https://github.com/RadioAstronomySoftwareGroup/pyuvdata
Tip revision: d5515e11b331a26e14ead3c2bd2bd0cb19220596 authored by Bryna Hazelton on 14 October 2021, 22:50:31 UTC
update the changelog for a new version
update the changelog for a new version
Tip revision: d5515e1
uvcal.py
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Primary container for radio interferometer calibration solutions."""
import copy
import numpy as np
import threading
import warnings
from ..uvbase import UVBase
from .. import parameter as uvp
from .. import telescopes as uvtel
from .. import utils as uvutils
__all__ = ["UVCal"]
class UVCal(UVBase):
"""
A class defining calibration solutions.
Currently supported file types: calfits
Attributes
----------
UVParameter objects: For full list see UVCal Parameters
(http://pyuvdata.readthedocs.io/en/latest/uvcal_parameters.html).
Some are always required, some are required for certain cal_types
and others are always optional.
"""
def __init__(self):
self._Nfreqs = uvp.UVParameter(
"Nfreqs", description="Number of frequency channels", expected_type=int
)
self._Njones = uvp.UVParameter(
"Njones",
description="Number of Jones calibration"
"parameters (Number of Jones matrix elements "
"calculated in calibration).",
expected_type=int,
)
desc = (
"Number of times with different calibrations calculated "
"(if a calibration is calculated over a range of integrations, "
"this gives the number of separate calibrations along the time axis)."
)
self._Ntimes = uvp.UVParameter("Ntimes", description=desc, expected_type=int)
self._history = uvp.UVParameter(
"history",
description="String of history, units English",
form="str",
expected_type=str,
)
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 = (
"Time range (in JD) that cal solutions are valid for."
"list: [start_time, end_time] in JD. Should only be set in Ntimes is 1."
)
self._time_range = uvp.UVParameter(
"time_range", description=desc, form=2, expected_type=float, required=False
)
desc = "Name of telescope. e.g. HERA. String."
self._telescope_name = uvp.UVParameter(
"telescope_name", description=desc, form="str", expected_type=str
)
desc = (
"Telescope location: xyz in ITRF (earth-centered frame). "
"Can also be accessed using telescope_location_lat_lon_alt or "
"telescope_location_lat_lon_alt_degrees properties"
)
self._telescope_location = uvp.LocationParameter(
"telescope_location",
description=desc,
acceptable_range=(6.35e6, 6.39e6),
tols=1e-3,
required=False,
)
desc = (
"Number of antennas that have data associated with them "
"(i.e. length of ant_array), which may be smaller than the number"
"of antennas in the telescope (i.e. length of antenna_numbers)."
)
self._Nants_data = uvp.UVParameter(
"Nants_data", description=desc, expected_type=int
)
desc = (
"Number of antennas in the antenna_numbers array. May be larger "
"than the number of antennas with gains associated with them."
)
self._Nants_telescope = uvp.UVParameter(
"Nants_telescope", description=desc, expected_type=int
)
desc = (
"Array of integer antenna numbers that appear in self.gain_array,"
" with shape (Nants_data,). "
"This array is ordered to match the inherent ordering of the zeroth"
" axis of self.gain_array."
)
self._ant_array = uvp.UVParameter(
"ant_array", description=desc, expected_type=int, form=("Nants_data",)
)
desc = (
"Array of antenna names with shape (Nants_telescope,). "
"Ordering of elements matches ordering of antenna_numbers."
)
self._antenna_names = uvp.UVParameter(
"antenna_names",
description=desc,
form=("Nants_telescope",),
expected_type=str,
)
desc = (
"Array of all integer-valued antenna numbers in the telescope with "
"shape (Nants_telescope,). Ordering of elements matches that of "
"antenna_names. This array is not necessarily identical to "
"ant_array, in that this array holds all antenna numbers "
"associated with the telescope, not just antennas with data, and "
"has an in principle non-specific ordering."
)
self._antenna_numbers = uvp.UVParameter(
"antenna_numbers",
description=desc,
form=("Nants_telescope",),
expected_type=int,
)
desc = (
"Array giving coordinates of antennas relative to "
"telescope_location (ITRF frame), shape (Nants_telescope, 3), "
"units meters. See the tutorial page in the documentation "
"for an example of how to convert this to topocentric frame."
)
self._antenna_positions = uvp.UVParameter(
"antenna_positions",
description=desc,
form=("Nants_telescope", 3),
expected_type=float,
tols=1e-3, # 1 mm
required=False,
)
self._spw_array = uvp.UVParameter(
"spw_array",
description="Array of spectral window numbers, shape (Nspws).",
form=("Nspws",),
expected_type=int,
)
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
desc = "Channel width of of a frequency bin. Units Hz."
self._channel_width = uvp.UVParameter(
"channel_width", description=desc, expected_type=float, tols=1e-3
)
desc = (
"Array of antenna polarization integers, shape (Njones). "
"linear pols -5:-8 (jxx, jyy, jxy, jyx)."
"circular pols -1:-4 (jrr, jll. jrl, jlr)."
)
self._jones_array = uvp.UVParameter(
"jones_array",
description=desc,
expected_type=int,
acceptable_vals=list(np.arange(-8, 0)),
form=("Njones",),
)
desc = (
"Array of calibration solution times, center of integration, "
"shape (Ntimes), units Julian Date"
)
self._time_array = uvp.UVParameter(
"time_array",
description=desc,
form=("Ntimes",),
expected_type=float,
tols=1e-3 / (60.0 * 60.0 * 24.0),
)
# standard angle tolerance: 10 mas in radians.
# Should perhaps be decreased to 1 mas in the future
radian_tol = 10 * 2 * np.pi * 1e-3 / (60.0 * 60.0 * 360.0)
desc = "Array of lsts, center of integration, shape (Ntimes), units radians"
self._lst_array = uvp.UVParameter(
"lst_array",
description=desc,
form=("Ntimes",),
expected_type=float,
tols=radian_tol,
required=False,
)
desc = "Integration time of a time bin, units seconds."
self._integration_time = uvp.UVParameter(
"integration_time", description=desc, expected_type=float, tols=1e-3
) # 1ms
desc = (
"The convention for applying the calibration solutions to data."
'Values are "divide" or "multiply", indicating that to calibrate '
"one should divide or multiply uncalibrated data by gains. "
"Mathematically this indicates the alpha exponent in the equation: "
"calibrated data = gain^alpha * uncalibrated data. A value of "
'"divide" represents alpha=-1 and "multiply" represents alpha=1.'
)
self._gain_convention = uvp.UVParameter(
"gain_convention",
form="str",
expected_type=str,
description=desc,
acceptable_vals=["divide", "multiply"],
)
desc = (
"Array of flags to be applied to calibrated data (logical OR "
"of input and flag generated by calibration). True is flagged. "
"Shape: (Nants_data, Nspws, Nfreqs, Ntimes, Njones), type = bool."
)
self._flag_array = uvp.UVParameter(
"flag_array",
description=desc,
form=("Nants_data", "Nspws", "Nfreqs", "Ntimes", "Njones"),
expected_type=bool,
)
desc = (
"Array of qualities of calibration solutions. "
"The shape depends on cal_type, if the cal_type is 'gain' or "
"'unknown', the shape is: (Nants_data, Nspws, Nfreqs, Ntimes, Njones), "
"if the cal_type is 'delay', the shape is "
"(Nants_data, Nspws, 1, Ntimes, Njones). The type is float."
)
self._quality_array = uvp.UVParameter(
"quality_array",
description=desc,
form=("Nants_data", "Nspws", "Nfreqs", "Ntimes", "Njones"),
expected_type=float,
)
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,
expected_type=str,
acceptable_vals=["east", "north"],
)
# --- cal_type parameters ---
desc = "cal type parameter. Values are delay, gain or unknown."
self._cal_type = uvp.UVParameter(
"cal_type",
form="str",
expected_type=str,
value="unknown",
description=desc,
acceptable_vals=["delay", "gain", "unknown"],
)
desc = (
'Required if cal_type = "gain". Array of gains, '
"shape: (Nants_data, Nspws, Nfreqs, Ntimes, Njones), type = complex float."
)
self._gain_array = uvp.UVParameter(
"gain_array",
description=desc,
required=False,
form=("Nants_data", "Nspws", "Nfreqs", "Ntimes", "Njones"),
expected_type=complex,
)
desc = (
'Required if cal_type = "delay". Array of delays with units of seconds. '
"Shape: (Nants_data, Nspws, 1, Ntimes, Njones), type = float."
)
self._delay_array = uvp.UVParameter(
"delay_array",
description=desc,
required=False,
form=("Nants_data", "Nspws", 1, "Ntimes", "Njones"),
expected_type=float,
)
desc = (
"Required if cal_type = 'delay'. Frequency range that solutions "
"are valid for. list: [start_frequency, end_frequency] in Hz."
)
self._freq_range = uvp.UVParameter(
"freq_range",
required=False,
description=desc,
form=2,
expected_type=float,
tols=1e-3,
)
# --- cal_style parameters ---
desc = "Style of calibration. Values are sky or redundant."
self._cal_style = uvp.UVParameter(
"cal_style",
form="str",
expected_type=str,
description=desc,
acceptable_vals=["sky", "redundant"],
)
desc = (
'Required if cal_style = "sky". Short string describing field '
"center or dominant source."
)
self._sky_field = uvp.UVParameter(
"sky_field", form="str", required=False, expected_type=str, description=desc
)
desc = 'Required if cal_style = "sky". Name of calibration catalog.'
self._sky_catalog = uvp.UVParameter(
"sky_catalog",
form="str",
required=False,
expected_type=str,
description=desc,
)
desc = 'Required if cal_style = "sky". Phase reference antenna.'
self._ref_antenna_name = uvp.UVParameter(
"ref_antenna_name",
form="str",
required=False,
expected_type=str,
description=desc,
)
desc = "Number of sources used."
self._Nsources = uvp.UVParameter(
"Nsources", required=False, expected_type=int, description=desc
)
desc = "Range of baselines used for calibration."
self._baseline_range = uvp.UVParameter(
"baseline_range",
form=2,
required=False,
expected_type=float,
description=desc,
)
desc = "Name of diffuse model."
self._diffuse_model = uvp.UVParameter(
"diffuse_model",
form="str",
required=False,
expected_type=str,
description=desc,
)
# --- truly optional parameters ---
desc = (
"The gain scale of the calibration, which indicates the units of the "
"calibrated visibilities. For example, Jy or K str."
)
self._gain_scale = uvp.UVParameter(
"gain_scale",
form="str",
expected_type=str,
description=desc,
required=False,
)
desc = (
"Array of input flags, True is flagged. shape: (Nants_data, Nspws, "
"Nfreqs, Ntimes, Njones), type = bool."
)
self._input_flag_array = uvp.UVParameter(
"input_flag_array",
description=desc,
required=False,
form=("Nants_data", "Nspws", "Nfreqs", "Ntimes", "Njones"),
expected_type=bool,
)
desc = "Origin (on github for e.g) of calibration software. Url and branch."
self._git_origin_cal = uvp.UVParameter(
"git_origin_cal",
form="str",
expected_type=str,
description=desc,
required=False,
)
desc = (
"Commit hash of calibration software (from git_origin_cal) used "
"to generate solutions."
)
self._git_hash_cal = uvp.UVParameter(
"git_hash_cal",
form="str",
expected_type=str,
description=desc,
required=False,
)
desc = "Name of observer who calculated solutions in this file."
self._observer = uvp.UVParameter(
"observer", form="str", description=desc, expected_type=str, required=False
)
desc = (
"Array of qualities of the calibration for entire arrays. "
'The shape depends on cal_type, if the cal_type is "gain" or '
'"unknown", the shape is: (Nspws, Nfreqs, Ntimes, Njones), '
'if the cal_type is "delay", the shape is (Nspws, 1, Ntimes, Njones), '
"type = float."
)
self._total_quality_array = uvp.UVParameter(
"total_quality_array",
description=desc,
form=("Nspws", "Nfreqs", "Ntimes", "Njones"),
expected_type=float,
required=False,
)
desc = (
"Any user supplied extra keywords, type=dict. Keys should be "
"8 character or less strings if writing to calfits 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,
)
super(UVCal, self).__init__()
def _set_gain(self):
"""Set cal_type to 'gain' and adjust required parameters."""
self.cal_type = "gain"
self._gain_array.required = True
self._delay_array.required = False
self._freq_range.required = False
self._quality_array.form = self._gain_array.form
self._total_quality_array.form = self._gain_array.form[1:]
def _set_delay(self):
"""Set cal_type to 'delay' and adjust required parameters."""
self.cal_type = "delay"
self._gain_array.required = False
self._delay_array.required = True
self._freq_range.required = True
self._quality_array.form = self._delay_array.form
self._total_quality_array.form = self._delay_array.form[1:]
def _set_unknown_cal_type(self):
"""Set cal_type to 'unknown' and adjust required parameters."""
self.cal_type = "unknown"
self._gain_array.required = False
self._delay_array.required = False
self._freq_range.required = False
self._quality_array.form = self._gain_array.form
self._total_quality_array.form = self._gain_array.form[1:]
def _set_sky(self):
"""Set cal_style to 'sky' and adjust required parameters."""
self.cal_style = "sky"
self._sky_field.required = True
self._sky_catalog.required = True
self._ref_antenna_name.required = True
def _set_redundant(self):
"""Set cal_style to 'redundant' and adjust required parameters."""
self.cal_style = "redundant"
self._sky_field.required = False
self._sky_catalog.required = False
self._ref_antenna_name.required = False
@property
def _data_params(self):
"""List of strings giving the data-like parameters."""
return [
"gain_array",
"delay_array",
"flag_array",
"quality_array",
"total_quality_array",
"input_flag_array",
]
@property
def data_like_parameters(self):
"""Iterate defined parameters which are data-like (not metadata-like)."""
for key in self._data_params:
if hasattr(self, key):
yield getattr(self, key)
@property
def metadata_only(self):
"""
Property that determines whether this is a metadata only object.
An object is metadata only if data_array, nsample_array and flag_array
are all None.
"""
metadata_only = all(d is None for d in self.data_like_parameters)
cal_type = self._cal_type.value
if cal_type is None:
cal_type = "unknown"
required_params = {
"gain": ["gain_array", "flag_array", "quality_array"],
"delay": ["delay_array", "flag_array", "quality_array"],
"unknown": ["flag_array", "quality_array"],
}
for param_name in self._data_params:
if param_name in required_params[cal_type]:
getattr(self, "_" + param_name).required = not metadata_only
return metadata_only
def set_telescope_params(self, overwrite=False):
"""
Set telescope related parameters.
If the telescope_name is in the known_telescopes, set the telescope
location to the value for the known telescope. Also set the antenna positions
if they are not set on the object and are available for the telescope.
Parameters
----------
overwrite : bool
Option to overwrite existing telescope-associated parameters with
the values from the known telescope.
Raises
------
ValueError
if the telescope_name is not in known telescopes
"""
telescope_obj = uvtel.get_telescope(self.telescope_name)
if telescope_obj is not False:
if self.telescope_location is None or overwrite is True:
warnings.warn(
"telescope_location is not set. Using known values "
f"for {telescope_obj.telescope_name}."
)
self.telescope_location = telescope_obj.telescope_location
if telescope_obj.antenna_positions is not None and (
self.antenna_positions is None or overwrite is True
):
ant_inds = []
telescope_ant_inds = []
# first try to match using names only
for index, antname in enumerate(self.antenna_names):
if antname in telescope_obj.antenna_names:
ant_inds.append(index)
telescope_ant_inds.append(
np.where(telescope_obj.antenna_names == antname)[0][0]
)
# next try using numbers
if len(ant_inds) != self.Nants_telescope:
for index, antnum in enumerate(self.antenna_numbers):
# only update if not already found
if (
index not in ant_inds
and antnum in telescope_obj.antenna_numbers
):
this_ant_ind = np.where(
telescope_obj.antenna_numbers == antnum
)[0][0]
# make sure we don't already have this antenna associated
# with another antenna
if this_ant_ind not in telescope_ant_inds:
ant_inds.append(index)
telescope_ant_inds.append(this_ant_ind)
if len(ant_inds) != self.Nants_telescope:
warnings.warn(
"Not all antennas have positions in the telescope object. "
"Not setting antenna_positions."
)
else:
warnings.warn(
"antenna_positions is not set. Using known values "
f"for {telescope_obj.telescope_name}."
)
telescope_ant_inds = np.array(telescope_ant_inds)
self.antenna_positions = telescope_obj.antenna_positions[
telescope_ant_inds, :
]
else:
raise ValueError(
f"Telescope {self.telescope_name} is not in known_telescopes."
)
def _set_lsts_helper(self):
latitude, longitude, altitude = self.telescope_location_lat_lon_alt_degrees
unique_times, inverse_inds = np.unique(self.time_array, return_inverse=True)
unique_lst_array = uvutils.get_lst_for_time(
unique_times, latitude, longitude, altitude
)
self.lst_array = unique_lst_array[inverse_inds]
return
def set_lsts_from_time_array(self, background=False):
"""Set the lst_array based from the time_array.
Parameters
----------
background : bool, False
When set to True, start the calculation on a threading.Thread in the
background and return the thread to the user.
Returns
-------
proc : None or threading.Thread instance
When background is set to True, a thread is returned which must be
joined before the lst_array exists on the UVData object.
"""
if not background:
self._set_lsts_helper()
return
else:
proc = threading.Thread(target=self._set_lsts_helper)
proc.start()
return proc
def check(self, check_extra=True, run_check_acceptability=True):
"""
Add some extra checks on top of checks on UVBase class.
Check that required parameters exist. Check that parameters have
appropriate shapes and optionally that the values are acceptable.
Parameters
----------
check_extra : bool
If true, check all parameters, otherwise only check required parameters.
run_check_acceptability : bool
Option to check if values in parameters are acceptable.
Returns
-------
bool
True if check passes
Raises
------
ValueError
if parameter shapes or types are wrong or do not have acceptable
values (if run_check_acceptability is True)
"""
# Make sure requirements are set properly for cal_style
if self.cal_style == "sky":
self._set_sky()
elif self.cal_style == "redundant":
self._set_redundant()
# If the telescope location is not set issue a deprecation warning
if self.telescope_location is None:
warnings.warn(
"The telescope_location is not set. It will be a required "
"parameter starting in pyuvdata version 2.3",
category=DeprecationWarning,
)
# If the antenna positions parameter is not set issue a deprecation warning
if self.antenna_positions is None:
warnings.warn(
"The antenna_positions parameter is not set. It will be a required "
"parameter starting in pyuvdata version 2.3",
category=DeprecationWarning,
)
# If the antenna positions parameter is not set issue a deprecation warning
if self.lst_array is None:
warnings.warn(
"The lst_array is not set. It will be a required "
"parameter starting in pyuvdata version 2.3",
category=DeprecationWarning,
)
# first run the basic check from UVBase
super(UVCal, self).check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
# require that all entries in ant_array exist in antenna_numbers
if not all(ant in self.antenna_numbers for ant in self.ant_array):
raise ValueError("All antennas in ant_array must be in antenna_numbers.")
# issue warning if extra_keywords keys are longer than 8 characters
for key in 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 calfits 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 calfits "
"files".format(key=key)
)
return True
def copy(self, metadata_only=False):
"""
Make and return a copy of the UVCal object.
Parameters
----------
metadata_only : bool
If True, only copy the metadata of the object.
Returns
-------
UVCal
Copy of self.
"""
if not metadata_only:
return super(UVCal, self).copy()
else:
uv = UVCal()
# include all attributes, not just UVParameter ones.
for attr in self.__iter__(uvparams_only=False):
# skip properties
if isinstance(getattr(type(self), attr, None), property):
continue
# skip data like parameters
# parameter names have a leading underscore we want to ignore
if attr.lstrip("_") in self._data_params:
continue
setattr(uv, attr, copy.deepcopy(getattr(self, attr)))
return uv
def _has_key(self, antnum=None, jpol=None):
"""
Check if this UVCal has the requested antenna or polarization.
Parameters
----------
antnum : int
Antenna number to check.
jpol : str or int
Antenna polarization string or integer to check.
Returns
-------
bool
Boolean indicator of whether the antenna and/or antenna
polarization is present on this object.
"""
if antnum is not None:
if antnum not in self.ant_array:
return False
if jpol is not None:
if isinstance(jpol, (str, np.str_)):
jpol = uvutils.jstr2num(jpol, x_orientation=self.x_orientation)
if jpol not in self.jones_array:
return False
return True
def ant2ind(self, antnum):
"""
Get the index in data arrays for an antenna number.
Parameters
----------
antnum : int
Antenna number to get index for.
Returns
-------
int
Antenna index in data arrays.
"""
if not self._has_key(antnum=antnum):
raise ValueError("{} not found in ant_array".format(antnum))
return np.argmin(np.abs(self.ant_array - antnum))
def jpol2ind(self, jpol):
"""
Get the index in data arrays for an antenna polarization.
Parameters
----------
jpol : int or str
Antenna polarization to get index for.
Returns
-------
int
Antenna polarization index in data arrays
"""
if isinstance(jpol, (str, np.str_)):
jpol = uvutils.jstr2num(jpol, x_orientation=self.x_orientation)
if not self._has_key(jpol=jpol):
raise ValueError("{} not found in jones_array".format(jpol))
return np.argmin(np.abs(self.jones_array - jpol))
def _slice_array(self, key, data_array, squeeze_pol=True):
"""
Slice a data array given a data key.
Parameters
----------
key : int or length 2 tuple of ints or int and str
Antenna or antenna and polarization to get slice for. If it's a length
2 tuple, the second value must be an antenna polarization int or string
parsable by jpol2ind.
data_array : :class: numpy ndarray
Array to get slice of. Must have the shape of the gain_array or delay_array.
squeeze_pol : bool
Option to squeeze pol dimension if possible.
Returns
-------
:class: numpy ndarray
Slice of the data_array for the key.
"""
key = uvutils._get_iterable(key)
if len(key) == 1:
# interpret as a single antenna
output = data_array[self.ant2ind(key[0]), 0, :, :, :]
if squeeze_pol and output.shape[-1] == 1:
output = output[:, :, 0]
return output
elif len(key) == 2:
# interpret as an antenna-pol pair
return data_array[self.ant2ind(key[0]), 0, :, :, self.jpol2ind(key[1])]
def _parse_key(self, ant, jpol=None):
"""
Parse key inputs and return a standard antenna-polarization key.
Parameters
----------
ant : int or length 2 tuple of ints or int and str
Antenna or antenna and polarization to get key for. If it's a length
2 tuple, the second value must be an antenna polarization int or string
parsable by jpol2ind.
jpol : int or str
Antenna polarization int or string parsable by jpol2ind. Only used
if `ant` is an integer.
Returns
-------
tuple
Standard key tuple.
"""
if isinstance(ant, (list, tuple)):
# interpret ant as (ant,) or (ant, jpol)
key = tuple(ant)
elif isinstance(ant, (int, np.integer)):
# interpret ant as antenna number
key = (ant,)
# add jpol if fed
if jpol is not None:
key += (jpol,)
return key
def get_gains(self, ant, jpol=None, squeeze_pol=True):
"""
Get the gain associated with an antenna and/or polarization.
Parameters
----------
ant : int or length 2 tuple of ints or int and str
Antenna or antenna and polarization to get gains for. If it's a length
2 tuple, the second value must be an antenna polarization int or string
parsable by jpol2ind.
jpol : int or str, optional
Instrumental polarization to request. Ex. 'Jxx'
squeeze_pol : bool
Option to squeeze pol dimension if possible.
Returns
-------
complex ndarray
Gain solution of shape (Nfreqs, Ntimes, Npol) or (Nfreqs, Ntimes)
if jpol is set or if squeeze_pol is True and Njones = 1.
"""
if self.cal_type != "gain":
raise ValueError("cal_type must be 'gain' for get_gains() method")
return self._slice_array(
self._parse_key(ant, jpol=jpol), self.gain_array, squeeze_pol=squeeze_pol
)
def get_flags(self, ant, jpol=None, squeeze_pol=True):
"""
Get the flags associated with an antenna and/or polarization.
Parameters
----------
ant : int or length 2 tuple of ints or int and str
Antenna or antenna and polarization to get gains for. If it's a length
2 tuple, the second value must be an antenna polarization int or string
parsable by jpol2ind.
jpol : int or str, optional
Instrumental polarization to request. Ex. 'Jxx'
squeeze_pol : bool
Option to squeeze pol dimension if possible.
Returns
-------
boolean ndarray
Flags of shape (Nfreqs, Ntimes, Npol) or (Nfreqs, Ntimes)
if jpol is set or if squeeze_pol is True and Njones = 1.
"""
return self._slice_array(
self._parse_key(ant, jpol=jpol), self.flag_array, squeeze_pol=squeeze_pol
)
def get_quality(self, ant, jpol=None, squeeze_pol=True):
"""
Get the qualities associated with an antenna and/or polarization.
Parameters
----------
ant : int or length 2 tuple of ints or int and str
Antenna or antenna and polarization to get gains for. If it's a length
2 tuple, the second value must be an antenna polarization int or string
parsable by jpol2ind.
jpol : int or str, optional
Instrumental polarization to request. Ex. 'Jxx'
squeeze_pol : bool
Option to squeeze pol dimension if possible.
Returns
-------
float ndarray
Qualities of shape (Nfreqs, Ntimes, Npol) or (Nfreqs, Ntimes)
if jpol is not None or if squeeze_pol is True and Njones = 1.
"""
return self._slice_array(
self._parse_key(ant, jpol=jpol), self.quality_array, squeeze_pol=squeeze_pol
)
def convert_to_gain(
self,
delay_convention="minus",
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Convert non-gain cal_types to gains.
For the delay cal_type the gain is calculated as:
gain = 1 * exp((+/-) * 2 * pi * j * delay * frequency)
where the (+/-) is dictated by the delay_convention
Parameters
----------
delay_convention : str
Exponent sign to use in the conversion, can be "plus" or "minus".
run_check : bool
Option to check for the existence and proper shapes of parameters
after converting.
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 parameters after
converting.
"""
if self.cal_type == "gain":
raise ValueError("The data is already a gain cal_type.")
elif self.cal_type == "delay":
if delay_convention == "minus":
conv = -1
elif delay_convention == "plus":
conv = 1
else:
raise ValueError('delay_convention can only be "minus" or "plus"')
self.history += " Converted from delays to gains using pyuvdata."
phase_array = np.zeros(
(self.Nants_data, self.Nspws, self.Nfreqs, self.Ntimes, self.Njones)
)
for si in range(self.Nspws):
temp = (
conv
* 2
* np.pi
* np.dot(
self.delay_array[:, si, 0, :, :, np.newaxis],
self.freq_array[si, np.newaxis, :],
)
)
temp = np.transpose(temp, (0, 3, 1, 2))
phase_array[:, si, :, :, :] = temp
gain_array = np.exp(1j * phase_array)
new_quality = np.repeat(
self.quality_array[:, :, :, :, :], self.Nfreqs, axis=2
)
self._set_gain()
self.gain_array = gain_array
self.quality_array = new_quality
self.delay_array = None
if self.total_quality_array is not None:
new_total_quality_array = np.repeat(
self.total_quality_array[:, :, :, :], self.Nfreqs, axis=1
)
self.total_quality_array = new_total_quality_array
# check if object is self-consistent
if run_check:
self.check(
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
else:
raise ValueError("cal_type is unknown, cannot convert to gain")
def __add__(
self,
other,
verbose_history=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
inplace=False,
):
"""
Combine two UVCal objects along antenna, frequency, time, and/or Jones axis.
Parameters
----------
other : :class: UVCal
Another UVCal object which will be added to self.
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 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 parameters after
combining objects.
inplace : bool
Option to overwrite self as we go, otherwise create a third object
as the sum of the two.
"""
if inplace:
this = self
else:
this = self.copy()
# Check that both objects are UVCal and valid
this.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not issubclass(other.__class__, this.__class__):
if not issubclass(this.__class__, other.__class__):
raise ValueError(
"Only UVCal (or subclass) objects can be added to "
"a UVCal (or subclass) object"
)
other.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
# Check objects are compatible
compatibility_params = [
"_cal_type",
"_integration_time",
"_channel_width",
"_telescope_name",
"_gain_convention",
"_x_orientation",
"_cal_style",
"_ref_antenna_name",
]
if this.cal_type == "delay":
compatibility_params.append("_freq_range")
warning_params = [
"_observer",
"_git_hash_cal",
"_sky_field",
"_sky_catalog",
"_Nsources",
"_baseline_range",
"_diffuse_model",
]
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)
for a in warning_params:
if getattr(this, a) != getattr(other, a):
msg = "UVParameter " + a[1:] + " does not match. Combining anyway."
warnings.warn(msg)
# Build up history string
history_update_string = " Combined data along "
n_axes = 0
# Check we don't have overlapping data
both_jones = np.intersect1d(this.jones_array, other.jones_array)
both_times = np.intersect1d(this.time_array, other.time_array)
if this.cal_type != "delay":
both_freq = np.intersect1d(this.freq_array[0, :], other.freq_array[0, :])
else:
# Make a non-empty array so we raise an error if other data is duplicated
both_freq = [0]
both_ants = np.intersect1d(this.ant_array, other.ant_array)
if len(both_jones) > 0:
if len(both_times) > 0:
if len(both_freq) > 0:
if len(both_ants) > 0:
raise ValueError(
"These objects have overlapping data and"
" cannot be combined."
)
temp = np.nonzero(~np.in1d(other.ant_array, this.ant_array))[0]
if len(temp) > 0:
anew_inds = temp
history_update_string += "antenna"
n_axes += 1
else:
anew_inds = []
temp = np.nonzero(~np.in1d(other.time_array, this.time_array))[0]
if len(temp) > 0:
tnew_inds = temp
if n_axes > 0:
history_update_string += ", time"
else:
history_update_string += "time"
n_axes += 1
else:
tnew_inds = []
# adding along frequency axis is not supported for delay-type cal files
if this.cal_type == "gain":
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 = []
else:
fnew_inds = []
temp = np.nonzero(~np.in1d(other.jones_array, this.jones_array))[0]
if len(temp) > 0:
jnew_inds = temp
if n_axes > 0:
history_update_string += ", jones"
else:
history_update_string += "jones"
n_axes += 1
else:
jnew_inds = []
# Initialize tqa variables
can_combine_tqa = True
if this.cal_type == "delay":
Nf_tqa = 1
else:
Nf_tqa = this.Nfreqs
# Pad out self to accommodate new data
if len(anew_inds) > 0:
this.ant_array = np.concatenate(
[this.ant_array, other.ant_array[anew_inds]]
)
order = np.argsort(this.ant_array)
this.ant_array = this.ant_array[order]
if not self.metadata_only:
zero_pad_data = np.zeros(
(
len(anew_inds),
this.Nspws,
this.quality_array.shape[2],
this.Ntimes,
this.Njones,
)
)
zero_pad_flags = np.zeros(
(len(anew_inds), this.Nspws, this.Nfreqs, this.Ntimes, this.Njones)
)
if this.cal_type == "delay":
this.delay_array = np.concatenate(
[this.delay_array, zero_pad_data], axis=0
)[order, :, :, :, :]
else:
this.gain_array = np.concatenate(
[this.gain_array, zero_pad_data], axis=0
)[order, :, :, :, :]
this.flag_array = np.concatenate(
[this.flag_array, 1 - zero_pad_flags], axis=0
).astype(np.bool_)[order, :, :, :, :]
this.quality_array = np.concatenate(
[this.quality_array, zero_pad_data], axis=0
)[order, :, :, :, :]
# If total_quality_array exists, we set it to None and warn the user
if (
this.total_quality_array is not None
or other.total_quality_array is not None
):
warnings.warn(
"Total quality array detected in at least one file; the "
"array in the new object will be set to 'None' because "
"whole-array values cannot be combined when adding antennas"
)
this.total_quality_array = None
can_combine_tqa = False
if this.input_flag_array is not None:
zero_pad = np.zeros(
(
len(anew_inds),
this.Nspws,
this.Nfreqs,
this.Ntimes,
this.Njones,
)
)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=0
).astype(np.bool_)[order, :, :, :, :]
elif other.input_flag_array is not None:
zero_pad = np.zeros(
(
len(anew_inds),
this.Nspws,
this.Nfreqs,
this.Ntimes,
this.Njones,
)
)
this.input_flag_array = np.array(
1
- np.zeros(
(
this.Nants_data,
this.Nspws,
this.Nfreqs,
this.Ntimes,
this.Njones,
)
)
).astype(np.bool_)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=0
).astype(np.bool_)[order, :, :, :, :]
if len(fnew_inds) > 0:
# Exploit the fact that quality array has the same dimensions as the
# main data.
# Also do not need to worry about different cases for gain v. delay type
zero_pad = np.zeros(
(
this.quality_array.shape[0],
this.Nspws,
len(fnew_inds),
this.Ntimes,
this.Njones,
)
)
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]
if not self.metadata_only:
this.gain_array = np.concatenate([this.gain_array, zero_pad], axis=2)[
:, :, order, :, :
]
this.flag_array = np.concatenate(
[this.flag_array, 1 - zero_pad], axis=2
).astype(np.bool_)[:, :, order, :, :]
this.quality_array = np.concatenate(
[this.quality_array, zero_pad], axis=2
)[:, :, order, :, :]
if this.total_quality_array is not None and can_combine_tqa:
zero_pad = np.zeros(
(this.Nspws, len(fnew_inds), this.Ntimes, this.Njones)
)
this.total_quality_array = np.concatenate(
[this.total_quality_array, zero_pad], axis=1
)[:, order, :, :]
elif other.total_quality_array is not None and can_combine_tqa:
zero_pad = np.zeros(
(this.Nspws, len(fnew_inds), this.Ntimes, this.Njones)
)
this.total_quality_array = np.zeros(
(this.Nspws, Nf_tqa, this.Ntimes, this.Njones)
)
this.total_quality_array = np.concatenate(
[this.total_quality_array, zero_pad], axis=1
)[:, order, :, :]
if this.input_flag_array is not None:
zero_pad = np.zeros(
(
this.input_flag_array.shape[0],
this.Nspws,
len(fnew_inds),
this.Ntimes,
this.Njones,
)
)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=2
).astype(np.bool_)[:, :, order, :, :]
elif other.input_flag_array is not None:
zero_pad = np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
len(fnew_inds),
this.Ntimes,
this.Njones,
)
)
this.input_flag_array = np.array(
1
- np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
this.flag_array.shape[3],
this.Njones,
)
)
).astype(np.bool_)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=2
).astype(np.bool_)[:, :, order, :, :]
if len(tnew_inds) > 0:
# Exploit the fact that quality array has the same dimensions as
# the main data
this.time_array = np.concatenate(
[this.time_array, other.time_array[tnew_inds]]
)
this.lst_array = np.concatenate(
[this.lst_array, other.lst_array[tnew_inds]]
)
order = np.argsort(this.time_array)
this.time_array = this.time_array[order]
this.lst_array = this.lst_array[order]
if not self.metadata_only:
zero_pad_data = np.zeros(
(
this.quality_array.shape[0],
this.Nspws,
this.quality_array.shape[2],
len(tnew_inds),
this.Njones,
)
)
zero_pad_flags = np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
len(tnew_inds),
this.Njones,
)
)
if this.cal_type == "delay":
this.delay_array = np.concatenate(
[this.delay_array, zero_pad_data], axis=3
)[:, :, :, order, :]
else:
this.gain_array = np.concatenate(
[this.gain_array, zero_pad_data], axis=3
)[:, :, :, order, :]
this.flag_array = np.concatenate(
[this.flag_array, 1 - zero_pad_flags], axis=3
).astype(np.bool_)[:, :, :, order, :]
this.quality_array = np.concatenate(
[this.quality_array, zero_pad_data], axis=3
)[:, :, :, order, :]
if this.total_quality_array is not None and can_combine_tqa:
zero_pad = np.zeros(
(
this.Nspws,
this.quality_array.shape[2],
len(tnew_inds),
this.Njones,
)
)
this.total_quality_array = np.concatenate(
[this.total_quality_array, zero_pad], axis=2
)[:, :, order, :]
elif other.total_quality_array is not None and can_combine_tqa:
zero_pad = np.zeros(
(
this.Nspws,
this.quality_array.shape[2],
len(tnew_inds),
this.Njones,
)
)
this.total_quality_array = np.zeros(
(this.Nspws, Nf_tqa, this.Ntimes, this.Njones)
)
this.total_quality_array = np.concatenate(
[this.total_quality_array, zero_pad], axis=2
)[:, :, order, :]
if this.input_flag_array is not None:
zero_pad = np.zeros(
(
this.input_flag_array.shape[0],
this.Nspws,
this.input_flag_array.shape[2],
len(tnew_inds),
this.Njones,
)
)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=3
).astype(np.bool_)[:, :, :, order, :]
elif other.input_flag_array is not None:
zero_pad = np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
len(tnew_inds),
this.Njones,
)
)
this.input_flag_array = np.array(
1
- np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
this.flag_array.shape[3],
this.Njones,
)
)
).astype(np.bool_)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=3
).astype(np.bool_)[:, :, :, order, :]
if len(jnew_inds) > 0:
# Exploit the fact that quality array has the same dimensions as
# the main data
this.jones_array = np.concatenate(
[this.jones_array, other.jones_array[jnew_inds]]
)
order = np.argsort(np.abs(this.jones_array))
this.jones_array = this.jones_array[order]
if not self.metadata_only:
zero_pad_data = np.zeros(
(
this.quality_array.shape[0],
this.Nspws,
this.quality_array.shape[2],
this.quality_array.shape[3],
len(jnew_inds),
)
)
zero_pad_flags = np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
this.flag_array.shape[3],
len(jnew_inds),
)
)
if this.cal_type == "delay":
this.delay_array = np.concatenate(
[this.delay_array, zero_pad_data], axis=4
)[:, :, :, :, order]
else:
this.gain_array = np.concatenate(
[this.gain_array, zero_pad_data], axis=4
)[:, :, :, :, order]
this.flag_array = np.concatenate(
[this.flag_array, 1 - zero_pad_flags], axis=4
).astype(np.bool_)[:, :, :, :, order]
this.quality_array = np.concatenate(
[this.quality_array, zero_pad_data], axis=4
)[:, :, :, :, order]
if this.total_quality_array is not None and can_combine_tqa:
zero_pad = np.zeros(
(
this.Nspws,
this.quality_array.shape[2],
this.quality_array.shape[3],
len(jnew_inds),
)
)
this.total_quality_array = np.concatenate(
[this.total_quality_array, zero_pad], axis=3
)[:, :, :, order]
elif other.total_quality_array is not None and can_combine_tqa:
zero_pad = np.zeros(
(
this.Nspws,
this.quality_array.shape[2],
this.quality_array.shape[3],
len(jnew_inds),
)
)
this.total_quality_array = np.zeros(
(this.Nspws, Nf_tqa, this.Ntimes, this.Njones)
)
this.total_quality_array = np.concatenate(
[this.total_quality_array, zero_pad], axis=3
)[:, :, :, order]
if this.input_flag_array is not None:
zero_pad = np.zeros(
(
this.input_flag_array.shape[0],
this.Nspws,
this.input_flag_array.shape[2],
this.input_flag_array.shape[3],
len(jnew_inds),
)
)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=4
).astype(np.bool_)[:, :, :, :, order]
elif other.input_flag_array is not None:
zero_pad = np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
this.flag_array.shape[3],
len(jnew_inds),
)
)
this.input_flag_array = np.array(
1
- np.zeros(
(
this.flag_array.shape[0],
this.Nspws,
this.flag_array.shape[2],
this.flag_array.shape[3],
this.Njones,
)
)
).astype(np.bool_)
this.input_flag_array = np.concatenate(
[this.input_flag_array, 1 - zero_pad], axis=4
).astype(np.bool_)[:, :, :, :, order]
# Now populate the data
if not self.metadata_only:
jones_t2o = np.nonzero(np.in1d(this.jones_array, other.jones_array))[0]
times_t2o = np.nonzero(np.in1d(this.time_array, other.time_array))[0]
freqs_t2o = np.nonzero(
np.in1d(this.freq_array[0, :], other.freq_array[0, :])
)[0]
ants_t2o = np.nonzero(np.in1d(this.ant_array, other.ant_array))[0]
if this.cal_type == "delay":
this.delay_array[
np.ix_(ants_t2o, [0], [0], times_t2o, jones_t2o)
] = other.delay_array
this.quality_array[
np.ix_(ants_t2o, [0], [0], times_t2o, jones_t2o)
] = other.quality_array
else:
this.gain_array[
np.ix_(ants_t2o, [0], freqs_t2o, times_t2o, jones_t2o)
] = other.gain_array
this.quality_array[
np.ix_(ants_t2o, [0], freqs_t2o, times_t2o, jones_t2o)
] = other.quality_array
this.flag_array[
np.ix_(ants_t2o, [0], freqs_t2o, times_t2o, jones_t2o)
] = other.flag_array
if this.total_quality_array is not None:
if other.total_quality_array is not None:
if this.cal_type == "delay":
this.total_quality_array[
np.ix_([0], [0], times_t2o, jones_t2o)
] = other.total_quality_array
else:
this.total_quality_array[
np.ix_([0], freqs_t2o, times_t2o, jones_t2o)
] = other.total_quality_array
if this.input_flag_array is not None:
if other.input_flag_array is not None:
this.input_flag_array[
np.ix_(ants_t2o, [0], freqs_t2o, times_t2o, jones_t2o)
] = other.input_flag_array
# Update N parameters (e.g. Npols)
this.Njones = this.jones_array.shape[0]
this.Ntimes = this.time_array.shape[0]
if this.cal_type == "gain":
this.Nfreqs = this.freq_array.shape[1]
this.Nants_data = len(
np.unique(this.ant_array.tolist() + other.ant_array.tolist())
)
# Check specific requirements
if this.cal_type == "gain" and 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."
)
elif np.max(freq_separation) > this.channel_width:
warnings.warn(
"Combined frequencies are not contiguous. This will make "
"it impossible to write this data out to some file types."
)
if this.Njones > 2:
jones_separation = np.diff(this.jones_array)
if np.min(jones_separation) < np.max(jones_separation):
warnings.warn(
"Combined Jones elements 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, run_check=True, check_extra=True, run_check_acceptability=True,
):
"""
Combine two UVCal objects in place.
Along antenna, frequency, time, and/or Jones axis.
Parameters
----------
other : :class: UVCal
Another UVCal object which will be added to self.
run_check : bool
Option to check for the existence and proper shapes of 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 parameters after
combining objects.
"""
self.__add__(
other,
inplace=True,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
return self
def select(
self,
antenna_nums=None,
antenna_names=None,
frequencies=None,
freq_chans=None,
times=None,
jones=None,
run_check=True,
check_extra=True,
run_check_acceptability=True,
inplace=True,
):
"""
Downselect data to keep on the object along various axes.
Axes that can be selected along include antennas, frequencies, times and
antenna polarization (jones).
The history attribute on the object will be updated to identify the
operations performed.
Parameters
----------
antenna_nums : array_like of int, optional
The antennas numbers to keep in the object (antenna positions and
names for the removed antennas will be retained).
This cannot be provided if `antenna_names` is also provided.
antenna_names : array_like of str, optional
The antennas names to keep in the object (antenna positions and
names for the removed antennas will be retained).
This cannot be provided if `antenna_nums` is also provided.
frequencies : array_like of float, optional
The frequencies to keep in the object, each value passed here should
exist in the freq_array.
freq_chans : array_like of int, optional
The frequency channel numbers to keep in the object.
times : array_like of float, optional
The times to keep in the object, each value passed here should
exist in the time_array.
jones : array_like of int or str, optional
The antenna polarizations numbers to keep in the object, each value
passed here should exist in the jones_array. If passing strings, the
canonical polarization strings (e.g. "Jxx", "Jrr") are supported and if the
`x_orientation` attribute is set, the physical dipole strings
(e.g. "Jnn", "Jee") are also supported.
run_check : bool
Option to check for the existence and proper shapes of parameters
after downselecting data on this object (the default is True,
meaning the check will be run).
check_extra : bool
Option to check optional parameters as well as required ones (the
default is True, meaning the optional parameters will be checked).
run_check_acceptability : bool
Option to check acceptable range of the values of parameters after
downselecting data on this object (the default is True, meaning the
acceptable range check will be done).
inplace : bool
Option to perform the select directly on self or return a new UVCal
object with just the selected data (the default is True, meaning the
select will be done on self).
"""
if inplace:
cal_object = self
else:
cal_object = self.copy()
# build up history string as we go
history_update_string = " Downselected to specific "
n_selects = 0
if antenna_names is not None:
if antenna_nums is not None:
raise ValueError(
"Only one of antenna_nums and antenna_names can be provided."
)
antenna_names = uvutils._get_iterable(antenna_names)
antenna_nums = []
for s in antenna_names:
if s not in cal_object.antenna_names:
raise ValueError(
f"Antenna name {s} is not present in the antenna_names array"
)
ind = np.where(np.array(cal_object.antenna_names) == s)[0][0]
antenna_nums.append(cal_object.antenna_numbers[ind])
if antenna_nums is not None:
antenna_nums = uvutils._get_iterable(antenna_nums)
history_update_string += "antennas"
n_selects += 1
ant_inds = np.zeros(0, dtype=np.int64)
for ant in antenna_nums:
if ant in cal_object.ant_array:
ant_inds = np.append(
ant_inds, np.where(cal_object.ant_array == ant)[0]
)
else:
raise ValueError(
f"Antenna number {ant} is not present in the array"
)
ant_inds = sorted(set(ant_inds))
cal_object.Nants_data = len(ant_inds)
cal_object.ant_array = cal_object.ant_array[ant_inds]
if not self.metadata_only:
cal_object.flag_array = cal_object.flag_array[ant_inds, :, :, :, :]
cal_object.quality_array = cal_object.quality_array[
ant_inds, :, :, :, :
]
if cal_object.cal_type == "delay":
cal_object.delay_array = cal_object.delay_array[
ant_inds, :, :, :, :
]
else:
cal_object.gain_array = cal_object.gain_array[ant_inds, :, :, :, :]
if cal_object.input_flag_array is not None:
cal_object.input_flag_array = cal_object.input_flag_array[
ant_inds, :, :, :, :
]
if cal_object.total_quality_array is not None:
warnings.warn(
"Cannot preserve total_quality_array when changing "
"number of antennas; discarding"
)
cal_object.total_quality_array = None
if times is not None:
times = uvutils._get_iterable(times)
if n_selects > 0:
history_update_string += ", times"
else:
history_update_string += "times"
n_selects += 1
time_inds = np.zeros(0, dtype=np.int64)
for jd in times:
if jd in cal_object.time_array:
time_inds = np.append(
time_inds, np.where(cal_object.time_array == jd)[0]
)
else:
raise ValueError(
"Time {t} is not present in the time_array".format(t=jd)
)
time_inds = sorted(set(time_inds))
cal_object.Ntimes = len(time_inds)
cal_object.time_array = cal_object.time_array[time_inds]
if cal_object.lst_array is not None:
cal_object.lst_array = cal_object.lst_array[time_inds]
if cal_object.Ntimes > 1:
time_separation = np.diff(cal_object.time_array)
if not np.isclose(
np.min(time_separation),
np.max(time_separation),
rtol=cal_object._time_array.tols[0],
atol=cal_object._time_array.tols[1],
):
warnings.warn(
"Selected times are not evenly spaced. This "
"is not supported by the calfits format."
)
if not self.metadata_only:
cal_object.flag_array = cal_object.flag_array[:, :, :, time_inds, :]
cal_object.quality_array = cal_object.quality_array[
:, :, :, time_inds, :
]
if cal_object.cal_type == "delay":
cal_object.delay_array = cal_object.delay_array[
:, :, :, time_inds, :
]
else:
cal_object.gain_array = cal_object.gain_array[:, :, :, time_inds, :]
if cal_object.input_flag_array is not None:
cal_object.input_flag_array = cal_object.input_flag_array[
:, :, :, time_inds, :
]
if cal_object.total_quality_array is not None:
cal_object.total_quality_array = cal_object.total_quality_array[
:, :, time_inds, :
]
if freq_chans is not None:
freq_chans = uvutils._get_iterable(freq_chans)
if frequencies is None:
frequencies = cal_object.freq_array[0, freq_chans]
else:
frequencies = uvutils._get_iterable(frequencies)
frequencies = np.sort(
list(set(frequencies) | set(cal_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 = cal_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))
cal_object.Nfreqs = len(freq_inds)
cal_object.freq_array = cal_object.freq_array[:, freq_inds]
if cal_object.Nfreqs > 1:
freq_separation = (
cal_object.freq_array[0, 1:] - cal_object.freq_array[0, :-1]
)
if not np.isclose(
np.min(freq_separation),
np.max(freq_separation),
rtol=cal_object._freq_array.tols[0],
atol=cal_object._freq_array.tols[1],
):
warnings.warn(
"Selected frequencies are not evenly spaced. This "
"is not supported by the calfits format"
)
if not self.metadata_only:
cal_object.flag_array = cal_object.flag_array[:, :, freq_inds, :, :]
if cal_object.cal_type == "delay":
pass
else:
cal_object.quality_array = cal_object.quality_array[
:, :, freq_inds, :, :
]
cal_object.gain_array = cal_object.gain_array[:, :, freq_inds, :, :]
if cal_object.input_flag_array is not None:
cal_object.input_flag_array = cal_object.input_flag_array[
:, :, freq_inds, :, :
]
if cal_object.cal_type == "delay":
pass
else:
if cal_object.total_quality_array is not None:
cal_object.total_quality_array = cal_object.total_quality_array[
:, freq_inds, :, :
]
if jones is not None:
jones = uvutils._get_iterable(jones)
if np.array(jones).ndim > 1:
jones = np.array(jones).flatten()
if n_selects > 0:
history_update_string += ", jones polarization terms"
else:
history_update_string += "jones polarization terms"
n_selects += 1
jones_inds = np.zeros(0, dtype=np.int64)
for j in jones:
if isinstance(j, str):
j_num = uvutils.jstr2num(j, x_orientation=self.x_orientation)
else:
j_num = j
if j_num in cal_object.jones_array:
jones_inds = np.append(
jones_inds, np.where(cal_object.jones_array == j_num)[0]
)
else:
raise ValueError(
"Jones term {j} is not present in the jones_array".format(j=j)
)
jones_inds = sorted(set(jones_inds))
cal_object.Njones = len(jones_inds)
cal_object.jones_array = cal_object.jones_array[jones_inds]
if len(jones_inds) > 2:
jones_separation = (
cal_object.jones_array[1:] - cal_object.jones_array[:-1]
)
if np.min(jones_separation) < np.max(jones_separation):
warnings.warn(
"Selected jones polarization terms are not evenly spaced. This "
"is not supported by the calfits format"
)
if not self.metadata_only:
cal_object.flag_array = cal_object.flag_array[:, :, :, :, jones_inds]
cal_object.quality_array = cal_object.quality_array[
:, :, :, :, jones_inds
]
if cal_object.cal_type == "delay":
cal_object.delay_array = cal_object.delay_array[
:, :, :, :, jones_inds
]
else:
cal_object.gain_array = cal_object.gain_array[
:, :, :, :, jones_inds
]
if cal_object.input_flag_array is not None:
cal_object.input_flag_array = cal_object.input_flag_array[
:, :, :, :, jones_inds
]
if cal_object.total_quality_array is not None:
cal_object.total_quality_array = cal_object.total_quality_array[
:, :, :, jones_inds
]
history_update_string += " using pyuvdata."
cal_object.history = cal_object.history + history_update_string
# check if object is self-consistent
if run_check:
cal_object.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return cal_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 == "calfits":
from . import calfits
other_obj = calfits.CALFITS()
else:
raise ValueError("filetype must be calfits.")
for p in self:
param = getattr(self, p)
setattr(other_obj, p, param)
return other_obj
def read_calfits(
self,
filename,
read_data=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Read in data from calfits file(s).
Parameters
----------
filename : str or list of str
The calfits file(s) to read from.
read_data : bool
Read in the gains or delays, quality arrays and flag arrays.
If set to False, only the metadata will be read in. Setting read_data to
False results in a metadata only object.
run_check : bool
Option to check for the existence and proper shapes of
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
parameters after reading in the file.
"""
from . import calfits
if isinstance(filename, (list, tuple)):
self.read_calfits(
filename[0],
read_data=read_data,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
if len(filename) > 1:
for f in filename[1:]:
uvcal2 = UVCal()
uvcal2.read_calfits(
f,
read_data=read_data,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self += uvcal2
del uvcal2
else:
calfits_obj = calfits.CALFITS()
calfits_obj.read_calfits(
filename,
read_data=read_data,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self._convert_from_filetype(calfits_obj)
del calfits_obj
def read_fhd_cal(
self,
cal_file,
obs_file,
layout_file=None,
settings_file=None,
raw=True,
read_data=True,
extra_history=None,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Read data from an FHD cal.sav file.
Parameters
----------
cal_file : str or list of str
The cal.sav file or list of files to read from.
obs_file : str or list of str
The obs.sav file or list of files to read from.
layout_file : str
The FHD layout file. Required for antenna_positions to be set.
settings_file : str or list of str, optional
The settings_file or list of files to read from. Optional,
but very useful for provenance.
raw : bool
Option to use the raw (per antenna, per frequency) solution or
to use the fitted (polynomial over phase/amplitude) solution.
Default is True (meaning use the raw solutions).
read_data : bool
Read in the gains, quality array and flag data. If set to False, only
the metadata will be read in. Setting read_data to False results in
a metadata only object. Note that if read_data is False, metadata is
derived entirely from the obs_file, which may result in slightly different
values than if it is derived from the cal file.
extra_history : str or list of str, optional
String(s) to add to the object's history parameter.
run_check : bool
Option to check for the existence and proper shapes of
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
parameters after reading in the file.
"""
from . import fhd_cal
if isinstance(cal_file, (list, tuple)):
if isinstance(obs_file, (list, tuple)):
if len(obs_file) != len(cal_file):
raise ValueError(
"Number of obs_files must match number of cal_files"
)
else:
raise ValueError("Number of obs_files must match number of cal_files")
if layout_file is not None:
if isinstance(layout_file, (list, tuple)):
if len(layout_file) != len(cal_file):
raise ValueError(
"Number of layout_files must match number of cal_files"
)
else:
raise ValueError(
"Number of layout_files must match number of cal_files"
)
layout_file_use = layout_file[0]
else:
layout_file_use = None
if settings_file is not None:
if isinstance(settings_file, (list, tuple)):
if len(settings_file) != len(cal_file):
raise ValueError(
"Number of settings_files must match number of cal_files"
)
else:
raise ValueError(
"Number of settings_files must match number of cal_files"
)
settings_file_use = settings_file[0]
else:
settings_file_use = None
self.read_fhd_cal(
cal_file[0],
obs_file[0],
layout_file=layout_file_use,
settings_file=settings_file_use,
raw=raw,
read_data=read_data,
extra_history=extra_history,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
if len(cal_file) > 1:
for ind, f in enumerate(cal_file[1:]):
uvcal2 = UVCal()
if settings_file is not None:
settings_file_use = settings_file[ind + 1]
if layout_file is not None:
layout_file_use = layout_file[ind + 1]
uvcal2.read_fhd_cal(
f,
obs_file[ind + 1],
layout_file=layout_file_use,
settings_file=settings_file_use,
raw=raw,
read_data=read_data,
extra_history=extra_history,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self += uvcal2
del uvcal2
else:
if isinstance(obs_file, (list, tuple)):
raise ValueError("Number of obs_files must match number of cal_files")
if layout_file is not None:
if isinstance(layout_file, (list, tuple)) and len(layout_file) > 1:
raise ValueError(
"Number of layout_files must match number of cal_files"
)
if settings_file is not None:
if isinstance(settings_file, (list, tuple)) and len(settings_file) > 1:
raise ValueError(
"Number of settings_files must match number of cal_files"
)
fhd_cal_obj = fhd_cal.FHDCal()
fhd_cal_obj.read_fhd_cal(
cal_file,
obs_file,
layout_file=layout_file,
settings_file=settings_file,
raw=raw,
read_data=read_data,
extra_history=extra_history,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self._convert_from_filetype(fhd_cal_obj)
del fhd_cal_obj
def write_calfits(
self,
filename,
run_check=True,
check_extra=True,
run_check_acceptability=True,
clobber=False,
):
"""
Write the data to a calfits file.
Parameters
----------
filename : str
The calfits file to write to.
run_check : bool
Option to check for the existence and proper shapes of
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
parameters before writing the file.
clobber : bool
Option to overwrite the filename if the file already exists.
Raises
------
ValueError
If the UVCal object is a metadata only object.
"""
if self.metadata_only:
raise ValueError(
"Cannot write out metadata only objects to a calfits file."
)
calfits_obj = self._convert_to_filetype("calfits")
calfits_obj.write_calfits(
filename,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
clobber=clobber,
)
del calfits_obj
