__all__ = ("Orbits",)
import warnings
import numpy as np
import pandas as pd
[docs]
class Orbits:
"""Orbits reads, checks for required values, and stores orbit
parameters for moving objects.
self.orbits stores the orbital parameters, as a pandas dataframe.
self.dataCols defines the columns required,
although obj_id, H, g, and sed_filename are optional.
"""
def __init__(self):
self.orbits = None
self.orb_format = None
# Specify the required columns/values in the self.orbits dataframe.
# Which columns are required depends on self.orb_format.
self.data_cols = {}
self.data_cols["COM"] = [
"obj_id",
"q",
"e",
"inc",
"Omega",
"argPeri",
"tPeri",
"epoch",
"H",
"g",
"sed_filename",
]
self.data_cols["KEP"] = [
"obj_id",
"a",
"e",
"inc",
"Omega",
"argPeri",
"meanAnomaly",
"epoch",
"H",
"g",
"sed_filename",
]
self.data_cols["CAR"] = [
"obj_id",
"x",
"y",
"z",
"xdot",
"ydot",
"zdot",
"epoch",
"H",
"g",
"sed_filename",
]
def __len__(self):
return len(self.orbits)
def __getitem__(self, i):
orb = Orbits()
orb.set_orbits(self.orbits.iloc[i])
return orb
def __iter__(self):
for i, orbit in self.orbits.iterrows():
orb = Orbits()
orb.set_orbits(orbit)
yield orb
def __eq__(self, other_orbits):
if isinstance(other_orbits, Orbits):
if self.orb_format != other_orbits.orb_format:
return False
for col in self.data_cols[self.orb_format]:
if not np.all(self.orbits[col].values == other_orbits.orbits[col].values):
return False
else:
return True
else:
return False
def __neq__(self, other_orbits):
if self == other_orbits:
return False
else:
return True
[docs]
def set_orbits(self, orbits):
"""Set and validate orbital parameters contain all required values.
Sets self.orbits and self.orb_format.
If objid is not present in orbits,
a sequential series of integers will be used.
If H is not present in orbits,
a default value of 20 will be used.
If g is not present in orbits,
a default value of 0.15 will be used.
If sed_filename is not present in orbits,
either C or S type will be assigned according to the
semi-major axis value.
Parameters
----------
orbits : `pd.DataFrame`, `pd.Series` or `np.ndarray`
Array-like object containing orbital parameter information.
"""
# Do we have a single item or multiples?
if isinstance(orbits, pd.Series):
# Passed a single SSO in Series, convert to a DataFrame.
orbits = pd.DataFrame([orbits])
elif isinstance(orbits, np.ndarray):
# Passed a numpy array, convert to DataFrame.
orbits = pd.DataFrame.from_records(orbits)
elif isinstance(orbits, np.record):
# This was a single object in a numpy array
orbits = pd.DataFrame.from_records([orbits], columns=orbits.dtype.names)
elif isinstance(orbits, pd.DataFrame):
# This was a pandas dataframe ..
# but we probably want to drop the index and recount.
orbits.reset_index(drop=True, inplace=True)
if "index" in orbits:
del orbits["index"]
n_sso = len(orbits)
# Error if orbits is empty
# (this avoids hard-to-interpret error messages from pyoorb).
if n_sso == 0:
raise ValueError("Length of the orbits dataframe was 0.")
# Discover which type of orbital parameters we have on disk.
self.orb_format = None
if "FORMAT" in orbits:
if ~(orbits["FORMAT"] == orbits["FORMAT"].iloc[0]).all():
raise ValueError("All orbital elements in the set should have the same FORMAT.")
self.orb_format = orbits["FORMAT"].iloc[0]
# Backwards compatibility .. a bit.
# CART is deprecated, so swap it to CAR.
if self.orb_format == "CART":
self.orb_format = "CAR"
del orbits["FORMAT"]
# Check that the orbit format is approximately right.
if self.orb_format == "COM":
if "q" not in orbits:
raise ValueError('The stated format was COM, but "q" not present in orbital elements?')
if self.orb_format == "KEP":
if "a" not in orbits:
raise ValueError('The stated format was KEP, but "a" not present in orbital elements?')
if self.orb_format == "CAR":
if "x" not in orbits:
raise ValueError('The stated format was CAR but "x" not present in orbital elements?')
if self.orb_format is None:
# Try to figure out the format, if it wasn't provided.
if "q" in orbits:
self.orb_format = "COM"
elif "a" in orbits:
self.orb_format = "KEP"
elif "x" in orbits:
self.orb_format = "CAR"
else:
raise ValueError(
"Can't determine orbital type, as neither q, a or x in input orbital elements.\n"
"Was attempting to base orbital element quantities on header row, "
"with columns: \n%s" % orbits.columns
)
# Check that the orbit epoch is within a 'reasonable' range,
# to detect possible column mismatches.
general_epoch = orbits["epoch"].head(1).values[0]
# Look for epochs between 1800 and 2200 -
# this is primarily to check if people used MJD (and not JD).
expect_min_epoch = -21503.0
expect_max_epoch = 124594.0
if general_epoch < expect_min_epoch or general_epoch > expect_max_epoch:
raise ValueError(
"The epoch detected for this orbit is odd - %f. "
"Expecting a value between %.1f and %.1f (MJD!)"
% (general_epoch, expect_min_epoch, expect_max_epoch)
)
# If these columns are not available in the input data,
# auto-generate them.
if "obj_id" not in orbits:
obj_id = np.arange(0, n_sso, 1)
orbits = orbits.assign(obj_id=obj_id)
if "H" not in orbits:
orbits = orbits.assign(H=20.0)
if "g" not in orbits:
orbits = orbits.assign(g=0.15)
if "sed_filename" not in orbits:
orbits = orbits.assign(sed_filename=self.assign_sed(orbits))
# Make sure we gave all the columns we need.
for col in self.data_cols[self.orb_format]:
if col not in orbits:
raise ValueError(
"Missing required orbital element %s for orbital format type %s" % (col, self.orb_format)
)
# Check to see if we have duplicates.
if len(orbits["obj_id"].unique()) != n_sso:
warnings.warn(
"There are duplicates in the orbit obj_id values" + " - was this intended? (continuing)."
)
# All is good.
self.orbits = orbits
[docs]
def assign_sed(self, orbits, random_seed=None):
"""Assign either a C or S type SED,
depending on the semi-major axis of the object.
P(C type) = 0 (a<2); 0.5*a - 1 (2<a<4); 1 (a > 4),
based on figure 23 from Ivezic et al 2001 (AJ, 122, 2749).
Parameters
----------
orbits : `pd.DataFrame`, `pd.Series` or `np.ndarray`
Array-like object containing orbital parameter information.
Returns
-------
sedvals : `np.ndarray`
Array containing the SED type for each object in 'orbits'.
"""
# using fig. 23 from Ivezic et al. 2001 (AJ, 122, 2749),
# we can approximate the sed types with a simple linear form:
# p(C) = 0 for a<2
# p(C) = 0.5*a-1 for 2<a<4
# p(C) = 1 for a>4
# where a is semi-major axis, and p(C) is the probability that
# an asteroid is C type, with p(S)=1-p(C) for S types.
if "a" in orbits:
a = orbits["a"]
elif "q" in orbits:
a = orbits["q"] / (1 - orbits["e"])
elif "x" in orbits:
# This isn't right, but it's a placeholder to make it work for now.
a = np.sqrt(orbits["x"] ** 2 + orbits["y"] ** 2 + orbits["z"] ** 2)
else:
raise ValueError("Need either a or q (plus e) in orbit data frame.")
if not hasattr(self, "_rng"):
if random_seed is not None:
self._rng = np.random.RandomState(random_seed)
else:
self._rng = np.random.RandomState(42)
chance = self._rng.random_sample(len(orbits))
prob_c = 0.5 * a - 1.0
# if chance <= prob_c:
sedvals = np.where(chance <= prob_c, "C.dat", "S.dat")
return sedvals
[docs]
def read_orbits(self, orbit_file, delim=None, skiprows=None):
"""Read orbits from a file.
This generates a pandas dataframe containing columns matching dataCols,
for the appropriate orbital parameter format.
(currently accepts COM, KEP or CAR formats).
After reading and standardizing the column names,
calls self.set_orbits to validate the
orbital parameters.
Expects angles in orbital element formats to be in degrees.
Note that readOrbits uses pandas.read_csv to read the data file
with the orbital parameters.
Thus, it should have column headers specifying the column names ..
unless skiprows = -1 or there is just no header line at all.
in which case it is assumed to be a standard DES format file,
with no header line.
Parameters
----------
orbit_file : `str`
The name of the input file with orbital parameter information.
delim : `str`, optional
The delimiter for the input orbit file.
Default is None, will use delim_whitespace=True.
skiprows : `int`, optional
The number of rows to skip before reading the header information.
Default is None, which will trigger a search of the file for
the header columns.
"""
names = None
# If skiprows is set, then we will assume the user has
# handled this so that the first line read has the header information.
# But, if skiprows is not set, then we have to do some checking to
# see if there is header information and which row it might start in.
if skiprows is None:
skiprows = -1
# Figure out whether the header is in the first line,
# or if there are rows to skip.
# We need to do a bit of juggling to do this before pandas
# reads the whole orbit file.
with open(orbit_file, "r") as fp:
headervalues = None
for line in fp:
values = line.split()
try:
# If it is a valid orbit line,
# we expect column 3 to be a number.
float(values[3])
# And if it worked, we're done here (it's an orbit) -
# go on to parsing header values.
break
except (ValueError, IndexError):
# This wasn't a valid number or there wasn't
# anything in the third value.
# So this is either the header line or it's a
# comment line before the header columns.
skiprows += 1
headervalues = values
if headervalues is not None: # (and skiprows > -1)
# There is a header, but we also need to check if there
# is a comment key at the start of the proper header line.
# (Because this varies as well).
linestart = headervalues[0]
if linestart == "#" or linestart == "!!" or linestart == "##":
names = headervalues[1:]
else:
names = headervalues
# Add 1 to skiprows, so that we skip the header column line.
skiprows += 1
# So now skiprows is a value.
# If it is -1, then there is no header information.
if skiprows == -1:
# No header; assume it's a typical DES file -
# we'll assign the column names based on the FORMAT.
names_com = (
"obj_id",
"FORMAT",
"q",
"e",
"i",
"node",
"argperi",
"t_p",
"H",
"epoch",
"INDEX",
"N_PAR",
"MOID",
"COMPCODE",
)
names_kep = (
"obj_id",
"FORMAT",
"a",
"e",
"i",
"node",
"argperi",
"meanAnomaly",
"H",
"epoch",
"INDEX",
"N_PAR",
"MOID",
"COMPCODE",
)
names_car = (
"obj_id",
"FORMAT",
"x",
"y",
"z",
"xdot",
"ydot",
"zdot",
"H",
"epoch",
"INDEX",
"N_PAR",
"MOID",
"COMPCODE",
)
# First use names_com, and then change if required.
orbits = pd.read_csv(orbit_file, delim_whitespace=True, header=None, names=names_com)
if orbits["FORMAT"][0] == "KEP":
orbits.columns = names_kep
elif orbits["FORMAT"][0] == "CAR":
orbits.columns = names_car
else:
if delim is None:
orbits = pd.read_csv(orbit_file, delim_whitespace=True, skiprows=skiprows, names=names)
else:
orbits = pd.read_csv(orbit_file, sep=delim, skiprows=skiprows, names=names)
# Drop some columns that are typically present in DES files
# but that we don't need.
if "INDEX" in orbits:
del orbits["INDEX"]
if "N_PAR" in orbits:
del orbits["N_PAR"]
if "MOID" in orbits:
del orbits["MOID"]
if "COMPCODE" in orbits:
del orbits["COMPCODE"]
if "tmp" in orbits:
del orbits["tmp"]
# Normalize the column names to standard values and
# identify the orbital element types.
sso_cols = orbits.columns.values.tolist()
# These are the alternative possibilities for various column headers
# (depending on file version, origin, etc.)
# that might need remapping to our standardized names.
alt_names = {}
alt_names["obj_id"] = [
"obj_id",
"objid",
"!!ObjID",
"!!OID",
"!!S3MID",
"OID",
"S3MID" "objid(int)",
"full_name",
"#name",
]
alt_names["q"] = ["q"]
alt_names["a"] = ["a"]
alt_names["e"] = ["e", "ecc"]
alt_names["inc"] = ["inc", "i", "i(deg)", "incl"]
alt_names["Omega"] = [
"Omega",
"omega",
"node",
"om",
"node(deg)",
"BigOmega",
"Omega/node",
"longNode",
]
alt_names["argPeri"] = [
"argPeri",
"argperi",
"omega/argperi",
"w",
"argperi(deg)",
"peri",
]
alt_names["tPeri"] = ["tPeri", "t_p", "timeperi", "t_peri", "T_peri"]
alt_names["epoch"] = ["epoch", "t_0", "Epoch", "epoch_mjd"]
alt_names["H"] = ["H", "magH", "magHv", "Hv", "H_v"]
alt_names["g"] = ["g", "phaseV", "phase", "gV", "phase_g", "G"]
alt_names["meanAnomaly"] = ["meanAnomaly", "meanAnom", "M", "ma"]
alt_names["sed_filename"] = ["sed_filename", "sed"]
alt_names["xdot"] = ["xdot", "xDot"]
alt_names["ydot"] = ["ydot", "yDot"]
alt_names["zdot"] = ["zdot", "zDot"]
# Update column names that match any of the alternatives above.
for name, alternatives in alt_names.items():
intersection = list(set(alternatives) & set(sso_cols))
if len(intersection) > 1:
raise ValueError(
"Received too many possible matches to %s in orbit file %s" % (name, orbit_file)
)
if len(intersection) == 1:
idx = sso_cols.index(intersection[0])
sso_cols[idx] = name
# Assign the new column names back to the orbits dataframe.
orbits.columns = sso_cols
# Failing on negative inclinations.
if "inc" in orbits.keys():
if np.min(orbits["inc"]) < 0:
negative_incs = np.where(orbits["inc"].values < 0)[0]
negative_ids = orbits["obj_id"].values[negative_incs]
ValueError("Negative orbital inclinations not supported. Problem obj_ids=%s" % negative_ids)
# Validate and assign orbits to self.
self.set_orbits(orbits)
[docs]
def update_orbits(self, neworb):
"""Update existing orbits with new values,
leaving OrbitIds, H, g, and sed_filenames in place.
Example use: transform orbital parameters (using PyOrbEphemerides)
and then replace original values.
Example use: propagate orbital parameters (using PyOrbEphemerides)
and then replace original values.
Parameters
----------
neworb: `pd.DataFrame`
"""
col_orig = ["obj_id", "sed_filename"]
new_order = ["obj_id"] + [n for n in neworb.columns] + ["sed_filename"]
updated_orbits = neworb.join(self.orbits[col_orig])[new_order]
self.set_orbits(updated_orbits)