# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Tests for common utility functions."""
import os
import copy
import pytest
import numpy as np
from astropy import units
from astropy.time import Time
from astropy.coordinates import SkyCoord, Angle
from pyuvdata import UVData, UVFlag, UVCal
import pyuvdata.utils as uvutils
import pyuvdata.tests as uvtest
from pyuvdata.data import DATA_PATH
ref_latlonalt = (-26.7 * np.pi / 180.0, 116.7 * np.pi / 180.0, 377.8)
ref_xyz = (-2562123.42683, 5094215.40141, -2848728.58869)
pytestmark = pytest.mark.filterwarnings(
"ignore:telescope_location is not set. Using known values",
"ignore:antenna_positions is not set. Using known values",
)
pytestmark = pytest.mark.filterwarnings(
"ignore:telescope_location is not set. Using known values for HERA.",
"ignore:antenna_positions is not set. Using known values for HERA.",
)
@pytest.fixture
def uvcalibrate_init_data():
uvdata = UVData()
uvdata.read(
os.path.join(DATA_PATH, "zen.2458098.45361.HH.uvh5_downselected"),
file_type="uvh5",
)
uvcal = UVCal()
uvcal.read_calfits(
os.path.join(DATA_PATH, "zen.2458098.45361.HH.omni.calfits_downselected")
)
yield uvdata, uvcal
@pytest.fixture
def uvcalibrate_data(uvcalibrate_init_data):
uvdata, uvcal = uvcalibrate_init_data
# fix the antenna names in the uvcal object to match the uvdata object
uvcal.antenna_names = np.array(
[name.replace("ant", "HH") for name in uvcal.antenna_names]
)
yield uvdata, uvcal
def test_XYZ_from_LatLonAlt():
"""Test conversion from lat/lon/alt to ECEF xyz with reference values."""
out_xyz = uvutils.XYZ_from_LatLonAlt(
ref_latlonalt[0], ref_latlonalt[1], ref_latlonalt[2]
)
# Got reference by forcing http://www.oc.nps.edu/oc2902w/coord/llhxyz.htm
# to give additional precision.
assert np.allclose(ref_xyz, out_xyz, rtol=0, atol=1e-3)
# test error checking
with pytest.raises(
ValueError,
match="latitude, longitude and altitude must all have the same length",
):
uvutils.XYZ_from_LatLonAlt(
ref_latlonalt[0],
ref_latlonalt[1],
np.array([ref_latlonalt[2], ref_latlonalt[2]]),
)
with pytest.raises(
ValueError,
match="latitude, longitude and altitude must all have the same length",
):
uvutils.XYZ_from_LatLonAlt(
ref_latlonalt[0],
np.array([ref_latlonalt[1], ref_latlonalt[1]]),
ref_latlonalt[2],
)
def test_LatLonAlt_from_XYZ():
"""Test conversion from ECEF xyz to lat/lon/alt with reference values."""
out_latlonalt = uvutils.LatLonAlt_from_XYZ(ref_xyz)
# Got reference by forcing http://www.oc.nps.edu/oc2902w/coord/llhxyz.htm
# to give additional precision.
assert np.allclose(ref_latlonalt, out_latlonalt, rtol=0, atol=1e-3)
pytest.raises(ValueError, uvutils.LatLonAlt_from_XYZ, ref_latlonalt)
# test passing multiple values
xyz_mult = np.stack((np.array(ref_xyz), np.array(ref_xyz)))
lat_vec, lon_vec, alt_vec = uvutils.LatLonAlt_from_XYZ(xyz_mult)
assert np.allclose(
ref_latlonalt, (lat_vec[1], lon_vec[1], alt_vec[1]), rtol=0, atol=1e-3
)
# check error if array transposed
with pytest.raises(ValueError) as cm:
uvutils.LatLonAlt_from_XYZ(xyz_mult.T)
assert str(cm.value).startswith(
"The expected shape of ECEF xyz array is (Npts, 3)."
)
# check error if only 2 coordinates
with pytest.raises(ValueError) as cm:
uvutils.LatLonAlt_from_XYZ(xyz_mult[:, 0:2])
assert str(cm.value).startswith(
"The expected shape of ECEF xyz array is (Npts, 3)."
)
# test error checking
pytest.raises(ValueError, uvutils.LatLonAlt_from_XYZ, ref_xyz[0:1])
def test_lla_xyz_lla_roundtrip():
"""Test roundtripping an array will yield the same values."""
np.random.seed(0)
lats = -30.721 + np.random.normal(0, 0.0005, size=30)
lons = 21.428 + np.random.normal(0, 0.0005, size=30)
alts = np.random.uniform(1051, 1054, size=30)
lats *= np.pi / 180.0
lons *= np.pi / 180.0
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
lats_new, lons_new, alts_new = uvutils.LatLonAlt_from_XYZ(xyz)
assert np.allclose(lats_new, lats)
assert np.allclose(lons_new, lons)
assert np.allclose(alts_new, alts)
@pytest.fixture(scope="module")
def enu_ecef_info():
"""Some setup info for ENU/ECEF calculations."""
center_lat = -30.7215261207 * np.pi / 180.0
center_lon = 21.4283038269 * np.pi / 180.0
center_alt = 1051.7
# fmt: off
lats = (np.array([-30.72218216, -30.72138101, -30.7212785, -30.7210011,
-30.72159853, -30.72206199, -30.72174614, -30.72188775,
-30.72183915, -30.72100138])
* np.pi / 180.0)
lons = (np.array([21.42728211, 21.42811727, 21.42814544, 21.42795736,
21.42686739, 21.42918772, 21.42785662, 21.4286408,
21.42750933, 21.42896567])
* np.pi / 180.0)
alts = np.array([1052.25, 1051.35, 1051.2, 1051., 1051.45, 1052.04, 1051.68,
1051.87, 1051.77, 1051.06])
# used pymap3d, which implements matlab code, as a reference.
x = [5109327.46674067, 5109339.76407785, 5109344.06370947,
5109365.11297147, 5109372.115673, 5109266.94314734,
5109329.89620962, 5109295.13656657, 5109337.21810468,
5109329.85680612]
y = [2005130.57953031, 2005221.35184577, 2005225.93775268,
2005214.8436201, 2005105.42364036, 2005302.93158317,
2005190.65566222, 2005257.71335575, 2005157.78980089,
2005304.7729239]
z = [-3239991.24516348, -3239914.4185286, -3239904.57048431,
-3239878.02656316, -3239935.20415493, -3239979.68381865,
-3239949.39266985, -3239962.98805772, -3239958.30386264,
-3239878.08403833]
east = [-97.87631659, -17.87126443, -15.17316938, -33.19049252, -137.60520964,
84.67346748, -42.84049408, 32.28083937, -76.1094745, 63.40285935]
north = [-72.7437482, 16.09066646, 27.45724573, 58.21544651, -8.02964511,
-59.41961437, -24.39698388, -40.09891961, -34.70965816, 58.18410876]
up = [0.54883333, -0.35004539, -0.50007736, -0.70035299, -0.25148791, 0.33916067,
-0.02019057, 0.16979185, 0.06945155, -0.64058124]
# fmt: on
yield (
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
)
def test_xyz_from_latlonalt(enu_ecef_info):
"""Test calculating xyz from lat lot alt."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
assert np.allclose(np.stack((x, y, z), axis=1), xyz, atol=1e-3)
def test_enu_from_ecef(enu_ecef_info):
"""Test calculating ENU from ECEF coordinates."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
enu = uvutils.ENU_from_ECEF(xyz, center_lat, center_lon, center_alt)
assert np.allclose(np.stack((east, north, up), axis=1), enu, atol=1e-3)
@pytest.mark.parametrize("shape_type", ["transpose", "Nblts,2", "Nblts,1"])
def test_enu_from_ecef_shape_errors(enu_ecef_info, shape_type):
"""Test ENU_from_ECEF input shape errors."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
if shape_type == "transpose":
xyz = xyz.T.copy()
elif shape_type == "Nblts,2":
xyz = xyz.copy()[:, 0:2]
elif shape_type == "Nblts,1":
xyz = xyz.copy()[:, 0:1]
# check error if array transposed
with pytest.raises(ValueError) as cm:
uvutils.ENU_from_ECEF(xyz, center_lat, center_lon, center_alt)
assert str(cm.value).startswith(
"The expected shape of ECEF xyz array is (Npts, 3)."
)
def test_enu_from_ecef_magnitude_error(enu_ecef_info):
"""Test ENU_from_ECEF input magnitude errors."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
# error checking
with pytest.raises(ValueError) as cm:
uvutils.ENU_from_ECEF(xyz / 2.0, center_lat, center_lon, center_alt)
assert str(cm.value).startswith(
"ECEF vector magnitudes must be on the order of the radius of the earth"
)
def test_ecef_from_enu_roundtrip(enu_ecef_info):
"""Test ECEF_from_ENU values."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
enu = uvutils.ENU_from_ECEF(xyz, center_lat, center_lon, center_alt)
# check that a round trip gives the original value.
xyz_from_enu = uvutils.ECEF_from_ENU(enu, center_lat, center_lon, center_alt)
assert np.allclose(xyz, xyz_from_enu, atol=1e-3)
@pytest.mark.parametrize("shape_type", ["transpose", "Nblts,2", "Nblts,1"])
def test_ecef_from_enu_shape_errors(enu_ecef_info, shape_type):
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
enu = uvutils.ENU_from_ECEF(xyz, center_lat, center_lon, center_alt)
if shape_type == "transpose":
enu = enu.copy().T
elif shape_type == "Nblts,2":
enu = enu.copy()[:, 0:2]
elif shape_type == "Nblts,1":
enu = enu.copy()[:, 0:1]
# check error if array transposed
with pytest.raises(ValueError) as cm:
uvutils.ECEF_from_ENU(enu, center_lat, center_lon, center_alt)
assert str(cm.value).startswith("The expected shape of the ENU array is (Npts, 3).")
def test_ecef_from_enu_single(enu_ecef_info):
"""Test single coordinate transform."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
# check passing a single value
enu_single = uvutils.ENU_from_ECEF(xyz[0, :], center_lat, center_lon, center_alt)
assert np.allclose(np.array((east[0], north[0], up[0])), enu_single, atol=1e-3)
def test_ecef_from_enu_single_roundtrip(enu_ecef_info):
"""Test single coordinate roundtrip."""
(
center_lat,
center_lon,
center_alt,
lats,
lons,
alts,
x,
y,
z,
east,
north,
up,
) = enu_ecef_info
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
# check passing a single value
enu = uvutils.ENU_from_ECEF(xyz, center_lat, center_lon, center_alt)
enu_single = uvutils.ENU_from_ECEF(xyz[0, :], center_lat, center_lon, center_alt)
assert np.allclose(np.array((east[0], north[0], up[0])), enu[0, :], atol=1e-3)
xyz_from_enu = uvutils.ECEF_from_ENU(enu_single, center_lat, center_lon, center_alt)
assert np.allclose(xyz[0, :], xyz_from_enu, atol=1e-3)
def test_mwa_ecef_conversion():
"""
Test based on comparing the antenna locations in a Cotter uvfits file to
the antenna locations in MWA_tools.
"""
test_data_file = os.path.join(DATA_PATH, "mwa128_ant_layouts.npz")
f = np.load(test_data_file)
# From the STABXYZ table in a cotter-generated uvfits file, obsid = 1066666832
xyz = f["stabxyz"]
# From the East/North/Height columns in a cotter-generated metafits file,
# obsid = 1066666832
enh = f["ENH"]
# From a text file antenna_locations.txt in MWA_Tools/scripts
txt_topo = f["txt_topo"]
# From the unphased uvw coordinates of obsid 1066666832, positions relative
# to antenna 0
# these aren't used in the current test, but are interesting and might help
# with phasing diagnosis in the future
uvw_topo = f["uvw_topo"]
# Sky coordinates are flipped for uvw derived values
uvw_topo = -uvw_topo
uvw_topo += txt_topo[0]
# transpose these arrays to get them into the right shape
txt_topo = txt_topo.T
uvw_topo = uvw_topo.T
# ARRAYX, ARRAYY, ARRAYZ in ECEF frame from Cotter file
arrcent = f["arrcent"]
lat, lon, alt = uvutils.LatLonAlt_from_XYZ(arrcent)
# The STABXYZ coordinates are defined with X through the local meridian,
# so rotate back to the prime meridian
new_xyz = uvutils.ECEF_from_rotECEF(xyz.T, lon)
# add in array center to get real ECEF
ecef_xyz = new_xyz + arrcent
enu = uvutils.ENU_from_ECEF(ecef_xyz, lat, lon, alt)
assert np.allclose(enu, enh)
# test other direction of ECEF rotation
rot_xyz = uvutils.rotECEF_from_ECEF(new_xyz, lon)
assert np.allclose(rot_xyz.T, xyz)
def test_phasing_funcs():
# these tests are based on a notebook where I tested against the mwa_tools
# phasing code
ra_hrs = 12.1
dec_degs = -42.3
mjd = 55780.1
array_center_xyz = np.array([-2559454.08, 5095372.14, -2849057.18])
lat_lon_alt = uvutils.LatLonAlt_from_XYZ(array_center_xyz)
obs_time = Time(mjd, format="mjd", location=(lat_lon_alt[1], lat_lon_alt[0]))
icrs_coord = SkyCoord(
ra=Angle(ra_hrs, unit="hr"), dec=Angle(dec_degs, unit="deg"), obstime=obs_time
)
gcrs_coord = icrs_coord.transform_to("gcrs")
# in east/north/up frame (relative to array center) in meters: (Nants, 3)
ants_enu = np.array([-101.94, 0156.41, 0001.24])
ant_xyz_abs = uvutils.ECEF_from_ENU(
ants_enu, lat_lon_alt[0], lat_lon_alt[1], lat_lon_alt[2]
)
array_center_coord = SkyCoord(
x=array_center_xyz[0] * units.m,
y=array_center_xyz[1] * units.m,
z=array_center_xyz[2] * units.m,
frame="itrs",
obstime=obs_time,
)
itrs_coord = SkyCoord(
x=ant_xyz_abs[0] * units.m,
y=ant_xyz_abs[1] * units.m,
z=ant_xyz_abs[2] * units.m,
frame="itrs",
obstime=obs_time,
)
gcrs_array_center = array_center_coord.transform_to("gcrs")
gcrs_from_itrs_coord = itrs_coord.transform_to("gcrs")
gcrs_rel = (
(gcrs_from_itrs_coord.cartesian - gcrs_array_center.cartesian).get_xyz().T
)
gcrs_uvw = uvutils.phase_uvw(gcrs_coord.ra.rad, gcrs_coord.dec.rad, gcrs_rel.value)
mwa_tools_calcuvw_u = -97.122828
mwa_tools_calcuvw_v = 50.388281
mwa_tools_calcuvw_w = -151.27976
assert np.allclose(gcrs_uvw[0, 0], mwa_tools_calcuvw_u, atol=1e-3)
assert np.allclose(gcrs_uvw[0, 1], mwa_tools_calcuvw_v, atol=1e-3)
assert np.allclose(gcrs_uvw[0, 2], mwa_tools_calcuvw_w, atol=1e-3)
# also test unphasing
temp2 = uvutils.unphase_uvw(
gcrs_coord.ra.rad, gcrs_coord.dec.rad, np.squeeze(gcrs_uvw)
)
assert np.allclose(gcrs_rel.value, temp2)
def test_pol_funcs():
""" Test utility functions to convert between polarization strings and numbers """
pol_nums = [-8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4]
pol_str = ["yx", "xy", "yy", "xx", "lr", "rl", "ll", "rr", "pI", "pQ", "pU", "pV"]
assert pol_nums == uvutils.polstr2num(pol_str)
assert pol_str == uvutils.polnum2str(pol_nums)
# Check individuals
assert -6 == uvutils.polstr2num("YY")
assert "pV" == uvutils.polnum2str(4)
# Check errors
pytest.raises(KeyError, uvutils.polstr2num, "foo")
pytest.raises(ValueError, uvutils.polstr2num, 1)
pytest.raises(ValueError, uvutils.polnum2str, 7.3)
# Check parse
assert uvutils.parse_polstr("xX") == "xx"
assert uvutils.parse_polstr("XX") == "xx"
assert uvutils.parse_polstr("i") == "pI"
def test_pol_funcs_x_orientation():
"""Test functions to convert between pol strings and numbers with x_orientation."""
pol_nums = [-8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4]
x_orient1 = "e"
pol_str = ["ne", "en", "nn", "ee", "lr", "rl", "ll", "rr", "pI", "pQ", "pU", "pV"]
assert pol_nums == uvutils.polstr2num(pol_str, x_orientation=x_orient1)
assert pol_str == uvutils.polnum2str(pol_nums, x_orientation=x_orient1)
# Check individuals
assert -6 == uvutils.polstr2num("NN", x_orientation=x_orient1)
assert "pV" == uvutils.polnum2str(4)
# Check errors
pytest.raises(KeyError, uvutils.polstr2num, "foo", x_orientation=x_orient1)
pytest.raises(ValueError, uvutils.polstr2num, 1, x_orientation=x_orient1)
pytest.raises(ValueError, uvutils.polnum2str, 7.3, x_orientation=x_orient1)
# Check parse
assert uvutils.parse_polstr("eE", x_orientation=x_orient1) == "ee"
assert uvutils.parse_polstr("xx", x_orientation=x_orient1) == "ee"
assert uvutils.parse_polstr("NN", x_orientation=x_orient1) == "nn"
assert uvutils.parse_polstr("yy", x_orientation=x_orient1) == "nn"
assert uvutils.parse_polstr("i", x_orientation=x_orient1) == "pI"
x_orient2 = "n"
pol_str = ["en", "ne", "ee", "nn", "lr", "rl", "ll", "rr", "pI", "pQ", "pU", "pV"]
assert pol_nums == uvutils.polstr2num(pol_str, x_orientation=x_orient2)
assert pol_str == uvutils.polnum2str(pol_nums, x_orientation=x_orient2)
# Check individuals
assert -6 == uvutils.polstr2num("EE", x_orientation=x_orient2)
assert "pV" == uvutils.polnum2str(4)
# Check errors
pytest.raises(KeyError, uvutils.polstr2num, "foo", x_orientation=x_orient2)
pytest.raises(ValueError, uvutils.polstr2num, 1, x_orientation=x_orient2)
pytest.raises(ValueError, uvutils.polnum2str, 7.3, x_orientation=x_orient2)
# Check parse
assert uvutils.parse_polstr("nN", x_orientation=x_orient2) == "nn"
assert uvutils.parse_polstr("xx", x_orientation=x_orient2) == "nn"
assert uvutils.parse_polstr("EE", x_orientation=x_orient2) == "ee"
assert uvutils.parse_polstr("yy", x_orientation=x_orient2) == "ee"
assert uvutils.parse_polstr("i", x_orientation=x_orient2) == "pI"
# check warnings for non-recognized x_orientation
with uvtest.check_warnings(UserWarning, "x_orientation not recognized"):
assert uvutils.polstr2num("xx", x_orientation="foo") == -5
with uvtest.check_warnings(UserWarning, "x_orientation not recognized"):
assert uvutils.polnum2str(-6, x_orientation="foo") == "yy"
def test_jones_num_funcs():
"""Test functions to convert between jones polarization strings and numbers."""
jnums = [-8, -7, -6, -5, -4, -3, -2, -1]
jstr = ["Jyx", "Jxy", "Jyy", "Jxx", "Jlr", "Jrl", "Jll", "Jrr"]
assert jnums == uvutils.jstr2num(jstr)
assert jstr, uvutils.jnum2str(jnums)
# Check shorthands
jstr = ["yx", "xy", "yy", "y", "xx", "x", "lr", "rl", "ll", "l", "rr", "r"]
jnums = [-8, -7, -6, -6, -5, -5, -4, -3, -2, -2, -1, -1]
assert jnums == uvutils.jstr2num(jstr)
# Check individuals
assert -6 == uvutils.jstr2num("jyy")
assert "Jxy" == uvutils.jnum2str(-7)
# Check errors
pytest.raises(KeyError, uvutils.jstr2num, "foo")
pytest.raises(ValueError, uvutils.jstr2num, 1)
pytest.raises(ValueError, uvutils.jnum2str, 7.3)
# check parse method
assert uvutils.parse_jpolstr("x") == "Jxx"
assert uvutils.parse_jpolstr("xy") == "Jxy"
assert uvutils.parse_jpolstr("XY") == "Jxy"
def test_jones_num_funcs_x_orientation():
"""Test functions to convert jones pol strings and numbers with x_orientation."""
jnums = [-8, -7, -6, -5, -4, -3, -2, -1]
x_orient1 = "east"
jstr = ["Jne", "Jen", "Jnn", "Jee", "Jlr", "Jrl", "Jll", "Jrr"]
assert jnums == uvutils.jstr2num(jstr, x_orientation=x_orient1)
assert jstr == uvutils.jnum2str(jnums, x_orientation=x_orient1)
# Check shorthands
jstr = ["ne", "en", "nn", "n", "ee", "e", "lr", "rl", "ll", "l", "rr", "r"]
jnums = [-8, -7, -6, -6, -5, -5, -4, -3, -2, -2, -1, -1]
assert jnums == uvutils.jstr2num(jstr, x_orientation=x_orient1)
# Check individuals
assert -6 == uvutils.jstr2num("jnn", x_orientation=x_orient1)
assert "Jen" == uvutils.jnum2str(-7, x_orientation=x_orient1)
# Check errors
pytest.raises(KeyError, uvutils.jstr2num, "foo", x_orientation=x_orient1)
pytest.raises(ValueError, uvutils.jstr2num, 1, x_orientation=x_orient1)
pytest.raises(ValueError, uvutils.jnum2str, 7.3, x_orientation=x_orient1)
# check parse method
assert uvutils.parse_jpolstr("e", x_orientation=x_orient1) == "Jee"
assert uvutils.parse_jpolstr("x", x_orientation=x_orient1) == "Jee"
assert uvutils.parse_jpolstr("y", x_orientation=x_orient1) == "Jnn"
assert uvutils.parse_jpolstr("en", x_orientation=x_orient1) == "Jen"
assert uvutils.parse_jpolstr("NE", x_orientation=x_orient1) == "Jne"
jnums = [-8, -7, -6, -5, -4, -3, -2, -1]
x_orient2 = "north"
jstr = ["Jen", "Jne", "Jee", "Jnn", "Jlr", "Jrl", "Jll", "Jrr"]
assert jnums == uvutils.jstr2num(jstr, x_orientation=x_orient2)
assert jstr == uvutils.jnum2str(jnums, x_orientation=x_orient2)
# Check shorthands
jstr = ["en", "ne", "ee", "e", "nn", "n", "lr", "rl", "ll", "l", "rr", "r"]
jnums = [-8, -7, -6, -6, -5, -5, -4, -3, -2, -2, -1, -1]
assert jnums == uvutils.jstr2num(jstr, x_orientation=x_orient2)
# Check individuals
assert -6 == uvutils.jstr2num("jee", x_orientation=x_orient2)
assert "Jne" == uvutils.jnum2str(-7, x_orientation=x_orient2)
# Check errors
pytest.raises(KeyError, uvutils.jstr2num, "foo", x_orientation=x_orient2)
pytest.raises(ValueError, uvutils.jstr2num, 1, x_orientation=x_orient2)
pytest.raises(ValueError, uvutils.jnum2str, 7.3, x_orientation=x_orient2)
# check parse method
assert uvutils.parse_jpolstr("e", x_orientation=x_orient2) == "Jee"
assert uvutils.parse_jpolstr("x", x_orientation=x_orient2) == "Jnn"
assert uvutils.parse_jpolstr("y", x_orientation=x_orient2) == "Jee"
assert uvutils.parse_jpolstr("en", x_orientation=x_orient2) == "Jen"
assert uvutils.parse_jpolstr("NE", x_orientation=x_orient2) == "Jne"
# check warnings for non-recognized x_orientation
with uvtest.check_warnings(UserWarning, "x_orientation not recognized"):
assert uvutils.jstr2num("x", x_orientation="foo") == -5
with uvtest.check_warnings(UserWarning, "x_orientation not recognized"):
assert uvutils.jnum2str(-6, x_orientation="foo") == "Jyy"
def test_conj_pol():
""" Test function to conjugate pols """
pol_nums = [-8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4]
cpol_nums = [-7, -8, -6, -5, -3, -4, -2, -1, 1, 2, 3, 4]
assert pol_nums == uvutils.conj_pol(cpol_nums)
assert uvutils.conj_pol(pol_nums) == cpol_nums
# fmt: off
pol_str = ['yx', 'xy', 'yy', 'xx', 'ee', 'nn', 'en', 'ne', 'lr', 'rl', 'll',
'rr', 'pI', 'pQ', 'pU', 'pV']
cpol_str = ['xy', 'yx', 'yy', 'xx', 'ee', 'nn', 'ne', 'en', 'rl', 'lr', 'll',
'rr', 'pI', 'pQ', 'pU', 'pV']
# fmt: on
assert pol_str == uvutils.conj_pol(cpol_str)
assert uvutils.conj_pol(pol_str) == cpol_str
assert [pol_str, pol_nums] == uvutils.conj_pol([cpol_str, cpol_nums])
# Test error with jones
cjstr = ["Jxy", "Jyx", "Jyy", "Jxx", "Jrl", "Jlr", "Jll", "Jrr"]
assert pytest.raises(KeyError, uvutils.conj_pol, cjstr)
# Test invalid pol
with pytest.raises(ValueError) as cm:
uvutils.conj_pol(2.3)
assert str(cm.value).startswith(
"Polarization not recognized, cannot be conjugated."
)
def test_redundancy_finder():
"""
Check that get_baseline_redundancies and get_antenna_redundancies return consistent
redundant groups for a test file with the HERA19 layout.
"""
uvd = UVData()
uvd.read_uvfits(
os.path.join(DATA_PATH, "fewant_randsrc_airybeam_Nsrc100_10MHz.uvfits")
)
uvd.select(times=uvd.time_array[0])
uvd.unphase_to_drift(use_ant_pos=True)
# uvw_array is now equivalent to baseline positions
uvd.conjugate_bls(convention="ant1<ant2", use_enu=True)
tol = 0.05 # meters
bl_positions = uvd.uvw_array
bl_pos_backup = copy.deepcopy(uvd.uvw_array)
pytest.raises(
ValueError,
uvutils.get_baseline_redundancies,
uvd.baseline_array,
bl_positions[0:2, 0:1],
)
baseline_groups, vec_bin_centers, lens = uvutils.get_baseline_redundancies(
uvd.baseline_array, bl_positions, tol=tol
)
for gi, gp in enumerate(baseline_groups):
for bl in gp:
bl_ind = np.where(uvd.baseline_array == bl)
bl_vec = bl_positions[bl_ind]
assert np.allclose(
np.sqrt(np.dot(bl_vec, vec_bin_centers[gi])), lens[gi], atol=tol
)
# Shift the baselines around in a circle. Check that the same baselines are
# recovered to the corresponding tolerance increase.
# This moves one baseline at a time by a fixed displacement and checks that
# the redundant groups are the same.
hightol = 0.25 # meters. Less than the smallest baseline in the file.
Nbls = uvd.Nbls
Nshifts = 5
shift_angs = np.linspace(0, 2 * np.pi, Nshifts)
base_shifts = np.stack(
(
(hightol - tol) * np.cos(shift_angs),
(hightol - tol) * np.sin(shift_angs),
np.zeros(Nshifts),
)
).T
for sh in base_shifts:
for bi in range(Nbls):
# Shift one baseline at a time.
bl_positions_new = uvd.uvw_array
bl_positions_new[bi] += sh
(
baseline_groups_new,
vec_bin_centers,
lens,
) = uvutils.get_baseline_redundancies(
uvd.baseline_array, bl_positions_new, tol=hightol
)
for gi, gp in enumerate(baseline_groups_new):
for bl in gp:
bl_ind = np.where(uvd.baseline_array == bl)
bl_vec = bl_positions[bl_ind]
assert np.allclose(
np.sqrt(np.abs(np.dot(bl_vec, vec_bin_centers[gi]))),
lens[gi],
atol=hightol,
)
# Compare baseline groups:
a = [tuple(el) for el in baseline_groups]
b = [tuple(el) for el in baseline_groups_new]
assert set(a) == set(b)
tol = 0.05
antpos, antnums = uvd.get_ENU_antpos()
baseline_groups_ants, vec_bin_centers, lens = uvutils.get_antenna_redundancies(
antnums, antpos, tol=tol, include_autos=False
)
# Under these conditions, should see 19 redundant groups in the file.
assert len(baseline_groups_ants) == 19
# Check with conjugated baseline redundancies returned
# Ensure at least one baseline has u==0 and v!=0 (for coverage of this case)
bl_positions[16, 0] = 0
(
baseline_groups,
vec_bin_centers,
lens,
conjugates,
) = uvutils.get_baseline_redundancies(
uvd.baseline_array, bl_positions, tol=tol, with_conjugates=True
)
# restore baseline (16,0) and repeat to get correct groups
bl_positions = bl_pos_backup
(
baseline_groups,
vec_bin_centers,
lens,
conjugates,
) = uvutils.get_baseline_redundancies(
uvd.baseline_array, bl_positions, tol=tol, with_conjugates=True
)
# Apply flips to compare with get_antenna_redundancies().
bl_gps_unconj = copy.deepcopy(baseline_groups)
for gi, gp in enumerate(bl_gps_unconj):
for bi, bl in enumerate(gp):
if bl in conjugates:
bl_gps_unconj[gi][bi] = uvutils.baseline_index_flip(bl, len(antnums))
bl_gps_unconj = [sorted(bgp) for bgp in bl_gps_unconj]
bl_gps_ants = [sorted(bgp) for bgp in baseline_groups_ants]
assert np.all(sorted(bl_gps_ants) == sorted(bl_gps_unconj))
for gi, gp in enumerate(baseline_groups):
for bl in gp:
bl_ind = np.where(uvd.baseline_array == bl)
bl_vec = bl_positions[bl_ind]
if bl in conjugates:
bl_vec *= -1
assert np.isclose(
np.sqrt(np.dot(bl_vec, vec_bin_centers[gi])), lens[gi], atol=tol
)
def test_high_tolerance_redundancy_error():
"""
Confirm that an error is raised if the redundancy tolerance is set too high,
such that baselines end up in multiple
"""
uvd = UVData()
uvd.read_uvfits(
os.path.join(DATA_PATH, "fewant_randsrc_airybeam_Nsrc100_10MHz.uvfits")
)
uvd.select(times=uvd.time_array[0])
uvd.unphase_to_drift(use_ant_pos=True)
# uvw_array is now equivalent to baseline positions
uvd.conjugate_bls(convention="ant1<ant2", use_enu=True)
bl_positions = uvd.uvw_array
tol = 20.05 # meters
with pytest.raises(ValueError) as cm:
(
baseline_groups,
vec_bin_centers,
lens,
conjugates,
) = uvutils.get_baseline_redundancies(
uvd.baseline_array, bl_positions, tol=tol, with_conjugates=True
)
assert "Some baselines are falling into" in str(cm.value)
def test_redundancy_conjugates():
"""
Check that redundancy finding with conjugation works.
Check that the correct baselines are flipped.
"""
Nants = 10
tol = 0.5
ant1_arr = np.tile(np.arange(Nants), Nants)
ant2_arr = np.repeat(np.arange(Nants), Nants)
Nbls = ant1_arr.size
bl_inds = uvutils.antnums_to_baseline(ant1_arr, ant2_arr, Nants)
maxbl = 100.0
bl_vecs = np.random.uniform(-maxbl, maxbl, (Nbls, 3))
bl_vecs[0, 0] = 0
bl_vecs[1, 0:2] = 0
expected_conjugates = []
for i, (u, v, w) in enumerate(bl_vecs):
uneg = u < -tol
uzer = np.isclose(u, 0.0, atol=tol)
vneg = v < -tol
vzer = np.isclose(v, 0.0, atol=tol)
wneg = w < -tol
if uneg or (uzer and vneg) or (uzer and vzer and wneg):
expected_conjugates.append(bl_inds[i])
bl_gps, vecs, lens, conjugates = uvutils.get_baseline_redundancies(
bl_inds, bl_vecs, tol=tol, with_conjugates=True
)
assert sorted(conjugates) == sorted(expected_conjugates)
def test_redundancy_finder_fully_redundant_array():
"""Test the redundancy finder for a fully redundant array."""
uvd = UVData()
uvd.read_uvfits(os.path.join(DATA_PATH, "test_redundant_array.uvfits"))
uvd.select(times=uvd.time_array[0])
tol = 1 # meters
bl_positions = uvd.uvw_array
(
baseline_groups,
vec_bin_centers,
lens,
conjugates,
) = uvutils.get_baseline_redundancies(
uvd.baseline_array, bl_positions, tol=tol, with_conjugates=True
)
# Only 1 set of redundant baselines
assert len(baseline_groups) == 1
# Should return the input baselines
assert baseline_groups[0].sort() == np.unique(uvd.baseline_array).sort()
@pytest.mark.parametrize("n_blocks", [1, 10])
def test_adjacency_lists(n_blocks):
"""Test the adjacency list method in utils."""
# n_blocks: in _adj_list, loop over chunks of vectors when computing distances.
# Make a grid.
Nx = 5
Lmax = 50
xbase = np.linspace(0, Lmax, Nx)
x, y, z = map(np.ndarray.flatten, np.meshgrid(xbase, xbase, xbase))
# Make more vectors by shifting by Lmax/Nx/3 in x, y, and z:
dx = (Lmax / Nx) / 3 # One third of cell size.
x = np.append(x, x + dx)
y = np.append(y, y + dx)
z = np.append(z, z + dx)
# Construct vectors
vecs = np.vstack((x, y, z)).T
Npts = x.size
# Reorder randomly.
np.random.shuffle(vecs)
# Tolerance = half of cell diagonal.
tol = Lmax / Nx * np.sqrt(2) / 2
adj = uvutils._adj_list(vecs, tol, n_blocks=n_blocks)
# Confirm that each adjacency set contains all of the vectors that
# are within the tolerance distance.
for vi in range(Npts):
for vj in range(Npts):
dist = np.linalg.norm(vecs[vi] - vecs[vj])
if dist < tol:
assert vj in adj[vi]
assert vi in adj[vj]
else:
assert vj not in adj[vi]
assert vi not in adj[vj]
# The way the grid is set up, every clique should have two elements.
assert all(len(vi) == 2 for vi in adj)
def test_strict_cliques():
# Adjacency lists comprising only isolated cliques.
adj_isol = [
{0, 1, 2},
{1, 0, 2},
{2, 0, 1},
{3},
{4},
{5, 6, 7, 8},
{5, 6, 7, 8},
{5, 6, 7, 8},
{5, 6, 7, 8},
]
adj_isol = [frozenset(st) for st in adj_isol]
exp_cliques = [[0, 1, 2], [3], [4], [5, 6, 7, 8]]
res = uvutils._find_cliques(adj_isol, strict=True)
assert res == exp_cliques
# Error if two cliques are not isolated
adj_link = adj_isol
adj_link[-1] = frozenset({5, 6, 7, 8, 1})
with pytest.raises(ValueError, match="Non-isolated cliques found in graph."):
uvutils._find_cliques(adj_link, strict=True),
def test_str_to_bytes():
test_str = "HERA"
with uvtest.check_warnings(
DeprecationWarning, "_str_to_bytes is deprecated and will be removed"
):
test_bytes = uvutils._str_to_bytes(test_str)
assert type(test_bytes) == bytes
assert test_bytes == b"\x48\x45\x52\x41"
return
def test_bytes_to_str():
test_bytes = b"\x48\x45\x52\x41"
with uvtest.check_warnings(
DeprecationWarning, "_bytes_to_str is deprecated and will be removed"
):
test_str = uvutils._bytes_to_str(test_bytes)
assert type(test_str) == str
assert test_str == "HERA"
return
def test_reorder_conj_pols_non_list():
pytest.raises(ValueError, uvutils.reorder_conj_pols, 4)
def test_reorder_conj_pols_strings():
pols = ["xx", "xy", "yx"]
corder = uvutils.reorder_conj_pols(pols)
assert np.array_equal(corder, [0, 2, 1])
def test_reorder_conj_pols_ints():
pols = [-5, -7, -8] # 'xx', 'xy', 'yx'
corder = uvutils.reorder_conj_pols(pols)
assert np.array_equal(corder, [0, 2, 1])
def test_reorder_conj_pols_missing_conj():
pols = ["xx", "xy"] # Missing 'yx'
pytest.raises(ValueError, uvutils.reorder_conj_pols, pols)
def test_collapse_mean_no_return_no_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i])
out = uvutils.collapse(data, "mean", axis=0)
out1 = uvutils.mean_collapse(data, axis=0)
# Actual values are tested in test_mean_no_weights
assert np.array_equal(out, out1)
def test_collapse_mean_returned_no_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i])
out, wo = uvutils.collapse(data, "mean", axis=0, return_weights=True)
out1, wo1 = uvutils.mean_collapse(data, axis=0, return_weights=True)
# Actual values are tested in test_mean_no_weights
assert np.array_equal(out, out1)
assert np.array_equal(wo, wo1)
def test_collapse_mean_returned_with_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wo = uvutils.collapse(data, "mean", weights=w, axis=0, return_weights=True)
out1, wo1 = uvutils.mean_collapse(data, weights=w, axis=0, return_weights=True)
# Actual values are tested in test_mean_weights
assert np.array_equal(out, out1)
assert np.array_equal(wo, wo1)
def test_collapse_mean_returned_with_weights_and_weights_square():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wo, wso = uvutils.collapse(
data, "mean", weights=w, axis=0, return_weights=True, return_weights_square=True
)
out1, wo1, wso1 = uvutils.mean_collapse(
data, weights=w, axis=0, return_weights=True, return_weights_square=True
)
# Actual values are tested in test_mean_weights
assert np.array_equal(out, out1)
assert np.array_equal(wo, wo1)
assert np.array_equal(wso, wso1)
def test_collapse_mean_returned_with_weights_square_no_return_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wso = uvutils.collapse(
data,
"mean",
weights=w,
axis=0,
return_weights=False,
return_weights_square=True,
)
out1, wso1 = uvutils.mean_collapse(
data, weights=w, axis=0, return_weights=False, return_weights_square=True
)
# Actual values are tested in test_mean_weights
assert np.array_equal(out, out1)
assert np.array_equal(wso, wso1)
def test_collapse_absmean_no_return_no_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = (-1) ** i * np.ones_like(data[:, i])
out = uvutils.collapse(data, "absmean", axis=0)
out1 = uvutils.absmean_collapse(data, axis=0)
# Actual values are tested in test_absmean_no_weights
assert np.array_equal(out, out1)
def test_collapse_quadmean_no_return_no_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i])
out = uvutils.collapse(data, "quadmean", axis=0)
out1 = uvutils.quadmean_collapse(data, axis=0)
# Actual values are tested elsewhere?
assert np.array_equal(out, out1)
def test_collapse_quadmean_returned_with_weights_and_weights_square():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wo, wso = uvutils.collapse(
data,
"quadmean",
weights=w,
axis=0,
return_weights=True,
return_weights_square=True,
)
out1, wo1, wso1 = uvutils.quadmean_collapse(
data, weights=w, axis=0, return_weights=True, return_weights_square=True
)
# Actual values are tested elsewhere?
assert np.array_equal(out, out1)
assert np.array_equal(wo, wo1)
assert np.array_equal(wso, wso1)
def test_collapse_quadmean_returned_with_weights_square_no_return_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wso = uvutils.collapse(
data,
"quadmean",
weights=w,
axis=0,
return_weights=False,
return_weights_square=True,
)
out1, wso1 = uvutils.quadmean_collapse(
data, weights=w, axis=0, return_weights=False, return_weights_square=True
)
# Actual values are tested elsewhere?
assert np.array_equal(out, out1)
assert np.array_equal(wso, wso1)
def test_collapse_quadmean_returned_without_weights_square_with_return_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wo = uvutils.collapse(
data,
"quadmean",
weights=w,
axis=0,
return_weights=True,
return_weights_square=False,
)
out1, wo1 = uvutils.quadmean_collapse(
data, weights=w, axis=0, return_weights=True, return_weights_square=False
)
# Actual values are tested elsewhere?
assert np.array_equal(out, out1)
assert np.array_equal(wo, wo1)
def test_collapse_quadmean_returned_with_weights_square_without_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wo = uvutils.collapse(
data,
"quadmean",
weights=w,
axis=0,
return_weights=False,
return_weights_square=True,
)
out1, wo1 = uvutils.quadmean_collapse(
data, weights=w, axis=0, return_weights=False, return_weights_square=True
)
# Actual values are tested elsewhere?
assert np.array_equal(out, out1)
assert np.array_equal(wo, wo1)
def test_collapse_or_no_return_no_weights():
# Fake data
data = np.zeros((50, 25), np.bool_)
data[0, 8] = True
o = uvutils.collapse(data, "or", axis=0)
o1 = uvutils.or_collapse(data, axis=0)
assert np.array_equal(o, o1)
def test_collapse_and_no_return_no_weights():
# Fake data
data = np.zeros((50, 25), np.bool_)
data[0, :] = True
o = uvutils.collapse(data, "and", axis=0)
o1 = uvutils.and_collapse(data, axis=0)
assert np.array_equal(o, o1)
def test_collapse_error():
pytest.raises(ValueError, uvutils.collapse, np.ones((2, 3)), "fooboo")
def test_mean_no_weights():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i])
out, wo = uvutils.mean_collapse(data, axis=0, return_weights=True)
assert np.array_equal(out, np.arange(data.shape[1]))
assert np.array_equal(wo, data.shape[0] * np.ones(data.shape[1]))
out, wo = uvutils.mean_collapse(data, axis=1, return_weights=True)
assert np.all(out == np.mean(np.arange(data.shape[1])))
assert len(out) == data.shape[0]
assert np.array_equal(wo, data.shape[1] * np.ones(data.shape[0]))
out, wo = uvutils.mean_collapse(data, return_weights=True)
assert out == np.mean(np.arange(data.shape[1]))
assert wo == data.size
out = uvutils.mean_collapse(data)
assert out == np.mean(np.arange(data.shape[1]))
def test_mean_weights_and_weights_square():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i]) + 1
w = 1.0 / data
out, wo, wso = uvutils.mean_collapse(
data, weights=w, axis=0, return_weights=True, return_weights_square=True
)
assert np.allclose(out * wo, data.shape[0])
assert np.allclose(wo, float(data.shape[0]) / (np.arange(data.shape[1]) + 1))
assert np.allclose(wso, float(data.shape[0]) / (np.arange(data.shape[1]) + 1) ** 2)
out, wo, wso = uvutils.mean_collapse(
data, weights=w, axis=1, return_weights=True, return_weights_square=True
)
assert np.allclose(out * wo, data.shape[1])
assert np.allclose(wo, np.sum(1.0 / (np.arange(data.shape[1]) + 1)))
assert np.allclose(wso, np.sum(1.0 / (np.arange(data.shape[1]) + 1) ** 2))
# Zero weights
w = np.ones_like(w)
w[0, :] = 0
w[:, 0] = 0
out, wo = uvutils.mean_collapse(data, weights=w, axis=0, return_weights=True)
ans = np.arange(data.shape[1]).astype(np.float64) + 1
ans[0] = np.inf
assert np.array_equal(out, ans)
ans = (data.shape[0] - 1) * np.ones(data.shape[1])
ans[0] = 0
assert np.all(wo == ans)
out, wo = uvutils.mean_collapse(data, weights=w, axis=1, return_weights=True)
ans = np.mean(np.arange(data.shape[1])[1:] + 1) * np.ones(data.shape[0])
ans[0] = np.inf
assert np.all(out == ans)
ans = (data.shape[1] - 1) * np.ones(data.shape[0])
ans[0] = 0
assert np.all(wo == ans)
def test_mean_infs():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i])
data[:, 0] = np.inf
data[0, :] = np.inf
out, wo = uvutils.mean_collapse(data, axis=0, return_weights=True)
ans = np.arange(data.shape[1]).astype(np.float64)
ans[0] = np.inf
assert np.array_equal(out, ans)
ans = (data.shape[0] - 1) * np.ones(data.shape[1])
ans[0] = 0
assert np.all(wo == ans)
out, wo = uvutils.mean_collapse(data, axis=1, return_weights=True)
ans = np.mean(np.arange(data.shape[1])[1:]) * np.ones(data.shape[0])
ans[0] = np.inf
assert np.all(out == ans)
ans = (data.shape[1] - 1) * np.ones(data.shape[0])
ans[0] = 0
assert np.all(wo == ans)
def test_absmean():
# Fake data
data1 = np.zeros((50, 25))
for i in range(data1.shape[1]):
data1[:, i] = (-1) ** i * np.ones_like(data1[:, i])
data2 = np.ones_like(data1)
out1 = uvutils.absmean_collapse(data1)
out2 = uvutils.absmean_collapse(data2)
assert out1 == out2
def test_quadmean():
# Fake data
data = np.zeros((50, 25))
for i in range(data.shape[1]):
data[:, i] = i * np.ones_like(data[:, i])
o1, w1 = uvutils.quadmean_collapse(data, return_weights=True)
o2, w2 = uvutils.mean_collapse(np.abs(data) ** 2, return_weights=True)
o3 = uvutils.quadmean_collapse(data) # without return_weights
o2 = np.sqrt(o2)
assert o1 == o2
assert w1 == w2
assert o1 == o3
def test_or_collapse():
# Fake data
data = np.zeros((50, 25), np.bool_)
data[0, 8] = True
o = uvutils.or_collapse(data, axis=0)
ans = np.zeros(25, np.bool_)
ans[8] = True
assert np.array_equal(o, ans)
o = uvutils.or_collapse(data, axis=1)
ans = np.zeros(50, np.bool_)
ans[0] = True
assert np.array_equal(o, ans)
o = uvutils.or_collapse(data)
assert o
def test_or_collapse_weights():
# Fake data
data = np.zeros((50, 25), np.bool_)
data[0, 8] = True
w = np.ones_like(data, np.float64)
o, wo = uvutils.or_collapse(data, axis=0, weights=w, return_weights=True)
ans = np.zeros(25, np.bool_)
ans[8] = True
assert np.array_equal(o, ans)
assert np.array_equal(wo, np.ones_like(o, dtype=np.float64))
w[0, 8] = 0.3
with uvtest.check_warnings(UserWarning, "Currently weights are"):
o = uvutils.or_collapse(data, axis=0, weights=w)
assert np.array_equal(o, ans)
def test_or_collapse_errors():
data = np.zeros(5)
pytest.raises(ValueError, uvutils.or_collapse, data)
def test_and_collapse():
# Fake data
data = np.zeros((50, 25), np.bool_)
data[0, :] = True
o = uvutils.and_collapse(data, axis=0)
ans = np.zeros(25, np.bool_)
assert np.array_equal(o, ans)
o = uvutils.and_collapse(data, axis=1)
ans = np.zeros(50, np.bool_)
ans[0] = True
assert np.array_equal(o, ans)
o = uvutils.and_collapse(data)
assert not o
def test_and_collapse_weights():
# Fake data
data = np.zeros((50, 25), np.bool_)
data[0, :] = True
w = np.ones_like(data, np.float64)
o, wo = uvutils.and_collapse(data, axis=0, weights=w, return_weights=True)
ans = np.zeros(25, np.bool_)
assert np.array_equal(o, ans)
assert np.array_equal(wo, np.ones_like(o, dtype=np.float64))
w[0, 8] = 0.3
with uvtest.check_warnings(UserWarning, "Currently weights are"):
o = uvutils.and_collapse(data, axis=0, weights=w)
assert np.array_equal(o, ans)
def test_and_collapse_errors():
data = np.zeros(5)
pytest.raises(ValueError, uvutils.and_collapse, data)
@pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values")
def test_uvcalibrate_apply_gains_oldfiles():
# read data
uvd = UVData()
uvd.read(os.path.join(DATA_PATH, "zen.2457698.40355.xx.HH.uvcAA.uvh5"))
# give it an x_orientation
uvd.x_orientation = "east"
uvc = UVCal()
uvc.read_calfits(os.path.join(DATA_PATH, "zen.2457698.40355.xx.gain.calfits"))
# assign gain scale manually
uvc.gain_scale = "Jy"
# downselect to match each other in shape (but not in actual values!)
uvd.select(frequencies=uvd.freq_array[0, :10])
uvc.select(times=uvc.time_array[:3])
key = (43, 72, "xx")
ant1 = (43, "Jxx")
ant2 = (72, "Jxx")
# division calibrate
uvc.gain_convention = "divide"
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc, prop_flags=True, flag_missing=False, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
ant_expected = {
"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."
}
missing_times = [2457698.4036761867, 2457698.4038004624]
time_expected = {
f"Time {this_time} exists on UVData but not on UVCal. "
"This will become an error in version 2.2"
for this_time in missing_times
}
freq_expected = {
f"Frequency {this_freq} exists on UVData but not on UVCal. "
"This will become an error in version 2.2"
for this_freq in uvd.freq_array[0, :]
}
assert warns == (ant_expected | time_expected | freq_expected)
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
assert uvdcal.vis_units == "Jy"
# test undo
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvdcal, uvc, prop_flags=True, flag_missing=False, inplace=False, undo=True
)
warns = {warn.message.args[0] for warn in warninfo}
assert warns == (ant_expected | time_expected | freq_expected)
np.testing.assert_array_almost_equal(uvd.get_data(key), uvdcal.get_data(key))
assert uvdcal.vis_units == "UNCALIB"
# multiplication calibrate
uvc.gain_convention = "multiply"
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc, prop_flags=False, flag_missing=False, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
assert warns == (ant_expected | time_expected | freq_expected)
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) * (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
assert uvdcal.vis_units == "Jy"
@pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values")
def test_uvcalibrate_delay_oldfiles():
uvd = UVData()
uvd.read(os.path.join(DATA_PATH, "zen.2457698.40355.xx.HH.uvcAA.uvh5"))
uvc = UVCal()
uvc.read_calfits(os.path.join(DATA_PATH, "zen.2457698.40355.xx.delay.calfits"))
# downselect to match each other in shape (but not in actual time values!)
uvc.select(times=uvc.time_array[:3], frequencies=uvd.freq_array[0, :])
uvc.gain_convention = "multiply"
ant_expected = [
"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."
]
missing_times = [2457698.4036761867, 2457698.4038004624]
time_expected = [
f"Time {this_time} exists on UVData but not on UVCal. "
"This will become an error in version 2.2"
for this_time in missing_times
]
with uvtest.check_warnings(DeprecationWarning, match=ant_expected + time_expected):
uvdcal = uvutils.uvcalibrate(
uvd, uvc, prop_flags=False, flag_missing=False, inplace=False
)
uvc.convert_to_gain()
with uvtest.check_warnings(DeprecationWarning, match=ant_expected + time_expected):
uvdcal2 = uvutils.uvcalibrate(
uvd, uvc, prop_flags=False, flag_missing=False, inplace=False
)
assert uvdcal == uvdcal2
@pytest.mark.parametrize("future_shapes", [True, False])
def test_uvcalibrate_divide(uvcalibrate_data, future_shapes):
uvd, uvc = uvcalibrate_data
if future_shapes:
uvd.use_future_array_shapes()
# set the gain_scale to "Jy" to test that vis units are set properly
assert uvc.gain_convention == "divide"
assert uvc.gain_scale is None
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
assert uvdcal.vis_units == "UNCALIB"
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
# test undo
uvdcal = uvutils.uvcalibrate(
uvdcal, uvc, prop_flags=True, flag_missing=False, inplace=False, undo=True
)
np.testing.assert_array_almost_equal(uvd.get_data(key), uvdcal.get_data(key))
assert uvdcal.vis_units == "UNCALIB"
@pytest.mark.parametrize("future_shapes", [True, False])
def test_uvcalibrate_multiply(uvcalibrate_data, future_shapes):
uvd, uvc = uvcalibrate_data
if future_shapes:
uvd.use_future_array_shapes()
# use multiply gain convention
uvc.gain_convention = "multiply"
# set the gain_scale to "Jy" to test that vis units are set properly in that case
uvc.gain_scale = "Jy"
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
key = (12, 23, "xx")
ant1 = (12, "Jxx")
ant2 = (23, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) * (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
assert uvdcal.vis_units == "Jy"
# test undo
uvdcal = uvutils.uvcalibrate(
uvdcal, uvc, prop_flags=True, flag_missing=False, inplace=False, undo=True
)
np.testing.assert_array_almost_equal(uvd.get_data(key), uvdcal.get_data(key))
assert uvdcal.vis_units == "UNCALIB"
@pytest.mark.filterwarnings("ignore:Combined frequencies are not contiguous.")
def test_uvcalibrate_dterm_handling(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
# test d-term exception
with pytest.raises(
ValueError, match="Cannot apply D-term calibration without -7 or -8"
):
uvutils.uvcalibrate(uvd, uvc, Dterm_cal=True)
# d-term not implemented error
uvcDterm = copy.deepcopy(uvc)
uvcDterm.jones_array = np.array([-7, -8])
uvcDterm = uvc + uvcDterm
with pytest.raises(
NotImplementedError, match="D-term calibration is not yet implemented."
):
uvutils.uvcalibrate(uvd, uvcDterm, Dterm_cal=True)
@pytest.mark.filterwarnings("ignore:Cannot preserve total_quality_array")
@pytest.mark.parametrize("future_shapes", [True, False])
def test_uvcalibrate_flag_propagation(uvcalibrate_data, future_shapes):
uvd, uvc = uvcalibrate_data
if future_shapes:
uvd.use_future_array_shapes()
# test flag propagation
uvc.flag_array[0] = True
uvc.gain_array[1] = 0.0
uvdcal = uvutils.uvcalibrate(
uvd, uvc, prop_flags=True, flag_missing=False, inplace=False
)
assert np.all(uvdcal.get_flags(1, 13, "xx")) # assert completely flagged
assert np.all(uvdcal.get_flags(0, 12, "xx")) # assert completely flagged
np.testing.assert_array_almost_equal(
uvd.get_data(1, 13, "xx"), uvdcal.get_data(1, 13, "xx")
)
np.testing.assert_array_almost_equal(
uvd.get_data(0, 12, "xx"), uvdcal.get_data(0, 12, "xx")
)
uvc_sub = uvc.select(antenna_nums=[1, 12], inplace=False)
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc_sub, prop_flags=True, flag_missing=False, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
uvdata_unique_nums = np.unique(np.append(uvd.ant_1_array, uvd.ant_2_array))
uvd.antenna_names = np.array(uvd.antenna_names)
missing_ants = uvdata_unique_nums.tolist()
missing_ants.remove(1)
missing_ants.remove(12)
missing_ant_names = [
uvd.antenna_names[np.where(uvd.antenna_numbers == antnum)[0][0]]
for antnum in missing_ants
]
ant_expected = {
f"Antennas {missing_ant_names} 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.",
}
assert not np.any(uvdcal.get_flags(13, 24, "xx")) # assert no flags exist
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc_sub, prop_flags=True, flag_missing=True, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
ant_expected = {
f"Antennas {missing_ant_names} 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."
}
assert warns == ant_expected
assert np.all(uvdcal.get_flags(13, 24, "xx")) # assert completely flagged
with pytest.warns(UserWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc_sub, prop_flags=True, ant_check=False, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
ant_expected = {
f"Antennas {missing_ant_names} 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."
}
assert warns == ant_expected
assert np.all(uvdcal.get_flags(13, 24, "xx")) # assert completely flagged
@pytest.mark.filterwarnings("ignore:Cannot preserve total_quality_array")
def test_uvcalibrate_flag_propagation_name_mismatch(uvcalibrate_init_data):
uvd, uvc = uvcalibrate_init_data
# test flag propagation
uvc.flag_array[0] = True
uvc.gain_array[1] = 0.0
with uvtest.check_warnings(
DeprecationWarning,
match="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.",
):
uvdcal = uvutils.uvcalibrate(
uvd, uvc, prop_flags=True, flag_missing=False, inplace=False
)
assert np.all(uvdcal.get_flags(1, 13, "xx")) # assert completely flagged
assert np.all(uvdcal.get_flags(0, 12, "xx")) # assert completely flagged
np.testing.assert_array_almost_equal(
uvd.get_data(1, 13, "xx"), uvdcal.get_data(1, 13, "xx")
)
np.testing.assert_array_almost_equal(
uvd.get_data(0, 12, "xx"), uvdcal.get_data(0, 12, "xx")
)
uvc_sub = uvc.select(antenna_nums=[1, 12], inplace=False)
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc_sub, prop_flags=True, flag_missing=False, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
uvdata_unique_nums = np.unique(np.append(uvd.ant_1_array, uvd.ant_2_array))
uvd.antenna_names = np.array(uvd.antenna_names)
missing_ants = uvdata_unique_nums.tolist()
missing_ants.remove(1)
missing_ants.remove(12)
missing_ant_names = [
uvd.antenna_names[np.where(uvd.antenna_numbers == antnum)[0][0]]
for antnum in missing_ants
]
present_ant_names = [
uvd.antenna_names[np.where(uvd.antenna_numbers == antnum)[0][0]]
for antnum in [1, 12]
]
ant_expected = {
f"Antennas {present_ant_names} 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.",
f"Antennas {missing_ant_names} 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.",
}
assert not np.any(uvdcal.get_flags(13, 24, "xx")) # assert no flags exist
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(
uvd, uvc_sub, prop_flags=True, flag_missing=True, inplace=False
)
warns = {warn.message.args[0] for warn in warninfo}
ant_expected = {
f"Antennas {present_ant_names} 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.",
f"Antennas {missing_ant_names} 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.",
}
assert warns == ant_expected
assert np.all(uvdcal.get_flags(13, 24, "xx")) # assert completely flagged
with uvtest.check_warnings(
UserWarning,
match="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.",
):
uvdcal = uvutils.uvcalibrate(
uvd, uvc_sub, prop_flags=True, ant_check=False, inplace=False
)
assert np.all(uvdcal.flag_array) # assert completely flagged
def test_uvcalibrate_extra_cal_antennas(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
# remove some antennas from the data
uvd.select(antenna_nums=[0, 1, 12, 13])
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
@pytest.mark.parametrize("future_shapes", [True, False])
def test_uvcalibrate_antenna_names_mismatch(uvcalibrate_init_data, future_shapes):
uvd, uvc = uvcalibrate_init_data
if future_shapes:
uvd.use_future_array_shapes()
with uvtest.check_warnings(
DeprecationWarning,
match="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.",
):
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
# now test that they're all flagged if ant_check is False
with uvtest.check_warnings(
UserWarning,
match="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.",
):
uvdcal = uvutils.uvcalibrate(uvd, uvc, ant_check=False, inplace=False)
assert np.all(uvdcal.flag_array) # assert completely flagged
def test_uvcalibrate_time_mismatch(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
# change times to get warnings
uvc.time_array = uvc.time_array + 1
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
warns = {warn.message.args[0] for warn in warninfo}
expected = {
f"Time {this_time} exists on UVData but not on UVCal. "
"This will become an error in version 2.2"
for this_time in np.unique(uvd.time_array)
}
assert warns == expected
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
def test_uvcalibrate_time_wrong_size(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
# downselect by one time to get error
uvc.select(times=uvc.time_array[1:])
with pytest.raises(
ValueError,
match="The uvcal object has more than one time but fewer than the "
"number of unique times on the uvdata object.",
):
uvutils.uvcalibrate(uvd, uvc, inplace=False)
@pytest.mark.parametrize("len_time_range", [0, 1])
def test_uvcalibrate_time_types(uvcalibrate_data, len_time_range):
uvd, uvc = uvcalibrate_data
# only one time
uvc.select(times=uvc.time_array[0])
if len_time_range == 0:
uvc.time_range = None
else:
# check cal runs fine with a good time range
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
# then change time_range to get warnings
uvc.time_range = np.array(uvc.time_range) + 1
with uvtest.check_warnings(
DeprecationWarning,
match=(
"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"
),
):
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
# set time_check=False to test the user warning
with uvtest.check_warnings(
UserWarning,
match=(
"Times do not match between UVData and UVCal "
"but time_check is False, so calibration "
"will be applied anyway."
),
):
uvdcal2 = uvutils.uvcalibrate(uvd, uvc, inplace=False, time_check=False)
assert uvdcal == uvdcal2
@pytest.mark.filterwarnings("ignore:Combined frequencies are not contiguous.")
def test_uvcalibrate_extra_cal_times(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
uvc2 = copy.deepcopy(uvc)
uvc2.time_array = uvc.time_array + 1
uvc_use = uvc + uvc2
uvdcal = uvutils.uvcalibrate(uvd, uvc_use, inplace=False)
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
def test_uvcalibrate_freq_mismatch(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
# change some frequencies to get warnings
maxf = np.max(uvc.freq_array)
uvc.freq_array[0, uvc.Nfreqs // 2 :] = uvc.freq_array[0, uvc.Nfreqs // 2 :] + maxf
with pytest.warns(DeprecationWarning) as warninfo:
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
warns = {warn.message.args[0] for warn in warninfo}
expected = {
f"Frequency {this_freq} exists on UVData but not on UVCal. "
"This will become an error in version 2.2"
for this_freq in uvd.freq_array[0, uvd.Nfreqs // 2 :]
}
assert warns == expected
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
@pytest.mark.filterwarnings("ignore:Combined frequencies are not evenly spaced.")
def test_uvcalibrate_extra_cal_freqs(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
uvc2 = copy.deepcopy(uvc)
uvc2.freq_array = uvc.freq_array + np.max(uvc.freq_array)
uvc_use = uvc + uvc2
uvdcal = uvutils.uvcalibrate(uvd, uvc_use, inplace=False)
key = (1, 13, "xx")
ant1 = (1, "Jxx")
ant2 = (13, "Jxx")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
def test_uvcalibrate_feedpol_mismatch(uvcalibrate_data):
uvd, uvc = uvcalibrate_data
# downselect the feed polarization to get warnings
uvc.select(jones=uvutils.jstr2num("Jnn", x_orientation=uvc.x_orientation))
with pytest.warns(
DeprecationWarning,
match=(
"Feed polarization e exists on UVData but not on UVCal. "
"This will become an error in version 2.2"
),
):
uvdcal = uvutils.uvcalibrate(uvd, uvc, inplace=False)
key = (1, 13, "nn")
ant1 = (1, "Jnn")
ant2 = (13, "Jnn")
np.testing.assert_array_almost_equal(
uvdcal.get_data(key),
uvd.get_data(key) / (uvc.get_gains(ant1) * uvc.get_gains(ant2).conj()).T,
)
@pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values")
@pytest.mark.parametrize("future_shapes", [True, False])
def test_apply_uvflag(future_shapes):
# load data and insert some flags
uvd = UVData()
uvd.read(os.path.join(DATA_PATH, "zen.2457698.40355.xx.HH.uvcAA.uvh5"))
uvd.flag_array[uvd.antpair2ind(9, 20)] = True
if future_shapes:
uvd.use_future_array_shapes()
# load a UVFlag into flag type
uvf = UVFlag(uvd)
uvf.to_flag()
# insert flags for 2 out of 3 times
uvf.flag_array[uvf.antpair2ind(9, 10)[:2]] = True
# apply flags and check for basic flag propagation
uvdf = uvutils.apply_uvflag(uvd, uvf, inplace=False)
assert np.all(uvdf.flag_array[uvdf.antpair2ind(9, 10)][:2])
# test inplace
uvdf = copy.deepcopy(uvd)
uvutils.apply_uvflag(uvdf, uvf, inplace=True)
assert np.all(uvdf.flag_array[uvdf.antpair2ind(9, 10)][:2])
# test flag missing
uvf2 = uvf.select(bls=uvf.get_antpairs()[:-1], inplace=False)
uvdf = uvutils.apply_uvflag(uvd, uvf2, inplace=False, flag_missing=True)
assert np.all(uvdf.flag_array[uvdf.antpair2ind(uvf.get_antpairs()[-1])])
uvdf = uvutils.apply_uvflag(uvd, uvf2, inplace=False, flag_missing=False)
assert not np.any(uvdf.flag_array[uvdf.antpair2ind(uvf.get_antpairs()[-1])])
# test force polarization
uvdf = copy.deepcopy(uvd)
uvdf2 = copy.deepcopy(uvd)
uvdf2.polarization_array[0] = -6
uvdf += uvdf2
uvdf = uvutils.apply_uvflag(uvdf, uvf, inplace=False, force_pol=True)
assert np.all(uvdf.flag_array[uvdf.antpair2ind(9, 10)][:2])
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvdf, uvf, inplace=False, force_pol=False)
assert "Input uvf and uvd polarizations do not match" in str(cm.value)
# test unflag first
uvdf = uvutils.apply_uvflag(uvd, uvf, inplace=False, unflag_first=True)
assert np.all(uvdf.flag_array[uvdf.antpair2ind(9, 10)][:2])
assert not np.any(uvdf.flag_array[uvdf.antpair2ind(9, 20)])
# convert uvf to waterfall and test
uvfw = copy.deepcopy(uvf)
uvfw.to_waterfall(method="or")
uvdf = uvutils.apply_uvflag(uvd, uvfw, inplace=False)
assert np.all(uvdf.flag_array[uvdf.antpair2ind(9, 10)][:2])
assert np.all(uvdf.flag_array[uvdf.antpair2ind(9, 20)][:2])
assert np.all(uvdf.flag_array[uvdf.antpair2ind(20, 22)][:2])
# test mode exception
uvfm = copy.deepcopy(uvf)
uvfm.mode = "metric"
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvd, uvfm)
assert "UVFlag must be flag mode" in str(cm.value)
# test polarization exception
uvd2 = copy.deepcopy(uvd)
uvd2.polarization_array[0] = -6
uvf2 = UVFlag(uvd)
uvf2.to_flag()
uvd2.polarization_array[0] = -8
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvd2, uvf2, force_pol=False)
assert "Input uvf and uvd polarizations do not match" in str(cm.value)
# test time and frequency mismatch exceptions
uvf2 = uvf.select(frequencies=uvf.freq_array[:, :2], inplace=False)
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvd, uvf2)
assert "UVFlag and UVData have mismatched frequency arrays" in str(cm.value)
uvf2 = copy.deepcopy(uvf)
uvf2.freq_array += 1.0
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvd, uvf2)
assert "UVFlag and UVData have mismatched frequency arrays" in str(cm.value)
uvf2 = uvf.select(times=np.unique(uvf.time_array)[:2], inplace=False)
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvd, uvf2)
assert "UVFlag and UVData have mismatched time arrays" in str(cm.value)
uvf2 = copy.deepcopy(uvf)
uvf2.time_array += 1.0
with pytest.raises(ValueError) as cm:
uvutils.apply_uvflag(uvd, uvf2)
assert "UVFlag and UVData have mismatched time arrays" in str(cm.value)
# assert implicit broadcasting works
uvf2 = uvf.select(frequencies=uvf.freq_array[:, :1], inplace=False)
uvd2 = uvutils.apply_uvflag(uvd, uvf2, inplace=False)
assert np.all(uvd2.get_flags(9, 10)[:2])
uvf2 = uvf.select(times=np.unique(uvf.time_array)[:1], inplace=False)
uvd2 = uvutils.apply_uvflag(uvd, uvf2, inplace=False)
assert np.all(uvd2.get_flags(9, 10))
def test_upos_tol_reds():
# Checks that the u-positive convention in get_antenna_redundancies
# is enforced to the specificed tolerance.
# Make a layout with two NS baselines, one with u ~ -2*eps, and another with u == 0
# This would previously cause one to be flipped, when they should be redundant.
eps = 1e-5
tol = 3 * eps
ant_pos = np.array(
[[-eps, 1.0, 0.0], [1.0, 1.0, 0.0], [eps, 0.0, 0.0], [1.0, 0.0, 0.0]]
)
ant_nums = np.arange(4)
red_grps, _, _ = uvutils.get_antenna_redundancies(ant_nums, ant_pos, tol=tol)
assert len(red_grps) == 4
class FakeClass:
def __init__(self):
pass
def test_parse_ants_error():
test_obj = FakeClass()
with pytest.raises(
ValueError,
match=(
"UVBased objects must have all the following attributes in order "
"to call 'parse_ants': "
),
):
uvutils.parse_ants(test_obj, ant_str="")
