# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Primary container for radio telescope antenna beams."""
import os
import warnings
import copy
import numpy as np
from scipy import interpolate
from astropy import units
from astropy.coordinates import Angle
from ..uvbase import UVBase
from .. import parameter as uvp
from .. import utils as uvutils
__all__ = ["UVBeam"]
[docs]class UVBeam(UVBase):
"""
A class for defining a radio telescope antenna beam.
Attributes
----------
UVParameter objects: For full list see UVBeam Parameters
(http://pyuvdata.readthedocs.io/en/latest/uvbeam_parameters.html).
Some are always required, some are required for certain beam_types,
antenna_types and pixel_coordinate_systems and others are always optional.
"""
coordinate_system_dict = {
"az_za": {
"axes": ["azimuth", "zen_angle"],
"description": "uniformly gridded azimuth, zenith angle coordinate system, "
"where az runs from East to North in radians",
},
"orthoslant_zenith": {
"axes": ["zenorth_x", "zenorth_y"],
"description": "orthoslant projection at zenith where y points North, "
"x point East",
},
"healpix": {
"axes": ["hpx_inds"],
"description": "HEALPix map with zenith at the north pole and "
"az, za coordinate axes (for the basis_vector_array) "
"where az runs from East to North",
},
}
interpolation_function_dict = {
"az_za_simple": {
"description": "scipy RectBivariate spline interpolation",
"func": "_interp_az_za_rect_spline",
},
"healpix_simple": {
"description": "healpix nearest-neighbor bilinear interpolation",
"func": "_interp_healpix_bilinear",
},
}
def __init__(self):
"""Create a new UVBeam object."""
# add the UVParameters to the class
self._Nfreqs = uvp.UVParameter(
"Nfreqs", description="Number of frequency channels", expected_type=int
)
self._Nspws = uvp.UVParameter(
"Nspws",
description="Number of spectral windows "
"(ie non-contiguous spectral chunks). "
"More than one spectral window is not "
"currently supported.",
expected_type=int,
)
desc = (
"Number of basis vectors specified at each pixel, options "
'are 2 or 3 (or 1 if beam_type is "power")'
)
self._Naxes_vec = uvp.UVParameter(
"Naxes_vec", description=desc, expected_type=int, acceptable_vals=[2, 3]
)
desc = (
"Number of basis vectors components specified at each pixel, options "
"are 2 or 3. Only required for E-field beams."
)
self._Ncomponents_vec = uvp.UVParameter(
"Ncomponents_vec",
description=desc,
expected_type=int,
acceptable_vals=[2, 3],
required=False,
)
desc = (
'Pixel coordinate system, options are: "'
+ '", "'.join(list(self.coordinate_system_dict.keys()))
+ '".'
)
for key in self.coordinate_system_dict:
desc = desc + (
' "'
+ key
+ '" is a '
+ self.coordinate_system_dict[key]["description"]
+ ". It has axes ["
+ ", ".join(self.coordinate_system_dict[key]["axes"])
+ "]."
)
self._pixel_coordinate_system = uvp.UVParameter(
"pixel_coordinate_system",
description=desc,
form="str",
expected_type=str,
acceptable_vals=list(self.coordinate_system_dict.keys()),
)
desc = (
"Number of elements along the first pixel axis. "
'Not required if pixel_coordinate_system is "healpix".'
)
self._Naxes1 = uvp.UVParameter(
"Naxes1", description=desc, expected_type=int, required=False
)
desc = (
"Coordinates along first pixel axis. "
'Not required if pixel_coordinate_system is "healpix".'
)
self._axis1_array = uvp.UVParameter(
"axis1_array",
description=desc,
expected_type=float,
required=False,
form=("Naxes1",),
)
desc = (
"Number of elements along the second pixel axis. "
'Not required if pixel_coordinate_system is "healpix".'
)
self._Naxes2 = uvp.UVParameter(
"Naxes2", description=desc, expected_type=int, required=False
)
desc = (
"Coordinates along second pixel axis. "
'Not required if pixel_coordinate_system is "healpix".'
)
self._axis2_array = uvp.UVParameter(
"axis2_array",
description=desc,
expected_type=float,
required=False,
form=("Naxes2",),
)
desc = (
"Healpix nside parameter. Only required if "
"pixel_coordinate_system is 'healpix'."
)
self._nside = uvp.UVParameter(
"nside", description=desc, expected_type=int, required=False
)
desc = (
'Healpix ordering parameter, allowed values are "ring" and "nested". '
'Only required if pixel_coordinate_system is "healpix".'
)
self._ordering = uvp.UVParameter(
"ordering",
description=desc,
expected_type=str,
required=False,
acceptable_vals=["ring", "nested"],
)
desc = (
"Number of healpix pixels. Only required if "
"pixel_coordinate_system is 'healpix'."
)
self._Npixels = uvp.UVParameter(
"Npixels", description=desc, expected_type=int, required=False
)
desc = (
"Healpix pixel numbers. Only required if "
"pixel_coordinate_system is 'healpix'."
)
self._pixel_array = uvp.UVParameter(
"pixel_array",
description=desc,
expected_type=int,
required=False,
form=("Npixels",),
)
desc = (
"String indicating beam type. Allowed values are 'efield', " "and 'power'."
)
self._beam_type = uvp.UVParameter(
"beam_type",
description=desc,
form="str",
expected_type=str,
acceptable_vals=["efield", "power"],
)
desc = (
"Beam basis vector components -- directions for which the "
"electric field values are recorded in the pixel coordinate system. "
'Not required if beam_type is "power". The shape depends on the '
'pixel_coordinate_system, if it is "healpix", the shape is: '
"(Naxes_vec, Ncomponents_vec, Npixels), otherwise it is "
"(Naxes_vec, Ncomponents_vec, Naxes2, Naxes1)"
)
self._basis_vector_array = uvp.UVParameter(
"basis_vector_array",
description=desc,
required=False,
expected_type=float,
form=("Naxes_vec", "Ncomponents_vec", "Naxes2", "Naxes1"),
acceptable_range=(0, 1),
tols=1e-3,
)
self._Nfeeds = uvp.UVParameter(
"Nfeeds",
description="Number of feeds. " 'Not required if beam_type is "power".',
expected_type=int,
acceptable_vals=[1, 2],
required=False,
)
desc = (
"Array of feed orientations. shape (Nfeeds). "
'options are: N/E or x/y or R/L. Not required if beam_type is "power".'
)
self._feed_array = uvp.UVParameter(
"feed_array",
description=desc,
required=False,
expected_type=str,
form=("Nfeeds",),
acceptable_vals=["N", "E", "x", "y", "R", "L"],
)
self._Npols = uvp.UVParameter(
"Npols",
description="Number of polarizations. "
'Only required if beam_type is "power".',
expected_type=int,
required=False,
)
desc = (
"Array of polarization integers, shape (Npols). "
"Uses the same convention as UVData: pseudo-stokes 1:4 (pI, pQ, pU, pV); "
"circular -1:-4 (RR, LL, RL, LR); linear -5:-8 (XX, YY, XY, YX). "
'Only required if beam_type is "power".'
)
self._polarization_array = uvp.UVParameter(
"polarization_array",
description=desc,
required=False,
expected_type=int,
form=("Npols",),
acceptable_vals=list(np.arange(-8, 0)) + list(np.arange(1, 5)),
)
desc = (
"Array of frequencies, center of the channel, "
"shape (Nspws, Nfreqs), units Hz"
)
self._freq_array = uvp.UVParameter(
"freq_array",
description=desc,
form=("Nspws", "Nfreqs"),
expected_type=float,
tols=1e-3,
) # mHz
self._spw_array = uvp.UVParameter(
"spw_array",
description="Array of spectral window " "Numbers, shape (Nspws)",
form=("Nspws",),
expected_type=int,
)
desc = (
"Normalization standard of data_array, options are: "
'"physical", "peak" or "solid_angle". Physical normalization '
"means that the frequency dependence of the antenna sensitivity "
"is included in the data_array while the frequency dependence "
"of the receiving chain is included in the bandpass_array. "
"Peak normalized means that for each frequency the data_array"
"is separately normalized such that the peak is 1 (so the beam "
"is dimensionless) and all direction-independent frequency "
'dependence is moved to the bandpass_array (if the beam_type is "efield", '
"then peak normalized means that the absolute value of the peak is 1). "
"Solid angle normalized means the peak normalized "
"beam is divided by the integral of the beam over the sphere, "
"so the beam has dimensions of 1/stradian."
)
self._data_normalization = uvp.UVParameter(
"data_normalization",
description=desc,
form="str",
expected_type=str,
acceptable_vals=["physical", "peak", "solid_angle"],
)
desc = (
"Depending on beam type, either complex E-field values "
"('efield' beam type) or power values ('power' beam type) for "
"beam model. Units are normalized to either peak or solid angle as "
"given by data_normalization. The shape depends on the beam_type "
"and pixel_coordinate_system, if it is 'healpix', the shape "
"is: (Naxes_vec, Nspws, Nfeeds or Npols, Nfreqs, Npixels), "
"otherwise it is "
"(Naxes_vec, Nspws, Nfeeds or Npols, Nfreqs, Naxes2, Naxes1)."
)
self._data_array = uvp.UVParameter(
"data_array",
description=desc,
expected_type=complex,
form=("Naxes_vec", "Nspws", "Nfeeds", "Nfreqs", "Naxes2", "Naxes1"),
tols=1e-3,
)
desc = (
"Frequency dependence of the beam. Depending on the data_normalization, "
"this may contain only the frequency dependence of the receiving "
'chain ("physical" normalization) or all the frequency dependence '
'("peak" normalization).'
)
self._bandpass_array = uvp.UVParameter(
"bandpass_array",
description=desc,
expected_type=float,
form=("Nspws", "Nfreqs"),
tols=1e-3,
)
# --------- metadata -------------
self._telescope_name = uvp.UVParameter(
"telescope_name",
description="Name of telescope " "(string)",
form="str",
expected_type=str,
)
self._feed_name = uvp.UVParameter(
"feed_name",
description="Name of physical feed " "(string)",
form="str",
expected_type=str,
)
self._feed_version = uvp.UVParameter(
"feed_version",
description="Version of physical feed " "(string)",
form="str",
expected_type=str,
)
self._model_name = uvp.UVParameter(
"model_name",
description="Name of beam model " "(string)",
form="str",
expected_type=str,
)
self._model_version = uvp.UVParameter(
"model_version",
description="Version of beam model " "(string)",
form="str",
expected_type=str,
)
self._history = uvp.UVParameter(
"history",
description="String of history, units English",
form="str",
expected_type=str,
)
# ---------- phased_array stuff -------------
desc = (
'String indicating antenna type. Allowed values are "simple", and '
'"phased_array"'
)
self._antenna_type = uvp.UVParameter(
"antenna_type",
form="str",
expected_type=str,
description=desc,
acceptable_vals=["simple", "phased_array"],
)
desc = (
'Required if antenna_type = "phased_array". Number of elements '
"in phased array"
)
self._Nelements = uvp.UVParameter(
"Nelements", required=False, description=desc, expected_type=int
)
desc = (
'Required if antenna_type = "phased_array". Element coordinate '
"system, options are: N-E or x-y"
)
self._element_coordinate_system = uvp.UVParameter(
"element_coordinate_system",
required=False,
description=desc,
expected_type=str,
acceptable_vals=["N-E", "x-y"],
)
desc = (
'Required if antenna_type = "phased_array". Array of element '
"locations in element coordinate system, shape: (2, Nelements)"
)
self._element_location_array = uvp.UVParameter(
"element_location_array",
required=False,
description=desc,
form=(2, "Nelements"),
expected_type=float,
)
desc = (
'Required if antenna_type = "phased_array". Array of element '
"delays, units: seconds, shape: (Nelements)"
)
self._delay_array = uvp.UVParameter(
"delay_array",
required=False,
description=desc,
form=("Nelements",),
expected_type=float,
)
desc = (
'Required if antenna_type = "phased_array". Array of element '
"gains, units: dB, shape: (Nelements)"
)
self._gain_array = uvp.UVParameter(
"gain_array",
required=False,
description=desc,
form=("Nelements",),
expected_type=float,
)
desc = (
'Required if antenna_type = "phased_array". Matrix of complex '
"element couplings, units: dB, "
"shape: (Nelements, Nelements, Nfeed, Nfeed, Nspws, Nfreqs)"
)
self._coupling_matrix = uvp.UVParameter(
"coupling_matrix",
required=False,
description=desc,
form=("Nelements", "Nelements", "Nfeed", "Nfeed", "Nspws", "Nfreqs"),
expected_type=complex,
)
# -------- extra, non-required parameters ----------
desc = (
"Orientation of the physical dipole corresponding to what is "
'labelled as the x polarization. Options are "east" '
'(indicating east/west orientation) and "north" (indicating '
"north/south orientation)"
)
self._x_orientation = uvp.UVParameter(
"x_orientation",
description=desc,
required=False,
expected_type=str,
acceptable_vals=["east", "north"],
)
desc = (
"String indicating interpolation function. Must be set to use "
'the interp_* methods. Allowed values are : "'
+ '", "'.join(list(self.interpolation_function_dict.keys()))
+ '".'
)
self._interpolation_function = uvp.UVParameter(
"interpolation_function",
required=False,
form="str",
expected_type=str,
description=desc,
acceptable_vals=list(self.interpolation_function_dict.keys()),
)
desc = (
"String indicating frequency interpolation kind. "
"See scipy.interpolate.interp1d for details. Default is linear."
)
self._freq_interp_kind = uvp.UVParameter(
"freq_interp_kind",
required=False,
form="str",
expected_type=str,
description=desc,
)
self.freq_interp_kind = "linear"
desc = (
"Any user supplied extra keywords, type=dict. Keys should be "
"8 character or less strings if writing to beam fits files. "
'Use the special key "comment" for long multi-line string comments.'
)
self._extra_keywords = uvp.UVParameter(
"extra_keywords",
required=False,
description=desc,
value={},
spoof_val={},
expected_type=dict,
)
desc = (
"Reference impedance of the beam model. The radiated E-farfield "
"or the realised gain depend on the impedance of the port used to "
"excite the simulation. This is the reference impedance (Z0) of "
"the simulation. units: Ohms"
)
self._reference_impedance = uvp.UVParameter(
"reference_impedance",
required=False,
description=desc,
expected_type=float,
tols=1e-3,
)
desc = "Array of receiver temperatures, shape (Nspws, Nfreqs), units K"
self._receiver_temperature_array = uvp.UVParameter(
"receiver_temperature_array",
required=False,
description=desc,
form=("Nspws", "Nfreqs"),
expected_type=float,
tols=1e-3,
)
desc = "Array of antenna losses, shape (Nspws, Nfreqs), units dB?"
self._loss_array = uvp.UVParameter(
"loss_array",
required=False,
description=desc,
form=("Nspws", "Nfreqs"),
expected_type=float,
tols=1e-3,
)
desc = "Array of antenna-amplifier mismatches, shape (Nspws, Nfreqs), units ?"
self._mismatch_array = uvp.UVParameter(
"mismatch_array",
required=False,
description=desc,
form=("Nspws", "Nfreqs"),
expected_type=float,
tols=1e-3,
)
desc = (
"S parameters of receiving chain, shape (4, Nspws, Nfreqs), "
"ordering: s11, s12, s21, s22. see "
"https://en.wikipedia.org/wiki/Scattering_parameters#Two-Port_S-Parameters"
)
self._s_parameters = uvp.UVParameter(
"s_parameters",
required=False,
description=desc,
form=(4, "Nspws", "Nfreqs"),
expected_type=float,
tols=1e-3,
)
super(UVBeam, self).__init__()
def _set_cs_params(self):
"""Set parameters depending on pixel_coordinate_system."""
if self.pixel_coordinate_system == "healpix":
self._Naxes1.required = False
self._axis1_array.required = False
self._Naxes2.required = False
self._axis2_array.required = False
self._nside.required = True
self._ordering.required = True
self._Npixels.required = True
self._pixel_array.required = True
self._basis_vector_array.form = ("Naxes_vec", "Ncomponents_vec", "Npixels")
if self.beam_type == "power":
self._data_array.form = (
"Naxes_vec",
"Nspws",
"Npols",
"Nfreqs",
"Npixels",
)
else:
self._data_array.form = (
"Naxes_vec",
"Nspws",
"Nfeeds",
"Nfreqs",
"Npixels",
)
else:
self._Naxes1.required = True
self._axis1_array.required = True
self._Naxes2.required = True
self._axis2_array.required = True
if self.pixel_coordinate_system == "az_za":
self._axis1_array.acceptable_range = [0, 2.0 * np.pi]
self._axis2_array.acceptable_range = [0, np.pi]
self._nside.required = False
self._ordering.required = False
self._Npixels.required = False
self._pixel_array.required = False
self._basis_vector_array.form = (
"Naxes_vec",
"Ncomponents_vec",
"Naxes2",
"Naxes1",
)
if self.beam_type == "power":
self._data_array.form = (
"Naxes_vec",
"Nspws",
"Npols",
"Nfreqs",
"Naxes2",
"Naxes1",
)
else:
self._data_array.form = (
"Naxes_vec",
"Nspws",
"Nfeeds",
"Nfreqs",
"Naxes2",
"Naxes1",
)
def _set_efield(self):
"""Set beam_type to 'efield' and adjust required parameters."""
self.beam_type = "efield"
self._Naxes_vec.acceptable_vals = [2, 3]
self._Ncomponents_vec.required = True
self._basis_vector_array.required = True
self._Nfeeds.required = True
self._feed_array.required = True
self._Npols.required = False
self._polarization_array.required = False
self._data_array.expected_type = complex
# call set_cs_params to fix data_array form
self._set_cs_params()
def _set_power(self):
"""Set beam_type to 'power' and adjust required parameters."""
self.beam_type = "power"
self._Naxes_vec.acceptable_vals = [1, 2, 3]
self._basis_vector_array.required = False
self._Ncomponents_vec.required = False
self._Nfeeds.required = False
self._feed_array.required = False
self._Npols.required = True
self._polarization_array.required = True
# If cross pols are included, the power beam is complex. Otherwise it's real
self._data_array.expected_type = float
for pol in self.polarization_array:
if pol in [3, 4, -3, -4, -7, -8]:
self._data_array.expected_type = complex
# call set_cs_params to fix data_array form
self._set_cs_params()
def _set_simple(self):
"""Set antenna_type to 'simple' and adjust required parameters."""
self.antenna_type = "simple"
self._Nelements.required = False
self._element_coordinate_system.required = False
self._element_location_array.required = False
self._delay_array.required = False
self._gain_array.required = False
self._coupling_matrix.required = False
def _set_phased_array(self):
"""Set antenna_type to 'phased_array' and adjust required parameters."""
self.antenna_type = "phased_array"
self._Nelements.required = True
self._element_coordinate_system.required = True
self._element_location_array.required = True
self._delay_array.required = True
self._gain_array.required = True
self._coupling_matrix.required = True
[docs] def check(self, check_extra=True, run_check_acceptability=True):
"""
Check that all required parameters are set reasonably.
Check that required parameters exist and have appropriate shapes.
Optionally check if the values are acceptable.
Parameters
----------
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check if values in required parameters are acceptable.
"""
# first make sure the required parameters and forms are set properly
# for the pixel_coordinate_system
self._set_cs_params()
# first run the basic check from UVBase
super(UVBeam, self).check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
# check that basis_vector_array are basis vectors
if self.basis_vector_array is not None:
if np.max(np.linalg.norm(self.basis_vector_array, axis=1)) > (1 + 1e-15):
raise ValueError("basis vectors must have lengths of 1 or less.")
# issue warning if extra_keywords keys are longer than 8 characters
for key in list(self.extra_keywords.keys()):
if len(key) > 8:
warnings.warn(
"key {key} in extra_keywords is longer than 8 "
"characters. It will be truncated to 8 if written "
"to a fits file format.".format(key=key)
)
# issue warning if extra_keywords values are lists, arrays or dicts
for key, value in self.extra_keywords.items():
if isinstance(value, (list, dict, np.ndarray)):
warnings.warn(
"{key} in extra_keywords is a list, array or dict, "
"which will raise an error when writing fits "
"files".format(key=key)
)
return True
[docs] def peak_normalize(self):
"""Convert to peak normalization."""
if self.data_normalization == "solid_angle":
raise NotImplementedError(
"Conversion from solid_angle to peak "
"normalization is not yet implemented"
)
for i in range(self.Nfreqs):
max_val = abs(self.data_array[:, :, :, i, :]).max()
self.data_array[:, :, :, i, :] /= max_val
self.bandpass_array[:, i] *= max_val
self.data_normalization = "peak"
[docs] def efield_to_power(
self,
calc_cross_pols=True,
keep_basis_vector=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
inplace=True,
):
"""
Convert E-field beam to power beam.
Parameters
----------
calc_cross_pols : bool
If True, calculate the crossed polarization beams
(e.g. 'xy' and 'yx'), otherwise only calculate the same
polarization beams (e.g. 'xx' and 'yy').
keep_basis_vector : bool
If True, keep the directionality information and
just multiply the efields for each basis vector separately
(caution: this is not what is standardly meant by the power beam).
inplace : bool
Option to apply conversion directly on self or to return a new
UVBeam object.
run_check : bool
Option to check for the existence and proper shapes of the required
parameters after converting to power.
run_check_acceptability : bool
Option to check acceptable range of the values of required parameters
after converting to power.
check_extra : bool
Option to check optional parameters as well as required ones.
"""
if inplace:
beam_object = self
else:
beam_object = self.copy()
if beam_object.beam_type != "efield":
raise ValueError("beam_type must be efield")
efield_data = beam_object.data_array
efield_naxes_vec = beam_object.Naxes_vec
feed_pol_order = [(0, 0)]
if beam_object.Nfeeds > 1:
feed_pol_order.append((1, 1))
if calc_cross_pols:
beam_object.Npols = beam_object.Nfeeds ** 2
if beam_object.Nfeeds > 1:
feed_pol_order.extend([(0, 1), (1, 0)])
else:
beam_object.Npols = beam_object.Nfeeds
pol_strings = []
for pair in feed_pol_order:
pol_strings.append(
beam_object.feed_array[pair[0]] + beam_object.feed_array[pair[1]]
)
beam_object.polarization_array = np.array(
[
uvutils.polstr2num(ps.upper(), x_orientation=self.x_orientation)
for ps in pol_strings
]
)
if not keep_basis_vector:
beam_object.Naxes_vec = 1
# adjust requirements, fix data_array form
beam_object._set_power()
power_data = np.zeros(
beam_object._data_array.expected_shape(beam_object), dtype=np.complex128
)
if keep_basis_vector:
for pol_i, pair in enumerate(feed_pol_order):
power_data[:, :, pol_i] = efield_data[:, :, pair[0]] * np.conj(
efield_data[:, :, pair[1]]
)
else:
for pol_i, pair in enumerate(feed_pol_order):
if efield_naxes_vec == 2:
for comp_i in range(2):
power_data[0, :, pol_i] += (
(
efield_data[0, :, pair[0]]
* np.conj(efield_data[0, :, pair[1]])
)
* beam_object.basis_vector_array[0, comp_i] ** 2
+ (
efield_data[1, :, pair[0]]
* np.conj(efield_data[1, :, pair[1]])
)
* beam_object.basis_vector_array[1, comp_i] ** 2
+ (
efield_data[0, :, pair[0]]
* np.conj(efield_data[1, :, pair[1]])
+ efield_data[1, :, pair[0]]
* np.conj(efield_data[0, :, pair[1]])
)
* (
beam_object.basis_vector_array[0, comp_i]
* beam_object.basis_vector_array[1, comp_i]
)
)
else:
raise ValueError(
"Conversion to power with 3-vector efields "
"is not currently supported because we have "
"no examples to work with."
)
power_data = np.real_if_close(power_data, tol=10)
beam_object.data_array = power_data
beam_object.Nfeeds = None
beam_object.feed_array = None
if not keep_basis_vector:
beam_object.basis_vector_array = None
beam_object.Ncomponents_vec = None
history_update_string = " Converted from efield to power using pyuvdata."
beam_object.history = beam_object.history + history_update_string
if run_check:
beam_object.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return beam_object
def _stokes_matrix(self, pol_index):
"""
Calculate Pauli matrices for pseudo-Stokes conversion.
Derived from https://arxiv.org/pdf/1401.2095.pdf, the Pauli
indices are reordered from the quantum mechanical
convention to an order which gives the ordering of the pseudo-Stokes vector
['pI', 'pQ', 'pU, 'pV'].
Parameters
----------
pol_index : int
Polarization index for which the Pauli matrix is generated, the index
must lie between 0 and 3 ('pI': 0, 'pQ': 1, 'pU': 2, 'pV':3).
Returns
-------
array of float
Pauli matrix for pol_index. Shape: (2, 2)
"""
if pol_index < 0:
raise ValueError("n must be positive integer.")
if pol_index > 4:
raise ValueError("n should lie between 0 and 3.")
if pol_index == 0:
pauli_mat = np.array([[1.0, 0.0], [0.0, 1.0]])
if pol_index == 1:
pauli_mat = np.array([[1.0, 0.0], [0.0, -1.0]])
if pol_index == 2:
pauli_mat = np.array([[0.0, 1.0], [1.0, 0.0]])
if pol_index == 3:
pauli_mat = np.array([[0.0, -1.0j], [1.0j, 0.0]])
return pauli_mat
def _construct_mueller(self, jones, pol_index1, pol_index2):
"""
Generate Mueller components.
Following https://arxiv.org/pdf/1802.04151.pdf. Using equation:
Mij = Tr(J sigma_i J^* sigma_j)
where sigma_i and sigma_j are Pauli matrices.
Parameters
----------
jones : array of float
Jones matrices containing the electric field for the dipole arms
or linear polarizations. Shape: (Npixels, 2, 2) for Healpix beams or
(Naxes1 * Naxes2, 2, 2) otherwise.
pol_index1 : int
Polarization index referring to the first index of Mij (i).
pol_index2 : int
Polarization index referring to the second index of Mij (j).
Returns
-------
array of float
Mueller array containing the Mij values, shape: (Npixels,) for Healpix beams
or (Naxes1 * Naxes2,) otherwise.
"""
pauli_mat1 = self._stokes_matrix(pol_index1)
pauli_mat2 = self._stokes_matrix(pol_index2)
mueller = 0.5 * np.einsum(
"...ab,...bc,...cd,...ad", pauli_mat1, jones, pauli_mat2, np.conj(jones)
)
mueller = np.abs(mueller)
return mueller
[docs] def efield_to_pstokes(
self,
inplace=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Convert E-field to pseudo-stokes power.
Following https://arxiv.org/pdf/1802.04151.pdf, using the equation:
M_ij = Tr(sigma_i J sigma_j J^*)
where sigma_i and sigma_j are Pauli matrices.
Parameters
----------
inplace : bool
Option to apply conversion directly on self or to return a new
UVBeam object.
run_check : bool
Option to check for the existence and proper shapes of the required
parameters after converting to power.
run_check_acceptability : bool
Option to check acceptable range of the values of required parameters
after converting to power.
check_extra : bool
Option to check optional parameters as well as required ones.
"""
if inplace:
beam_object = self
else:
beam_object = self.copy()
if beam_object.beam_type != "efield":
raise ValueError("beam_type must be efield.")
efield_data = beam_object.data_array
_sh = beam_object.data_array.shape
Nfreqs = beam_object.Nfreqs
if self.pixel_coordinate_system != "healpix":
Naxes2, Naxes1 = beam_object.Naxes2, beam_object.Naxes1
npix = Naxes1 * Naxes2
efield_data = efield_data.reshape(efield_data.shape[:-2] + (npix,))
_sh = efield_data.shape
# construct jones matrix containing the electric field
pol_strings = ["pI", "pQ", "pU", "pV"]
power_data = np.zeros(
(1, 1, len(pol_strings), _sh[-2], _sh[-1]), dtype=np.complex128
)
beam_object.polarization_array = np.array(
[
uvutils.polstr2num(ps.upper(), x_orientation=self.x_orientation)
for ps in pol_strings
]
)
for fq_i in range(Nfreqs):
jones = np.zeros((_sh[-1], 2, 2), dtype=np.complex128)
pol_strings = ["pI", "pQ", "pU", "pV"]
jones[:, 0, 0] = efield_data[0, 0, 0, fq_i, :]
jones[:, 0, 1] = efield_data[0, 0, 1, fq_i, :]
jones[:, 1, 0] = efield_data[1, 0, 0, fq_i, :]
jones[:, 1, 1] = efield_data[1, 0, 1, fq_i, :]
for pol_i in range(len(pol_strings)):
power_data[:, :, pol_i, fq_i, :] = self._construct_mueller(
jones, pol_i, pol_i
)
if self.pixel_coordinate_system != "healpix":
power_data = power_data.reshape(power_data.shape[:-1] + (Naxes2, Naxes1))
beam_object.data_array = power_data
beam_object.polarization_array = np.array(
[
uvutils.polstr2num(ps.upper(), x_orientation=self.x_orientation)
for ps in pol_strings
]
)
beam_object.Naxes_vec = 1
beam_object._set_power()
history_update_string = (
" Converted from efield to pseudo-stokes power using pyuvdata."
)
beam_object.Npols = beam_object.Nfeeds ** 2
beam_object.history = beam_object.history + history_update_string
beam_object.Nfeeds = None
beam_object.feed_array = None
beam_object.basis_vector_array = None
beam_object.Ncomponents_vec = None
if run_check:
beam_object.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return beam_object
def _interp_freq(self, freq_array, kind="linear", tol=1.0):
"""
Interpolate function along frequency axis.
Parameters
----------
freq_array : array_like of floats
Frequency values to interpolate to.
kind : str
Interpolation method to use frequency.
See scipy.interpolate.interp1d for details.
Returns
-------
interp_data : array_like of float or complex
The array of interpolated data values,
shape: (Naxes_vec, Nspws, Nfeeds or Npols, freq_array.size,
Npixels or (Naxis2, Naxis1))
interp_bandpass : array_like of float
The interpolated bandpass. shape: (Nspws, freq_array.size)
"""
assert isinstance(freq_array, np.ndarray)
assert freq_array.ndim == 1
# use the beam at nearest neighbors if not kind is 'nearest'
if kind == "nearest":
freq_dists = np.abs(self.freq_array - freq_array.reshape(-1, 1))
nearest_inds = np.argmin(freq_dists, axis=1)
interp_arrays = [
self.data_array[:, :, :, nearest_inds, :],
self.bandpass_array[:, nearest_inds],
]
# otherwise interpolate the beam
else:
if self.Nfreqs == 1:
raise ValueError("Only one frequency in UVBeam so cannot interpolate.")
if np.min(freq_array) < np.min(self.freq_array) or np.max(
freq_array
) > np.max(self.freq_array):
raise ValueError(
"at least one interpolation frequency is outside of "
"the UVBeam freq_array range."
)
def get_lambda(real_lut, imag_lut=None):
# Returns function objects for interpolation reuse
if imag_lut is None:
return lambda freqs: real_lut(freqs)
else:
return lambda freqs: (real_lut(freqs) + 1j * imag_lut(freqs))
interp_arrays = []
for data, ax in zip([self.data_array, self.bandpass_array], [3, 1]):
if np.iscomplexobj(data):
# interpolate real and imaginary parts separately
real_lut = interpolate.interp1d(
self.freq_array[0, :], data.real, kind=kind, axis=ax
)
imag_lut = interpolate.interp1d(
self.freq_array[0, :], data.imag, kind=kind, axis=ax
)
lut = get_lambda(real_lut, imag_lut)
else:
lut = interpolate.interp1d(
self.freq_array[0, :], data, kind=kind, axis=ax
)
lut = get_lambda(lut)
interp_arrays.append(lut(freq_array))
return tuple(interp_arrays)
def _interp_az_za_rect_spline(
self,
az_array,
za_array,
freq_array,
freq_interp_kind="linear",
freq_interp_tol=1.0,
polarizations=None,
reuse_spline=False,
spline_opts=None,
):
"""
Interpolate in az_za coordinate system with a simple spline.
Parameters
----------
az_array : array_like of floats
Azimuth values to interpolate to in radians, specifying the azimuth
positions for every interpolation point (same length as `za_array`).
za_array : array_like of floats
Zenith values to interpolate to in radians, specifying the zenith
positions for every interpolation point (same length as `az_array`).
freq_array : array_like of floats
Frequency values to interpolate to.
freq_interp_kind : str
Interpolation method to use frequency.
See scipy.interpolate.interp1d for details.
polarizations : list of str
polarizations to interpolate if beam_type is 'power'.
Default is all polarizations in self.polarization_array.
reuse_spline : bool
Save the interpolation functions for reuse.
spline_opts : dict
Options (kx, ky, s) for numpy.RectBivariateSpline.
Returns
-------
interp_data : array_like of float or complex
The array of interpolated data values,
shape: (Naxes_vec, Nspws, Nfeeds or Npols, Nfreqs, az_array.size)
interp_basis_vector : array_like of float
The array of interpolated basis vectors,
shape: (Naxes_vec, Ncomponents_vec, az_array.size)
interp_bandpass : array_like of float
The interpolated bandpass. shape: (Nspws, freq_array.size)
"""
if self.pixel_coordinate_system != "az_za":
raise ValueError('pixel_coordinate_system must be "az_za"')
if freq_array is not None:
assert isinstance(freq_array, np.ndarray)
input_data_array, interp_bandpass = self._interp_freq(
freq_array, kind=freq_interp_kind, tol=freq_interp_tol
)
input_nfreqs = freq_array.size
else:
input_data_array = self.data_array
input_nfreqs = self.Nfreqs
freq_array = self.freq_array[0]
interp_bandpass = self.bandpass_array[0]
if az_array is None or za_array is None:
return input_data_array, self.basis_vector_array, interp_bandpass
assert isinstance(az_array, np.ndarray)
assert isinstance(za_array, np.ndarray)
assert az_array.ndim == 1
assert az_array.shape == za_array.shape
npoints = az_array.size
axis1_diff = np.diff(self.axis1_array)[0]
axis2_diff = np.diff(self.axis2_array)[0]
max_axis_diff = np.max([axis1_diff, axis2_diff])
phi_length = np.abs(self.axis1_array[0] - self.axis1_array[-1]) + axis1_diff
phi_vals, theta_vals = np.meshgrid(self.axis1_array, self.axis2_array)
assert input_data_array.shape[3] == input_nfreqs
if np.iscomplexobj(input_data_array):
data_type = np.complex128
else:
data_type = np.float64
if np.isclose(phi_length, 2 * np.pi, atol=axis1_diff):
# phi wraps around, extend array in each direction to improve interpolation
extend_length = 3
phi_use = np.concatenate(
(
np.flip(phi_vals[:, :extend_length] * (-1) - axis1_diff, axis=1),
phi_vals,
phi_vals[:, -1 * extend_length :] + extend_length * axis1_diff,
),
axis=1,
)
theta_use = np.concatenate(
(
theta_vals[:, :extend_length],
theta_vals,
theta_vals[:, -1 * extend_length :],
),
axis=1,
)
low_slice = input_data_array[:, :, :, :, :, :extend_length]
high_slice = input_data_array[:, :, :, :, :, -1 * extend_length :]
data_use = np.concatenate((high_slice, input_data_array, low_slice), axis=5)
else:
phi_use = phi_vals
theta_use = theta_vals
data_use = input_data_array
if self.basis_vector_array is not None:
if np.any(self.basis_vector_array[0, 1, :] > 0) or np.any(
self.basis_vector_array[1, 0, :] > 0
):
# Input basis vectors are not aligned to the native theta/phi
# coordinate system
raise NotImplementedError(
"interpolation for input basis "
"vectors that are not aligned to the "
"native theta/phi coordinate system "
"is not yet supported"
)
else:
# The basis vector array comes in defined at the rectangular grid.
# Redefine it for the interpolation points
interp_basis_vector = np.zeros(
[self.Naxes_vec, self.Ncomponents_vec, npoints]
)
interp_basis_vector[0, 0, :] = np.ones(npoints) # theta hat
interp_basis_vector[1, 1, :] = np.ones(npoints) # phi hat
else:
interp_basis_vector = None
def get_lambda(real_lut, imag_lut=None):
# Returns function objects for interpolation reuse
if imag_lut is None:
return lambda za, az: real_lut(za, az, grid=False)
else:
return lambda za, az: (
real_lut(za, az, grid=False) + 1j * imag_lut(za, az, grid=False)
)
# Npols is only defined for power beams. For E-field beams need Nfeeds.
if self.beam_type == "power":
# get requested polarization indices
if polarizations is None:
Npol_feeds = self.Npols
pol_inds = np.arange(Npol_feeds)
else:
pols = [
uvutils.polstr2num(p, x_orientation=self.x_orientation)
for p in polarizations
]
pol_inds = []
for pol in pols:
if pol not in self.polarization_array:
raise ValueError(
"Requested polarization {} not found "
"in self.polarization_array".format(pol)
)
pol_inds.append(np.where(self.polarization_array == pol)[0][0])
pol_inds = np.asarray(pol_inds)
Npol_feeds = len(pol_inds)
else:
Npol_feeds = self.Nfeeds
pol_inds = np.arange(Npol_feeds)
data_shape = (self.Naxes_vec, self.Nspws, Npol_feeds, input_nfreqs, npoints)
interp_data = np.zeros(data_shape, dtype=data_type)
if spline_opts is None or not isinstance(spline_opts, dict):
spline_opts = {}
if reuse_spline and not hasattr(self, "saved_interp_functions"):
int_dict = {}
self.saved_interp_functions = int_dict
for index1 in range(self.Nspws):
for index3 in range(input_nfreqs):
freq = freq_array[index3]
for index0 in range(self.Naxes_vec):
for pol_return_ind, index2 in enumerate(pol_inds):
do_interp = True
key = (index1, freq, index2, index0)
if reuse_spline:
if key in self.saved_interp_functions.keys():
do_interp = False
lut = self.saved_interp_functions[key]
if do_interp:
if np.iscomplexobj(data_use):
# interpolate real and imaginary parts separately
real_lut = interpolate.RectBivariateSpline(
theta_use[:, 0],
phi_use[0, :],
data_use[index0, index1, index2, index3, :].real,
**spline_opts,
)
imag_lut = interpolate.RectBivariateSpline(
theta_use[:, 0],
phi_use[0, :],
data_use[index0, index1, index2, index3, :].imag,
**spline_opts,
)
lut = get_lambda(real_lut, imag_lut)
else:
lut = interpolate.RectBivariateSpline(
theta_use[:, 0],
phi_use[0, :],
data_use[index0, index1, index2, index3, :],
**spline_opts,
)
lut = get_lambda(lut)
if reuse_spline:
self.saved_interp_functions[key] = lut
if index0 == 0 and index1 == 0 and index2 == 0 and index3 == 0:
for point_i in range(npoints):
pix_dists = np.sqrt(
(theta_use - za_array[point_i]) ** 2.0
+ (phi_use - az_array[point_i]) ** 2.0
)
if np.min(pix_dists) > (max_axis_diff * 2.0):
raise ValueError(
"at least one interpolation location "
"is outside of the UVBeam pixel coverage."
)
interp_data[index0, index1, pol_return_ind, index3, :] = lut(
za_array, az_array
)
return interp_data, interp_basis_vector, interp_bandpass
def _interp_healpix_bilinear(
self,
az_array,
za_array,
freq_array,
freq_interp_kind="linear",
freq_interp_tol=1.0,
polarizations=None,
reuse_spline=False,
):
"""
Interpolate in Healpix coordinate system with a simple bilinear function.
Parameters
----------
az_array : array_like of floats
Azimuth values to interpolate to in radians, specifying the azimuth
positions for every interpolation point (same length as `za_array`).
za_array : array_like of floats
Zenith values to interpolate to in radians, specifying the zenith
positions for every interpolation point (same length as `az_array`).
freq_array : array_like of floats
Frequency values to interpolate to.
freq_interp_kind : str
Interpolation method to use frequency.
See scipy.interpolate.interp1d for details.
polarizations : list of str
polarizations to interpolate if beam_type is 'power'.
Default is all polarizations in self.polarization_array.
Returns
-------
interp_data : array_like of float or complex
The array of interpolated data values,
shape: (Naxes_vec, Nspws, Nfeeds or Npols, Nfreqs, az_array.size)
interp_basis_vector : array_like of float
The array of interpolated basis vectors,
shape: (Naxes_vec, Ncomponents_vec, az_array.size)
interp_bandpass : array_like of float
The interpolated bandpass. shape: (Nspws, freq_array.size)
"""
try:
from astropy_healpix import HEALPix
except ImportError as e: # pragma: no cover
raise ImportError(
"astropy_healpix is not installed but is "
"required for healpix functionality. "
'Install "astropy-healpix" using conda or pip.'
) from e
if self.pixel_coordinate_system != "healpix":
raise ValueError('pixel_coordinate_system must be "healpix"')
if not self.Npixels == 12 * self.nside ** 2:
raise ValueError(
"simple healpix interpolation requires full sky healpix maps."
)
if not np.max(np.abs(np.diff(self.pixel_array))) == 1:
raise ValueError(
"simple healpix interpolation requires healpix pixels to be in order."
)
if freq_array is not None:
assert isinstance(freq_array, np.ndarray)
input_data_array, interp_bandpass = self._interp_freq(
freq_array, kind=freq_interp_kind, tol=freq_interp_tol
)
input_nfreqs = freq_array.size
else:
input_data_array = self.data_array
input_nfreqs = self.Nfreqs
freq_array = self.freq_array[0]
interp_bandpass = self.bandpass_array[0]
if az_array is None or za_array is None:
return input_data_array, self.basis_vector_array, interp_bandpass
assert isinstance(az_array, np.ndarray)
assert isinstance(za_array, np.ndarray)
assert az_array.ndim == 1
assert az_array.shape == za_array.shape
npoints = az_array.size
# Npols is only defined for power beams. For E-field beams need Nfeeds.
if self.beam_type == "power":
# get requested polarization indices
if polarizations is None:
Npol_feeds = self.Npols
pol_inds = np.arange(Npol_feeds)
else:
pols = [
uvutils.polstr2num(p, x_orientation=self.x_orientation)
for p in polarizations
]
pol_inds = []
for pol in pols:
if pol not in self.polarization_array:
raise ValueError(
f"Requested polarization {pol} not found "
"in self.polarization_array"
)
pol_inds.append(np.where(self.polarization_array == pol)[0][0])
pol_inds = np.asarray(pol_inds)
Npol_feeds = len(pol_inds)
else:
Npol_feeds = self.Nfeeds
pol_inds = np.arange(Npol_feeds)
if np.iscomplexobj(input_data_array):
data_type = np.complex128
else:
data_type = np.float64
interp_data = np.zeros(
(self.Naxes_vec, self.Nspws, Npol_feeds, input_nfreqs, len(az_array)),
dtype=data_type,
)
if self.basis_vector_array is not None:
if np.any(self.basis_vector_array[0, 1, :] > 0) or np.any(
self.basis_vector_array[1, 0, :] > 0
):
""" Input basis vectors are not aligned to the native theta/phi
coordinate system """
raise NotImplementedError(
"interpolation for input basis "
"vectors that are not aligned to the "
"native theta/phi coordinate system "
"is not yet supported"
)
else:
""" The basis vector array comes in defined at the rectangular grid.
Redefine it for the interpolation points """
interp_basis_vector = np.zeros(
[self.Naxes_vec, self.Ncomponents_vec, npoints]
)
interp_basis_vector[0, 0, :] = np.ones(npoints) # theta hat
interp_basis_vector[1, 1, :] = np.ones(npoints) # phi hat
else:
interp_basis_vector = None
hp_obj = HEALPix(nside=self.nside, order=self.ordering)
lat_array = Angle(np.pi / 2, units.radian) - Angle(za_array, units.radian)
lon_array = Angle(az_array, units.radian)
for index1 in range(self.Nspws):
for index3 in range(input_nfreqs):
for index0 in range(self.Naxes_vec):
for index2 in range(Npol_feeds):
if np.iscomplexobj(input_data_array):
# interpolate real and imaginary parts separately
real_hmap = hp_obj.interpolate_bilinear_lonlat(
lon_array,
lat_array,
input_data_array[
index0, index1, pol_inds[index2], index3, :
].real,
)
imag_hmap = hp_obj.interpolate_bilinear_lonlat(
lon_array,
lat_array,
input_data_array[
index0, index1, pol_inds[index2], index3, :
].imag,
)
hmap = real_hmap + 1j * imag_hmap
else:
# interpolate once
hmap = hp_obj.interpolate_bilinear_lonlat(
lon_array,
lat_array,
input_data_array[
index0, index1, pol_inds[index2], index3, :
],
)
interp_data[index0, index1, index2, index3, :] = hmap
return interp_data, interp_basis_vector, interp_bandpass
[docs] def interp(
self,
az_array=None,
za_array=None,
az_za_grid=False,
healpix_nside=None,
healpix_inds=None,
freq_array=None,
freq_interp_tol=1.0,
polarizations=None,
return_bandpass=False,
reuse_spline=False,
spline_opts=None,
new_object=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Interpolate beam to given frequency, az & za locations or Healpix pixel centers.
Parameters
----------
az_array : array_like of floats, optional
Azimuth values to interpolate to in radians, either specifying the
azimuth positions for every interpolation point or specifying the
azimuth vector for a meshgrid if az_za_grid is True.
za_array : array_like of floats, optional
Zenith values to interpolate to in radians, either specifying the
zenith positions for every interpolation point or specifying the
zenith vector for a meshgrid if az_za_grid is True.
az_za_grid : bool
Option to treat the `az_array` and `za_array` as the input vectors
for points on a mesh grid.
healpix_nside : int, optional
HEALPix nside parameter if interpolating to HEALPix pixels.
healpix_inds : array_like of int, optional
HEALPix indices to interpolate to. Defaults to all indices in the
map if `healpix_nside` is set and `az_array` and `za_array` are None.
freq_array : array_like of floats, optional
Frequency values to interpolate to.
freq_interp_tol : float
Frequency distance tolerance [Hz] of nearest neighbors.
If *all* elements in freq_array have nearest neighbor distances within
the specified tolerance then return the beam at each nearest neighbor,
otherwise interpolate the beam.
polarizations : list of str
polarizations to interpolate if beam_type is 'power'.
Default is all polarizations in self.polarization_array.
new_object : bool
Option to return a new UVBeam object with the interpolated data,
if possible. Note that this is only possible for Healpix pixels or
if az_za_grid is True and `az_array` and `za_array` are evenly spaced
or for frequency only interpolation.
reuse_spline : bool
Save the interpolation functions for reuse. Only applies for
`az_za_simple` interpolation.
spline_opts : dict
Provide options to numpy.RectBivariateSpline. This includes spline
order parameters `kx` and `ky`, and smoothing parameter `s`.
Only applies for `az_za_simple` interpolation.
run_check : bool
Only used if new_object is True. Option to check for the existence
and proper shapes of required parameters on the new object.
check_extra : bool
Only used if new_object is True. Option to check optional parameters
as well as required ones on the new object.
run_check_acceptability : bool
Only used if new_object is True. Option to check acceptable range
of the values of required parameters on the new object.
Returns
-------
array_like of float or complex or a UVBeam object
Either an array of interpolated values or a UVBeam object if
`new_object` is True. The shape of the interpolated data will be:
(Naxes_vec, Nspws, Nfeeds or Npols, Nfreqs or freq_array.size if
freq_array is passed, Npixels/(Naxis1, Naxis2) or az_array.size if
az/za_arrays are passed)
interp_basis_vector : array_like of float, optional
an array of interpolated basis vectors (or self.basis_vector_array
if az/za_arrays are not passed), shape: (Naxes_vec, Ncomponents_vec,
Npixels/(Naxis1, Naxis2) or az_array.size if az/za_arrays are passed)
interp_bandpass : array_like of float, optional
The interpolated bandpass, only returned if `return_bandpass` is True.
shape: (Nspws, freq_array.size)
"""
if self.interpolation_function is None:
raise ValueError("interpolation_function must be set on object first")
if self.freq_interp_kind is None:
raise ValueError("freq_interp_kind must be set on object first")
if new_object:
if not az_za_grid and az_array is not None:
raise ValueError(
"A new object can only be returned if "
"az_za_grid is True or for Healpix pixels or "
"for frequency only interpolation."
)
kind_use = self.freq_interp_kind
if freq_array is not None:
# get frequency distances
freq_dists = np.abs(self.freq_array - freq_array.reshape(-1, 1))
nearest_dist = np.min(freq_dists, axis=1)
interp_bool = np.any(nearest_dist >= freq_interp_tol)
# use the beam at nearest neighbors if not interp_bool
if not interp_bool:
kind_use = "nearest"
if az_za_grid:
az_array_use, za_array_use = np.meshgrid(az_array, za_array)
az_array_use = az_array_use.flatten()
za_array_use = za_array_use.flatten()
else:
az_array_use = copy.copy(az_array)
za_array_use = copy.copy(za_array)
if healpix_nside is not None or healpix_inds is not None:
if healpix_nside is None:
raise ValueError("healpix_nside must be set if healpix_inds is set.")
if az_array is not None or za_array is not None:
raise ValueError(
"healpix_nside and healpix_inds can not be "
"set if az_array or za_array is set."
)
try:
from astropy_healpix import HEALPix
except ImportError as e: # pragma: no cover
raise ImportError(
"astropy_healpix is not installed but is "
"required for healpix functionality. "
'Install "astropy-healpix" using conda or pip.'
) from e
hp_obj = HEALPix(nside=healpix_nside)
if healpix_inds is None:
healpix_inds = np.arange(hp_obj.npix)
hpx_lon, hpx_lat = hp_obj.healpix_to_lonlat(healpix_inds)
za_array_use = (Angle(np.pi / 2, units.radian) - hpx_lat).radian
az_array_use = hpx_lon.radian
interp_func = self.interpolation_function_dict[self.interpolation_function][
"func"
]
extra_keyword_dict = {}
if interp_func == "_interp_az_za_rect_spline":
extra_keyword_dict["reuse_spline"] = reuse_spline
extra_keyword_dict["spline_opts"] = spline_opts
interp_data, interp_basis_vector, interp_bandpass = getattr(self, interp_func)(
az_array_use,
za_array_use,
freq_array,
freq_interp_kind=kind_use,
polarizations=polarizations,
**extra_keyword_dict,
)
# return just the interpolated arrays
if not new_object:
if return_bandpass:
return interp_data, interp_basis_vector, interp_bandpass
else:
return interp_data, interp_basis_vector
# return a new UVBeam object with interpolated data
else:
# make a new object
new_uvb = self.copy()
history_update_string = " Interpolated"
if freq_array is not None:
history_update_string += " in frequency"
new_uvb.Nfreqs = freq_array.size
new_uvb.freq_array = freq_array.reshape(1, -1)
new_uvb.bandpass_array = interp_bandpass
new_uvb.freq_interp_kind = kind_use
if az_array is not None:
if freq_array is not None:
history_update_string += " and"
if new_uvb.pixel_coordinate_system != "az_za":
input_desc = self.coordinate_system_dict[
new_uvb.pixel_coordinate_system
]["description"]
output_desc = self.coordinate_system_dict["az_za"]["description"]
history_update_string += (
" from " + input_desc + " to " + output_desc
)
new_uvb.pixel_coordinate_system = "az_za"
new_uvb.Npixels = None
new_uvb.pixel_array = None
new_uvb.nside = None
new_uvb.ordering = None
else:
history_update_string += " to a new azimuth/zenith angle grid"
interp_data = interp_data.reshape(
interp_data.shape[:-1] + (za_array.size, az_array.size)
)
if interp_basis_vector is not None:
interp_basis_vector = interp_basis_vector.reshape(
interp_basis_vector.shape[:-1] + (za_array.size, az_array.size)
)
new_uvb.axis1_array = az_array
new_uvb.axis2_array = za_array
new_uvb.Naxes1 = new_uvb.axis1_array.size
new_uvb.Naxes2 = new_uvb.axis2_array.size
elif healpix_nside is not None:
if freq_array is not None:
history_update_string += " and"
if new_uvb.pixel_coordinate_system != "healpix":
input_desc = self.coordinate_system_dict[
new_uvb.pixel_coordinate_system
]["description"]
output_desc = self.coordinate_system_dict["healpix"]["description"]
history_update_string += (
" from " + input_desc + " to " + output_desc
)
new_uvb.pixel_coordinate_system = "healpix"
new_uvb.Naxes1 = None
new_uvb.axis1_array = None
new_uvb.Naxes2 = None
new_uvb.axis2_array = None
else:
history_update_string += " to a new healpix grid"
new_uvb.pixel_array = healpix_inds
new_uvb.Npixels = new_uvb.pixel_array.size
new_uvb.nside = healpix_nside
new_uvb.ordering = "ring"
history_update_string += (
" using pyuvdata with interpolation_function = "
+ new_uvb.interpolation_function
)
if freq_array is not None:
history_update_string += (
" and freq_interp_kind = " + new_uvb.freq_interp_kind
)
history_update_string += "."
new_uvb.history = new_uvb.history + history_update_string
new_uvb.data_array = interp_data
if new_uvb.basis_vector_array is not None:
new_uvb.basis_vector_array = interp_basis_vector
if hasattr(new_uvb, "saved_interp_functions"):
delattr(new_uvb, "saved_interp_functions")
new_uvb._set_cs_params()
if run_check:
new_uvb.check(
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
return new_uvb
[docs] def to_healpix(
self,
nside=None,
run_check=True,
check_extra=True,
run_check_acceptability=True,
inplace=True,
):
"""
Convert beam to the healpix coordinate system.
The interpolation is done using the interpolation method specified in
self.interpolation_function.
Note that this interpolation isn't perfect. Interpolating an Efield beam
and then converting to power gives a different result than converting
to power and then interpolating at about a 5% level.
Parameters
----------
nside : int
The nside to use for the Healpix map. If not specified, use
the nside that gives the closest resolution that is higher than the
input resolution.
run_check : bool
Option to check for the existence and proper shapes of required
parameters after converting to healpix.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of required parameters
after combining objects
inplace : bool
Option to perform the select directly on self or return a new UVBeam
object.
"""
try:
from astropy_healpix import HEALPix
except ImportError as e: # pragma: no cover
raise ImportError(
"astropy_healpix is not installed but is "
"required for healpix functionality. "
'Install "astropy-healpix" using conda or pip.'
) from e
if nside is None:
min_res = np.min(
np.array([np.diff(self.axis1_array)[0], np.diff(self.axis2_array)[0]])
)
nside_min_res = np.sqrt(3 / np.pi) * np.radians(60.0) / min_res
nside = int(2 ** np.ceil(np.log2(nside_min_res)))
hp_obj = HEALPix(nside=nside)
assert hp_obj.pixel_resolution.to_value(units.radian) < min_res
else:
hp_obj = HEALPix(nside=nside)
pixels = np.arange(hp_obj.npix)
hpx_lon, hpx_lat = hp_obj.healpix_to_lonlat(pixels)
hpx_theta = (Angle(np.pi / 2, units.radian) - hpx_lat).radian
hpx_phi = hpx_lon.radian
phi_vals, theta_vals = np.meshgrid(self.axis1_array, self.axis2_array)
# Don't ask for interpolation to pixels that aren't inside the beam area
pix_dists = np.sqrt(
(theta_vals.ravel() - hpx_theta.reshape(-1, 1)) ** 2
+ (phi_vals.ravel() - hpx_phi.reshape(-1, 1)) ** 2
)
inds_to_use = np.argwhere(
np.min(pix_dists, axis=1)
< hp_obj.pixel_resolution.to_value(units.radian) * 2
).squeeze(1)
if inds_to_use.size < hp_obj.npix:
pixels = pixels[inds_to_use]
beam_object = self.interp(
healpix_nside=nside,
healpix_inds=pixels,
new_object=True,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
if not inplace:
return beam_object
else:
for p in beam_object:
param = getattr(beam_object, p)
setattr(self, p, param)
def _get_beam(self, pol):
"""
Get the healpix power beam map corresponding to the specififed polarization.
pseudo-stokes I: 'pI', Q: 'pQ', U: 'pU' and V: 'pV' or linear dipole
polarization: 'XX', 'YY', etc.
Parameters
----------
pol : str or int
polarization string or integer, Ex. a pseudo-stokes pol 'pI', or
a linear pol 'XX'.
Returns
-------
UVBeam
healpix beam
"""
# assert map is in healpix coords
assert (
self.pixel_coordinate_system == "healpix"
), "pixel_coordinate_system must be healpix"
# assert beam_type is power
assert self.beam_type == "power", "beam_type must be power"
if isinstance(pol, (str, np.str_)):
pol = uvutils.polstr2num(pol, x_orientation=self.x_orientation)
pol_array = self.polarization_array
if pol in pol_array:
stokes_p_ind = np.where(np.isin(pol_array, pol))[0][0]
beam = self.data_array[0, 0, stokes_p_ind]
else:
raise ValueError("Do not have the right polarization information")
return beam
[docs] def get_beam_area(self, pol="pI"):
"""
Compute the integral of the beam in units of steradians.
Pseudo-Stokes 'pI' (I), 'pQ'(Q), 'pU'(U), 'pV'(V) beam and linear
dipole 'XX', 'XY', 'YX' and 'YY' are supported.
See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154
or arxiv:1502.05072 and Kohn et al. (2018) or
https://arxiv.org/pdf/1802.04151.pdf for details.
Parameters
----------
pol : str or int
polarization string or integer, Ex. a pseudo-stokes pol 'pI', or a
linear pol 'XX'.
Returns
-------
omega : float
Integral of the beam across the sky, units: steradians.
"""
if isinstance(pol, (str, np.str_)):
pol = uvutils.polstr2num(pol, x_orientation=self.x_orientation)
if self.beam_type != "power":
raise ValueError("beam_type must be power")
if self.Naxes_vec > 1:
raise ValueError("Expect scalar for power beam, found vector")
if self._data_normalization.value != "peak":
raise ValueError("beam must be peak normalized")
if self.pixel_coordinate_system != "healpix":
raise ValueError("Currently only healpix format supported")
nside = self.nside
# get beam
beam = self._get_beam(pol)
# get integral
omega = np.sum(beam, axis=-1) * np.pi / (3.0 * nside ** 2)
return omega
[docs] def get_beam_sq_area(self, pol="pI"):
"""
Compute the integral of the beam^2 in units of steradians.
Pseudo-Stokes 'pI' (I), 'pQ'(Q), 'pU'(U), 'pV'(V) beam and
linear dipole 'XX', 'XY', 'YX' and 'YY' are supported.
See Equations 4 and 5 of Moore et al. (2017) ApJ 836, 154
or arxiv:1502.05072 for details.
Parameters
----------
pol : str or int
polarization string or integer, Ex. a pseudo-stokes pol 'pI', or a
linear pol 'XX'.
Returns
-------
omega : float
Integral of the beam^2 across the sky, units: steradians.
"""
if isinstance(pol, (str, np.str_)):
pol = uvutils.polstr2num(pol, x_orientation=self.x_orientation)
if self.beam_type != "power":
raise ValueError("beam_type must be power")
if self.Naxes_vec > 1:
raise ValueError("Expect scalar for power beam, found vector")
if self._data_normalization.value != "peak":
raise ValueError("beam must be peak normalized")
if self.pixel_coordinate_system != "healpix":
raise ValueError("Currently only healpix format supported")
nside = self.nside
# get beam
beam = self._get_beam(pol)
# get integral
omega = np.sum(beam ** 2, axis=-1) * np.pi / (3.0 * nside ** 2)
return omega
def __add__(
self,
other,
verbose_history=False,
inplace=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Combine two UVBeam objects.
Objects can be added along frequency, feed or polarization
(for efield or power beams), and/or pixel axes.
Parameters
----------
other : UVBeam object
UVBeam object to add to self.
inplace : bool
Option to overwrite self as we go, otherwise create a third object
as the sum of the two.
verbose_history : bool
Option to allow more verbose history. If True and if the histories for the
two objects are different, the combined object will keep all the history of
both input objects (if many objects are combined in succession this can
lead to very long histories). If False and if the histories for the two
objects are different, the combined object will have the history of the
first object and only the parts of the second object history that are unique
(this is done word by word and can result in hard to interpret histories).
run_check : bool
Option to check for the existence and proper shapes of
required parameters after combining objects.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
required parameters after combining objects.
"""
if inplace:
this = self
else:
this = self.copy()
# Check that both objects are UVBeam and valid
this.check(check_extra=check_extra, run_check_acceptability=False)
if not issubclass(other.__class__, this.__class__):
if not issubclass(this.__class__, other.__class__):
raise ValueError(
"Only UVBeam (or subclass) objects can be added "
"to a UVBeam (or subclass) object"
)
other.check(check_extra=check_extra, run_check_acceptability=False)
# Check objects are compatible
compatibility_params = [
"_beam_type",
"_data_normalization",
"_telescope_name",
"_feed_name",
"_feed_version",
"_model_name",
"_model_version",
"_pixel_coordinate_system",
"_Naxes_vec",
"_nside",
"_ordering",
]
for a in compatibility_params:
if getattr(this, a) != getattr(other, a):
msg = (
"UVParameter " + a[1:] + " does not match. Cannot combine objects."
)
raise ValueError(msg)
# check for presence of optional parameters with a frequency axis in
# both objects
optional_freq_params = [
"_receiver_temperature_array",
"_loss_array",
"_mismatch_array",
"_s_parameters",
]
for attr in optional_freq_params:
this_attr = getattr(this, attr)
other_attr = getattr(other, attr)
if (
this_attr.value is None or other_attr.value is None
) and this_attr != other_attr:
warnings.warn(
"Only one of the UVBeam objects being combined "
"has optional parameter {attr}. After the sum the "
"final object will not have {attr}".format(attr=attr)
)
if this_attr.value is not None:
this_attr.value = None
setattr(this, attr, this_attr)
# Build up history string
history_update_string = " Combined data along "
n_axes = 0
# Check we don't have overlapping data
if this.beam_type == "power":
both_pol = np.intersect1d(this.polarization_array, other.polarization_array)
else:
both_pol = np.intersect1d(this.feed_array, other.feed_array)
both_freq = np.intersect1d(this.freq_array[0, :], other.freq_array[0, :])
if this.pixel_coordinate_system == "healpix":
both_pixels = np.intersect1d(this.pixel_array, other.pixel_array)
else:
both_axis1 = np.intersect1d(this.axis1_array, other.axis1_array)
both_axis2 = np.intersect1d(this.axis2_array, other.axis2_array)
if len(both_pol) > 0:
if len(both_freq) > 0:
if self.pixel_coordinate_system == "healpix":
if len(both_pixels) > 0:
raise ValueError(
"These objects have overlapping data and"
" cannot be combined."
)
else:
if len(both_axis1) > 0:
if len(both_axis2) > 0:
raise ValueError(
"These objects have overlapping data and"
" cannot be combined."
)
if this.pixel_coordinate_system == "healpix":
temp = np.nonzero(~np.in1d(other.pixel_array, this.pixel_array))[0]
if len(temp) > 0:
pix_new_inds = temp
history_update_string += "healpix pixel"
n_axes += 1
else:
pix_new_inds = []
else:
temp = np.nonzero(~np.in1d(other.axis1_array, this.axis1_array))[0]
if len(temp) > 0:
ax1_new_inds = temp
history_update_string += "first image"
n_axes += 1
else:
ax1_new_inds = []
temp = np.nonzero(~np.in1d(other.axis2_array, this.axis2_array))[0]
if len(temp) > 0:
ax2_new_inds = temp
if n_axes > 0:
history_update_string += ", second image"
else:
history_update_string += "second image"
n_axes += 1
else:
ax2_new_inds = []
temp = np.nonzero(~np.in1d(other.freq_array[0, :], this.freq_array[0, :]))[0]
if len(temp) > 0:
fnew_inds = temp
if n_axes > 0:
history_update_string += ", frequency"
else:
history_update_string += "frequency"
n_axes += 1
else:
fnew_inds = []
if this.beam_type == "power":
temp = np.nonzero(
~np.in1d(other.polarization_array, this.polarization_array)
)[0]
if len(temp) > 0:
pnew_inds = temp
if n_axes > 0:
history_update_string += ", polarization"
else:
history_update_string += "polarization"
n_axes += 1
else:
pnew_inds = []
else:
temp = np.nonzero(~np.in1d(other.feed_array, this.feed_array))[0]
if len(temp) > 0:
pnew_inds = temp
if n_axes > 0:
history_update_string += ", feed"
else:
history_update_string += "feed"
n_axes += 1
else:
pnew_inds = []
# Pad out self to accommodate new data
if this.pixel_coordinate_system == "healpix":
if len(pix_new_inds) > 0:
data_pix_axis = 4
data_pad_dims = tuple(
list(this.data_array.shape[0:data_pix_axis])
+ [len(pix_new_inds)]
+ list(this.data_array.shape[data_pix_axis + 1 :])
)
data_zero_pad = np.zeros(data_pad_dims)
this.pixel_array = np.concatenate(
[this.pixel_array, other.pixel_array[pix_new_inds]]
)
order = np.argsort(this.pixel_array)
this.pixel_array = this.pixel_array[order]
this.data_array = np.concatenate(
[this.data_array, data_zero_pad], axis=data_pix_axis
)[:, :, :, :, order]
if this.beam_type == "efield":
basisvec_pix_axis = 2
basisvec_pad_dims = tuple(
list(this.basis_vector_array.shape[0:basisvec_pix_axis])
+ [len(pix_new_inds)]
+ list(this.basis_vector_array.shape[basisvec_pix_axis + 1 :])
)
basisvec_zero_pad = np.zeros(basisvec_pad_dims)
this.basis_vector_array = np.concatenate(
[this.basis_vector_array, basisvec_zero_pad],
axis=basisvec_pix_axis,
)[:, :, order]
else:
if len(ax1_new_inds) > 0:
data_ax1_axis = 5
data_pad_dims = tuple(
list(this.data_array.shape[0:data_ax1_axis])
+ [len(ax1_new_inds)]
+ list(this.data_array.shape[data_ax1_axis + 1 :])
)
data_zero_pad = np.zeros(data_pad_dims)
this.axis1_array = np.concatenate(
[this.axis1_array, other.axis1_array[ax1_new_inds]]
)
order = np.argsort(this.axis1_array)
this.axis1_array = this.axis1_array[order]
this.data_array = np.concatenate(
[this.data_array, data_zero_pad], axis=data_ax1_axis
)[:, :, :, :, :, order]
if this.beam_type == "efield":
basisvec_ax1_axis = 3
basisvec_pad_dims = tuple(
list(this.basis_vector_array.shape[0:basisvec_ax1_axis])
+ [len(ax1_new_inds)]
+ list(this.basis_vector_array.shape[basisvec_ax1_axis + 1 :])
)
basisvec_zero_pad = np.zeros(basisvec_pad_dims)
this.basis_vector_array = np.concatenate(
[this.basis_vector_array, basisvec_zero_pad],
axis=basisvec_ax1_axis,
)[:, :, :, order]
if len(ax2_new_inds) > 0:
data_ax2_axis = 4
data_pad_dims = tuple(
list(this.data_array.shape[0:data_ax2_axis])
+ [len(ax2_new_inds)]
+ list(this.data_array.shape[data_ax2_axis + 1 :])
)
data_zero_pad = np.zeros(data_pad_dims)
this.axis2_array = np.concatenate(
[this.axis2_array, other.axis2_array[ax2_new_inds]]
)
order = np.argsort(this.axis2_array)
this.axis2_array = this.axis2_array[order]
this.data_array = np.concatenate(
[this.data_array, data_zero_pad], axis=data_ax2_axis
)[:, :, :, :, order, ...]
if this.beam_type == "efield":
basisvec_ax2_axis = 2
basisvec_pad_dims = tuple(
list(this.basis_vector_array.shape[0:basisvec_ax2_axis])
+ [len(ax2_new_inds)]
+ list(this.basis_vector_array.shape[basisvec_ax2_axis + 1 :])
)
basisvec_zero_pad = np.zeros(basisvec_pad_dims)
this.basis_vector_array = np.concatenate(
[this.basis_vector_array, basisvec_zero_pad],
axis=basisvec_ax2_axis,
)[:, :, order, ...]
if len(fnew_inds) > 0:
faxis = 3
data_pad_dims = tuple(
list(this.data_array.shape[0:faxis])
+ [len(fnew_inds)]
+ list(this.data_array.shape[faxis + 1 :])
)
data_zero_pad = np.zeros(data_pad_dims)
this.freq_array = np.concatenate(
[this.freq_array, other.freq_array[:, fnew_inds]], axis=1
)
order = np.argsort(this.freq_array[0, :])
this.freq_array = this.freq_array[:, order]
this.bandpass_array = np.concatenate(
[this.bandpass_array, np.zeros((1, len(fnew_inds)))], axis=1
)[:, order]
this.data_array = np.concatenate(
[this.data_array, data_zero_pad], axis=faxis
)[:, :, :, order, ...]
if this.receiver_temperature_array is not None:
this.receiver_temperature_array = np.concatenate(
[this.receiver_temperature_array, np.zeros((1, len(fnew_inds)))],
axis=1,
)[:, order]
if this.loss_array is not None:
this.loss_array = np.concatenate(
[this.loss_array, np.zeros((1, len(fnew_inds)))], axis=1
)[:, order]
if this.mismatch_array is not None:
this.mismatch_array = np.concatenate(
[this.mismatch_array, np.zeros((1, len(fnew_inds)))], axis=1
)[:, order]
if this.s_parameters is not None:
this.s_parameters = np.concatenate(
[this.s_parameters, np.zeros((4, 1, len(fnew_inds)))], axis=2
)[:, :, order]
if len(pnew_inds) > 0:
paxis = 2
data_pad_dims = tuple(
list(this.data_array.shape[0:paxis])
+ [len(pnew_inds)]
+ list(this.data_array.shape[paxis + 1 :])
)
data_zero_pad = np.zeros(data_pad_dims)
if this.beam_type == "power":
this.polarization_array = np.concatenate(
[this.polarization_array, other.polarization_array[pnew_inds]]
)
order = np.argsort(np.abs(this.polarization_array))
this.polarization_array = this.polarization_array[order]
else:
this.feed_array = np.concatenate(
[this.feed_array, other.feed_array[pnew_inds]]
)
order = np.argsort(this.feed_array)
this.feed_array = this.feed_array[order]
this.data_array = np.concatenate(
[this.data_array, data_zero_pad], axis=paxis
)[:, :, order, ...]
# Now populate the data
if this.beam_type == "power":
this.Npols = this.polarization_array.shape[0]
pol_t2o = np.nonzero(
np.in1d(this.polarization_array, other.polarization_array)
)[0]
else:
this.Nfeeds = this.feed_array.shape[0]
pol_t2o = np.nonzero(np.in1d(this.feed_array, other.feed_array))[0]
freq_t2o = np.nonzero(np.in1d(this.freq_array[0, :], other.freq_array[0, :]))[0]
if this.pixel_coordinate_system == "healpix":
this.Npixels = this.pixel_array.shape[0]
pix_t2o = np.nonzero(np.in1d(this.pixel_array, other.pixel_array))[0]
this.data_array[
np.ix_(np.arange(this.Naxes_vec), [0], pol_t2o, freq_t2o, pix_t2o)
] = other.data_array
if this.beam_type == "efield":
this.basis_vector_array[
np.ix_(np.arange(this.Naxes_vec), np.arange(2), pix_t2o)
] = other.basis_vector_array
else:
this.Naxes1 = this.axis1_array.shape[0]
this.Naxes2 = this.axis2_array.shape[0]
ax1_t2o = np.nonzero(np.in1d(this.axis1_array, other.axis1_array))[0]
ax2_t2o = np.nonzero(np.in1d(this.axis2_array, other.axis2_array))[0]
this.data_array[
np.ix_(
np.arange(this.Naxes_vec), [0], pol_t2o, freq_t2o, ax2_t2o, ax1_t2o
)
] = other.data_array
if this.beam_type == "efield":
this.basis_vector_array[
np.ix_(np.arange(this.Naxes_vec), np.arange(2), ax2_t2o, ax1_t2o)
] = other.basis_vector_array
this.bandpass_array[np.ix_([0], freq_t2o)] = other.bandpass_array
if this.receiver_temperature_array is not None:
this.receiver_temperature_array[
np.ix_([0], freq_t2o)
] = other.receiver_temperature_array
if this.loss_array is not None:
this.loss_array[np.ix_([0], freq_t2o)] = other.loss_array
if this.mismatch_array is not None:
this.mismatch_array[np.ix_([0], freq_t2o)] = other.mismatch_array
if this.s_parameters is not None:
this.s_parameters[np.ix_(np.arange(4), [0], freq_t2o)] = other.s_parameters
this.Nfreqs = this.freq_array.shape[1]
# Check specific requirements
if this.Nfreqs > 1:
freq_separation = np.diff(this.freq_array[0, :])
if not np.isclose(
np.min(freq_separation),
np.max(freq_separation),
rtol=this._freq_array.tols[0],
atol=this._freq_array.tols[1],
):
warnings.warn(
"Combined frequencies are not evenly spaced. This will "
"make it impossible to write this data out to some file types."
)
if self.beam_type == "power" and this.Npols > 2:
pol_separation = np.diff(this.polarization_array)
if np.min(pol_separation) < np.max(pol_separation):
warnings.warn(
"Combined polarizations are not evenly spaced. This will "
"make it impossible to write this data out to some file types."
)
if n_axes > 0:
history_update_string += " axis using pyuvdata."
histories_match = uvutils._check_histories(this.history, other.history)
this.history += history_update_string
if not histories_match:
if verbose_history:
this.history += " Next object history follows. " + other.history
else:
extra_history = uvutils._combine_history_addition(
this.history, other.history
)
if extra_history is not None:
this.history += (
" Unique part of next object history follows. "
+ extra_history
)
# Check final object is self-consistent
if run_check:
this.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return this
def __iadd__(self, other):
"""
Add in place.
Parameters
----------
other : UVBeam object
Another UVBeam object to adding to self.
"""
self.__add__(other, inplace=True)
return self
[docs] def select(
self,
axis1_inds=None,
axis2_inds=None,
pixels=None,
frequencies=None,
freq_chans=None,
feeds=None,
polarizations=None,
inplace=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Downselect data to keep on the object along various axes.
Axes that can be selected along include image axis indices or pixels
(if healpix), frequencies and feeds or polarizations (if power).
The history attribute on the object will be updated to identify the
operations performed.
Parameters
----------
axis1_indss : array_like of int, optional
The indices along the first image axis to keep in the object.
Cannot be set if pixel_coordinate_system is "healpix".
axis2_inds : array_like of int, optional
The indices along the second image axis to keep in the object.
Cannot be set if pixel_coordinate_system is "healpix".
pixels : array_like of int, optional
The healpix pixels to keep in the object.
Cannot be set if pixel_coordinate_system is not "healpix".
frequencies : array_like of float, optional
The frequencies to keep in the object.
freq_chans : array_like of int, optional
The frequency channel numbers to keep in the object.
feeds : array_like of int, optional
The feeds to keep in the object. Cannot be set if the beam_type is "power".
polarizations : array_like of int, optional
The polarizations to keep in the object.
Cannot be set if the beam_type is "efield".
inplace : bool
Option to perform the select directly on self or return
a new UVBeam object, which is a subselection of self.
run_check : bool
Option to check for the existence and proper shapes of
required parameters after downselecting data on this object.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
required parameters after downselecting data on this object.
"""
if inplace:
beam_object = self
else:
beam_object = self.copy()
# build up history string as we go
history_update_string = " Downselected to specific "
n_selects = 0
if axis1_inds is not None:
if beam_object.pixel_coordinate_system == "healpix":
raise ValueError(
"axis1_inds cannot be used with healpix coordinate system"
)
history_update_string += "parts of first image axis"
n_selects += 1
axis1_inds = sorted(set(axis1_inds))
if min(axis1_inds) < 0 or max(axis1_inds) > beam_object.Naxes1 - 1:
raise ValueError("axis1_inds must be > 0 and < Naxes1")
beam_object.Naxes1 = len(axis1_inds)
beam_object.axis1_array = beam_object.axis1_array[axis1_inds]
if beam_object.Naxes1 > 1:
axis1_spacing = np.diff(beam_object.axis1_array)
if not np.isclose(
np.min(axis1_spacing),
np.max(axis1_spacing),
rtol=beam_object._axis1_array.tols[0],
atol=beam_object._axis1_array.tols[1],
):
warnings.warn(
"Selected values along first image axis are "
"not evenly spaced. This is not supported by "
"the regularly gridded beam fits format"
)
beam_object.data_array = beam_object.data_array[:, :, :, :, :, axis1_inds]
if beam_object.beam_type == "efield":
beam_object.basis_vector_array = beam_object.basis_vector_array[
:, :, :, axis1_inds
]
if axis2_inds is not None:
if beam_object.pixel_coordinate_system == "healpix":
raise ValueError(
"axis2_inds cannot be used with healpix coordinate system"
)
if n_selects > 0:
history_update_string += ", parts of second image axis"
else:
history_update_string += "parts of second image axis"
n_selects += 1
axis2_inds = sorted(set(axis2_inds))
if min(axis2_inds) < 0 or max(axis2_inds) > beam_object.Naxes2 - 1:
raise ValueError("axis2_inds must be > 0 and < Naxes2")
beam_object.Naxes2 = len(axis2_inds)
beam_object.axis2_array = beam_object.axis2_array[axis2_inds]
if beam_object.Naxes2 > 1:
axis2_spacing = np.diff(beam_object.axis2_array)
if not np.isclose(
np.min(axis2_spacing),
np.max(axis2_spacing),
rtol=beam_object._axis2_array.tols[0],
atol=beam_object._axis2_array.tols[1],
):
warnings.warn(
"Selected values along second image axis are "
"not evenly spaced. This is not supported by "
"the regularly gridded beam fits format"
)
beam_object.data_array = beam_object.data_array[:, :, :, :, axis2_inds, :]
if beam_object.beam_type == "efield":
beam_object.basis_vector_array = beam_object.basis_vector_array[
:, :, axis2_inds, :
]
if pixels is not None:
if beam_object.pixel_coordinate_system != "healpix":
raise ValueError(
"pixels can only be used with healpix coordinate system"
)
history_update_string += "healpix pixels"
n_selects += 1
pix_inds = np.zeros(0, dtype=np.int64)
for p in pixels:
if p in beam_object.pixel_array:
pix_inds = np.append(
pix_inds, np.where(beam_object.pixel_array == p)[0]
)
else:
raise ValueError(
"Pixel {p} is not present in the pixel_array".format(p=p)
)
pix_inds = sorted(set(pix_inds))
beam_object.Npixels = len(pix_inds)
beam_object.pixel_array = beam_object.pixel_array[pix_inds]
beam_object.data_array = beam_object.data_array[:, :, :, :, pix_inds]
if beam_object.beam_type == "efield":
beam_object.basis_vector_array = beam_object.basis_vector_array[
:, :, pix_inds
]
if freq_chans is not None:
freq_chans = uvutils._get_iterable(freq_chans)
if frequencies is None:
frequencies = beam_object.freq_array[0, freq_chans]
else:
frequencies = uvutils._get_iterable(frequencies)
frequencies = np.sort(
list(set(frequencies) | set(beam_object.freq_array[0, freq_chans]))
)
if frequencies is not None:
frequencies = uvutils._get_iterable(frequencies)
if n_selects > 0:
history_update_string += ", frequencies"
else:
history_update_string += "frequencies"
n_selects += 1
freq_inds = np.zeros(0, dtype=np.int64)
# this works because we only allow one SPW. This will have to be
# reworked when we support more.
freq_arr_use = beam_object.freq_array[0, :]
for f in frequencies:
if f in freq_arr_use:
freq_inds = np.append(freq_inds, np.where(freq_arr_use == f)[0])
else:
raise ValueError(
"Frequency {f} is not present in the freq_array".format(f=f)
)
freq_inds = sorted(set(freq_inds))
beam_object.Nfreqs = len(freq_inds)
beam_object.freq_array = beam_object.freq_array[:, freq_inds]
beam_object.bandpass_array = beam_object.bandpass_array[:, freq_inds]
if beam_object.Nfreqs > 1:
freq_separation = (
beam_object.freq_array[0, 1:] - beam_object.freq_array[0, :-1]
)
if not np.isclose(
np.min(freq_separation),
np.max(freq_separation),
rtol=beam_object._freq_array.tols[0],
atol=beam_object._freq_array.tols[1],
):
warnings.warn(
"Selected frequencies are not evenly spaced. This "
"is not supported by the regularly gridded beam fits format"
)
if beam_object.pixel_coordinate_system == "healpix":
beam_object.data_array = beam_object.data_array[:, :, :, freq_inds, :]
else:
beam_object.data_array = beam_object.data_array[
:, :, :, freq_inds, :, :
]
if beam_object.antenna_type == "phased_array":
beam_object.coupling_matrix = beam_object.coupling_matrix[
:, :, :, :, :, freq_inds
]
if beam_object.receiver_temperature_array is not None:
rx_temp_array = beam_object.receiver_temperature_array
beam_object.receiver_temperature_array = rx_temp_array[:, freq_inds]
if beam_object.loss_array is not None:
beam_object.loss_array = beam_object.loss_array[:, freq_inds]
if beam_object.mismatch_array is not None:
beam_object.mismatch_array = beam_object.mismatch_array[:, freq_inds]
if beam_object.s_parameters is not None:
beam_object.s_parameters = beam_object.s_parameters[:, :, freq_inds]
if feeds is not None:
if beam_object.beam_type == "power":
raise ValueError("feeds cannot be used with power beams")
feeds = uvutils._get_iterable(feeds)
if n_selects > 0:
history_update_string += ", feeds"
else:
history_update_string += "feeds"
n_selects += 1
feed_inds = np.zeros(0, dtype=np.int64)
for f in feeds:
if f in beam_object.feed_array:
feed_inds = np.append(
feed_inds, np.where(beam_object.feed_array == f)[0]
)
else:
raise ValueError(
"Feed {f} is not present in the feed_array".format(f=f)
)
feed_inds = sorted(set(feed_inds))
beam_object.Nfeeds = len(feed_inds)
beam_object.feed_array = beam_object.feed_array[feed_inds]
if beam_object.pixel_coordinate_system == "healpix":
beam_object.data_array = beam_object.data_array[:, :, feed_inds, :, :]
else:
beam_object.data_array = beam_object.data_array[
:, :, feed_inds, :, :, :
]
if polarizations is not None:
if beam_object.beam_type == "efield":
raise ValueError("polarizations cannot be used with efield beams")
polarizations = uvutils._get_iterable(polarizations)
if n_selects > 0:
history_update_string += ", polarizations"
else:
history_update_string += "polarizations"
n_selects += 1
pol_inds = np.zeros(0, dtype=np.int64)
for p in polarizations:
if p in beam_object.polarization_array:
pol_inds = np.append(
pol_inds, np.where(beam_object.polarization_array == p)[0]
)
else:
raise ValueError(
"polarization {p} is not present in the"
" polarization_array".format(p=p)
)
pol_inds = sorted(set(pol_inds))
beam_object.Npols = len(pol_inds)
beam_object.polarization_array = beam_object.polarization_array[pol_inds]
if len(pol_inds) > 2:
pol_separation = (
beam_object.polarization_array[1:]
- beam_object.polarization_array[:-1]
)
if np.min(pol_separation) < np.max(pol_separation):
warnings.warn(
"Selected polarizations are not evenly spaced. This "
"is not supported by the regularly gridded beam fits format"
)
if beam_object.pixel_coordinate_system == "healpix":
beam_object.data_array = beam_object.data_array[:, :, pol_inds, :, :]
else:
beam_object.data_array = beam_object.data_array[:, :, pol_inds, :, :, :]
history_update_string += " using pyuvdata."
beam_object.history = beam_object.history + history_update_string
# check if object is self-consistent
if run_check:
beam_object.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return beam_object
def _convert_from_filetype(self, other):
for p in other:
param = getattr(other, p)
setattr(self, p, param)
def _convert_to_filetype(self, filetype):
if filetype == "beamfits":
from . import beamfits
other_obj = beamfits.BeamFITS()
else:
raise ValueError("filetype must be beamfits")
for p in self:
param = getattr(self, p)
setattr(other_obj, p, param)
return other_obj
[docs] def read_beamfits(
self, filename, run_check=True, check_extra=True, run_check_acceptability=True
):
"""
Read in data from a beamfits file.
Parameters
----------
filename : str or list of str
The beamfits file or list of files to read from.
run_check : bool
Option to check for the existence and proper shapes of
required parameters after reading in the file.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptabilit : bool
Option to check acceptable range of the values of
required parameters after reading in the file.
"""
from . import beamfits
if isinstance(filename, (list, tuple)):
self.read_beamfits(
filename[0],
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
if len(filename) > 1:
for f in filename[1:]:
beam2 = UVBeam()
beam2.read_beamfits(
f,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self += beam2
del beam2
else:
beamfits_obj = beamfits.BeamFITS()
beamfits_obj.read_beamfits(
filename,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self._convert_from_filetype(beamfits_obj)
del beamfits_obj
def _read_cst_beam_yaml(self, filename):
"""
Parse a CST beam yaml file.
Paramters
---------
filename : str
Filename to parse.
Returns
-------
dict
Containing all the info from the yaml file.
"""
import yaml
with open(filename, "r") as file:
settings_dict = yaml.safe_load(file)
required_keys = [
"telescope_name",
"feed_name",
"feed_version",
"model_name",
"model_version",
"history",
"frequencies",
"filenames",
"feed_pol",
]
for key in required_keys:
if key not in settings_dict:
raise ValueError(
"{key} is a required key in CST settings files "
"but is not present.".format(key=key)
)
return settings_dict
[docs] def read_cst_beam(
self,
filename,
beam_type="power",
feed_pol=None,
rotate_pol=None,
frequency=None,
telescope_name=None,
feed_name=None,
feed_version=None,
model_name=None,
model_version=None,
history=None,
x_orientation=None,
reference_impedance=None,
extra_keywords=None,
frequency_select=None,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Read in data from a cst file.
Parameters
----------
filename : str
Either a settings yaml file or a cst text file or
list of cst text files to read from. If a list is passed,
the files are combined along the appropriate axes.
Settings yaml files must include the following keywords:
| - telescope_name (str)
| - feed_name (str)
| - feed_version (str)
| - model_name (str)
| - model_version (str)
| - history (str)
| - frequencies (list(float))
| - cst text filenames (list(str)) -- path relative to yaml file
| - feed_pol (str) or (list(str))
and they may include the following optional keywords:
| - x_orientation (str): Optional but strongly encouraged!
| - ref_imp (float): beam model reference impedance
| - sim_beam_type (str): e.g. 'E-farfield'
| - all other fields will go into the extra_keywords attribute
More details and an example are available in the docs
(cst_settings_yaml.rst).
Specifying any of the associated keywords to this function will
override the values in the settings file.
beam_type : str
What beam_type to read in ('power' or 'efield').
feed_pol : str
The feed or polarization or list of feeds or polarizations the
files correspond to.
Defaults to 'x' (meaning x for efield or xx for power beams).
rotate_pol : bool
If True, assume the structure in the simulation is symmetric under
90 degree rotations about the z-axis (so that the y polarization can be
constructed by rotating the x polarization or vice versa).
Default: True if feed_pol is a single value or a list with all
the same values in it, False if it is a list with varying values.
frequency : float or list of float, optional
The frequency or list of frequencies corresponding to the filename(s).
This is assumed to be in the same order as the files.
If not passed, the code attempts to parse it from the filenames.
telescope_name : str, optional
The name of the telescope corresponding to the filename(s).
feed_name : str, optional
The name of the feed corresponding to the filename(s).
feed_version : str, optional
The version of the feed corresponding to the filename(s).
model_name : str, optional
The name of the model corresponding to the filename(s).
model_version : str, optional
The version of the model corresponding to the filename(s).
history : str, optional
A string detailing the history of the filename(s).
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization. Options are "east" (indicating
east/west orientation) and "north" (indicating north/south orientation)
reference_impedance : float, optional
The reference impedance of the model(s).
extra_keywords : dict, optional
A dictionary containing any extra_keywords.
frequency_select : list of float, optional
Only used if the file is a yaml file. Indicates which frequencies
to include (only read in files for those frequencies)
run_check : bool
Option to check for the existence and proper shapes of
required parameters after reading in the file.
check_extra : bool
Option to check optional parameters as well as
required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
required parameters after reading in the file.
"""
from . import cst_beam
if isinstance(filename, np.ndarray):
if len(filename.shape) > 1:
raise ValueError("filename can not be a multi-dimensional array")
filename = filename.tolist()
if isinstance(filename, (list, tuple)):
if len(filename) == 1:
filename = filename[0]
if not isinstance(filename, (list, tuple)) and filename.endswith("yaml"):
settings_dict = self._read_cst_beam_yaml(filename)
if not isinstance(settings_dict["filenames"], list):
raise ValueError("filenames in yaml file must be a list.")
if not isinstance(settings_dict["frequencies"], list):
raise ValueError("frequencies in yaml file must be a list.")
yaml_dir = os.path.dirname(filename)
cst_filename = [
os.path.join(yaml_dir, f) for f in settings_dict["filenames"]
]
overriding_keywords = {
"feed_pol": feed_pol,
"frequency": frequency,
"telescope_name": telescope_name,
"feed_name": feed_name,
"feed_version": feed_version,
"model_name": model_name,
"model_version": model_version,
"history": history,
}
if "ref_imp" in settings_dict:
overriding_keywords["reference_impedance"] = reference_impedance
if "x_orientation" in settings_dict:
overriding_keywords["x_orientation"] = reference_impedance
for key, val in overriding_keywords.items():
if val is not None:
warnings.warn(
"The {key} keyword is set, overriding the "
"value in the settings yaml file.".format(key=key)
)
if feed_pol is None:
feed_pol = settings_dict["feed_pol"]
if frequency is None:
frequency = settings_dict["frequencies"]
if telescope_name is None:
telescope_name = settings_dict["telescope_name"]
if feed_name is None:
feed_name = settings_dict["feed_name"]
if feed_version is None:
feed_version = str(settings_dict["feed_version"])
if model_name is None:
model_name = settings_dict["model_name"]
if model_version is None:
model_version = str(settings_dict["model_version"])
if history is None:
history = settings_dict["history"]
if reference_impedance is None and "ref_imp" in settings_dict:
reference_impedance = float(settings_dict["ref_imp"])
if x_orientation is None and "x_orientation" in settings_dict:
x_orientation = settings_dict["x_orientation"]
if extra_keywords is None:
extra_keywords = {}
known_keys = [
"telescope_name",
"feed_name",
"feed_version",
"model_name",
"model_version",
"history",
"frequencies",
"filenames",
"feed_pol",
"ref_imp",
"x_orientation",
]
# One of the standard paramters in the settings yaml file is
# longer than 8 characters.
# This causes warnings and straight truncation when writing to
# beamfits files
# To avoid these, this defines a standard renaming of that paramter
rename_extra_keys_map = {"sim_beam_type": "sim_type"}
for key, value in settings_dict.items():
if key not in known_keys:
if key in rename_extra_keys_map.keys():
extra_keywords[rename_extra_keys_map[key]] = value
else:
extra_keywords[key] = value
if frequency_select is not None:
freq_inds = []
for freq in frequency_select:
freq_array = np.array(frequency, dtype=np.float64)
close_inds = np.where(
np.isclose(
freq_array,
freq,
rtol=self._freq_array.tols[0],
atol=self._freq_array.tols[1],
)
)[0]
if close_inds.size > 0:
for ind in close_inds:
freq_inds.append(ind)
else:
raise ValueError(f"frequency {freq} not in frequency list")
freq_inds = np.array(freq_inds)
frequency = freq_array[freq_inds].tolist()
cst_filename = np.array(cst_filename)[freq_inds].tolist()
if len(cst_filename) == 1:
cst_filename = cst_filename[0]
if isinstance(feed_pol, list):
if rotate_pol is None:
# if a mix of feed pols, don't rotate by default
# do this here in case selections confuse this test
if np.any(np.array(feed_pol) != feed_pol[0]):
rotate_pol = False
else:
rotate_pol = True
feed_pol = np.array(feed_pol)[freq_inds].tolist()
else:
cst_filename = filename
if feed_pol is None:
# default to x (done here in case it's specified in a yaml file)
feed_pol = "x"
if history is None:
# default to empty (done here in case it's specified in a yaml file)
history = ""
if isinstance(frequency, np.ndarray):
if len(frequency.shape) > 1:
raise ValueError("frequency can not be a multi-dimensional array")
frequency = frequency.tolist()
if isinstance(frequency, (list, tuple)):
if len(frequency) == 1:
frequency = frequency[0]
if isinstance(feed_pol, np.ndarray):
if len(feed_pol.shape) > 1:
raise ValueError("frequency can not be a multi-dimensional array")
feed_pol = feed_pol.tolist()
if isinstance(feed_pol, (list, tuple)):
if len(feed_pol) == 1:
feed_pol = feed_pol[0]
if isinstance(cst_filename, (list, tuple)):
if frequency is not None:
if isinstance(frequency, (list, tuple)):
if not len(frequency) == len(cst_filename):
raise ValueError(
"If frequency and filename are both "
"lists they need to be the same length"
)
freq = frequency[0]
else:
freq = frequency
else:
freq = None
if isinstance(feed_pol, (list, tuple)):
if not len(feed_pol) == len(cst_filename):
raise ValueError(
"If feed_pol and filename are both "
"lists they need to be the same length"
)
pol = feed_pol[0]
if rotate_pol is None:
# if a mix of feed pols, don't rotate by default
if np.any(np.array(feed_pol) != feed_pol[0]):
rotate_pol = False
else:
rotate_pol = True
else:
pol = feed_pol
if rotate_pol is None:
rotate_pol = True
if isinstance(freq, (list, tuple)):
raise ValueError("frequency can not be a nested list")
if isinstance(pol, (list, tuple)):
raise ValueError("feed_pol can not be a nested list")
self.read_cst_beam(
cst_filename[0],
beam_type=beam_type,
feed_pol=pol,
rotate_pol=rotate_pol,
frequency=freq,
telescope_name=telescope_name,
feed_name=feed_name,
feed_version=feed_version,
model_name=model_name,
model_version=model_version,
history=history,
x_orientation=x_orientation,
reference_impedance=reference_impedance,
extra_keywords=extra_keywords,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
for file_i, f in enumerate(cst_filename[1:]):
if isinstance(f, (list, tuple)):
raise ValueError("filename can not be a nested list")
if isinstance(frequency, (list, tuple)):
freq = frequency[file_i + 1]
elif frequency is not None:
freq = frequency
else:
freq = None
if isinstance(feed_pol, (list, tuple)):
pol = feed_pol[file_i + 1]
else:
pol = feed_pol
beam2 = UVBeam()
beam2.read_cst_beam(
f,
beam_type=beam_type,
feed_pol=pol,
rotate_pol=rotate_pol,
frequency=freq,
telescope_name=telescope_name,
feed_name=feed_name,
feed_version=feed_version,
model_name=model_name,
model_version=model_version,
history=history,
x_orientation=x_orientation,
reference_impedance=reference_impedance,
extra_keywords=extra_keywords,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self += beam2
if len(cst_filename) > 1:
del beam2
else:
if isinstance(frequency, (list, tuple)):
raise ValueError("Too many frequencies specified")
if isinstance(feed_pol, (list, tuple)):
raise ValueError("Too many feed_pols specified")
if rotate_pol is None:
rotate_pol = True
cst_beam_obj = cst_beam.CSTBeam()
cst_beam_obj.read_cst_beam(
cst_filename,
beam_type=beam_type,
feed_pol=feed_pol,
rotate_pol=rotate_pol,
frequency=frequency,
telescope_name=telescope_name,
feed_name=feed_name,
feed_version=feed_version,
model_name=model_name,
model_version=model_version,
history=history,
x_orientation=x_orientation,
reference_impedance=reference_impedance,
extra_keywords=extra_keywords,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self._convert_from_filetype(cst_beam_obj)
del cst_beam_obj
[docs] def read_mwa_beam(
self,
h5filepath,
delays=None,
amplitudes=None,
pixels_per_deg=5,
freq_range=None,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Read in the full embedded element MWA beam.
Note that the azimuth convention in for the UVBeam object is different than the
azimuth convention in the mwa_pb repo. In that repo, the azimuth convention is
changed from the native FEKO convention (the FEKO convention is the same as the
UVBeam convention). The convention in the mwa_pb repo has a different zero point
and a different direction (so it is in a left handed coordinate system).
Parameters
----------
h5filepath : str
path to input h5 file containing the MWA full embedded element spherical
harmonic modes. Download via
`wget http://cerberus.mwa128t.org/mwa_full_embedded_element_pattern.h5`
delays : array of ints
Array of MWA beamformer delay steps. Should be shape (n_pols, n_dipoles).
amplitudes : array of floats
Array of dipole amplitudes, these are absolute values
(i.e. relatable to physical units).
Should be shape (n_pols, n_dipoles).
pixels_per_deg : float
Number of theta/phi pixels per degree. Sets the resolution of the beam.
freq_range : array_like of float
Range of frequencies to include in Hz, defaults to all available
frequencies. Must be length 2.
run_check : bool
Option to check for the existence and proper shapes of
required parameters after reading in the file.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
required parameters after reading in the file.
"""
from . import mwa_beam
mwabeam_obj = mwa_beam.MWABeam()
mwabeam_obj.read_mwa_beam(
h5filepath,
delays=delays,
amplitudes=amplitudes,
pixels_per_deg=pixels_per_deg,
freq_range=freq_range,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self._convert_from_filetype(mwabeam_obj)
del mwabeam_obj
[docs] def write_beamfits(
self,
filename,
run_check=True,
check_extra=True,
run_check_acceptability=True,
clobber=False,
):
"""
Write the data to a beamfits file.
Parameters
----------
filename : str
The beamfits file to write to.
run_check : bool
Option to check for the existence and proper shapes of
required parameters before writing the file.
check_extra : bool
Option to check optional parameters as well as
required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
required parameters before writing the file.
clobber : bool
Option to overwrite the filename if the file already exists.
"""
beamfits_obj = self._convert_to_filetype("beamfits")
beamfits_obj.write_beamfits(
filename,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
clobber=clobber,
)
del beamfits_obj