def __init__(self,
filename,
*,
template="./labels/m3/l2_template.xml",
clean=False,
**pdr_kwargs):
# default values for known attributes populated later
self.RFL_IMAGE = np.array([])
self.SUPPL_IMAGE = np.array([])
self.filename = None
self.LABEL = None
self.rfl_file = None
self.sup_file = None
# read the label with PDR
super().__init__(filename, **pdr_kwargs)
# roll images into HDULists of appropriate types
self.fits_rfl_image = fitsify(self.RFL_IMAGE.astype("float32"))
self.fits_sup_image = fitsify(self.SUPPL_IMAGE.astype("float32"))
if clean:
del self.RFL_IMAGE
del self.SUPPL_IMAGE
self.template = template
self.pds4_root = (
interpret_m3_basename(self.filename)["basename"].lower() + "_l2")
def __init__(self,
filename,
*,
template="./labels/m3/l1b_template.xml",
clean=False,
**pdr_kwargs):
# default values for known attributes populated later
self.OBS_IMAGE = np.array([])
self.RDN_IMAGE = np.array([])
self.LOC_IMAGE = np.array([])
self.UTC_TIME_TABLE = None
self.filename = None
self.LABEL = None
self.rdn_file = None
self.loc_file = None
self.obs_file = None
self.tim_file = None
# read the label with PDR
super().__init__(filename, **pdr_kwargs)
# roll images into HDULists of appropriate types
self.fits_loc_image = fitsify(self.LOC_IMAGE.astype("float64"))
self.fits_rdn_image = fitsify(self.RDN_IMAGE.astype("float32"))
self.fits_obs_image = fitsify(self.OBS_IMAGE.astype("float32"))
if clean:
# LOC_IMAGE doesn't get deleted here because it's used in tag
# mapping.
# we may change that later.
del self.RDN_IMAGE
del self.OBS_IMAGE
self.template = template
self.pds4_root = (
interpret_m3_basename(self.filename)["basename"].lower() + "_l1b")
def write_pds4(
self,
output_directory,
write_product_files=True,
write_label_file=True,
):
"""
output file objects and label for a PDS4 product corresponding to
this object.
(string containing output directory -> None)
"""
print("Converting " + self.filename + " to PDS4.")
# all of the images we need to convert in this way seem to have ENVI
# headers that are handled well by rasterio; moreover, since they
# don't have PDS3
# labels, we can't assemble a struct format specification without
# referencing
# the ENVI headers _anyway_, so just using rasterio.
self.fits_image_file = output_directory + self.pds4_root + ".fits"
if write_product_files:
# these are 2D images; they have a single 'band' to rasterio
envi_img = rasterio.open(self.filename).read(1)
self.FITS_IMAGE = fitsify(envi_img)
print("Writing image to " + self.fits_image_file)
self.FITS_IMAGE.writeto(self.fits_image_file, overwrite=True)
self.write_label(output_directory,
write_label_file,
label_name=self.pds4_root)
def __init__(self,
filename,
*,
template="./labels/m3/l0_template.xml",
clean=False,
**pdr_kwargs):
# default values for known attributes populated later
self.LABEL = None
self.L0_LINE_PREFIX_TABLE = None
self.L0_IMAGE = np.array([])
self.filename = None
self.fits_image_file = None
self.clock_file = None
super().__init__(filename, **pdr_kwargs)
# stymie numpy's enthusiasm for 64-bit integers and roll into an
# HDUList
self.FITS_IMAGE = fitsify(self.L0_IMAGE.astype("int16"))
self.PREFIX_DATAFRAME = pd.DataFrame(self.L0_LINE_PREFIX_TABLE)
# replace missing-byte strings (which are present in most of the files,
# and _supposed_ to be present in all) with 'n/a' and put that info
# at the end (so that order of most of the fields corresponds to
# 1-indexed
# byte order rather than 2-indexed)
self.PREFIX_DATAFRAME[23] = self.PREFIX_DATAFRAME[0].apply(
null_bytes_to_na)
self.PREFIX_DATAFRAME.drop(0, axis=1, inplace=True)
if clean:
del self.L0_IMAGE
self.template = template
self.pds4_root = (
interpret_m3_basename(self.filename)["basename"].lower() + "_l0")
def __init__(self,
filename,
*,
template="./labels/m3/bad_detector_element_template.xml"):
self.filename = None
self.template = template
self.fits_image_file = None
super().__init__(filename)
envi_img = rasterio.open(self.filename).read(1)
self.FITS_IMAGE = fitsify(envi_img)
self.pds4_root = (
interpret_m3_basename(self.filename)["basename"].lower() + "_bde")
def __init__(
self,
filename,
*,
template_directory="./labels/clementine/",
image_index=None,
template_override=None,
**pdr_kwargs
):
# default values for known attributes populated later
self.LABEL = {}
self.IMAGE = np.array([])
self.IMAGE_HISTOGRAM = np.array([])
self.filename = None
self.fits_image_file = None
self.histogram_file = None
self.image_index = image_index
super().__init__(filename, **pdr_kwargs)
# stymie numpy's enthusiasm for 64-bit integers and roll into an
# HDUList
self.FITS_IMAGE = fitsify(self.IMAGE.astype("uint8"))
if template_override is None:
self.template = template_directory + 'edr_template.xml'
else:
self.template = template_override
# 'basename' of the PDS4 product
self.pds4_root = format_edr_time(self.LABEL["START_TIME"]) \
+ "_" + self.LABEL["INSTRUMENT_ID"].lower() \
+ "_" + self.LABEL["TARGET_NAME"].lower()
if self.LABEL["FILTER_NAME"] != "N/A":
self.pds4_root += "_" + self.LABEL["FILTER_NAME"].lower()
if self.LABEL["CENTER_FILTER_WAVELENGTH"] != "N/A":
self.pds4_root += "_" + str(
self.LABEL["CENTER_FILTER_WAVELENGTH"].value)