def CartesianToEquatorial(pos, observer=[0,0,0]):
"""
Convert Cartesian position coordinates to equatorial right ascension
and declination, using the specified observer location.
.. note::
RA and DEC will be returned in degrees, with RA in the range [0,360]
and DEC in the range [-90, 90].
Parameters
----------
pos : array_like
a N x 3 array holding the Cartesian position coordinates
observer : array_like
a length 3 array holding the observer location
Returns
-------
ra, dec : array_like
the right ascension and declination coordinates, in degrees. RA
will be in the range [0,360] and DEC in the range [-90, 90]
"""
# recenter based on observer
pos = pos - observer
s = da.hypot(pos[:,0], pos[:,1])
lon = da.arctan2(pos[:,1], pos[:,0])
lat = da.arctan2(pos[:,2], s)
# convert to degrees
lon = da.rad2deg(lon)
lat = da.rad2deg(lat)
# wrap lon to [0,360]
lon = da.mod(lon-360., 360.)
return lon, lat
def get_alt_az(utc_time, lon, lat):
"""Return sun altitude and azimuth from *utc_time*, *lon*, and *lat*.
lon,lat in degrees
What is the unit of the returned angles and heights!? FIXME!
"""
lon = da.deg2rad(lon)
lat = da.deg2rad(lat)
ra_, dec = sun_ra_dec(utc_time)
h__ = _local_hour_angle(utc_time, lon, ra_)
return (da.arcsin(
da.sin(lat) * np.sin(dec) + da.cos(lat) * np.cos(dec) * np.cos(h__)),
da.arctan2(
-np.sin(h__),
(da.cos(lat) * np.tan(dec) - da.sin(lat) * np.cos(h__))))
def test_arithmetic():
x = np.arange(5).astype('f4') + 2
y = np.arange(5).astype('i8') + 2
z = np.arange(5).astype('i4') + 2
a = da.from_array(x, chunks=(2,))
b = da.from_array(y, chunks=(2,))
c = da.from_array(z, chunks=(2,))
assert eq(a + b, x + y)
assert eq(a * b, x * y)
assert eq(a - b, x - y)
assert eq(a / b, x / y)
assert eq(b & b, y & y)
assert eq(b | b, y | y)
assert eq(b ^ b, y ^ y)
assert eq(a // b, x // y)
assert eq(a ** b, x ** y)
assert eq(a % b, x % y)
assert eq(a > b, x > y)
assert eq(a < b, x < y)
assert eq(a >= b, x >= y)
assert eq(a <= b, x <= y)
assert eq(a == b, x == y)
assert eq(a != b, x != y)
assert eq(a + 2, x + 2)
assert eq(a * 2, x * 2)
assert eq(a - 2, x - 2)
assert eq(a / 2, x / 2)
assert eq(b & True, y & True)
assert eq(b | True, y | True)
assert eq(b ^ True, y ^ True)
assert eq(a // 2, x // 2)
assert eq(a ** 2, x ** 2)
assert eq(a % 2, x % 2)
assert eq(a > 2, x > 2)
assert eq(a < 2, x < 2)
assert eq(a >= 2, x >= 2)
assert eq(a <= 2, x <= 2)
assert eq(a == 2, x == 2)
assert eq(a != 2, x != 2)
assert eq(2 + b, 2 + y)
assert eq(2 * b, 2 * y)
assert eq(2 - b, 2 - y)
assert eq(2 / b, 2 / y)
assert eq(True & b, True & y)
assert eq(True | b, True | y)
assert eq(True ^ b, True ^ y)
assert eq(2 // b, 2 // y)
assert eq(2 ** b, 2 ** y)
assert eq(2 % b, 2 % y)
assert eq(2 > b, 2 > y)
assert eq(2 < b, 2 < y)
assert eq(2 >= b, 2 >= y)
assert eq(2 <= b, 2 <= y)
assert eq(2 == b, 2 == y)
assert eq(2 != b, 2 != y)
assert eq(-a, -x)
assert eq(abs(a), abs(x))
assert eq(~(a == b), ~(x == y))
assert eq(~(a == b), ~(x == y))
assert eq(da.logaddexp(a, b), np.logaddexp(x, y))
assert eq(da.logaddexp2(a, b), np.logaddexp2(x, y))
assert eq(da.exp(b), np.exp(y))
assert eq(da.log(a), np.log(x))
assert eq(da.log10(a), np.log10(x))
assert eq(da.log1p(a), np.log1p(x))
assert eq(da.expm1(b), np.expm1(y))
assert eq(da.sqrt(a), np.sqrt(x))
assert eq(da.square(a), np.square(x))
assert eq(da.sin(a), np.sin(x))
assert eq(da.cos(b), np.cos(y))
assert eq(da.tan(a), np.tan(x))
assert eq(da.arcsin(b/10), np.arcsin(y/10))
assert eq(da.arccos(b/10), np.arccos(y/10))
assert eq(da.arctan(b/10), np.arctan(y/10))
assert eq(da.arctan2(b*10, a), np.arctan2(y*10, x))
assert eq(da.hypot(b, a), np.hypot(y, x))
assert eq(da.sinh(a), np.sinh(x))
assert eq(da.cosh(b), np.cosh(y))
assert eq(da.tanh(a), np.tanh(x))
assert eq(da.arcsinh(b*10), np.arcsinh(y*10))
assert eq(da.arccosh(b*10), np.arccosh(y*10))
assert eq(da.arctanh(b/10), np.arctanh(y/10))
assert eq(da.deg2rad(a), np.deg2rad(x))
assert eq(da.rad2deg(a), np.rad2deg(x))
assert eq(da.logical_and(a < 1, b < 4), np.logical_and(x < 1, y < 4))
assert eq(da.logical_or(a < 1, b < 4), np.logical_or(x < 1, y < 4))
assert eq(da.logical_xor(a < 1, b < 4), np.logical_xor(x < 1, y < 4))
assert eq(da.logical_not(a < 1), np.logical_not(x < 1))
assert eq(da.maximum(a, 5 - a), np.maximum(a, 5 - a))
assert eq(da.minimum(a, 5 - a), np.minimum(a, 5 - a))
assert eq(da.fmax(a, 5 - a), np.fmax(a, 5 - a))
assert eq(da.fmin(a, 5 - a), np.fmin(a, 5 - a))
assert eq(da.isreal(a + 1j * b), np.isreal(x + 1j * y))
assert eq(da.iscomplex(a + 1j * b), np.iscomplex(x + 1j * y))
assert eq(da.isfinite(a), np.isfinite(x))
assert eq(da.isinf(a), np.isinf(x))
assert eq(da.isnan(a), np.isnan(x))
assert eq(da.signbit(a - 3), np.signbit(x - 3))
assert eq(da.copysign(a - 3, b), np.copysign(x - 3, y))
assert eq(da.nextafter(a - 3, b), np.nextafter(x - 3, y))
assert eq(da.ldexp(c, c), np.ldexp(z, z))
assert eq(da.fmod(a * 12, b), np.fmod(x * 12, y))
assert eq(da.floor(a * 0.5), np.floor(x * 0.5))
assert eq(da.ceil(a), np.ceil(x))
assert eq(da.trunc(a / 2), np.trunc(x / 2))
assert eq(da.degrees(b), np.degrees(y))
assert eq(da.radians(a), np.radians(x))
assert eq(da.rint(a + 0.3), np.rint(x + 0.3))
assert eq(da.fix(a - 2.5), np.fix(x - 2.5))
assert eq(da.angle(a + 1j), np.angle(x + 1j))
assert eq(da.real(a + 1j), np.real(x + 1j))
assert eq((a + 1j).real, np.real(x + 1j))
assert eq(da.imag(a + 1j), np.imag(x + 1j))
assert eq((a + 1j).imag, np.imag(x + 1j))
assert eq(da.conj(a + 1j * b), np.conj(x + 1j * y))
assert eq((a + 1j * b).conj(), (x + 1j * y).conj())
assert eq(da.clip(b, 1, 4), np.clip(y, 1, 4))
assert eq(da.fabs(b), np.fabs(y))
assert eq(da.sign(b - 2), np.sign(y - 2))
l1, l2 = da.frexp(a)
r1, r2 = np.frexp(x)
assert eq(l1, r1)
assert eq(l2, r2)
l1, l2 = da.modf(a)
r1, r2 = np.modf(x)
assert eq(l1, r1)
assert eq(l2, r2)
assert eq(da.around(a, -1), np.around(x, -1))
def test_arithmetic():
x = np.arange(5).astype('f4') + 2
y = np.arange(5).astype('i8') + 2
z = np.arange(5).astype('i4') + 2
a = da.from_array(x, chunks=(2, ))
b = da.from_array(y, chunks=(2, ))
c = da.from_array(z, chunks=(2, ))
assert eq(a + b, x + y)
assert eq(a * b, x * y)
assert eq(a - b, x - y)
assert eq(a / b, x / y)
assert eq(b & b, y & y)
assert eq(b | b, y | y)
assert eq(b ^ b, y ^ y)
assert eq(a // b, x // y)
assert eq(a**b, x**y)
assert eq(a % b, x % y)
assert eq(a > b, x > y)
assert eq(a < b, x < y)
assert eq(a >= b, x >= y)
assert eq(a <= b, x <= y)
assert eq(a == b, x == y)
assert eq(a != b, x != y)
assert eq(a + 2, x + 2)
assert eq(a * 2, x * 2)
assert eq(a - 2, x - 2)
assert eq(a / 2, x / 2)
assert eq(b & True, y & True)
assert eq(b | True, y | True)
assert eq(b ^ True, y ^ True)
assert eq(a // 2, x // 2)
assert eq(a**2, x**2)
assert eq(a % 2, x % 2)
assert eq(a > 2, x > 2)
assert eq(a < 2, x < 2)
assert eq(a >= 2, x >= 2)
assert eq(a <= 2, x <= 2)
assert eq(a == 2, x == 2)
assert eq(a != 2, x != 2)
assert eq(2 + b, 2 + y)
assert eq(2 * b, 2 * y)
assert eq(2 - b, 2 - y)
assert eq(2 / b, 2 / y)
assert eq(True & b, True & y)
assert eq(True | b, True | y)
assert eq(True ^ b, True ^ y)
assert eq(2 // b, 2 // y)
assert eq(2**b, 2**y)
assert eq(2 % b, 2 % y)
assert eq(2 > b, 2 > y)
assert eq(2 < b, 2 < y)
assert eq(2 >= b, 2 >= y)
assert eq(2 <= b, 2 <= y)
assert eq(2 == b, 2 == y)
assert eq(2 != b, 2 != y)
assert eq(-a, -x)
assert eq(abs(a), abs(x))
assert eq(~(a == b), ~(x == y))
assert eq(~(a == b), ~(x == y))
assert eq(da.logaddexp(a, b), np.logaddexp(x, y))
assert eq(da.logaddexp2(a, b), np.logaddexp2(x, y))
assert eq(da.exp(b), np.exp(y))
assert eq(da.log(a), np.log(x))
assert eq(da.log10(a), np.log10(x))
assert eq(da.log1p(a), np.log1p(x))
assert eq(da.expm1(b), np.expm1(y))
assert eq(da.sqrt(a), np.sqrt(x))
assert eq(da.square(a), np.square(x))
assert eq(da.sin(a), np.sin(x))
assert eq(da.cos(b), np.cos(y))
assert eq(da.tan(a), np.tan(x))
assert eq(da.arcsin(b / 10), np.arcsin(y / 10))
assert eq(da.arccos(b / 10), np.arccos(y / 10))
assert eq(da.arctan(b / 10), np.arctan(y / 10))
assert eq(da.arctan2(b * 10, a), np.arctan2(y * 10, x))
assert eq(da.hypot(b, a), np.hypot(y, x))
assert eq(da.sinh(a), np.sinh(x))
assert eq(da.cosh(b), np.cosh(y))
assert eq(da.tanh(a), np.tanh(x))
assert eq(da.arcsinh(b * 10), np.arcsinh(y * 10))
assert eq(da.arccosh(b * 10), np.arccosh(y * 10))
assert eq(da.arctanh(b / 10), np.arctanh(y / 10))
assert eq(da.deg2rad(a), np.deg2rad(x))
assert eq(da.rad2deg(a), np.rad2deg(x))
assert eq(da.logical_and(a < 1, b < 4), np.logical_and(x < 1, y < 4))
assert eq(da.logical_or(a < 1, b < 4), np.logical_or(x < 1, y < 4))
assert eq(da.logical_xor(a < 1, b < 4), np.logical_xor(x < 1, y < 4))
assert eq(da.logical_not(a < 1), np.logical_not(x < 1))
assert eq(da.maximum(a, 5 - a), np.maximum(a, 5 - a))
assert eq(da.minimum(a, 5 - a), np.minimum(a, 5 - a))
assert eq(da.fmax(a, 5 - a), np.fmax(a, 5 - a))
assert eq(da.fmin(a, 5 - a), np.fmin(a, 5 - a))
assert eq(da.isreal(a + 1j * b), np.isreal(x + 1j * y))
assert eq(da.iscomplex(a + 1j * b), np.iscomplex(x + 1j * y))
assert eq(da.isfinite(a), np.isfinite(x))
assert eq(da.isinf(a), np.isinf(x))
assert eq(da.isnan(a), np.isnan(x))
assert eq(da.signbit(a - 3), np.signbit(x - 3))
assert eq(da.copysign(a - 3, b), np.copysign(x - 3, y))
assert eq(da.nextafter(a - 3, b), np.nextafter(x - 3, y))
assert eq(da.ldexp(c, c), np.ldexp(z, z))
assert eq(da.fmod(a * 12, b), np.fmod(x * 12, y))
assert eq(da.floor(a * 0.5), np.floor(x * 0.5))
assert eq(da.ceil(a), np.ceil(x))
assert eq(da.trunc(a / 2), np.trunc(x / 2))
assert eq(da.degrees(b), np.degrees(y))
assert eq(da.radians(a), np.radians(x))
assert eq(da.rint(a + 0.3), np.rint(x + 0.3))
assert eq(da.fix(a - 2.5), np.fix(x - 2.5))
assert eq(da.angle(a + 1j), np.angle(x + 1j))
assert eq(da.real(a + 1j), np.real(x + 1j))
assert eq((a + 1j).real, np.real(x + 1j))
assert eq(da.imag(a + 1j), np.imag(x + 1j))
assert eq((a + 1j).imag, np.imag(x + 1j))
assert eq(da.conj(a + 1j * b), np.conj(x + 1j * y))
assert eq((a + 1j * b).conj(), (x + 1j * y).conj())
assert eq(da.clip(b, 1, 4), np.clip(y, 1, 4))
assert eq(da.fabs(b), np.fabs(y))
assert eq(da.sign(b - 2), np.sign(y - 2))
l1, l2 = da.frexp(a)
r1, r2 = np.frexp(x)
assert eq(l1, r1)
assert eq(l2, r2)
l1, l2 = da.modf(a)
r1, r2 = np.modf(x)
assert eq(l1, r1)
assert eq(l2, r2)
assert eq(da.around(a, -1), np.around(x, -1))
USEcalibr0V_i = []
USEcalibr0V_time = [['2018-04-17T17:28', '2018-04-17T17:38']] # [['2018-04-17T19:10', '2018-04-17T19:20']]
nAveragePrefer = 128 # 1200
# Filter
min_P = 0.5
# Displey
vecScale = 1
DISP_TimeZoom = [['2018-04-17T20:00', '2018-04-18T00:00']]
maxGsumMinus1 = 0.3 # 0.1
TimeShiftedFromUTC_s = 0
# Angles in radians:
fPitch = lambda Gxyz: -da.arctan2(Gxyz[0, :], da.sqrt(da.sum(da.square(Gxyz[1:, :]), 0)))
fRoll = lambda Gxyz: da.arctan2(Gxyz[1, :], Gxyz[2,
:]) # da.arctan2(Gxyz[1,:], da.sqrt(da.sum(da.square(Gxyz[(0,2),:]), 0))) #=da.arctan2(Gxyz[1,:], da.sqrt(da.square(Gxyz[0,:])+da.square(Gxyz[2,:])) )
fInclination = lambda Gxyz: da.arctan2(da.sqrt(da.sum(da.square(Gxyz[:-1, :]), 0)), Gxyz[2, :])
fHeading = lambda H, p, r: da.arctan2(H[2, :] * da.sin(r) - H[1, :] * da.cos(r),
H[0, :] * da.cos(p) + (H[1, :] * da.sin(r) + H[2, :] * da.cos(r)) * da.sin(p))
fG = lambda Axyz, Ag, Cg: da.dot(Ag.T, (Axyz - Cg[0, :]).T)
fGi = lambda Ax, Ay, Az, Ag, Cg, i: da.dot(Ag.T, (da.column_stack((Ax, Ay, Az))[slice(*i)] - Cg[0, :]).T)
fbinningClip = lambda x, bin2_iStEn, bin1_nAverage: da.mean(da.reshape(x[slice(*bin2_iStEn)], (-1, bin1_nAverage)), 1)
fbinning = lambda x, bin1_nAverage: da.mean(da.reshape(x, (-1, bin1_nAverage)), 1)
repeat3shift1 = lambda A2: [A2[t:(len(A2) - 2 + t)] for t in range(3)]
median3cols = lambda a, b, c: da.where(a < b, da.where(c < a, a, da.where(b < c, b, c)),
da.where(a < c, a, da.where(c < b, b, c)))
median3 = lambda x: da.hstack((np.NaN, median3cols(*repeat3shift1(x)), np.NaN))
def CartesianToEquatorial(pos, observer=[0,0,0], frame='icrs'):
"""
Convert Cartesian position coordinates to equatorial right ascension
and declination, using the specified observer location.
.. note::
RA and DEC will be returned in degrees, with RA in the range [0,360]
and DEC in the range [-90, 90].
Parameters
----------
pos : array_like
a N x 3 array holding the Cartesian position coordinates
observer : array_like
a length 3 array holding the observer location
frame : string
A string, 'icrs' or 'galactic'. The frame of the input position.
Use 'icrs' if the cartesian position is already in Equatorial.
Returns
-------
ra, dec : array_like
the right ascension and declination coordinates, in degrees. RA
will be in the range [0,360] and DEC in the range [-90, 90]
"""
# split x, y, z to signify that we do not need to have pos
# as a full chunk in the last dimension.
# this is useful when we use apply_gufunc.
x, y, z = [pos[..., i] - observer[i] for i in range(3)]
if frame == 'icrs':
# FIXME: Convert these to a gufunc that uses astropy?
# might be a step backward.
# from equatorial to equatorial
s = da.hypot(x, y)
lon = da.arctan2(y, x)
lat = da.arctan2(z, s)
# convert to degrees
lon = da.rad2deg(lon)
lat = da.rad2deg(lat)
# wrap lon to [0,360]
lon = da.mod(lon-360., 360.)
ra, dec = lon, lat
else:
from astropy.coordinates import SkyCoord
def cart_to_eq(x, y, z):
try:
sc = SkyCoord(x, y, z, representation_type='cartesian', frame=frame)
scg = sc.transform_to(frame='icrs')
scg.representation_type = 'unitspherical'
except:
sc = SkyCoord(x, y, z, representation='cartesian', frame=frame)
scg = sc.transform_to(frame='icrs')
scg.representation = 'unitspherical'
ra, dec = scg.ra.value, scg.dec.value
return ra, dec
dtype = pos.dtype
ra, dec = da.apply_gufunc(cart_to_eq, '(),(),()->(),()', x, y, z, output_dtypes=[dtype, dtype])
return da.stack((ra, dec), axis=0)
from distributed import Client, progress
import dask.array as da
dask_client = Client(
scheduler_file="/global/cscratch1/sd/rkube/scheduler.json")
with np.load("../dask_fft_data_s0000.npz") as df:
num_channels, num_fft = df["fft_data"].shape
print(num_channels, num_fft)
fft_data = da.from_array(df["fft_data"], chunks=(1, num_fft))
dask_client.persist(fft_data)
# Calculate the crosspower using the array interface
res1 = (fft_data[:2, :] * fft_data[-2:, :].conj()).mean(axis=1)
print("type res1 = ", type(res1))
res2 = da.arctan2(res1.real, res1.imag).real
print("type res2 = ", type(res2))
print("result res2 = ", res2.compute())
# Calculate the crosspower using the distributed interface
def cross_phase(ft_data, ch1, ch2):
_tmp1 = (ft_data[ch1, :] * ft_data[ch2, :].conj()).mean().compute()
print("** crosspower: type(tmp1) =", type(_tmp1))
_tmp2 = np.arctan2(_tmp1.real, _tmp1.imag).real
#_tmp2 = _tmp1.real + _tmp1.imag
return (_tmp2)
def sinfit(array, periods, dim=None, coord=None, unit='s'):
"""
Least squares sinusoidal fit.
Fit sinusoidal functions ``y = A[p] * sin(2 * pi * ax * f[1] + phi[1])``
Parameters
----------
array : xarray.DataArray
Data to be fitted
periods: float or list of float
The periods of the sinusoidal functions to be fitted
dim : str, optional
The dimension along which the data will be fitted. If not precised,
the first dimension will be used
unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional
If the fit uses a datetime dimension, the unit of the period may be
specified here.
Returns
-------
modes : Dataset
A Dataset with the amplitude and the phase for each periods
"""
if dim is None:
dim = array.dims[0]
if _utils.is_scalar(periods):
periods = [
periods,
]
n = 2 * len(periods) + 1
# Sort frequencies in ascending order
periods.sort(reverse=True)
# Re-order the array to place the fitting dimension as the first dimension
# + stack the other dimensions
array_stacked = _order_and_stack(array, dim)
dim_chunk = array.chunks[array.get_axis_num(dim)][0]
# Check if the dimension is associated with a numpy.datetime
# and normalize to use periods and time in seconds
if coord is None:
coord = array[dim]
if _utils.is_datetime(coord):
# Use the 1e-9 to scale nanoseconds to seconds (by default, xarray use
# datetime in nanoseconds
t = coord.data.astype('f8') * 1e-9
freqs = 1. / pd.to_timedelta(periods, unit=unit).total_seconds()
else:
t = coord.data
freqs = 1. / periods
# Build coefficient matrix for the fit using the exponential form
x = da.vstack([da.cos(2 * np.pi * f * t) for f in reversed(freqs)] + [
da.ones(len(t), chunks=dim_chunk),
] + [da.sin(2 * np.pi * f * t) for f in freqs]).T
x = x.rechunk((dim_chunk, n))
# Solve the least-square system
c, _, _, _ = da.linalg.lstsq(x, array_stacked.data)
# Get cosine (a) and sine (b) ampitudes
b = c[0:n // 2, ][::-1]
a = c[n // 2 + 1:, ]
# Compute amplitude and phase
amplitude = da.sqrt(a**2 + b**2)
phase = da.arctan2(b, a) * 180. / np.pi
# Store the results
new_dims = ('periods', ) + array_stacked.dims[1:]
new_coords = {
co: array_stacked.coords[co]
for co in array_stacked.coords if co != dim
}
var_dict = {
'amplitude': (new_dims, amplitude),
'phase': (new_dims, phase),
'offset': (array_stacked.dims[1:], c[n // 2, ])
}
ds = xr.Dataset(var_dict, coords=new_coords)
ds = ds.assign_coords(periods=periods)
ds['periods'].attrs['units'] = unit
# Unstack the data
modes = _unstack(ds)
return modes
assert mapper is not None
self.fullname, self.unit, self.mapper, self.column, self.extras = fullname, unit, mapper, column, extras
self.conjugate = conjugate
self.axis = axis
_identity = lambda x: x
# this dict maps short axis names into full DataMapper objects
data_mappers = OrderedDict(
_=DataMapper("", "", _identity),
amp=DataMapper("Amplitude", "", abs),
logamp=DataMapper("Log-amplitude", "", lambda x: da.log10(abs(x))),
phase=DataMapper(
"Phase", "deg",
lambda x: da.arctan2(da.imag(x), da.real(x)) * 180 / math.pi),
real=DataMapper("Real", "", da.real),
imag=DataMapper("Imag", "", da.imag),
TIME=DataMapper("Time", "s", axis=0, column="TIME", mapper=_identity),
ROW=DataMapper("Row number",
"",
column=False,
axis=0,
extras=["rows"],
mapper=lambda x, rows: rows),
BASELINE=DataMapper("Baseline",
"",
column=False,
axis=0,
extras=["baselines"],
mapper=lambda x, baselines: baselines),
def CartesianToEquatorial(pos, observer=[0,0,0], frame='icrs'):
"""
Convert Cartesian position coordinates to equatorial right ascension
and declination, using the specified observer location.
.. note::
RA and DEC will be returned in degrees, with RA in the range [0,360]
and DEC in the range [-90, 90].
Parameters
----------
pos : array_like
a N x 3 array holding the Cartesian position coordinates
observer : array_like
a length 3 array holding the observer location
frame : string
A string, 'icrs' or 'galactic'. The frame of the input position.
Use 'icrs' if the cartesian position is already in Equatorial.
Returns
-------
ra, dec : array_like
the right ascension and declination coordinates, in degrees. RA
will be in the range [0,360] and DEC in the range [-90, 90]
"""
# split x, y, z to signify that we do not need to have pos
# as a full chunk in the last dimension.
# this is useful when we use apply_gufunc.
x, y, z = [pos[..., i] - observer[i] for i in range(3)]
if frame == 'icrs':
# FIXME: Convert these to a gufunc that uses astropy?
# might be a step backward.
# from equatorial to equatorial
s = da.hypot(x, y)
lon = da.arctan2(y, x)
lat = da.arctan2(z, s)
# convert to degrees
lon = da.rad2deg(lon)
lat = da.rad2deg(lat)
# wrap lon to [0,360]
lon = da.mod(lon-360., 360.)
ra, dec = lon, lat
else:
from astropy.coordinates import SkyCoord
def cart_to_eq(x, y, z):
try:
sc = SkyCoord(x, y, z, representation_type='cartesian', frame=frame)
scg = sc.transform_to(frame='icrs')
scg.representation_type = 'unitspherical'
except:
sc = SkyCoord(x, y, z, representation='cartesian', frame=frame)
scg = sc.transform_to(frame='icrs')
scg.representation = 'unitspherical'
ra, dec = scg.ra.value, scg.dec.value
return ra, dec
dtype = pos.dtype
ra, dec = da.apply_gufunc(cart_to_eq, '(),(),()->(),()', x, y, z, output_dtypes=[dtype, dtype])
return da.stack((ra, dec), axis=0)
