utils.py
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Commonly used utility functions."""
import re
import copy
import warnings
from collections.abc import Iterable
import numpy as np
from scipy.spatial.distance import cdist
from astropy.time import Time
from astropy.coordinates import Angle
from astropy.utils import iers
from . import _utils
def _str_to_bytes(s):
warnings.warn(
"_str_to_bytes is deprecated and will be removed in pyuvdata version 2.2. "
"For an input string s, this function is a thin wrapper on s.encode('utf8'). "
"The use of encode is preferred over calling this function.",
DeprecationWarning,
)
return s.encode("utf8")
def _bytes_to_str(b):
warnings.warn(
"_bytes_to_str is deprecated and will be removed in pyuvdata version 2.2. "
"For an input string s, this function is a thin wrapper on s.decode('utf8'). "
"The use of decode is preferred over calling this function.",
DeprecationWarning,
)
return b.decode("utf8")
__all__ = [
"POL_STR2NUM_DICT",
"POL_NUM2STR_DICT",
"CONJ_POL_DICT",
"JONES_STR2NUM_DICT",
"JONES_NUM2STR_DICT",
"LatLonAlt_from_XYZ",
"XYZ_from_LatLonAlt",
"rotECEF_from_ECEF",
"ECEF_from_rotECEF",
"ENU_from_ECEF",
"ECEF_from_ENU",
"phase_uvw",
"unphase_uvw",
"uvcalibrate",
"apply_uvflag",
"get_lst_for_time",
"polstr2num",
"polnum2str",
"jstr2num",
"jnum2str",
"parse_polstr",
"parse_jpolstr",
"conj_pol",
"reorder_conj_pols",
"baseline_to_antnums",
"antnums_to_baseline",
"baseline_index_flip",
"get_baseline_redundancies",
"get_antenna_redundancies",
"collapse",
"mean_collapse",
"absmean_collapse",
"quadmean_collapse",
"or_collapse",
"and_collapse",
]
# fmt: off
# polarization constants
# maps polarization strings to polarization integers
POL_STR2NUM_DICT = {"pI": 1, "pQ": 2, "pU": 3, "pV": 4,
"I": 1, "Q": 2, "U": 3, "V": 4, # support straight stokes names
"rr": -1, "ll": -2, "rl": -3, "lr": -4,
"xx": -5, "yy": -6, "xy": -7, "yx": -8}
# maps polarization integers to polarization strings
POL_NUM2STR_DICT = {1: "pI", 2: "pQ", 3: "pU", 4: "pV",
-1: "rr", -2: "ll", -3: "rl", -4: "lr",
-5: "xx", -6: "yy", -7: "xy", -8: "yx"}
# maps how polarizations change when antennas are swapped
CONJ_POL_DICT = {"xx": "xx", "yy": "yy", "xy": "yx", "yx": "xy",
"ee": "ee", "nn": "nn", "en": "ne", "ne": "en",
"rr": "rr", "ll": "ll", "rl": "lr", "lr": "rl",
"I": "I", "Q": "Q", "U": "U", "V": "V",
"pI": "pI", "pQ": "pQ", "pU": "pU", "pV": "pV"}
# maps jones matrix element strings to jones integers
# Add entries that don't start with "J" to allow shorthand versions
JONES_STR2NUM_DICT = {"Jxx": -5, "Jyy": -6, "Jxy": -7, "Jyx": -8,
"xx": -5, "x": -5, "yy": -6, "y": -6, "xy": -7, "yx": -8,
"Jrr": -1, "Jll": -2, "Jrl": -3, "Jlr": -4,
"rr": -1, "r": -1, "ll": -2, "l": -2, "rl": -3, "lr": -4}
# maps jones integers to jones matrix element strings
JONES_NUM2STR_DICT = {-1: "Jrr", -2: "Jll", -3: "Jrl", -4: "Jlr",
-5: "Jxx", -6: "Jyy", -7: "Jxy", -8: "Jyx"}
# maps uvdata pols to input feed polarizations
POL_TO_FEED_DICT = {"xx": ["x", "x"], "yy": ["y", "y"],
"xy": ["x", "y"], "yx": ["y", "x"],
"ee": ["e", "e"], "nn": ["n", "n"],
"en": ["e", "n"], "ne": ["n", "e"],
"rr": ["r", "r"], "ll": ["l", "l"],
"rl": ["r", "l"], "lr": ["l", "r"]}
# fmt: on
def _get_iterable(x):
"""Return iterable version of input."""
if isinstance(x, Iterable):
return x
else:
return (x,)
def _fits_gethduaxis(hdu, axis):
"""
Make axis arrays for fits files.
Parameters
----------
hdu : astropy.io.fits HDU object
The HDU to make an axis array for.
axis : int
The axis number of interest (1-based).
Returns
-------
ndarray of float
Array of values for the specified axis.
"""
ax = str(axis)
axis_num = hdu.header["NAXIS" + ax]
val = hdu.header["CRVAL" + ax]
delta = hdu.header["CDELT" + ax]
index = hdu.header["CRPIX" + ax] - 1
return delta * (np.arange(axis_num) - index) + val
def _fits_indexhdus(hdulist):
"""
Get a dict of table names and HDU numbers from a FITS HDU list.
Parameters
----------
hdulist : list of astropy.io.fits HDU objects
List of HDUs to get names for
Returns
-------
dict
dictionary with table names as keys and HDU number as values.
"""
tablenames = {}
for i in range(len(hdulist)):
try:
tablenames[hdulist[i].header["EXTNAME"]] = i
except (KeyError):
continue
return tablenames
def _get_fits_extra_keywords(header, keywords_to_skip=None):
"""
Get any extra keywords and return as dict.
Parameters
----------
header : FITS header object
header object to get extra_keywords from.
keywords_to_skip : list of str
list of keywords to not include in extra keywords in addition to standard
FITS keywords.
Returns
-------
dict
dict of extra keywords.
"""
# List standard FITS header items that are still should not be included in
# extra_keywords
# These are the beginnings of FITS keywords to ignore, the actual keywords
# often include integers following these names (e.g. NAXIS1, CTYPE3)
std_fits_substrings = [
"HISTORY",
"SIMPLE",
"BITPIX",
"EXTEND",
"BLOCKED",
"GROUPS",
"PCOUNT",
"BSCALE",
"BZERO",
"NAXIS",
"PTYPE",
"PSCAL",
"PZERO",
"CTYPE",
"CRVAL",
"CRPIX",
"CDELT",
"CROTA",
"CUNIT",
]
if keywords_to_skip is not None:
std_fits_substrings.extend(keywords_to_skip)
extra_keywords = {}
# find all the other header items and keep them as extra_keywords
for key in header:
# check if key contains any of the standard FITS substrings
if np.any([sub in key for sub in std_fits_substrings]):
continue
if key == "COMMENT":
extra_keywords[key] = str(header.get(key))
elif key != "":
extra_keywords[key] = header.get(key)
return extra_keywords
def _check_history_version(history, version_string):
"""Check if version_string is present in history string."""
if version_string.replace(" ", "") in history.replace("\n", "").replace(" ", ""):
return True
else:
return False
def _check_histories(history1, history2):
"""Check if two histories are the same."""
if history1.replace("\n", "").replace(" ", "") == history2.replace(
"\n", ""
).replace(" ", ""):
return True
else:
return False
def _combine_histories(history1, history2):
"""Combine histories with minimal repeats."""
hist2_words = history2.split(" ")
add_hist = ""
test_hist1 = " " + history1 + " "
for i, word in enumerate(hist2_words):
if " " + word + " " not in test_hist1:
add_hist += " " + word
keep_going = i + 1 < len(hist2_words)
while keep_going:
if (hist2_words[i + 1] == " ") or (
" " + hist2_words[i + 1] + " " not in test_hist1
):
add_hist += " " + hist2_words[i + 1]
del hist2_words[i + 1]
keep_going = i + 1 < len(hist2_words)
else:
keep_going = False
return history1 + add_hist
def baseline_to_antnums(baseline, Nants_telescope):
"""
Get the antenna numbers corresponding to a given baseline number.
Parameters
----------
baseline : int or array_like of ints
baseline number
Nants_telescope : int
number of antennas
Returns
-------
int or array_like of int
first antenna number(s)
int or array_like of int
second antenna number(s)
"""
if Nants_telescope > 2048:
raise Exception(
"error Nants={Nants}>2048 not supported".format(Nants=Nants_telescope)
)
return_array = isinstance(baseline, (np.ndarray, list, tuple))
ant1, ant2 = _utils.baseline_to_antnums(
np.ascontiguousarray(baseline, dtype=np.int64)
)
if return_array:
return ant1, ant2
else:
return ant1.item(0), ant2.item(0)
def antnums_to_baseline(ant1, ant2, Nants_telescope, attempt256=False):
"""
Get the baseline number corresponding to two given antenna numbers.
Parameters
----------
ant1 : int or array_like of int
first antenna number
ant2 : int or array_like of int
second antenna number
Nants_telescope : int
number of antennas
attempt256 : bool
Option to try to use the older 256 standard used in
many uvfits files (will use 2048 standard if there are more
than 256 antennas). Default is False.
Returns
-------
int or array of int
baseline number corresponding to the two antenna numbers.
"""
if Nants_telescope is not None and Nants_telescope > 2048:
raise Exception(
"cannot convert ant1, ant2 to a baseline index "
"with Nants={Nants}>2048.".format(Nants=Nants_telescope)
)
return_array = isinstance(ant1, (np.ndarray, list, tuple))
baseline = _utils.antnums_to_baseline(
np.ascontiguousarray(ant1, dtype=np.int64),
np.ascontiguousarray(ant2, dtype=np.int64),
attempt256=attempt256,
)
if return_array:
return baseline
else:
return baseline.item(0)
def baseline_index_flip(baseline, Nants_telescope):
"""Change baseline number to reverse antenna order."""
ant1, ant2 = baseline_to_antnums(baseline, Nants_telescope)
return antnums_to_baseline(ant2, ant1, Nants_telescope)
def _x_orientation_rep_dict(x_orientation):
"""Create replacement dict based on x_orientation."""
if x_orientation.lower() == "east" or x_orientation.lower() == "e":
return {"x": "e", "y": "n"}
elif x_orientation.lower() == "north" or x_orientation.lower() == "n":
return {"x": "n", "y": "e"}
else:
raise ValueError("x_orientation not recognized.")
def polstr2num(pol, x_orientation=None):
"""
Convert polarization str to number according to AIPS Memo 117.
Prefer 'pI', 'pQ', 'pU' and 'pV' to make it clear that these are pseudo-Stokes,
not true Stokes, but also supports 'I', 'Q', 'U', 'V'.
Parameters
----------
pol : str
polarization string
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to allow for
converting from E/N strings. See corresonding parameter on UVData
for more details.
Returns
-------
int
Number corresponding to string
Raises
------
ValueError
If the pol string cannot be converted to a polarization number.
Warns
-----
UserWarning
If the x_orientation not recognized.
"""
dict_use = copy.deepcopy(POL_STR2NUM_DICT)
if x_orientation is not None:
try:
rep_dict = _x_orientation_rep_dict(x_orientation)
for key, value in POL_STR2NUM_DICT.items():
new_key = key.replace("x", rep_dict["x"]).replace("y", rep_dict["y"])
dict_use[new_key] = value
except ValueError:
warnings.warn("x_orientation not recognized.")
poldict = {k.lower(): v for k, v in dict_use.items()}
if isinstance(pol, str):
out = poldict[pol.lower()]
elif isinstance(pol, Iterable):
out = [poldict[key.lower()] for key in pol]
else:
raise ValueError(
"Polarization {p} cannot be converted to a polarization number.".format(
p=pol
)
)
return out
def polnum2str(num, x_orientation=None):
"""
Convert polarization number to str according to AIPS Memo 117.
Uses 'pI', 'pQ', 'pU' and 'pV' to make it clear that these are pseudo-Stokes,
not true Stokes
Parameters
----------
num : int
polarization number
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to convert to
E/N strings. See corresonding parameter on UVData for more details.
Returns
-------
str
String corresponding to polarization number
Raises
------
ValueError
If the polarization number cannot be converted to a polarization string.
Warns
-----
UserWarning
If the x_orientation not recognized.
"""
dict_use = copy.deepcopy(POL_NUM2STR_DICT)
if x_orientation is not None:
try:
rep_dict = _x_orientation_rep_dict(x_orientation)
for key, value in POL_NUM2STR_DICT.items():
new_val = value.replace("x", rep_dict["x"]).replace("y", rep_dict["y"])
dict_use[key] = new_val
except ValueError:
warnings.warn("x_orientation not recognized.")
if isinstance(num, (int, np.int32, np.int64)):
out = dict_use[num]
elif isinstance(num, Iterable):
out = [dict_use[i] for i in num]
else:
raise ValueError(
"Polarization {p} cannot be converted to string.".format(p=num)
)
return out
def jstr2num(jstr, x_orientation=None):
"""
Convert jones polarization str to number according to calfits memo.
Parameters
----------
jstr : str
antenna (jones) polarization string
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to allow for
converting from E/N strings. See corresonding parameter on UVData
for more details.
Returns
-------
int
antenna (jones) polarization number corresponding to string
Raises
------
ValueError
If the jones string cannot be converted to a polarization number.
Warns
-----
UserWarning
If the x_orientation not recognized.
"""
dict_use = copy.deepcopy(JONES_STR2NUM_DICT)
if x_orientation is not None:
try:
rep_dict = _x_orientation_rep_dict(x_orientation)
for key, value in JONES_STR2NUM_DICT.items():
new_key = key.replace("x", rep_dict["x"]).replace("y", rep_dict["y"])
dict_use[new_key] = value
except ValueError:
warnings.warn("x_orientation not recognized.")
jdict = {k.lower(): v for k, v in dict_use.items()}
if isinstance(jstr, str):
out = jdict[jstr.lower()]
elif isinstance(jstr, Iterable):
out = [jdict[key.lower()] for key in jstr]
else:
raise ValueError(
"Jones polarization {j} cannot be converted to index.".format(j=jstr)
)
return out
def jnum2str(jnum, x_orientation=None):
"""
Convert jones polarization number to str according to calfits memo.
Parameters
----------
num : int
antenna (jones) polarization number
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to convert to
E/N strings. See corresonding parameter on UVData for more details.
Returns
-------
str
antenna (jones) polarization string corresponding to number
Raises
------
ValueError
If the jones polarization number cannot be converted to a jones
polarization string.
Warns
-----
UserWarning
If the x_orientation not recognized.
"""
dict_use = copy.deepcopy(JONES_NUM2STR_DICT)
if x_orientation is not None:
try:
rep_dict = _x_orientation_rep_dict(x_orientation)
for key, value in JONES_NUM2STR_DICT.items():
new_val = value.replace("x", rep_dict["x"]).replace("y", rep_dict["y"])
dict_use[key] = new_val
except ValueError:
warnings.warn("x_orientation not recognized.")
if isinstance(jnum, (int, np.int32, np.int64)):
out = dict_use[jnum]
elif isinstance(jnum, Iterable):
out = [dict_use[i] for i in jnum]
else:
raise ValueError(
"Jones polarization {j} cannot be converted to string.".format(j=jnum)
)
return out
def parse_polstr(polstr, x_orientation=None):
"""
Parse a polarization string and return pyuvdata standard polarization string.
See utils.POL_STR2NUM_DICT for options.
Parameters
----------
polstr : str
polarization string
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to allow for
converting from E/N strings. See corresonding parameter on UVData
for more details.
Returns
-------
str
AIPS Memo 117 standard string
Raises
------
ValueError
If the pol string cannot be converted to a polarization number.
Warns
-----
UserWarning
If the x_orientation not recognized.
"""
return polnum2str(
polstr2num(polstr, x_orientation=x_orientation), x_orientation=x_orientation
)
def parse_jpolstr(jpolstr, x_orientation=None):
"""
Parse a Jones polarization string and return pyuvdata standard jones string.
See utils.JONES_STR2NUM_DICT for options.
Parameters
----------
jpolstr : str
Jones polarization string
Returns
-------
str
calfits memo standard string
Raises
------
ValueError
If the jones string cannot be converted to a polarization number.
Warns
-----
UserWarning
If the x_orientation not recognized.
"""
return jnum2str(
jstr2num(jpolstr, x_orientation=x_orientation), x_orientation=x_orientation
)
def conj_pol(pol):
"""
Return the polarization for the conjugate baseline.
For example, (1, 2, 'xy') = conj(2, 1, 'yx').
The returned polarization is determined by assuming the antenna pair is
reversed in the data, and finding the correct polarization correlation
which will yield the requested baseline when conjugated. Note this means
changing the polarization for linear cross-pols, but keeping auto-pol
(e.g. xx) and Stokes the same.
Parameters
----------
pol : str or int
Polarization string or integer.
Returns
-------
cpol : str or int
Polarization as if antennas are swapped (type matches input)
"""
cpol_dict = {k.lower(): v for k, v in CONJ_POL_DICT.items()}
if isinstance(pol, str):
cpol = cpol_dict[pol.lower()]
elif isinstance(pol, Iterable):
cpol = [conj_pol(p) for p in pol]
elif isinstance(pol, (int, np.int32, np.int64)):
cpol = polstr2num(cpol_dict[polnum2str(pol).lower()])
else:
raise ValueError("Polarization not recognized, cannot be conjugated.")
return cpol
def reorder_conj_pols(pols):
"""
Reorder multiple pols, swapping pols that are conjugates of one another.
For example ('xx', 'xy', 'yx', 'yy') -> ('xx', 'yx', 'xy', 'yy')
This is useful for the _key2inds function in the case where an antenna
pair is specified but the conjugate pair exists in the data. The conjugated
data should be returned in the order of the polarization axis, so after
conjugating the data, the pols need to be reordered.
For example, if a file contains antpair (0, 1) and pols 'xy' and 'yx', but
the user requests antpair (1, 0), they should get:
[(1x, 0y), (1y, 0x)] = [conj(0y, 1x), conj(0x, 1y)]
Parameters
----------
pols : array_like of str or int
Polarization array (strings or ints).
Returns
-------
conj_order : ndarray of int
Indices to reorder polarization array.
"""
if not isinstance(pols, Iterable):
raise ValueError("reorder_conj_pols must be given an array of polarizations.")
cpols = np.array([conj_pol(p) for p in pols]) # Array needed for np.where
conj_order = [np.where(cpols == p)[0][0] if p in cpols else -1 for p in pols]
if -1 in conj_order:
raise ValueError(
"Not all conjugate pols exist in the polarization array provided."
)
return conj_order
def LatLonAlt_from_XYZ(xyz, check_acceptability=True):
"""
Calculate lat/lon/alt from ECEF x,y,z.
Parameters
----------
xyz : ndarray of float
numpy array, shape (Npts, 3), with ECEF x,y,z coordinates.
check_acceptability : bool
Flag to check XYZ coordinates are reasonable.
Returns
-------
latitude : ndarray or float
latitude, numpy array (if Npts > 1) or value (if Npts = 1) in radians
longitude : ndarray or float
longitude, numpy array (if Npts > 1) or value (if Npts = 1) in radians
altitude : ndarray or float
altitude, numpy array (if Npts > 1) or value (if Npts = 1) in meters
"""
# convert to a numpy array
xyz = np.asarray(xyz)
if xyz.ndim > 1 and xyz.shape[1] != 3:
raise ValueError("The expected shape of ECEF xyz array is (Npts, 3).")
squeeze = xyz.ndim == 1
if squeeze:
xyz = xyz[np.newaxis, :]
xyz = np.ascontiguousarray(xyz.T, dtype=np.float64)
# checking for acceptable values
if check_acceptability:
norms = np.linalg.norm(xyz, axis=0)
if not all(np.logical_and(norms >= 6.35e6, norms <= 6.39e6)):
raise ValueError("xyz values should be ECEF x, y, z coordinates in meters")
# this helper function returns one 2D array because it is less overhead for cython
lla = _utils._lla_from_xyz(xyz)
if squeeze:
return lla[0, 0], lla[1, 0], lla[2, 0]
return lla[0], lla[1], lla[2]
def XYZ_from_LatLonAlt(latitude, longitude, altitude):
"""
Calculate ECEF x,y,z from lat/lon/alt values.
Parameters
----------
latitude : ndarray or float
latitude, numpy array (if Npts > 1) or value (if Npts = 1) in radians
longitude : ndarray or float
longitude, numpy array (if Npts > 1) or value (if Npts = 1) in radians
altitude : ndarray or float
altitude, numpy array (if Npts > 1) or value (if Npts = 1) in meters
Returns
-------
xyz : ndarray of float
numpy array, shape (Npts, 3), with ECEF x,y,z coordinates.
"""
latitude = np.ascontiguousarray(latitude, dtype=np.float64)
longitude = np.ascontiguousarray(longitude, dtype=np.float64)
altitude = np.ascontiguousarray(altitude, dtype=np.float64)
n_pts = latitude.size
if longitude.size != n_pts:
raise ValueError(
"latitude, longitude and altitude must all have the same length"
)
if altitude.size != n_pts:
raise ValueError(
"latitude, longitude and altitude must all have the same length"
)
xyz = _utils._xyz_from_latlonalt(latitude, longitude, altitude)
xyz = xyz.T
if n_pts == 1:
return xyz[0]
return xyz
def rotECEF_from_ECEF(xyz, longitude):
"""
Get rotated ECEF positions such that the x-axis goes through the longitude.
Miriad and uvfits expect antenna positions in this frame
(with longitude of the array center/telescope location)
Parameters
----------
xyz : ndarray of float
numpy array, shape (Npts, 3), with ECEF x,y,z coordinates.
longitude : float
longitude in radians to rotate coordinates to
(usually the array center/telescope location).
Returns
-------
ndarray of float
Rotated ECEF coordinates, shape (Npts, 3).
"""
angle = -1 * longitude
rot_matrix = np.array(
[
[np.cos(angle), -1 * np.sin(angle), 0],
[np.sin(angle), np.cos(angle), 0],
[0, 0, 1],
]
)
return rot_matrix.dot(xyz.T).T
def ECEF_from_rotECEF(xyz, longitude):
"""
Calculate ECEF from a rotated ECEF (Inverse of rotECEF_from_ECEF).
Parameters
----------
xyz : ndarray of float
numpy array, shape (Npts, 3), with rotated ECEF x,y,z coordinates.
longitude : float
longitude in radians giving the x direction of the rotated coordinates
(usually the array center/telescope location).
Returns
-------
ndarray of float
ECEF coordinates, shape (Npts, 3).
"""
angle = longitude
rot_matrix = np.array(
[
[np.cos(angle), -1 * np.sin(angle), 0],
[np.sin(angle), np.cos(angle), 0],
[0, 0, 1],
]
)
return rot_matrix.dot(xyz.T).T
def ENU_from_ECEF(xyz, latitude, longitude, altitude):
"""
Calculate local ENU (east, north, up) coordinates from ECEF coordinates.
Parameters
----------
xyz : ndarray of float
numpy array, shape (Npts, 3), with ECEF x,y,z coordinates.
latitude : float
Latitude of center of ENU coordinates in radians.
longitude : float
Longitude of center of ENU coordinates in radians.
altitude : float
Altitude of center of ENU coordinates in radians.
Returns
-------
ndarray of float
numpy array, shape (Npts, 3), with local ENU coordinates
"""
xyz = np.asarray(xyz)
if xyz.ndim > 1 and xyz.shape[1] != 3:
raise ValueError("The expected shape of ECEF xyz array is (Npts, 3).")
squeeze = False
if xyz.ndim == 1:
squeeze = True
xyz = xyz[np.newaxis, :]
xyz = np.ascontiguousarray(xyz.T, dtype=np.float64)
# check that these are sensible ECEF values -- their magnitudes need to be
# on the order of Earth's radius
ecef_magnitudes = np.linalg.norm(xyz, axis=0)
sensible_radius_range = (6.35e6, 6.39e6)
if np.any(ecef_magnitudes <= sensible_radius_range[0]) or np.any(
ecef_magnitudes >= sensible_radius_range[1]
):
raise ValueError(
"ECEF vector magnitudes must be on the order of the radius of the earth"
)
# the cython utility expects (3, Npts) for faster manipulation
# transpose after we get the array back to match the expected shape
enu = _utils._ENU_from_ECEF(
xyz,
np.ascontiguousarray(latitude, dtype=np.float64),
np.ascontiguousarray(longitude, dtype=np.float64),
np.ascontiguousarray(altitude, dtype=np.float64),
)
enu = enu.T
if squeeze:
enu = np.squeeze(enu)
return enu
def ECEF_from_ENU(enu, latitude, longitude, altitude):
"""
Calculate ECEF coordinates from local ENU (east, north, up) coordinates.
Parameters
----------
enu : ndarray of float
numpy array, shape (Npts, 3), with local ENU coordinates.
latitude : float
Latitude of center of ENU coordinates in radians.
longitude : float
Longitude of center of ENU coordinates in radians.
altitude : float
Altitude of center of ENU coordinates in radians.
Returns
-------
xyz : ndarray of float
numpy array, shape (Npts, 3), with ECEF x,y,z coordinates.
"""
enu = np.asarray(enu)
if enu.ndim > 1 and enu.shape[1] != 3:
raise ValueError("The expected shape of the ENU array is (Npts, 3).")
squeeze = False
if enu.ndim == 1:
squeeze = True
enu = enu[np.newaxis, :]
enu = np.ascontiguousarray(enu.T, dtype=np.float64)
# the cython utility expects (3, Npts) for faster manipulation
# transpose after we get the array back to match the expected shape
xyz = _utils._ECEF_FROM_ENU(
enu,
np.ascontiguousarray(latitude, dtype=np.float64),
np.ascontiguousarray(longitude, dtype=np.float64),
np.ascontiguousarray(altitude, dtype=np.float64),
)
xyz = xyz.T
if squeeze:
xyz = np.squeeze(xyz)
return xyz
def phase_uvw(ra, dec, initial_uvw):
"""
Calculate phased uvws/positions from unphased ones in an icrs or gcrs frame.
This code expects input uvws or positions relative to the telescope
location in the same frame that ra/dec are in (e.g. icrs or gcrs) and
returns phased ones in the same frame.
Note that this code is nearly identical to ENU_from_ECEF, except that it
uses an arbitrary phasing center rather than a coordinate center.
Parameters
----------
ra : float
Right ascension of phase center.
dec : float
Declination of phase center.
initial_uvw : ndarray of float
Unphased uvws or positions relative to the array center,
shape (Nlocs, 3).
Returns
-------
uvw : ndarray of float
uvw array in the same frame as initial_uvws, ra and dec.
"""
if initial_uvw.ndim == 1:
initial_uvw = initial_uvw[np.newaxis, :]
return _utils._phase_uvw(
np.float64(ra),
np.float64(dec),
np.ascontiguousarray(initial_uvw.T, dtype=np.float64),
).T
def unphase_uvw(ra, dec, uvw):
"""
Calculate unphased uvws/positions from phased ones in an icrs or gcrs frame.
This code expects phased uvws or positions in the same frame that ra/dec
are in (e.g. icrs or gcrs) and returns unphased ones in the same frame.
Parameters
----------
ra : float
Right ascension of phase center.
dec : float
Declination of phase center.
uvw : ndarray of float
Phased uvws or positions relative to the array center,
shape (Nlocs, 3).
Returns
-------
unphased_uvws : ndarray of float
Unphased uvws or positions relative to the array center,
shape (Nlocs, 3).
"""
if uvw.ndim == 1:
uvw = uvw[np.newaxis, :]
return _utils._unphase_uvw(
np.float64(ra), np.float64(dec), np.ascontiguousarray(uvw.T, dtype=np.float64),
).T
def get_lst_for_time(jd_array, latitude, longitude, altitude):
"""
Get the lsts for a set of jd times at an earth location.
Parameters
----------
jd_array : ndarray of float
JD times to get lsts for.
latitude : float
Latitude of location to get lst for in degrees.
longitude : float
Longitude of location to get lst for in degrees.
altitude : float
Altitude of location to get lst for in meters.
Returns
-------
ndarray of float
LSTs in radians corresponding to the jd_array.
"""
lst_array = np.zeros_like(jd_array)
jd, reverse_inds = np.unique(jd_array, return_inverse=True)
times = Time(
jd,
format="jd",
location=(Angle(longitude, unit="deg"), Angle(latitude, unit="deg")),
)
if iers.conf.auto_max_age is None: # pragma: no cover
delta, status = times.get_delta_ut1_utc(return_status=True)
if np.any(
np.isin(status, (iers.TIME_BEFORE_IERS_RANGE, iers.TIME_BEYOND_IERS_RANGE))
):
warnings.warn(
"time is out of IERS range, setting delta ut1 utc to "
"extrapolated value"
)
times.delta_ut1_utc = delta
lst_array = times.sidereal_time("apparent").radian[reverse_inds]
return lst_array
def _adj_list(vecs, tol, n_blocks=None):
"""Identify neighbors of each vec in vecs, to distance tol."""
n_items = len(vecs)
max_items = 2 ** 10 # Max array size used is max_items**2. Avoid using > 1 GiB
if n_blocks is None:
n_blocks = max(n_items // max_items, 1)
# We may sort blocks so that some pairs of blocks may be skipped.
# Reorder vectors by x.
order = np.argsort(vecs[:, 0])
blocks = np.array_split(order, n_blocks)
adj = [{k} for k in range(n_items)] # Adjacency lists
for b1 in blocks:
for b2 in blocks:
v1, v2 = vecs[b1], vecs[b2]
# Check for no overlap, with tolerance.
xmin1 = v1[0, 0] - tol
xmax1 = v1[-1, 0] + tol
xmin2 = v2[0, 0] - tol
xmax2 = v2[-1, 0] + tol
if max(xmin1, xmin2) > min(xmax1, xmax2):
continue
adj_mat = cdist(vecs[b1], vecs[b2]) < tol
for bi, col in enumerate(adj_mat):
adj[b1[bi]] = adj[b1[bi]].union(b2[col])
return [frozenset(g) for g in adj]
def _find_cliques(adj, strict=False):
n_items = len(adj)
loc_gps = []
visited = np.zeros(n_items, dtype=bool)
for k in range(n_items):
if visited[k]:
continue
a0 = adj[k]
visited[k] = True
if all(adj[it].__hash__() == a0.__hash__() for it in a0):
group = list(a0)
group.sort()
visited[list(a0)] = True
loc_gps.append(group)
# Require all adjacency lists to be isolated maximal cliques:
if strict:
if not all(sorted(st) in loc_gps for st in adj):
raise ValueError("Non-isolated cliques found in graph.")
return loc_gps
def find_clusters(location_ids, location_vectors, tol, strict=False):
"""
Find clusters of vectors (e.g. redundant baselines, times).
Parameters
----------
location_ids : array_like of int
ID labels for locations.
location_vectors : array_like of float
location vectors, can be multidimensional
tol : float
tolerance for clusters
strict : bool
Require that all adjacency lists be isolated maximal cliques.
This ensures that vectors do not fall into multiple clusters.
Default: False
Returns
-------
list of list of location_ids
"""
location_vectors = np.asarray(location_vectors)
location_ids = np.asarray(location_ids)
if location_vectors.ndim == 1:
location_vectors = location_vectors[:, np.newaxis]
adj = _adj_list(location_vectors, tol) # adj = list of sets
loc_gps = _find_cliques(adj, strict=strict)
loc_gps = [np.sort(location_ids[gp]).tolist() for gp in loc_gps]
return loc_gps
def get_baseline_redundancies(baselines, baseline_vecs, tol=1.0, with_conjugates=False):
"""
Find redundant baseline groups.
Parameters
----------
baselines : array_like of int
Baseline numbers, shape (Nbls,)
baseline_vecs : array_like of float
Baseline vectors in meters, shape (Nbls, 3)
tol : float
Absolute tolerance of redundancy, in meters.
with_conjugates : bool
Option to include baselines that are redundant when flipped.
Returns
-------
baseline_groups : list of lists of int
list of lists of redundant baseline numbers
vec_bin_centers : list of array_like of float
List of vectors describing redundant group centers
lengths : list of float
List of redundant group baseline lengths in meters
baseline_ind_conj : list of int
List of baselines that are redundant when reversed. Only returned if
with_conjugates is True
"""
Nbls = baselines.shape[0]
if not baseline_vecs.shape == (Nbls, 3):
raise ValueError("Baseline vectors must be shape (Nbls, 3)")
baseline_vecs = copy.copy(baseline_vecs) # Protect the vectors passed in.
if with_conjugates:
conjugates = []
for bv in baseline_vecs:
uneg = bv[0] < -tol
uzer = np.isclose(bv[0], 0.0, atol=tol)
vneg = bv[1] < -tol
vzer = np.isclose(bv[1], 0.0, atol=tol)
wneg = bv[2] < -tol
conjugates.append(uneg or (uzer and vneg) or (uzer and vzer and wneg))
conjugates = np.array(conjugates, dtype=bool)
baseline_vecs[conjugates] *= -1
baseline_ind_conj = baselines[conjugates]
bl_gps, vec_bin_centers, lens = get_baseline_redundancies(
baselines, baseline_vecs, tol=tol, with_conjugates=False
)
return bl_gps, vec_bin_centers, lens, baseline_ind_conj
try:
bl_gps = find_clusters(baselines, baseline_vecs, tol, strict=True)
except ValueError as exc:
raise ValueError(
"Some baselines are falling into multiple"
" redundant groups. Lower the tolerance to resolve ambiguity."
) from exc
n_unique = len(bl_gps)
vec_bin_centers = np.zeros((n_unique, 3))
for gi, gp in enumerate(bl_gps):
inds = [np.where(i == baselines)[0] for i in gp]
vec_bin_centers[gi] = np.mean(baseline_vecs[inds, :], axis=0)
lens = np.sqrt(np.sum(vec_bin_centers ** 2, axis=1))
return bl_gps, vec_bin_centers, lens
def get_antenna_redundancies(
antenna_numbers, antenna_positions, tol=1.0, include_autos=False
):
"""
Find redundant baseline groups based on antenna positions.
Parameters
----------
antenna_numbers : array_like of int
Antenna numbers, shape (Nants,).
antenna_positions : array_like of float
Antenna position vectors in the ENU (topocentric) frame in meters,
shape (Nants, 3).
tol : float
Redundancy tolerance in meters.
include_autos : bool
Option to include autocorrelations.
Returns
-------
baseline_groups : list of lists of int
list of lists of redundant baseline numbers
vec_bin_centers : list of array_like of float
List of vectors describing redundant group centers
lengths : list of float
List of redundant group baseline lengths in meters
Notes
-----
The baseline numbers refer to antenna pairs (a1, a2) such that
the baseline vector formed from ENU antenna positions,
blvec = enu[a1] - enu[a2]
is close to the other baselines in the group.
This is achieved by putting baselines in a form of the u>0
convention, but with a tolerance in defining the signs of
vector components.
To guarantee that the same baseline numbers are present in a UVData
object, ``UVData.conjugate_bls('u>0', uvw_tol=tol)``, where `tol` is
the tolerance used here.
"""
Nants = antenna_numbers.size
bls = []
bl_vecs = []
for aj in range(Nants):
mini = aj + 1
if include_autos:
mini = aj
for ai in range(mini, Nants):
anti, antj = antenna_numbers[ai], antenna_numbers[aj]
bidx = antnums_to_baseline(antj, anti, Nants)
bv = antenna_positions[ai] - antenna_positions[aj]
bl_vecs.append(bv)
bls.append(bidx)
bls = np.array(bls)
bl_vecs = np.array(bl_vecs)
gps, vecs, lens, conjs = get_baseline_redundancies(
bls, bl_vecs, tol=tol, with_conjugates=True
)
# Flip the baselines in the groups.
for gi, gp in enumerate(gps):
for bi, bl in enumerate(gp):
if bl in conjs:
gps[gi][bi] = baseline_index_flip(bl, Nants)
return gps, vecs, lens
def mean_collapse(
arr, weights=None, axis=None, return_weights=False, return_weights_square=False
):
"""
Collapse by averaging data.
This is similar to np.average, except it handles infs (by giving them
zero weight) and zero weight axes (by forcing result to be inf with zero
output weight).
Parameters
----------
arr : array
Input array to process.
weights: ndarray, optional
weights for average. If none, will default to equal weight for all
non-infinite data.
axis : int or tuple, optional
Axis or axes to collapse (passed to np.sum). Default is all.
return_weights : bool
Whether to return sum of weights.
return_weights_square: bool
Whether to return the sum of the square of the weights. Default is False.
"""
arr = copy.deepcopy(arr) # avoid changing outside
if weights is None:
weights = np.ones_like(arr)
else:
weights = copy.deepcopy(weights)
weights = weights * np.logical_not(np.isinf(arr))
arr[np.isinf(arr)] = 0
weight_out = np.sum(weights, axis=axis)
if return_weights_square:
weights_square = weights ** 2
weights_square_out = np.sum(weights_square, axis=axis)
out = np.sum(weights * arr, axis=axis)
where = weight_out > 1e-10
out = np.true_divide(out, weight_out, where=where)
out = np.where(where, out, np.inf)
if return_weights and return_weights_square:
return out, weight_out, weights_square_out
elif return_weights:
return out, weight_out
elif return_weights_square:
return out, weights_square_out
else:
return out
def absmean_collapse(
arr, weights=None, axis=None, return_weights=False, return_weights_square=False
):
"""
Collapse by averaging absolute value of data.
Parameters
----------
arr : array
Input array to process.
weights: ndarray, optional
weights for average. If none, will default to equal weight for all
non-infinite data.
axis : int or tuple, optional
Axis or axes to collapse (passed to np.sum). Default is all.
return_weights : bool
Whether to return sum of weights.
return_weights_square: bool
whether to return the sum of the squares of the weights. Default is False.
"""
return mean_collapse(
np.abs(arr),
weights=weights,
axis=axis,
return_weights=return_weights,
return_weights_square=return_weights_square,
)
def quadmean_collapse(
arr, weights=None, axis=None, return_weights=False, return_weights_square=False
):
"""
Collapse by averaging in quadrature.
Parameters
----------
arr : array
Input array to process.
weights: ndarray, optional
weights for average. If none, will default to equal weight for all
non-infinite data.
axis : int or tuple, optional
Axis or axes to collapse (passed to np.sum). Default is all.
return_weights : bool
Whether to return sum of weights.
return_weights_square: bool
whether to return the sum of the squares of the weights. Default is False.
"""
out = mean_collapse(
np.abs(arr) ** 2,
weights=weights,
axis=axis,
return_weights=return_weights,
return_weights_square=return_weights_square,
)
if return_weights and return_weights_square:
return np.sqrt(out[0]), out[1], out[2]
elif return_weights or return_weights_square:
return np.sqrt(out[0]), out[1]
else:
return np.sqrt(out)
def or_collapse(
arr, weights=None, axis=None, return_weights=False, return_weights_square=False
):
"""
Collapse using OR operation.
Parameters
----------
arr : array
Input array to process.
weights: ndarray, optional
NOT USED, but kept for symmetry with other collapsing functions.
axis : int or tuple, optional
Axis or axes to collapse (take OR over). Default is all.
return_weights : bool
Whether to return dummy weights array.
NOTE: the dummy weights will simply be an array of ones
return_weights_square: bool
NOT USED, but kept for symmetry with other collapsing functions.
"""
if arr.dtype != np.bool_:
raise ValueError("Input to or_collapse function must be boolean array")
out = np.any(arr, axis=axis)
if (weights is not None) and not np.all(weights == weights.reshape(-1)[0]):
warnings.warn("Currently weights are not handled when OR-ing boolean arrays.")
if return_weights:
return out, np.ones_like(out, dtype=np.float64)
else:
return out
def and_collapse(
arr, weights=None, axis=None, return_weights=False, return_weights_square=False
):
"""
Collapse using AND operation.
Parameters
----------
arr : array
Input array to process.
weights: ndarray, optional
NOT USED, but kept for symmetry with other collapsing functions.
axis : int or tuple, optional
Axis or axes to collapse (take AND over). Default is all.
return_weights : bool
Whether to return dummy weights array.
NOTE: the dummy weights will simply be an array of ones
return_weights_square: bool
NOT USED, but kept for symmetry with other collapsing functions.
"""
if arr.dtype != np.bool_:
raise ValueError("Input to and_collapse function must be boolean array")
out = np.all(arr, axis=axis)
if (weights is not None) and not np.all(weights == weights.reshape(-1)[0]):
warnings.warn("Currently weights are not handled when AND-ing boolean arrays.")
if return_weights:
return out, np.ones_like(out, dtype=np.float64)
else:
return out
def collapse(
arr, alg, weights=None, axis=None, return_weights=False, return_weights_square=False
):
"""
Parent function to collapse an array with a given algorithm.
Parameters
----------
arr : array
Input array to process.
alg : str
Algorithm to use. Must be defined in this function with
corresponding subfunction above.
weights: ndarray, optional
weights for collapse operation (e.g. weighted mean).
NOTE: Some subfunctions do not use the weights. See corresponding
doc strings.
axis : int or tuple, optional
Axis or axes to collapse. Default is all.
return_weights : bool
Whether to return sum of weights.
return_weights_square: bool
Whether to return the sum of the squares of the weights. Default is False.
"""
collapse_dict = {
"mean": mean_collapse,
"absmean": absmean_collapse,
"quadmean": quadmean_collapse,
"or": or_collapse,
"and": and_collapse,
}
try:
out = collapse_dict[alg](
arr,
weights=weights,
axis=axis,
return_weights=return_weights,
return_weights_square=return_weights_square,
)
except KeyError:
raise ValueError(
"Collapse algorithm must be one of: "
+ ", ".join(collapse_dict.keys())
+ "."
)
return out
def uvcalibrate(
uvdata,
uvcal,
inplace=True,
prop_flags=True,
flag_missing=True,
Dterm_cal=False,
delay_convention="minus",
undo=False,
time_check=True,
ant_check=True,
):
"""
Calibrate a UVData object with a UVCal object.
Parameters
----------
uvdata : UVData object
UVData object to calibrate.
uvcal : UVCal object
UVCal object containing the calibration.
inplace : bool, optional
if True edit uvdata in place, else return a calibrated copy
prop_flags : bool, optional
if True, propagate calibration flags to data flags
and doesn't use flagged gains. Otherwise, uses flagged gains and
does not propagate calibration flags to data flags.
flag_missing : bool, optional
Deprecated in favor of ant_check.
If True, flag baselines in uvdata otherwise don't flag and
don't calibrate the baseline if a participating antenna or polarization
is missing in uvcal.
Dterm_cal : bool, optional
Calibrate the off-diagonal terms in the Jones matrix if present
in uvcal. Default is False. Currently not implemented.
delay_convention : str, optional
Exponent sign to use in conversion of 'delay' to 'gain' cal_type
if the input uvcal is not inherently 'gain' cal_type. Default to 'minus'.
undo : bool, optional
If True, undo the provided calibration. i.e. apply the calibration with
flipped gain_convention. Flag propagation rules apply the same.
time_check : bool
Option to check that times match between the UVCal and UVData
objects if UVCal has a single time or time range. Times are always
checked if UVCal has multiple times.
ant_check : bool
Option to check that all antennas with data on the UVData
object have calibration solutions in the UVCal object. If this option is
set to False, uvcalibrate will proceed without erroring and data for
antennas without calibrations will be flagged.
Returns
-------
UVData, optional
Returns if not inplace
"""
if not inplace:
uvdata = uvdata.copy()
# Check whether the UVData antennas *that have data associated with them*
# have associated data in the UVCal object
uvdata_unique_nums = np.unique(np.append(uvdata.ant_1_array, uvdata.ant_2_array))
uvdata.antenna_names = np.asarray(uvdata.antenna_names)
uvdata_used_antnames = np.array(
[
uvdata.antenna_names[np.where(uvdata.antenna_numbers == antnum)][0]
for antnum in uvdata_unique_nums
]
)
uvcal_unique_nums = np.unique(uvcal.ant_array)
uvcal.antenna_names = np.asarray(uvcal.antenna_names)
uvcal_used_antnames = np.array(
[
uvcal.antenna_names[np.where(uvcal.antenna_numbers == antnum)][0]
for antnum in uvcal_unique_nums
]
)
ant_arr_match = uvcal_used_antnames.tolist() == uvdata_used_antnames.tolist()
if not ant_arr_match:
# check more carefully
name_missing = []
for this_ant_name in uvdata_used_antnames:
wh_ant_match = np.nonzero(uvcal_used_antnames == this_ant_name)
if wh_ant_match[0].size == 0:
name_missing.append(this_ant_name)
use_ant_nums = False
if len(name_missing) > 0:
if len(name_missing) == uvdata_used_antnames.size:
# all antenna_names with data on UVData are missing on UVCal.
if not ant_check:
warnings.warn(
"All antenna names with data on UVData are missing "
"on UVCal. Since ant_check is False, calibration will "
"proceed but all data will be flagged."
)
else:
# this entire clause will be replaced with just raising a
# ValueError in version 2.2
# old behavior only required that antenna numbers were present,
# not names. Check numbers
number_missing = []
for this_ant_name in uvdata_used_antnames:
uvdata_ant_num = uvdata.antenna_numbers[
np.where(uvdata.antenna_names == this_ant_name)[0][0]
]
if uvdata_ant_num not in uvcal_unique_nums:
number_missing.append(this_ant_name)
if len(number_missing) == 0:
# all have matching numbers on UVCal
use_ant_nums = True
warnings.warn(
"All antenna names with data on UVData are missing "
"on UVCal. They do all have matching antenna numbers on "
"UVCal. Currently the data will be calibrated using the "
"matching antenna number, but that will be deprecated in "
"version 2.2 and this will become an error.",
DeprecationWarning,
)
elif len(number_missing) < len(name_missing):
# Some have matching numbers on UVCal
use_ant_nums = True
both_missing = sorted(set(number_missing) & set(name_missing))
only_name_missing = sorted(
set(name_missing) - set(number_missing)
)
warnings.warn(
f"Antennas {only_name_missing} have data on UVData but "
"are missing on UVCal. They do have matching antenna "
"numbers on UVCal. Currently the data for these antennas "
"will be calibrated using the matching antenna number, "
"but that will be deprecated in "
"version 2.2 and this will become an error.",
DeprecationWarning,
)
if flag_missing is True:
warnings.warn(
f"Antennas {both_missing} have data on UVData but "
"are missing on UVCal. Currently calibration will "
"proceed and since flag_missing is True, the data "
"for these antennas will be flagged. This will "
"become an error in version 2.2, to continue "
"calibration and flag missing antennas in the "
"future, set ant_check=False.",
DeprecationWarning,
)
else:
warnings.warn(
f"Antennas {both_missing} have data on UVData but "
"are missing on UVCal. Currently calibration will "
"proceed and since flag_missing is False, the data "
"for these antennas will not be calibrated or "
"flagged. This will become an error in version 2.2, "
"to continue calibration and flag missing "
"antennas in the future, set ant_check=False.",
DeprecationWarning,
)
else:
# Only some antenna_names with data on UVData are missing on UVCal
if not ant_check:
warnings.warn(
f"Antennas {name_missing} have data on UVData but are missing "
"on UVCal. Since ant_check is False, calibration will "
"proceed and the data for these antennas will be flagged."
)
else:
# this entire clause will be replaced with just raising a
# ValueError in version 2.2
if flag_missing is True:
warnings.warn(
f"Antennas {name_missing} have data on UVData but "
"are missing on UVCal. Currently calibration will "
"proceed and since flag_missing is True, the data "
"for these antennas will be flagged. This will "
"become an error in version 2.2, to continue "
"calibration and flag missing antennas in the "
"future, set ant_check=False.",
DeprecationWarning,
)
else:
warnings.warn(
f"Antennas {name_missing} have data on UVData but "
"are missing on UVCal. Currently calibration will "
"proceed and since flag_missing is False, the data "
"for these antennas will not be calibrated or "
"flagged. This will become an error in version 2.2, "
"to continue calibration and flag missing "
"antennas in the future, set ant_check=False.",
DeprecationWarning,
)
uvdata_times = np.unique(uvdata.time_array)
downselect_cal_times = False
if uvcal.Ntimes > 1:
if uvcal.Ntimes < uvdata.Ntimes:
raise ValueError(
"The uvcal object has more than one time but fewer than the "
"number of unique times on the uvdata object."
)
uvcal_times = np.unique(uvcal.time_array)
try:
time_arr_match = np.allclose(
uvcal_times,
uvdata_times,
atol=uvdata._time_array.tols[1],
rtol=uvdata._time_array.tols[0],
)
except ValueError:
time_arr_match = False
if not time_arr_match:
# check more carefully
uvcal_times_to_keep = []
for this_time in uvdata_times:
wh_time_match = np.nonzero(
np.isclose(
uvcal.time_array - this_time,
0,
atol=uvdata._time_array.tols[1],
rtol=uvdata._time_array.tols[0],
)
)
if wh_time_match[0].size > 0:
uvcal_times_to_keep.append(uvcal.time_array[wh_time_match][0])
else:
warnings.warn(
f"Time {this_time} exists on UVData but not on UVCal. "
"This will become an error in version 2.2",
DeprecationWarning,
)
if len(uvcal_times_to_keep) < uvcal.Ntimes:
downselect_cal_times = True
elif uvcal.time_range is None:
# only one UVCal time, no time_range.
# This cannot match if UVData.Ntimes > 1.
# If they are both NTimes = 1, then check if they're close.
if uvdata.Ntimes > 1 or not np.isclose(
uvdata_times,
uvcal.time_array,
atol=uvdata._time_array.tols[1],
rtol=uvdata._time_array.tols[0],
):
if not time_check:
warnings.warn(
"Times do not match between UVData and UVCal "
"but time_check is False, so calibration "
"will be applied anyway."
)
else:
warnings.warn(
"Times do not match between UVData and UVCal. "
"Set time_check=False to apply calibration anyway. "
"This will become an error in version 2.2",
DeprecationWarning,
)
else:
# time_array is length 1 and time_range exists: check uvdata_times in time_range
if (
np.min(uvdata_times) < uvcal.time_range[0]
or np.max(uvdata_times) > uvcal.time_range[1]
):
if not time_check:
warnings.warn(
"Times do not match between UVData and UVCal "
"but time_check is False, so calibration "
"will be applied anyway."
)
else:
warnings.warn(
"Times do not match between UVData and UVCal. "
"Set time_check=False to apply calibration anyway. "
"This will become an error in version 2.2",
DeprecationWarning,
)
downselect_cal_freq = False
try:
freq_arr_match = np.allclose(
np.sort(uvcal.freq_array[0, :]),
np.sort(uvdata.freq_array[0, :]),
atol=uvdata._freq_array.tols[1],
rtol=uvdata._freq_array.tols[0],
)
except ValueError:
freq_arr_match = False
if freq_arr_match is False:
# check more carefully
uvcal_freqs_to_keep = []
for this_freq in uvdata.freq_array[0, :]:
wh_freq_match = np.nonzero(
np.isclose(
uvcal.freq_array - this_freq,
0,
atol=uvdata._freq_array.tols[1],
rtol=uvdata._freq_array.tols[0],
)
)
if wh_freq_match[0].size > 0:
uvcal_freqs_to_keep.append(uvcal.freq_array[wh_freq_match][0])
else:
warnings.warn(
f"Frequency {this_freq} exists on UVData but not on UVCal. "
"This will become an error in version 2.2",
DeprecationWarning,
)
if len(uvcal_freqs_to_keep) < uvcal.Nfreqs:
downselect_cal_freq = True
uvdata_pol_strs = polnum2str(
uvdata.polarization_array, x_orientation=uvdata.x_orientation
)
uvcal_pol_strs = jnum2str(uvcal.jones_array, x_orientation=uvcal.x_orientation)
uvdata_feed_pols = {
feed for pol in uvdata_pol_strs for feed in POL_TO_FEED_DICT[pol]
}
for feed in uvdata_feed_pols:
# get diagonal jones str
jones_str = parse_jpolstr(feed, x_orientation=uvcal.x_orientation)
if jones_str not in uvcal_pol_strs:
warnings.warn(
f"Feed polarization {feed} exists on UVData but not on UVCal. "
"This will become an error in version 2.2",
DeprecationWarning,
)
# downselect UVCal times, frequencies
if downselect_cal_freq or downselect_cal_times:
if not downselect_cal_times:
uvcal_times_to_keep = None
elif not downselect_cal_freq:
uvcal_freqs_to_keep = None
# handle backwards compatibility: prevent downselecting to nothing
# or to shapes that don't match
if downselect_cal_times and len(uvcal_times_to_keep) < uvdata.Ntimes:
downselect_cal_times = False
uvcal_times_to_keep = None
if downselect_cal_freq and len(uvcal_freqs_to_keep) < uvdata.Nfreqs:
downselect_cal_freq = False
uvcal_freqs_to_keep = None
if downselect_cal_freq or downselect_cal_times:
uvcal_use = uvcal.select(
times=uvcal_times_to_keep, frequencies=uvcal_freqs_to_keep, inplace=False
)
new_uvcal = True
else:
uvcal_use = uvcal
new_uvcal = False
# input checks
if uvcal_use.cal_type == "delay":
if not new_uvcal:
# make a copy to convert to gain
uvcal_use = uvcal_use.copy()
new_uvcal = True
uvcal_use.convert_to_gain(delay_convention=delay_convention)
# D-term calibration
if Dterm_cal:
# check for D-terms
if -7 not in uvcal_use.jones_array and -8 not in uvcal_use.jones_array:
raise ValueError(
"Cannot apply D-term calibration without -7 or -8"
"Jones polarization in uvcal object."
)
raise NotImplementedError("D-term calibration is not yet implemented.")
# No D-term calibration
else:
# key is number, value is name
uvdata_ant_dict = dict(zip(uvdata.antenna_numbers, uvdata.antenna_names))
# opposite: key is name, value is number
uvcal_ant_dict = dict(zip(uvcal.antenna_names, uvcal.antenna_numbers))
# iterate over keys
for key in uvdata.get_antpairpols():
# get indices for this key
blt_inds = uvdata.antpair2ind(key)
pol_ind = np.argmin(
np.abs(
uvdata.polarization_array - polstr2num(key[2], uvdata.x_orientation)
)
)
# try to get gains for each antenna
ant1_num = key[0]
ant2_num = key[1]
feed1, feed2 = POL_TO_FEED_DICT[key[2]]
try:
uvcal_ant1_num = uvcal_ant_dict[uvdata_ant_dict[ant1_num]]
except KeyError:
if use_ant_nums:
# backwards compatibility: use antenna numbers instead
# this will be removed in version 2.2
uvcal_ant1_num = ant1_num
else:
uvcal_ant1_num = None
try:
uvcal_ant2_num = uvcal_ant_dict[uvdata_ant_dict[ant2_num]]
except KeyError:
if use_ant_nums:
# backwards compatibility: use antenna numbers instead
# this will be removed in version 2.2
uvcal_ant2_num = ant2_num
else:
uvcal_ant2_num = None
uvcal_key1 = (uvcal_ant1_num, feed1)
uvcal_key2 = (uvcal_ant2_num, feed2)
if uvcal_ant1_num is None or uvcal_ant2_num is None:
uvdata.flag_array[blt_inds, 0, :, pol_ind] = True
continue
elif not uvcal_use._has_key(*uvcal_key1) or not uvcal_use._has_key(
*uvcal_key2
):
if flag_missing:
uvdata.flag_array[blt_inds, 0, :, pol_ind] = True
continue
gain = (
uvcal_use.get_gains(uvcal_key1)
* np.conj(uvcal_use.get_gains(uvcal_key2))
).T # tranpose to match uvdata shape
flag = (uvcal_use.get_flags(uvcal_key1) | uvcal_use.get_flags(uvcal_key2)).T
# propagate flags
if prop_flags:
mask = np.isclose(gain, 0.0) | flag
gain[mask] = 1.0
uvdata.flag_array[blt_inds, 0, :, pol_ind] += mask
# apply to data
mult_gains = uvcal_use.gain_convention == "multiply"
if undo:
mult_gains = not mult_gains
if mult_gains:
uvdata.data_array[blt_inds, 0, :, pol_ind] *= gain
else:
uvdata.data_array[blt_inds, 0, :, pol_ind] /= gain
# update attributes
uvdata.history += "\nCalibrated with pyuvdata.utils.uvcalibrate."
if undo:
uvdata.vis_units = "UNCALIB"
else:
if uvcal_use.gain_scale is not None:
uvdata.vis_units = uvcal_use.gain_scale
if not inplace:
return uvdata
def apply_uvflag(
uvd, uvf, inplace=True, unflag_first=False, flag_missing=True, force_pol=True
):
"""
Apply flags from a UVFlag to a UVData instantiation.
Note that if uvf.Nfreqs or uvf.Ntimes is 1, it will broadcast flags across
that axis.
Parameters
----------
uvd : UVData object
UVData object to add flags to.
uvf : UVFlag object
A UVFlag object in flag mode.
inplace : bool
If True overwrite flags in uvd, otherwise return new object
unflag_first : bool
If True, completely unflag the UVData before applying flags.
Else, OR the inherent uvd flags with uvf flags.
flag_missing : bool
If input uvf is a baseline type and antpairs in uvd do not exist in uvf,
flag them in uvd. Otherwise leave them untouched.
force_pol : bool
If True, broadcast flags to all polarizations if they do not match.
Only works if uvf.Npols == 1.
Returns
-------
UVData
If not inplace, returns new UVData object with flags applied
"""
# assertions
if uvf.mode != "flag":
raise ValueError("UVFlag must be flag mode")
if not inplace:
uvd = uvd.copy()
# make a deepcopy by default b/c it is generally edited inplace downstream
uvf = uvf.copy()
# convert to baseline type
if uvf.type != "baseline":
# edits inplace
uvf.to_baseline(uvd, force_pol=force_pol)
else:
# make sure polarizations match or force_pol
uvd_pols, uvf_pols = (
uvd.polarization_array.tolist(),
uvf.polarization_array.tolist(),
)
if set(uvd_pols) != set(uvf_pols):
if uvf.Npols == 1 and force_pol:
# if uvf is 1pol we can make them match: also edits inplace
uvf.polarization_array = uvd.polarization_array
uvf.Npols = len(uvf.polarization_array)
uvf_pols = uvf.polarization_array.tolist()
else:
raise ValueError("Input uvf and uvd polarizations do not match")
# make sure polarization ordering is correct: also edits inplace
uvf.polarization_array = uvf.polarization_array[
[uvd_pols.index(pol) for pol in uvf_pols]
]
# check time and freq shapes match: if Ntimes or Nfreqs is 1, allow
# implicit broadcasting
if uvf.Ntimes == 1:
mismatch_times = False
elif uvf.Ntimes == uvd.Ntimes:
tdiff = np.unique(uvf.time_array) - np.unique(uvd.time_array)
mismatch_times = np.any(tdiff > np.max(np.abs(uvf._time_array.tols)))
else:
mismatch_times = True
if mismatch_times:
raise ValueError("UVFlag and UVData have mismatched time arrays.")
if uvf.Nfreqs == 1:
mismatch_freqs = False
elif uvf.Nfreqs == uvd.Nfreqs:
fdiff = np.unique(uvf.freq_array) - np.unique(uvd.freq_array)
mismatch_freqs = np.any(fdiff > np.max(np.abs(uvf._freq_array.tols)))
else:
mismatch_freqs = True
if mismatch_freqs:
raise ValueError("UVFlag and UVData have mismatched frequency arrays.")
# unflag if desired
if unflag_first:
uvd.flag_array[:] = False
# iterate over antpairs and apply flags: TODO need to be able to handle
# conjugated antpairs
uvf_antpairs = uvf.get_antpairs()
for ap in uvd.get_antpairs():
uvd_ap_inds = uvd.antpair2ind(ap)
if ap not in uvf_antpairs:
if flag_missing:
uvd.flag_array[uvd_ap_inds] = True
continue
uvf_ap_inds = uvf.antpair2ind(*ap)
# addition of boolean is OR
uvd.flag_array[uvd_ap_inds] += uvf.flag_array[uvf_ap_inds]
uvd.history += "\nFlagged with pyuvdata.utils.apply_uvflags."
if not inplace:
return uvd
def parse_ants(uv, ant_str, print_toggle=False, x_orientation=None):
"""
Get antpair and polarization from parsing an aipy-style ant string.
Used to support the the select function.
Generates two lists of antenna pair tuples and polarization indices based
on parsing of the string ant_str. If no valid polarizations (pseudo-Stokes
params, or combinations of [lr] or [xy]) or antenna numbers are found in
ant_str, ant_pairs_nums and polarizations are returned as None.
Parameters
----------
uv : UVBase Object
A UVBased object that supports the following functions and parameters:
- get_ants
- get_antpairs
- get_pols
These are used to construct the baseline ant_pair_nums
and polarizations returned.
ant_str : str
String containing antenna information to parse. Can be 'all',
'auto', 'cross', or combinations of antenna numbers and polarization
indicators 'l' and 'r' or 'x' and 'y'. Minus signs can also be used
in front of an antenna number or baseline to exclude it from being
output in ant_pairs_nums. If ant_str has a minus sign as the first
character, 'all,' will be appended to the beginning of the string.
See the tutorial for examples of valid strings and their behavior.
print_toggle : bool
Boolean for printing parsed baselines for a visual user check.
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to allow for
converting from E/N strings. If input uv object has an `x_orientation`
parameter and the input to this function is `None`, the value from the
object will be used. Any input given to this function will override the
value on the uv object. See corresonding parameter on UVData
for more details.
Returns
-------
ant_pairs_nums : list of tuples of int or None
List of tuples containing the parsed pairs of antenna numbers, or
None if ant_str is 'all' or a pseudo-Stokes polarizations.
polarizations : list of int or None
List of desired polarizations or None if ant_str does not contain a
polarization specification.
"""
required_attrs = ["get_ants", "get_antpairs", "get_pols"]
if not all(hasattr(uv, attr) for attr in required_attrs):
raise ValueError(
"UVBased objects must have all the following attributes in order "
f"to call 'parse_ants': {required_attrs}."
)
if x_orientation is None and (
hasattr(uv, "x_orientation") and uv.x_orientation is not None
):
x_orientation = uv.x_orientation
ant_re = r"(\(((-?\d+[lrxy]?,?)+)\)|-?\d+[lrxy]?)"
bl_re = "(^(%s_%s|%s),?)" % (ant_re, ant_re, ant_re)
str_pos = 0
ant_pairs_nums = []
polarizations = []
ants_data = uv.get_ants()
ant_pairs_data = uv.get_antpairs()
pols_data = uv.get_pols()
warned_ants = []
warned_pols = []
if ant_str.startswith("-"):
ant_str = "all," + ant_str
while str_pos < len(ant_str):
m = re.search(bl_re, ant_str[str_pos:])
if m is None:
if ant_str[str_pos:].upper().startswith("ALL"):
if len(ant_str[str_pos:].split(",")) > 1:
ant_pairs_nums = uv.get_antpairs()
elif ant_str[str_pos:].upper().startswith("AUTO"):
for pair in ant_pairs_data:
if pair[0] == pair[1] and pair not in ant_pairs_nums:
ant_pairs_nums.append(pair)
elif ant_str[str_pos:].upper().startswith("CROSS"):
for pair in ant_pairs_data:
if not (pair[0] == pair[1] or pair in ant_pairs_nums):
ant_pairs_nums.append(pair)
elif ant_str[str_pos:].upper().startswith("PI"):
polarizations.append(polstr2num("pI"))
elif ant_str[str_pos:].upper().startswith("PQ"):
polarizations.append(polstr2num("pQ"))
elif ant_str[str_pos:].upper().startswith("PU"):
polarizations.append(polstr2num("pU"))
elif ant_str[str_pos:].upper().startswith("PV"):
polarizations.append(polstr2num("pV"))
else:
raise ValueError("Unparsible argument {s}".format(s=ant_str))
comma_cnt = ant_str[str_pos:].find(",")
if comma_cnt >= 0:
str_pos += comma_cnt + 1
else:
str_pos = len(ant_str)
else:
m = m.groups()
str_pos += len(m[0])
if m[2] is None:
ant_i_list = [m[8]]
ant_j_list = list(uv.get_ants())
else:
if m[3] is None:
ant_i_list = [m[2]]
else:
ant_i_list = m[3].split(",")
if m[6] is None:
ant_j_list = [m[5]]
else:
ant_j_list = m[6].split(",")
for ant_i in ant_i_list:
include_i = True
if type(ant_i) == str and ant_i.startswith("-"):
ant_i = ant_i[1:] # nibble the - off the string
include_i = False
for ant_j in ant_j_list:
include_j = True
if type(ant_j) == str and ant_j.startswith("-"):
ant_j = ant_j[1:]
include_j = False
pols = None
ant_i, ant_j = str(ant_i), str(ant_j)
if not ant_i.isdigit():
ai = re.search(r"(\d+)([x,y,l,r])", ant_i).groups()
if not ant_j.isdigit():
aj = re.search(r"(\d+)([x,y,l,r])", ant_j).groups()
if ant_i.isdigit() and ant_j.isdigit():
ai = [ant_i, ""]
aj = [ant_j, ""]
elif ant_i.isdigit() and not ant_j.isdigit():
if "x" in ant_j or "y" in ant_j:
pols = ["x" + aj[1], "y" + aj[1]]
else:
pols = ["l" + aj[1], "r" + aj[1]]
ai = [ant_i, ""]
elif not ant_i.isdigit() and ant_j.isdigit():
if "x" in ant_i or "y" in ant_i:
pols = [ai[1] + "x", ai[1] + "y"]
else:
pols = [ai[1] + "l", ai[1] + "r"]
aj = [ant_j, ""]
elif not ant_i.isdigit() and not ant_j.isdigit():
pols = [ai[1] + aj[1]]
ant_tuple = (abs(int(ai[0])), abs(int(aj[0])))
# Order tuple according to order in object
if ant_tuple in ant_pairs_data:
pass
elif ant_tuple[::-1] in ant_pairs_data:
ant_tuple = ant_tuple[::-1]
else:
if not (
ant_tuple[0] in ants_data or ant_tuple[0] in warned_ants
):
warned_ants.append(ant_tuple[0])
if not (
ant_tuple[1] in ants_data or ant_tuple[1] in warned_ants
):
warned_ants.append(ant_tuple[1])
if pols is not None:
for pol in pols:
if not (pol.lower() in pols_data or pol in warned_pols):
warned_pols.append(pol)
continue
if include_i and include_j:
if ant_tuple not in ant_pairs_nums:
ant_pairs_nums.append(ant_tuple)
if pols is not None:
for pol in pols:
if (
pol.lower() in pols_data
and polstr2num(pol, x_orientation=x_orientation)
not in polarizations
):
polarizations.append(
polstr2num(pol, x_orientation=x_orientation)
)
elif not (
pol.lower() in pols_data or pol in warned_pols
):
warned_pols.append(pol)
else:
if pols is not None:
for pol in pols:
if pol.lower() in pols_data:
if uv.Npols == 1 and [pol.lower()] == pols_data:
ant_pairs_nums.remove(ant_tuple)
if (
polstr2num(pol, x_orientation=x_orientation)
in polarizations
):
polarizations.remove(
polstr2num(
pol, x_orientation=x_orientation,
)
)
elif not (
pol.lower() in pols_data or pol in warned_pols
):
warned_pols.append(pol)
elif ant_tuple in ant_pairs_nums:
ant_pairs_nums.remove(ant_tuple)
if ant_str.upper() == "ALL":
ant_pairs_nums = None
elif len(ant_pairs_nums) == 0:
if not ant_str.upper() in ["AUTO", "CROSS"]:
ant_pairs_nums = None
if len(polarizations) == 0:
polarizations = None
else:
polarizations.sort(reverse=True)
if print_toggle:
print("\nParsed antenna pairs:")
if ant_pairs_nums is not None:
for pair in ant_pairs_nums:
print(pair)
print("\nParsed polarizations:")
if polarizations is not None:
for pol in polarizations:
print(polnum2str(pol, x_orientation=x_orientation))
if len(warned_ants) > 0:
warnings.warn(
"Warning: Antenna number {a} passed, but not present "
"in the ant_1_array or ant_2_array".format(
a=(",").join(map(str, warned_ants))
)
)
if len(warned_pols) > 0:
warnings.warn(
"Warning: Polarization {p} is not present in "
"the polarization_array".format(p=(",").join(warned_pols).upper())
)
return ant_pairs_nums, polarizations
