")[0]
[0]]
self.wcs_2d = _astropy.pywcs.WCS(naxis=2)
self.wcs_2d.wcs.crpix = self.wcs.wcs.crpix[[
self.lon_axis, self.lat_axis
]]
self.wcs_2d.wcs.cdelt = self.wcs.wcs.cdelt[[
self.lon_axis, self.lat_axis
]]
self.wcs_2d.wcs.crval = self.wcs.wcs.crval[[
self.lon_axis, self.lat_axis
]]
self.wcs_2d.wcs.cunit = [
str(self.wcs.wcs.cunit[self.lon_axis]),
str(self.wcs.wcs.cunit[self.lat_axis])
]
self.wcs_2d.wcs.ctype = [
self.wcs.wcs.ctype[self.lon_axis],
self.wcs.wcs.ctype[self.lat_axis]
]
self._p0 = self.wcs.wcs.crpix[self.spec_axis]
self._dz = self.wcs.wcs.cdelt[self.spec_axis]
self._z0 = self.wcs.wcs.crval[self.spec_axis]
self.spec_unit = str(self.wcs.wcs.cunit[self.spec_axis])
if self.spectral_factor == "auto":
self.spectral_factor = float(
max(self.domain_dimensions[[self.lon_axis,
self.lat_axis]]))
self.spectral_factor /= self.domain_dimensions[self.spec_axis]
mylog.info("Setting the spectral factor to %f" %
(self.spectral_factor))
Dz = self.domain_right_edge[
self.spec_axis] - self.domain_left_edge[self.spec_axis]
dre = self.domain_right_edge
dre[self.spec_axis] = (self.domain_left_edge[self.spec_axis] +
self.spectral_factor * Dz)
self.domain_right_edge = dre
self._dz /= self.spectral_factor
self._p0 = (self._p0 - 0.5) * self.spectral_factor + 0.5
else:
self.wcs_2d = self.wcs
self.spec_axis = 2
self.spec_name = "z"
self.spec_unit = "code_length"
def spec2pixel(self, spec_value):
sv = self.arr(spec_value).in_units(self.spec_unit)
return self.arr((sv.v - self._z0) / self._dz + self._p0, "code_length")
def pixel2spec(self, pixel_value):
pv = self.arr(pixel_value, "code_length")
return self.arr((pv.v - self._p0) * self._dz + self._z0,
self.spec_unit)
@classmethod
def _is_valid(cls, *args, **kwargs):
ext = args[0].rsplit(".", 1)[-1]
if ext.upper() in ("GZ", "FZ"):
# We don't know for sure that there will be > 1
ext = args[0].rsplit(".", 1)[0].rsplit(".", 1)[-1]
if ext.upper() not in ("FITS", "FTS"):
return False
elif isinstance(_astropy.pyfits, NotAModule):
raise RuntimeError(
"This appears to be a FITS file, but AstroPy is not installed."
)
try:
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
category=UserWarning,
append=True)
fileh = _astropy.pyfits.open(args[0])
valid = fileh[0].header["naxis"] >= 2
if len(fileh) > 1:
valid = fileh[1].header["naxis"] >= 2 or valid
fileh.close()
return valid
except:
pass
return False
@classmethod
def _guess_candidates(cls, base, directories, files):
candidates = []
for fn, fnl in ((_, _.lower()) for _ in files):
if fnl.endswith(".fits") or \
fnl.endswith(".fits.gz") or \
fnl.endswith(".fits.fz"):
candidates.append(fn)
# FITS files don't preclude subdirectories
return candidates, True
def close(self):
self._handle.close()
