Revision 8b22682704c00bd278c44dae1686f726d261b718 authored by Steven Murray on 09 January 2023, 21:13:32 UTC, committed by Steven Murray on 09 January 2023, 21:13:32 UTC
1 parent e377413
uvflag.py
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2019 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Primary container for radio interferometer flag manipulation."""
import copy
import os
import pathlib
import threading
import warnings
import h5py
import numpy as np
from .. import parameter as uvp
from .. import telescopes as uvtel
from .. import utils as uvutils
from ..uvbase import UVBase
from ..uvcal import UVCal
from ..uvdata import UVData
__all__ = ["UVFlag", "flags2waterfall", "and_rows_cols", "lst_from_uv"]
def and_rows_cols(waterfall):
"""Perform logical and over rows and cols of a waterfall.
For a 2D flag waterfall, flag pixels only if fully flagged along
time and/or frequency
Parameters
----------
waterfall : 2D boolean array of shape (Ntimes, Nfreqs)
Returns
-------
wf : 2D array
A 2D array (size same as input) where only times/integrations
that were fully flagged are flagged.
"""
wf = np.zeros_like(waterfall, dtype=np.bool_)
Ntimes, Nfreqs = waterfall.shape
wf[:, (np.sum(waterfall, axis=0) / Ntimes) == 1] = True
wf[(np.sum(waterfall, axis=1) / Nfreqs) == 1] = True
return wf
def lst_from_uv(uv):
"""Calculate the lst_array for a UVData or UVCal object.
Parameters
----------
uv : a UVData or UVCal object.
Object from which lsts are calculated
Returns
-------
lst_array: array of float
lst_array corresponding to time_array and at telescope location.
Units are radian.
"""
warnings.warn(
"The lst_from_uv function is deprecated. Use the `set_lsts_from_time_array` "
"method on the input object.",
category=DeprecationWarning,
)
if not isinstance(uv, (UVCal, UVData)):
raise ValueError(
"Function lst_from_uv can only operate on UVCal or UVData object."
)
uv.set_lsts_from_time_array()
return copy.deepcopy(uv.lst_array)
def flags2waterfall(uv, flag_array=None, keep_pol=False):
"""Convert a flag array to a 2D waterfall of dimensions (Ntimes, Nfreqs).
Averages over baselines and polarizations (in the case of visibility data),
or antennas and jones parameters (in case of calibrationd data).
Parameters
----------
uv : A UVData or UVCal object
Object defines the times and frequencies, and supplies the
flag_array to convert (if flag_array not specified)
flag_array : Optional,
flag array to convert instead of uv.flag_array.
Must have same dimensions as uv.flag_array.
keep_pol : bool
Option to keep the polarization axis intact.
Returns
-------
waterfall : 2D array or 3D array
Waterfall of averaged flags, for example fraction of baselines
which are flagged for every time and frequency (in case of UVData input)
Size is (Ntimes, Nfreqs) or (Ntimes, Nfreqs, Npols).
"""
if not isinstance(uv, (UVData, UVCal)):
raise ValueError(
"flags2waterfall() requires a UVData or UVCal object as "
"the first argument."
)
if flag_array is None:
flag_array = uv.flag_array
if uv.flag_array.shape != flag_array.shape:
raise ValueError("Flag array must align with UVData or UVCal object.")
if isinstance(uv, UVCal):
if keep_pol:
waterfall = np.swapaxes(np.mean(flag_array, axis=(0, 1)), 0, 1)
else:
waterfall = np.mean(flag_array, axis=(0, 1, 4)).T
else:
if keep_pol:
waterfall = np.zeros((uv.Ntimes, uv.Nfreqs, uv.Npols))
for i, t in enumerate(np.unique(uv.time_array)):
waterfall[i, :] = np.mean(
flag_array[uv.time_array == t, 0, :, :], axis=0
)
else:
waterfall = np.zeros((uv.Ntimes, uv.Nfreqs))
for i, t in enumerate(np.unique(uv.time_array)):
waterfall[i, :] = np.mean(
flag_array[uv.time_array == t, 0, :, :], axis=(0, 2)
)
return waterfall
class UVFlag(UVBase):
"""Object to handle flag arrays and waterfalls for interferometric datasets.
Supports reading/writing, and stores all relevant information to combine
flags and apply to data.
Initialization of the UVFlag object requires some parameters. Metadata is
copied from indata object. If indata is subclass of UVData or UVCal,
the weights_array will be set to all ones.
Lists or tuples are iterated through, treating each entry with an
individual UVFlag init.
Parameters
----------
indata : UVData, UVCal, str, pathlib.Path, list of compatible combination
Input to initialize UVFlag object. If str, assumed to be path to previously
saved UVFlag object. UVData and UVCal objects cannot be directly combined,
unless waterfall is True.
mode : {"metric", "flag"}, optional
The mode determines whether the object has a floating point metric_array
or a boolean flag_array.
copy_flags : bool, optional
Whether to copy flags from indata to new UVFlag object
waterfall : bool, optional
Whether to immediately initialize as a waterfall object, with flag/metric
axes: time, frequency, polarization.
history : str, optional
History string to attach to object.
extra_keywords : dict, optional
A dictionary of metadata values not explicitly specified by another
parameter.
label: str, optional
String used for labeling the object (e.g. 'FM').
use_future_array_shapes : bool
Option to convert to the future planned array shapes before the changes go
into effect by removing the spectral window axis.
run_check : bool
Option to check for the existence and proper shapes of parameters
after creating UVFlag object.
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
creating UVFlag object.
Attributes
----------
UVParameter objects :
For full list see the UVFlag Parameters Documentation.
(https://pyuvdata.readthedocs.io/en/latest/uvflag_parameters.html)
Some are always required, some are required for certain phase_types
and others are always optional.
"""
def __init__(
self,
indata=None,
mode="metric",
copy_flags=False,
waterfall=False,
history="",
label="",
use_future_array_shapes=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Initialize the object."""
# 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 = (
"The mode determines whether the object has a "
"floating point metric_array or a boolean flag_array. "
'Options: {"metric", "flag"}. Default is "metric".'
)
self._mode = uvp.UVParameter(
"mode",
description=desc,
form="str",
expected_type=str,
acceptable_vals=["metric", "flag"],
)
desc = (
"String used for labeling the object (e.g. 'FM'). "
"Default is empty string."
)
self._label = uvp.UVParameter(
"label", description=desc, form="str", expected_type=str
)
desc = (
"The type of object defines the form of some arrays "
"and also how metrics/flags are combined. "
"Accepted types:'waterfall', 'baseline', 'antenna'"
)
self._type = uvp.UVParameter(
"type",
description=desc,
form="str",
expected_type=str,
acceptable_vals=["antenna", "baseline", "waterfall"],
)
self._Ntimes = uvp.UVParameter(
"Ntimes", description="Number of times", expected_type=int
)
desc = "Number of baselines. Only Required for 'baseline' type objects."
self._Nbls = uvp.UVParameter(
"Nbls", description=desc, expected_type=int, required=False
)
self._Nblts = uvp.UVParameter(
"Nblts",
description="Number of baseline-times "
"(i.e. number of spectra). Not necessarily "
"equal to Nbls * Ntimes",
expected_type=int,
)
self._Nspws = uvp.UVParameter(
"Nspws",
description="Number of spectral windows "
"(ie non-contiguous spectral chunks).",
expected_type=int,
required=False, # will be required starting in version 2.4
)
self._Nfreqs = uvp.UVParameter(
"Nfreqs", description="Number of frequency channels", expected_type=int
)
self._Npols = uvp.UVParameter(
"Npols", description="Number of polarizations", expected_type=int
)
desc = (
"Floating point metric information, only available in metric mode. "
"The shape depends on the `type` parameter and on the "
"`future_array_shapes` parameter. For 'baseline' type objects, "
"the shape is (Nblts, 1, Nfreq, Npols) or (Nblts, Nfreqs, Npols) if "
"future_array_shapes=True. For 'antenna' type objects, the shape is "
"(Nants_data, 1, Nfreqs, Ntimes, Npols) or "
"(Nants_data, Nfreqs, Ntimes, Npols) if future_array_shapes=True. "
"For 'waterfall' type objects, the shape is (Nfreq, Ntimes, Npols)."
)
self._metric_array = uvp.UVParameter(
"metric_array",
description=desc,
form=("Nblts", 1, "Nfreqs", "Npols"),
expected_type=float,
required=False,
)
desc = (
"Boolean flag, True is flagged, only available in flag mode. "
"The shape depends on the `type` parameter and on the "
"`future_array_shapes` parameter. For 'baseline' type objects, "
"the shape is (Nblts, 1, Nfreq, Npols) or (Nblts, Nfreqs, Npols) if "
"future_array_shapes=True. For 'antenna' type objects, the shape is "
"(Nants_data, 1, Nfreqs, Ntimes, Npols) or "
"(Nants_data, Nfreqs, Ntimes, Npols) if future_array_shapes=True. "
"For 'waterfall' type objects, the shape is (Nfreq, Ntimes, Npols)."
)
self._flag_array = uvp.UVParameter(
"flag_array",
description=desc,
form=("Nblts", 1, "Nfreqs", "Npols"),
expected_type=bool,
required=False,
)
desc = (
"Floating point weight information, only available in metric mode."
"The shape depends on the `type` parameter and on the "
"`future_array_shapes` parameter. For 'baseline' type objects, "
"the shape is (Nblts, 1, Nfreq, Npols) or (Nblts, Nfreqs, Npols) if "
"future_array_shapes=True. For 'antenna' type objects, the shape is "
"(Nants_data, 1, Nfreqs, Ntimes, Npols) or "
"(Nants_data, Nfreqs, Ntimes, Npols) if future_array_shapes=True. "
"For 'waterfall' type objects, the shape is (Nfreq, Ntimes, Npols)."
)
self._weights_array = uvp.UVParameter(
"weights_array",
description=desc,
form=("Nblts", 1, "Nfreqs", "Npols"),
expected_type=float,
)
desc = (
"Floating point weight information about sum of squares of weights "
"when weighted data is converted from baseline to waterfall type."
"Only available in metric mode, the shape is (Nfreq, Ntimes, Npols)."
)
# TODO: should this be set to None when converting back to baseline or antenna?
# If not, should the shape be adjusted?
self._weights_square_array = uvp.UVParameter(
"weights_square_array",
description=desc,
form=("Ntimes", "Nfreqs", "Npols"),
expected_type=float,
required=False,
)
desc = (
"Array of times in Julian Date, center of integration. The shape depends "
"on the `type` parameter. For 'baseline' type object, shape is (Nblts), "
"for 'antenna' and 'waterfall' type objects, shape is (Ntimes)."
)
self._time_array = uvp.UVParameter(
"time_array",
description=desc,
form=("Nblts",),
expected_type=float,
tols=1e-3 / (60.0 * 60.0 * 24.0),
) # 1 ms in days
desc = (
"Array of lsts radians, center of integration. The shape depends "
"on the `type` parameter. For 'baseline' type object, shape is (Nblts), "
"for 'antenna' and 'waterfall' type objects, shape is (Ntimes)."
)
self._lst_array = uvp.UVParameter(
"lst_array",
description=desc,
form=("Nblts",),
expected_type=float,
tols=radian_tol,
)
desc = (
"Array of first antenna numbers, shape (Nblts). Only available for "
"'baseline' type objects."
)
self._ant_1_array = uvp.UVParameter(
"ant_1_array",
description=desc,
expected_type=int,
form=("Nblts",),
required=False,
)
desc = (
"Array of second antenna numbers, shape (Nblts). Only available for "
"'baseline' type objects."
)
self._ant_2_array = uvp.UVParameter(
"ant_2_array",
description=desc,
expected_type=int,
form=("Nblts",),
required=False,
)
desc = (
"Array of antenna numbers, shape (Nants_data), only available for "
"'antenna' type objects. "
)
self._ant_array = uvp.UVParameter(
"ant_array",
description=desc,
expected_type=int,
form=("Nants_data",),
required=False,
)
desc = (
"Array of baseline indices, shape (Nblts). "
"Only available for 'baseline' type objects. "
"type = int; baseline = 2048 * ant1 + ant2 + 2^16"
)
self._baseline_array = uvp.UVParameter(
"baseline_array",
description=desc,
expected_type=int,
form=("Nblts",),
required=False,
)
desc = (
"Array of frequencies in Hz, center of the channel. Shape (1, Nfreqs) or "
"(Nfreqs,) if type is 'waterfall' or if future_array_shapes=True."
)
self._freq_array = uvp.UVParameter(
"freq_array",
description=desc,
form=(1, "Nfreqs"),
expected_type=float,
tols=1e-3,
) # mHz
desc = "Width of frequency channels (Hz). Shape (Nfreqs,), type = float."
self._channel_width = uvp.UVParameter(
"channel_width",
description=desc,
form=("Nfreqs",),
expected_type=float,
tols=1e-3,
required=False, # will be required starting in version 2.4
) # 1 mHz
self._spw_array = uvp.UVParameter(
"spw_array",
description="Array of spectral window numbers, shape (Nspws).",
form=("Nspws",),
expected_type=int,
required=False, # will be required starting in version 2.4
)
desc = (
"Required if Nspws > 1. Maps individual channels along the "
"frequency axis to individual spectral windows, as listed in the "
"spw_array. Shape (Nfreqs), type = int."
)
self._flex_spw_id_array = uvp.UVParameter(
"flex_spw_id_array",
description=desc,
form=("Nfreqs",),
expected_type=int,
required=False,
)
desc = (
"Array of polarization integers, shape (Npols). "
"AIPS Memo 117 says: pseudo-stokes 1:4 (pI, pQ, pU, pV); "
"circular -1:-4 (RR, LL, RL, LR); linear -5:-8 (XX, YY, XY, YX). "
"NOTE: AIPS Memo 117 actually calls the pseudo-Stokes polarizations "
'"Stokes", but this is inaccurate as visibilities cannot be in '
"true Stokes polarizations for physical antennas. We adopt the "
"term pseudo-Stokes to refer to linear combinations of instrumental "
"visibility polarizations (e.g. pI = xx + yy)."
)
self._polarization_array = uvp.UVParameter(
"polarization_array",
description=desc,
expected_type=int,
acceptable_vals=list(np.arange(-8, 0)) + list(np.arange(1, 5)),
form=("Npols",),
)
self._telescope_name = uvp.UVParameter(
"telescope_name",
description="Name of telescope or array (string).",
form="str",
expected_type=str,
required=False, # will be required starting in version 2.4
)
self._telescope_location = uvp.LocationParameter(
"telescope_location",
description=desc,
acceptable_range=(6.35e6, 6.39e6),
tols=1e-3,
required=False, # will be required starting in version 2.4
)
self._history = uvp.UVParameter(
"history",
description="String of history, units English",
form="str",
expected_type=str,
)
desc = (
"Any user supplied extra keywords, type=dict."
"Use the special key 'comment' for long multi-line string comments."
"Default is an empty dictionary."
)
self._extra_keywords = uvp.UVParameter(
"extra_keywords",
required=False,
description=desc,
value={},
spoof_val={},
expected_type=dict,
)
desc = "Flag indicating that this object is using the future array shapes."
self._future_array_shapes = uvp.UVParameter(
"future_array_shapes", description=desc, expected_type=bool, value=False
)
# ---antenna information ---
desc = (
"Number of antennas in the array. Only available for 'baseline' type "
"objects, used for calculating baseline numbers. "
"May be larger than the number of antennas with data."
)
self._Nants_telescope = uvp.UVParameter(
"Nants_telescope", description=desc, expected_type=int, required=False
)
desc = (
"Number of antennas with data present. "
"Only available for 'baseline' or 'antenna' type objects."
"May be smaller than the number of antennas in the array"
)
self._Nants_data = uvp.UVParameter(
"Nants_data", description=desc, expected_type=int, required=False
)
desc = (
"List of antenna names, shape (Nants_telescope), with numbers given by "
"antenna_numbers (which can be matched to ant_1_array and ant_2_array for "
"baseline type or ant_array for antenna type objects). Required for "
"baseline or antenna type objects. There must be one entry here for each "
"unique entry in ant_1_array and ant_2_array (for baseline type) or "
"ant_array (for antenna type), but there may be extras as well. "
)
self._antenna_names = uvp.UVParameter(
"antenna_names",
description=desc,
form=("Nants_telescope",),
expected_type=str,
required=False, # will be required starting in version 2.4
)
desc = (
"List of integer antenna numbers corresponding to antenna_names, "
"shape (Nants_telescope). Required for baseline or antenna type objects. "
"There must be one entry here for each unique entry in ant_1_array and "
"ant_2_array (for baseline type) or ant_array (for antenna type), but "
"there may be extras as well. Note that these are not indices -- they do "
"not need to start at zero or be continuous."
)
self._antenna_numbers = uvp.UVParameter(
"antenna_numbers",
description=desc,
form=("Nants_telescope",),
expected_type=int,
required=False, # will be required starting in version 2.4
)
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, # will be required starting in version 2.4
)
# --extra information ---
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 = (
"List of strings containing the unique basenames (not the full path) of "
"input files."
)
self._filename = uvp.UVParameter(
"filename", required=False, description=desc, expected_type=str
)
# initialize the underlying UVBase properties
super(UVFlag, self).__init__()
self.history = "" # Added to at the end
self.label = "" # Added to at the end
if isinstance(indata, (list, tuple)):
self.__init__(
indata[0],
mode=mode,
copy_flags=copy_flags,
waterfall=waterfall,
history=history,
label=label,
use_future_array_shapes=use_future_array_shapes,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
if len(indata) > 1:
for i in indata[1:]:
fobj = UVFlag(
i,
mode=mode,
copy_flags=copy_flags,
waterfall=waterfall,
history=history,
use_future_array_shapes=use_future_array_shapes,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
self.__add__(
fobj,
run_check=run_check,
inplace=True,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
del fobj
elif issubclass(indata.__class__, (str, pathlib.Path)):
# Given a path, read indata
self.read(
indata,
history,
use_future_array_shapes=use_future_array_shapes,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
elif issubclass(indata.__class__, UVData):
self.from_uvdata(
indata,
mode=mode,
copy_flags=copy_flags,
waterfall=waterfall,
history=history,
label=label,
use_future_array_shapes=use_future_array_shapes,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
elif issubclass(indata.__class__, UVCal):
self.from_uvcal(
indata,
mode=mode,
copy_flags=copy_flags,
waterfall=waterfall,
history=history,
label=label,
use_future_array_shapes=use_future_array_shapes,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
elif indata is not None:
raise ValueError(
"input to UVFlag.__init__ must be one of: "
"list, tuple, string, pathlib.Path, UVData, or UVCal."
)
@property
def _data_params(self):
"""List of strings giving the data-like parameters."""
if not hasattr(self, "mode") or self.mode is None:
return None
elif self.mode == "flag":
return ["flag_array"]
elif self.mode == "metric":
if self.weights_square_array is None:
return ["metric_array", "weights_array"]
else:
return ["metric_array", "weights_array", "weights_square_array"]
else:
raise ValueError(
"Invalid mode. Mode must be one of "
+ ", ".join(["{}"] * len(self._mode.acceptable_vals)).format(
*self._mode.acceptable_vals
)
)
@property
def data_like_parameters(self):
"""Return iterator of defined parameters which are data-like."""
for key in self._data_params:
if hasattr(self, key):
yield getattr(self, key)
@property
def pol_collapsed(self):
"""Determine if this object has had pols collapsed."""
if not hasattr(self, "polarization_array") or self.polarization_array is None:
return False
elif isinstance(self.polarization_array.item(0), str):
return True
else:
return False
def _check_pol_state(self):
if self.pol_collapsed:
# collapsed pol objects have a different type for
# the polarization array.
self._polarization_array.expected_type = str
self._polarization_array.acceptable_vals = None
else:
self._polarization_array.expected_type = uvp._get_generic_type(int)
self._polarization_array.acceptable_vals = list(np.arange(-8, 0)) + list(
np.arange(1, 5)
)
def _set_future_array_shapes(self):
"""
Set future_array_shapes to True and adjust required parameters.
This method should not be called directly by users; instead it is called
by file-reading methods and `use_future_array_shapes` to indicate the
`future_array_shapes` is True and define expected parameter shapes.
"""
self.future_array_shapes = True
self._freq_array.form = ("Nfreqs",)
data_like_params = ["metric_array", "weights_array", "flag_array"]
if self.type == "baseline":
for param_name in data_like_params:
getattr(self, "_" + param_name).form = ("Nblts", "Nfreqs", "Npols")
elif self.type == "antenna":
for param_name in data_like_params:
getattr(self, "_" + param_name).form = (
"Nants_data",
"Nfreqs",
"Ntimes",
"Npols",
)
def use_future_array_shapes(self):
"""
Change the array shapes of this object to match the planned future shapes.
This method sets allows users to convert to the planned array shapes changes
before the changes go into effect. This method sets the `future_array_shapes`
parameter on this object to True.
"""
self._set_future_array_shapes()
if not self.type == "waterfall":
# remove the length-1 spw axis for all data-like parameters
for param_name in self._data_params:
if param_name == "weights_square_array":
continue
setattr(self, param_name, (getattr(self, param_name))[:, 0])
# remove the length-1 spw axis for the freq_array
self.freq_array = self.freq_array[0, :]
def use_current_array_shapes(self):
"""
Change the array shapes of this object to match the current future shapes.
This method sets allows users to convert back to the current array shapes.
This method sets the `future_array_shapes` parameter on this object to False.
"""
self.future_array_shapes = False
if not self.type == "waterfall":
if self.type == "baseline":
for param_name in self._data_params:
if param_name == "weights_square_array":
continue
getattr(self, "_" + param_name).form = (
"Nblts",
1,
"Nfreqs",
"Npols",
)
elif self.type == "antenna":
for param_name in self._data_params:
if param_name == "weights_square_array":
continue
getattr(self, "_" + param_name).form = (
"Nants_data",
1,
"Nfreqs",
"Ntimes",
"Npols",
)
for param_name in self._data_params:
if param_name == "weights_square_array":
continue
setattr(
self, param_name, (getattr(self, param_name))[:, np.newaxis, :, :]
)
self._freq_array.form = (1, "Nfreqs")
self.freq_array = self.freq_array[np.newaxis, :]
def _set_mode_flag(self):
"""Set the mode and required parameters consistent with a flag object."""
self.mode = "flag"
self._flag_array.required = True
self._metric_array.required = False
self._weights_array.required = False
if self.weights_square_array is not None:
self.weights_square_array = None
return
def _set_mode_metric(self):
"""Set the mode and required parameters consistent with a metric object."""
self.mode = "metric"
self._flag_array.required = False
self._metric_array.required = True
self._weights_array.required = True
if self.weights_array is None and self.metric_array is not None:
self.weights_array = np.ones_like(self.metric_array, dtype=float)
return
def _set_type_antenna(self):
"""Set the type and required propertis consistent with an antenna object."""
self.type = "antenna"
self._ant_array.required = True
self._baseline_array.required = False
self._ant_1_array.required = False
self._ant_2_array.required = False
self._Nants_telescope.required = False
self._Nants_data.required = True
self._Nbls.required = False
self._Nblts.required = False
self._Nspws.required = True # this should be removed in version 2.4
if self.future_array_shapes:
self._metric_array.form = ("Nants_data", "Nfreqs", "Ntimes", "Npols")
self._flag_array.form = ("Nants_data", "Nfreqs", "Ntimes", "Npols")
self._weights_array.form = ("Nants_data", "Nfreqs", "Ntimes", "Npols")
else:
self._metric_array.form = ("Nants_data", 1, "Nfreqs", "Ntimes", "Npols")
self._flag_array.form = ("Nants_data", 1, "Nfreqs", "Ntimes", "Npols")
self._weights_array.form = ("Nants_data", 1, "Nfreqs", "Ntimes", "Npols")
self._freq_array.form = (1, "Nfreqs")
self._time_array.form = ("Ntimes",)
self._lst_array.form = ("Ntimes",)
def _set_type_baseline(self):
"""Set the type and required propertis consistent with a baseline object."""
self.type = "baseline"
self._ant_array.required = False
self._baseline_array.required = True
self._ant_1_array.required = True
self._ant_2_array.required = True
self._Nants_telescope.required = True
self._Nants_data.required = True
self._Nbls.required = True
self._Nblts.required = True
self._Nspws.required = True # this should be removed in version 2.4
if self.time_array is not None:
self.Nblts = len(self.time_array)
if self.future_array_shapes:
self._metric_array.form = ("Nblts", "Nfreqs", "Npols")
self._flag_array.form = ("Nblts", "Nfreqs", "Npols")
self._weights_array.form = ("Nblts", "Nfreqs", "Npols")
else:
self._metric_array.form = ("Nblts", 1, "Nfreqs", "Npols")
self._flag_array.form = ("Nblts", 1, "Nfreqs", "Npols")
self._weights_array.form = ("Nblts", 1, "Nfreqs", "Npols")
self._freq_array.form = (1, "Nfreqs")
self._time_array.form = ("Nblts",)
self._lst_array.form = ("Nblts",)
def _set_type_waterfall(self):
"""Set the type and required propertis consistent with a waterfall object."""
self.type = "waterfall"
self._ant_array.required = False
self._baseline_array.required = False
self._ant_1_array.required = False
self._ant_2_array.required = False
self._Nants_telescope.required = False
self._Nants_data.required = False
self._Nbls.required = False
self._Nblts.required = False
self._Nspws.required = False # this should be removed in version 2.4
self._metric_array.form = ("Ntimes", "Nfreqs", "Npols")
self._flag_array.form = ("Ntimes", "Nfreqs", "Npols")
self._weights_array.form = ("Ntimes", "Nfreqs", "Npols")
self._time_array.form = ("Ntimes",)
self._lst_array.form = ("Ntimes",)
if not self.future_array_shapes:
self._freq_array.form = ("Nfreqs",)
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)
"""
# set the flex_spw_id_array to required if Nspws > 1
if self.Nspws is not None and self.Nspws > 1:
self._flex_spw_id_array.required = True
# Issue a deprecation warnings for parameters that are not set but will be
# required in v2.4
params_to_warn = [
"telescope_name",
"telescope_location",
"channel_width",
"spw_array",
"Nspws",
"antenna_names",
"antenna_numbers",
"antenna_positions",
]
for param in params_to_warn:
if getattr(self, param) is None:
warnings.warn(
f"The {param} is not set. It will be a required "
"parameter starting in pyuvdata version 2.4",
category=DeprecationWarning,
)
# first run the basic check from UVBase
super().check(check_extra, run_check_acceptability)
# Check internal consistency of numbers which don't explicitly correspond
# to the shape of another array.
if self.type == "baseline":
if self.Nants_data != int(
np.union1d(self.ant_1_array, self.ant_2_array).size
):
raise ValueError(
"Nants_data must be equal to the number of unique "
"values in ant_1_array and ant_2_array"
)
if self.Nbls != len(np.unique(self.baseline_array)):
raise ValueError(
"Nbls must be equal to the number of unique "
"baselines in the baseline_array"
)
if self.Ntimes != len(np.unique(self.time_array)):
raise ValueError(
"Ntimes must be equal to the number of unique "
"times in the time_array"
)
if self.antenna_numbers is not None:
if not set(np.unique(self.ant_1_array)).issubset(self.antenna_numbers):
raise ValueError(
"All antennas in ant_1_array must be in antenna_numbers."
)
if not set(np.unique(self.ant_2_array)).issubset(self.antenna_numbers):
raise ValueError(
"All antennas in ant_2_array must be in antenna_numbers."
)
elif self.type == "antenna":
if self.antenna_numbers is not None:
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."
)
if self.flex_spw_id_array is not None:
# Check that all values in flex_spw_id_array are entries in the spw_array
if not np.all(np.isin(self.flex_spw_id_array, self.spw_array)):
raise ValueError(
"All values in the flex_spw_id_array must exist in the spw_array."
)
return True
def clear_unused_attributes(self):
"""Remove unused attributes.
Useful when changing type or mode or to save memory.
Will set all non-required attributes to None, except x_orientation,
extra_keywords, weights_square_array and filename.
"""
optional_attrs_to_keep = [
"telescope_name",
"telescope_location",
"channel_width",
"spw_array",
"Nspws",
"flex_spw_id_array",
"antenna_names",
"antenna_numbers",
"antenna_positions",
"Nants_telescope",
"x_orientation",
"weights_square_array",
"extra_keywords",
"filename",
]
for p in self:
attr = getattr(self, p)
if (
not attr.required
and attr.value is not None
and attr.name not in optional_attrs_to_keep
):
attr.value = None
setattr(self, p, attr)
def __eq__(self, other, check_history=True, check_extra=True):
"""Check Equality of two UVFlag objects.
Parameters
----------
other: UVFlag
object to check against
check_history : bool
Include the history keyword when comparing UVFlag objects.
check_extra : bool
Include non-required parameters when comparing UVFlag objects.
"""
if check_history:
return super(UVFlag, self).__eq__(other, check_extra=check_extra)
else:
# initial check that the classes are the same
# then strip the histories
if isinstance(other, self.__class__):
_h1 = self.history
self.history = None
_h2 = other.history
other.history = None
truth = super(UVFlag, self).__eq__(other, check_extra=check_extra)
self.history = _h1
other.history = _h2
return truth
else:
print("Classes do not match")
return False
def __ne__(self, other, check_history=True, check_extra=True):
"""Not Equal."""
return not self.__eq__(
other, check_history=check_history, check_extra=check_extra
)
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 set_telescope_params(self, overwrite=False, warn=True):
"""
Set telescope related parameters.
If the telescope_name is in the known_telescopes, set any missing
telescope-associated parameters (e.g. telescope location) to the value
for the known 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:
params_set = []
telescope_params = list(telescope_obj.__iter__())
# ensure that the Nants_telescope comes first so shapes work out below
telescope_params.remove("_Nants_telescope")
telescope_params.insert(0, "_Nants_telescope")
for p in telescope_params:
telescope_param = getattr(telescope_obj, p)
if p in self:
self_param = getattr(self, p)
else:
continue
if telescope_param.value is not None and (
overwrite is True or self_param.value is None
):
telescope_shape = telescope_param.expected_shape(telescope_obj)
self_shape = self_param.expected_shape(self)
if telescope_shape == self_shape:
params_set.append(self_param.name)
prop_name = self_param.name
setattr(self, prop_name, getattr(telescope_obj, prop_name))
else:
# Note dropped handling for antenna diameters that appears in
# UVData because they don't exist on UVFlag.
warnings.warn(
f"{self_param.name} is not set but cannot be set using "
f"known values for {telescope_obj.telescope_name} "
"because the expected shapes don't match."
)
if len(params_set) > 0:
params_set_str = ", ".join(params_set)
if warn:
warnings.warn(
f"{params_set_str} are not set or being overwritten. Using "
f"known values for {telescope_obj.telescope_name}."
)
else:
raise ValueError(
f"Telescope {self.telescope_name} is not in known_telescopes."
)
def antpair2ind(self, ant1, ant2):
"""Get blt indices for given (ordered) antenna pair.
Parameters
----------
ant1 : int or array_like of int
Number of the first antenna
ant2 : int or array_like of int
Number of the second antenna
Returns
-------
int or array_like of int
baseline number(s) corresponding to the input antenna number
"""
if self.type != "baseline":
raise ValueError(
"UVFlag object of type " + self.type + " does not "
"contain antenna pairs to index."
)
return np.where((self.ant_1_array == ant1) & (self.ant_2_array == ant2))[0]
def baseline_to_antnums(self, baseline):
"""Get the antenna numbers corresponding to a given baseline number.
Parameters
----------
baseline : int
baseline number
Returns
-------
tuple
Antenna numbers corresponding to baseline.
"""
assert self.type == "baseline", "Must be 'baseline' type UVFlag object."
return uvutils.baseline_to_antnums(baseline, self.Nants_telescope)
def antnums_to_baseline(self, ant1, ant2, attempt256=False):
"""
Get the baseline number corresponding to two given antenna numbers.
Parameters
----------
ant1 : int or array_like of int
first antenna number
ant2 : int or array_like of int
second antenna number
attempt256 : bool
Option to try to use the older 256 standard used in many uvfits files
(will use 2048 standard if there are more than 256 antennas).
Returns
-------
int or array of int
baseline number corresponding to the two antenna numbers.
"""
assert self.type == "baseline", "Must be 'baseline' type UVFlag object."
return uvutils.antnums_to_baseline(
ant1, ant2, self.Nants_telescope, attempt256=attempt256
)
def get_baseline_nums(self):
"""Return numpy array of unique baseline numbers in data."""
assert self.type == "baseline", "Must be 'baseline' type UVFlag object."
return np.unique(self.baseline_array)
def get_antpairs(self):
"""Return list of unique antpair tuples (ant1, ant2) in data."""
assert self.type == "baseline", "Must be 'baseline' type UVFlag object."
return list(zip(*self.baseline_to_antnums(self.get_baseline_nums())))
def get_ants(self):
"""
Get the unique antennas that have data associated with them.
Returns
-------
ndarray of int
Array of unique antennas with data associated with them.
"""
if self.type == "baseline":
return np.unique(np.append(self.ant_1_array, self.ant_2_array))
elif self.type == "antenna":
return np.unique(self.ant_array)
elif self.type == "waterfall":
raise ValueError("A waterfall type UVFlag object has no sense of antennas.")
def get_pols(self):
"""
Get the polarizations in the data.
Returns
-------
list of str
list of polarizations (as strings) in the data.
"""
return uvutils.polnum2str(
self.polarization_array, x_orientation=self.x_orientation
)
def parse_ants(self, ant_str, print_toggle=False):
"""
Get antpair and polarization from parsing an aipy-style ant string.
Used to support the select function. This function is only useable when
the UVFlag type is 'baseline'. Generates two lists of antenna pair tuples
and polarization indices based on parsing of the string ant_str. If no
valid polarizations (pseudo-Stokes params, or combinations of [lr] or
[xy]) or antenna numbers are found in ant_str, ant_pairs_nums and
polarizations are returned as None.
Parameters
----------
ant_str : str
String containing antenna information to parse. Can be 'all',
'auto', 'cross', or combinations of antenna numbers and polarization
indicators 'l' and 'r' or 'x' and 'y'. Minus signs can also be used
in front of an antenna number or baseline to exclude it from being
output in ant_pairs_nums. If ant_str has a minus sign as the first
character, 'all,' will be added to the beginning of the string.
See the tutorial for examples of valid strings and their behavior.
print_toggle : bool
Boolean for printing parsed baselines for a visual user check.
Returns
-------
ant_pairs_nums : list of tuples of int or None
List of tuples containing the parsed pairs of antenna numbers, or
None if ant_str is 'all' or a pseudo-Stokes polarizations.
polarizations : list of int or None
List of desired polarizations or None if ant_str does not contain a
polarization specification.
"""
if self.type != "baseline":
raise ValueError(
"UVFlag objects can only call 'parse_ants' function "
"if type is 'baseline'."
)
return uvutils.parse_ants(
self,
ant_str=ant_str,
print_toggle=print_toggle,
x_orientation=self.x_orientation,
)
def collapse_pol(
self,
method="quadmean",
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Collapse the polarization axis using a given method.
If the original UVFlag object has more than one polarization,
the resulting polarization_array will be a single element array with a
comma separated string encoding the original polarizations.
Parameters
----------
method : str, {"quadmean", "absmean", "mean", "or", "and"}
How to collapse the dimension(s).
run_check : bool
Option to check for the existence and proper shapes of parameters
after collapsing polarizations.
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
collapsing polarizations.
"""
method = method.lower()
if self.mode == "flag":
darr = self.flag_array
else:
darr = self.metric_array
if len(self.polarization_array) > 1:
if self.mode == "metric":
_weights = self.weights_array
else:
_weights = np.ones_like(darr)
# Collapse pol dimension. But note we retain a polarization axis.
d, w = uvutils.collapse(
darr, method, axis=-1, weights=_weights, return_weights=True
)
darr = np.expand_dims(d, axis=d.ndim)
if self.mode == "metric":
self.weights_array = np.expand_dims(w, axis=w.ndim)
self.polarization_array = np.array(
[",".join(map(str, self.polarization_array))], dtype=np.str_
)
self.Npols = len(self.polarization_array)
self._check_pol_state()
else:
warnings.warn(
"Cannot collapse polarization axis when only one pol present."
)
return
if ((method == "or") or (method == "and")) and (self.mode == "flag"):
self.flag_array = darr
else:
self.metric_array = darr
self._set_mode_metric()
self.clear_unused_attributes()
self.history += "Pol axis collapse. "
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
def to_waterfall(
self,
method="quadmean",
keep_pol=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
return_weights_square=False,
):
"""Convert an 'antenna' or 'baseline' type object to waterfall.
Parameters
----------
method : str, {"quadmean", "absmean", "mean", "or", "and"}
How to collapse the dimension(s).
keep_pol : bool
Whether to also collapse the polarization dimension
If keep_pol is False, and the original UVFlag object has more
than one polarization, the resulting polarization_array
will be a single element array with a comma separated string
encoding the original polarizations.
run_check : bool
Option to check for the existence and proper shapes of parameters
after converting to waterfall type.
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 to waterfall type.
return_weights_square: bool
Option to compute the sum of the squares of the weights when
collapsing baseline object to waterfall. Not used if type is not
baseline to begin with. Fills an optional parameter if so.
"""
method = method.lower()
if self.type == "waterfall" and (
keep_pol or (len(self.polarization_array) == 1)
):
warnings.warn("This object is already a waterfall. Nothing to change.")
return
if (not keep_pol) and (len(self.polarization_array) > 1):
self.collapse_pol(method)
if self.mode == "flag":
darr = self.flag_array
else:
darr = self.metric_array
if self.type == "antenna":
if self.future_array_shapes:
collapse_axes = (0,)
else:
collapse_axes = (0, 1)
d, w = uvutils.collapse(
darr,
method,
axis=collapse_axes,
weights=self.weights_array,
return_weights=True,
)
darr = np.swapaxes(d, 0, 1)
if self.mode == "metric":
self.weights_array = np.swapaxes(w, 0, 1)
elif self.type == "baseline":
Nt = len(np.unique(self.time_array))
Nf = self.freq_array.size
Np = len(self.polarization_array)
d = np.zeros((Nt, Nf, Np))
w = np.zeros((Nt, Nf, Np))
if return_weights_square:
ws = np.zeros((Nt, Nf, Np))
for i, t in enumerate(np.unique(self.time_array)):
ind = self.time_array == t
if self.mode == "metric":
_weights = self.weights_array[ind, :, :]
else:
_weights = np.ones_like(darr[ind, :, :], dtype=float)
if return_weights_square:
d[i, :, :], w[i, :, :], ws[i, :, :] = uvutils.collapse(
darr[ind, :, :],
method,
axis=0,
weights=_weights,
return_weights=True,
return_weights_square=return_weights_square,
)
else:
d[i, :, :], w[i, :, :] = uvutils.collapse(
darr[ind, :, :],
method,
axis=0,
weights=_weights,
return_weights=True,
return_weights_square=return_weights_square,
)
darr = d
if self.mode == "metric":
self.weights_array = w
if return_weights_square:
self.weights_square_array = ws
self.time_array, ri = np.unique(self.time_array, return_index=True)
self.lst_array = self.lst_array[ri]
if ((method == "or") or (method == "and")) and (self.mode == "flag"):
# If using a boolean operation (AND/OR) and in flag mode, stay in flag
# flags should be bool, but somehow it is cast as float64
# is reacasting to bool like this best?
self.flag_array = darr.astype(bool)
else:
# Otherwise change to (or stay in) metric
self.metric_array = darr
self._set_mode_metric()
self.freq_array = self.freq_array.flatten()
self._set_type_waterfall()
self.history += 'Collapsed to type "waterfall". ' # + self.pyuvdata_version_str
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
self.clear_unused_attributes()
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
def to_baseline(
self,
uv,
force_pol=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Convert a UVFlag object of type "waterfall" or "antenna" to type "baseline".
Broadcasts the flag array to all baselines.
This function does NOT apply flags to uv (see utils.apply_uvflag for that).
Parameters
----------
uv : UVData or UVFlag object
Object with type baseline to match.
force_pol : bool
If True, will use 1 pol to broadcast to any other pol.
Otherwise, will require polarizations match.
For example, this keyword is useful if one flags on all
pols combined, and wants to broadcast back to individual pols.
run_check : bool
Option to check for the existence and proper shapes of parameters
after converting to baseline type.
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 to baseline type.
"""
if self.type == "baseline":
return
if not (
issubclass(uv.__class__, UVData)
or (isinstance(uv, UVFlag) and uv.type == "baseline")
):
raise ValueError(
"Must pass in UVData object or UVFlag object of type "
'"baseline" to match.'
)
# write it out this rather than comparing the UVParameters because
# future_array_shapes might be different. In the future, when shapes are not
# variable, this can be done by comparing the UVParameters.
if self.Nfreqs != uv.Nfreqs or not np.allclose(
np.squeeze(self.freq_array),
np.squeeze(uv.freq_array),
rtol=self._freq_array.tols[0],
atol=self._freq_array.tols[1],
):
raise ValueError(
"The freq_array on uv is not the same as the freq_array on this "
f"object. The value on this object is {self.freq_array}; the value "
f"on uv is {uv.freq_array}"
)
warn_compatibility_params = [
"telescope_name",
"telescope_location",
"antenna_names",
"antenna_numbers",
"antenna_positions",
"channel_width",
"spw_array",
]
if self.Nspws is not None and self.Nspws > 1:
warn_compatibility_params.append("flex_spw_id_array")
for param in warn_compatibility_params:
if (
issubclass(uv.__class__, UVData)
and param == "channel_width"
and not (uv.future_array_shapes or uv.flex_spw)
):
if not np.allclose(
self.channel_width,
np.full(uv.Nfreqs, uv.channel_width),
rtol=self._channel_width.tols[0],
atol=self._channel_width.tols[1],
):
warnings.warn(
"channel_width is not the same this object and on uv. The "
f"value on this object is {self.channel_width}; the value on "
f"uv is {uv.channel_width}. This will become an error in "
"version 2.4.",
DeprecationWarning,
)
else:
# compare the UVParameter objects to properly handle tolerances
this_param = getattr(self, "_" + param)
uv_param = getattr(uv, "_" + param)
if this_param.value is not None and this_param != uv_param:
warnings.warn(
f"{param} is not the same this object and on uv. The value on "
f"this object is {this_param.value}; the value on uv is "
f"{uv_param.value}. This will become an error in version 2.4.",
DeprecationWarning,
)
# Deal with polarization
if force_pol and self.polarization_array.size == 1:
# Use single pol for all pols, regardless
self.polarization_array = uv.polarization_array
# Broadcast arrays
if self.mode == "flag":
self.flag_array = self.flag_array.repeat(
self.polarization_array.size, axis=-1
)
else:
self.metric_array = self.metric_array.repeat(
self.polarization_array.size, axis=-1
)
self.weights_array = self.weights_array.repeat(
self.polarization_array.size, axis=-1
)
self.Npols = len(self.polarization_array)
self._check_pol_state()
# Now the pol axes should match regardless of force_pol.
if not np.array_equal(uv.polarization_array, self.polarization_array):
if self.polarization_array.size == 1:
raise ValueError(
"Polarizations do not match. Try keyword force_pol"
+ " if you wish to broadcast to all polarizations."
)
else:
raise ValueError("Polarizations could not be made to match.")
if self.type == "waterfall":
# Populate arrays
if self.mode == "flag":
if (
issubclass(uv.__class__, UVData)
and uv.future_array_shapes != self.future_array_shapes
):
if uv.future_array_shapes:
arr = np.zeros_like(uv.flag_array[:, np.newaxis, :, :])
else:
arr = np.zeros_like(uv.flag_array[:, 0, :, :])
else:
arr = np.zeros_like(uv.flag_array)
sarr = self.flag_array
elif self.mode == "metric":
if (
issubclass(uv.__class__, UVData)
and uv.future_array_shapes != self.future_array_shapes
):
if uv.future_array_shapes:
arr = np.zeros_like(
uv.flag_array[:, np.newaxis, :, :], dtype=np.float64
)
warr = np.zeros_like(
uv.flag_array[:, np.newaxis, :, :], dtype=np.float64
)
else:
arr = np.zeros_like(uv.flag_array[:, 0, :, :], dtype=np.float64)
warr = np.zeros_like(
uv.flag_array[:, 0, :, :], dtype=np.float64
)
else:
arr = np.zeros_like(uv.flag_array, dtype=np.float64)
warr = np.zeros_like(uv.flag_array, dtype=np.float64)
sarr = self.metric_array
for i, t in enumerate(np.unique(self.time_array)):
ti = np.where(
np.isclose(
uv.time_array,
t,
rtol=max(self._time_array.tols[0], uv._time_array.tols[0]),
atol=max(self._time_array.tols[1], uv._time_array.tols[1]),
)
)
if self.future_array_shapes:
arr[ti] = sarr[i][np.newaxis, :, :]
if self.mode == "metric":
warr[ti] = self.weights_array[i][np.newaxis, :, :]
else:
arr[ti] = sarr[i][np.newaxis, np.newaxis, :, :]
if self.mode == "metric":
warr[ti] = self.weights_array[i][np.newaxis, np.newaxis, :, :]
if self.mode == "flag":
self.flag_array = arr
elif self.mode == "metric":
self.metric_array = arr
self.weights_array = warr
elif self.type == "antenna":
if self.mode == "metric":
raise NotImplementedError(
"Cannot currently convert from antenna type, metric mode to "
"baseline type UVFlag object."
)
ants_data = np.unique(uv.ant_1_array.tolist() + uv.ant_2_array.tolist())
new_ants = np.setdiff1d(ants_data, self.ant_array)
if new_ants.size > 0:
self.ant_array = np.append(self.ant_array, new_ants).tolist()
# make new flags of the same shape but with first axis the
# size of the new ants
flag_shape = list(self.flag_array.shape)
flag_shape[0] = new_ants.size
new_flags = np.full(flag_shape, True, dtype=bool)
self.flag_array = np.append(self.flag_array, new_flags, axis=0)
if self.future_array_shapes:
baseline_flags = np.full(
(uv.Nblts, self.Nfreqs, self.Npols), True, dtype=bool
)
else:
baseline_flags = np.full(
(uv.Nblts, 1, self.Nfreqs, self.Npols), True, dtype=bool
)
for blt_index, bl in enumerate(uv.baseline_array):
uvf_t_index = np.nonzero(
np.isclose(
uv.time_array[blt_index],
self.time_array,
rtol=max(self._time_array.tols[0], uv._time_array.tols[0]),
atol=max(self._time_array.tols[1], uv._time_array.tols[1]),
)
)[0]
if uvf_t_index.size > 0:
# if the time is found in the uvflag object time_array
# input the or'ed data from each antenna
ant1, ant2 = uv.baseline_to_antnums(bl)
ant1_index = np.nonzero(np.array(self.ant_array) == ant1)
ant2_index = np.nonzero(np.array(self.ant_array) == ant2)
if self.future_array_shapes:
or_flag = np.logical_or(
self.flag_array[ant1_index, :, uvf_t_index, :],
self.flag_array[ant2_index, :, uvf_t_index, :],
)
else:
or_flag = np.logical_or(
self.flag_array[ant1_index, :, :, uvf_t_index, :],
self.flag_array[ant2_index, :, :, uvf_t_index, :],
)
baseline_flags[blt_index] = or_flag.copy()
self.flag_array = baseline_flags
# Check the frequency array for shape, broadcast to (1, Nfreqs) if needed
if not self.future_array_shapes:
self.freq_array = np.atleast_2d(self.freq_array)
if self.Nspws is None:
self.Nspws = uv.Nspws
self.spw_array = uv.spw_array
if self.Nspws > 1:
self.flex_spw_id_array = uv.flex_spw_id_array
self.baseline_array = uv.baseline_array
self.Nbls = np.unique(self.baseline_array).size
self.ant_1_array = uv.ant_1_array
self.ant_2_array = uv.ant_2_array
self.Nants_data = int(np.union1d(self.ant_1_array, self.ant_2_array).size)
self.time_array = uv.time_array
self.lst_array = uv.lst_array
self.Nblts = self.time_array.size
if self.telescope_name is None and self.telescope_location is None:
self.telescope_name = uv.telescope_name
self.telescope_location = uv.telescope_location
if (
self.antenna_numbers is None
and self.antenna_names is None
and self.antenna_positions is None
):
self.antenna_numbers = uv.antenna_numbers
self.antenna_names = uv.antenna_names
self.antenna_positions = uv.antenna_positions
self.Nants_telescope = uv.Nants_telescope
self._set_type_baseline()
self.clear_unused_attributes()
self.history += 'Broadcast to type "baseline". '
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
def to_antenna(
self,
uv,
force_pol=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Convert a UVFlag object of type "waterfall" to type "antenna".
Broadcasts the flag array to all antennas.
This function does NOT apply flags to uv (see utils.apply_uvflag for that).
Parameters
----------
uv : UVCal or UVFlag object
object of type antenna to match.
force_pol : bool
If True, will use 1 pol to broadcast to any other pol.
Otherwise, will require polarizations match.
For example, this keyword is useful if one flags on all
pols combined, and wants to broadcast back to individual pols.
run_check : bool
Option to check for the existence and proper shapes of parameters
after converting to antenna type.
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 to antenna type.
"""
if self.type == "antenna":
return
if not (
issubclass(uv.__class__, UVCal)
or (isinstance(uv, UVFlag) and uv.type == "antenna")
):
raise ValueError(
"Must pass in UVCal object or UVFlag object of type "
'"antenna" to match.'
)
if self.type != "waterfall":
raise ValueError(
'Cannot convert from type "' + self.type + '" to "antenna".'
)
# write it out this rather than comparing the UVParameters because
# future_array_shapes might be different. In the future, when shapes are not
# variable, this can be done by comparing the UVParameters.
if self.Nfreqs != uv.Nfreqs or not np.allclose(
np.squeeze(self.freq_array),
np.squeeze(uv.freq_array),
rtol=self._freq_array.tols[0],
atol=self._freq_array.tols[1],
):
raise ValueError(
"The freq_array on uv is not the same as the freq_array on this "
f"object. The value on this object is {self.freq_array}; the value "
f"on uv is {uv.freq_array}"
)
warn_compatibility_params = [
"telescope_name",
"telescope_location",
"antenna_names",
"antenna_numbers",
"antenna_positions",
"channel_width",
"spw_array",
]
if self.Nspws is not None and self.Nspws > 1:
warn_compatibility_params.append("flex_spw_id_array")
for param in warn_compatibility_params:
if (
issubclass(uv.__class__, UVCal)
and param == "channel_width"
and not (uv.future_array_shapes or uv.flex_spw)
):
if not np.allclose(
self.channel_width,
np.full(uv.Nfreqs, uv.channel_width),
rtol=self._channel_width.tols[0],
atol=self._channel_width.tols[1],
):
warnings.warn(
"channel_width is not the same this object and on uv. The "
f"value on this object is {self.channel_width}; the value on "
f"uv is {uv.channel_width}. This will become an error in "
"version 2.4.",
DeprecationWarning,
)
else:
# compare the UVParameter objects to properly handle tolerances
this_param = getattr(self, "_" + param)
uv_param = getattr(uv, "_" + param)
if this_param.value is not None and this_param != uv_param:
warnings.warn(
f"{param} is not the same this object and on uv. The value on "
f"this object is {this_param.value}; the value on uv is "
f"{uv_param.value}. This will become an error in version 2.4.",
DeprecationWarning,
)
# Deal with polarization
if issubclass(uv.__class__, UVCal):
polarr = uv.jones_array
else:
polarr = uv.polarization_array
if force_pol and self.polarization_array.size == 1:
# Use single pol for all pols, regardless
self.polarization_array = polarr
# Broadcast arrays
if self.mode == "flag":
self.flag_array = self.flag_array.repeat(
self.polarization_array.size, axis=-1
)
else:
self.metric_array = self.metric_array.repeat(
self.polarization_array.size, axis=-1
)
self.weights_array = self.weights_array.repeat(
self.polarization_array.size, axis=-1
)
self.Npols = len(self.polarization_array)
self._check_pol_state()
# Now the pol axes should match regardless of force_pol.
if not np.array_equal(polarr, self.polarization_array):
if self.polarization_array.size == 1:
raise ValueError(
"Polarizations do not match. Try keyword force_pol"
+ "if you wish to broadcast to all polarizations."
)
else:
raise ValueError("Polarizations could not be made to match.")
# Populate arrays
if self.mode == "flag":
if self.future_array_shapes:
self.flag_array = np.swapaxes(self.flag_array, 0, 1)[
np.newaxis, :, :, :
]
else:
self.flag_array = np.swapaxes(self.flag_array, 0, 1)[
np.newaxis, np.newaxis, :, :, :
]
self.flag_array = self.flag_array.repeat(len(uv.ant_array), axis=0)
elif self.mode == "metric":
if self.future_array_shapes:
self.metric_array = np.swapaxes(self.metric_array, 0, 1)[
np.newaxis, :, :, :
]
self.weights_array = np.swapaxes(self.weights_array, 0, 1)[
np.newaxis, :, :, :
]
else:
self.metric_array = np.swapaxes(self.metric_array, 0, 1)[
np.newaxis, np.newaxis, :, :, :
]
self.weights_array = np.swapaxes(self.weights_array, 0, 1)[
np.newaxis, np.newaxis, :, :, :
]
self.metric_array = self.metric_array.repeat(len(uv.ant_array), axis=0)
self.weights_array = self.weights_array.repeat(len(uv.ant_array), axis=0)
self.ant_array = uv.ant_array
self.Nants_data = len(uv.ant_array)
# Check the frequency array for Nspws, otherwise broadcast to 1,Nfreqs
if not self.future_array_shapes:
self.freq_array = np.atleast_2d(self.freq_array)
if self.telescope_name is None and self.telescope_location is None:
self.telescope_name = uv.telescope_name
self.telescope_location = uv.telescope_location
if (
self.antenna_numbers is None
and self.antenna_names is None
and self.antenna_positions is None
):
self.antenna_numbers = uv.antenna_numbers
self.antenna_names = uv.antenna_names
self.antenna_positions = uv.antenna_positions
self.Nants_telescope = uv.Nants_telescope
if self.Nspws is None:
self.Nspws = uv.Nspws
self.spw_array = uv.spw_array
if self.Nspws > 1:
self.flex_spw_id_array = uv.flex_spw_id_array
self._set_type_antenna()
self.history += 'Broadcast to type "antenna". '
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
def to_flag(
self,
threshold=np.inf,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Convert to flag mode.
This function is NOT SMART. Removes metric_array and creates a
flag_array from a simple threshold on the metric values.
Parameters
----------
threshold : float
Metric value over which the corresponding flag is
set to True. Default is np.inf, which results in flags of all False.
run_check : bool
Option to check for the existence and proper shapes of parameters
after converting to flag mode.
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 to flag mode.
"""
if self.mode == "flag":
return
elif self.mode == "metric":
self.flag_array = np.where(self.metric_array >= threshold, True, False)
self._set_mode_flag()
else:
raise ValueError(
"Unknown UVFlag mode: " + self.mode + ". Cannot convert to flag."
)
self.history += 'Converted to mode "flag". '
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
self.clear_unused_attributes()
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
def to_metric(
self,
convert_wgts=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Convert to metric mode.
This function is NOT SMART. Simply recasts flag_array as float
and uses this as the metric array.
Parameters
----------
convert_wgts : bool
if True convert self.weights_array to ones
unless a column or row is completely flagged, in which case
convert those pixels to zero. This is used when reinterpretting
flags as metrics to calculate flag fraction. Zero weighting
completely flagged rows/columns prevents those from counting
against a threshold along the other dimension.
run_check : bool
Option to check for the existence and proper shapes of parameters
after converting to metric mode.
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 to metric mode.
"""
if self.mode == "metric":
return
elif self.mode == "flag":
self.metric_array = self.flag_array.astype(np.float64)
self._set_mode_metric()
if convert_wgts:
self.weights_array = np.ones_like(self.weights_array)
if self.type == "waterfall":
for i in range(self.Npols):
self.weights_array[:, :, i] *= ~and_rows_cols(
self.flag_array[:, :, i]
)
elif self.type == "baseline":
for i in range(self.Npols):
for ap in self.get_antpairs():
inds = self.antpair2ind(*ap)
if self.future_array_shapes:
self.weights_array[inds, :, i] *= ~and_rows_cols(
self.flag_array[inds, :, i]
)
else:
self.weights_array[inds, 0, :, i] *= ~and_rows_cols(
self.flag_array[inds, 0, :, i]
)
elif self.type == "antenna":
for i in range(self.Npols):
for j in range(self.weights_array.shape[0]):
if self.future_array_shapes:
self.weights_array[j, :, :, i] *= ~and_rows_cols(
self.flag_array[j, :, :, i]
)
else:
self.weights_array[j, 0, :, :, i] *= ~and_rows_cols(
self.flag_array[j, 0, :, :, i]
)
else:
raise ValueError(
"Unknown UVFlag mode: " + self.mode + ". Cannot convert to metric."
)
self.history += 'Converted to mode "metric". '
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
self.clear_unused_attributes()
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
def __add__(
self,
other,
inplace=False,
axis="time",
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Add two UVFlag objects together along a given axis.
Parameters
----------
other : UVFlag
object to combine with self.
axis : str
Axis along which to combine UVFlag objects.
run_check : bool
Option to check for the existence and proper shapes of parameters
after combining two 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 two objects.
inplace : bool
Option to perform the select directly on self or return a new UVData
object with just the selected data.
Returns
-------
uvf : UVFlag
If inplace==False, return new UVFlag object.
"""
# Handle in place
if inplace:
this = self
else:
this = self.copy()
# Check that objects are compatible
if not isinstance(other, this.__class__):
raise ValueError("Only UVFlag objects can be added to a UVFlag object")
if this.type != other.type:
raise ValueError(
"UVFlag object of type " + other.type + " cannot be "
"added to object of type " + this.type + "."
)
if this.mode != other.mode:
raise ValueError(
"UVFlag object of mode " + other.mode + " cannot be "
"added to object of mode " + this.type + "."
)
# check that both objects have the same array shapes
if this.future_array_shapes != other.future_array_shapes:
raise ValueError(
"Both objects must have the same `future_array_shapes` parameter. "
"Use the `use_future_array_shapes` or `use_current_array_shapes` "
"methods to convert them."
)
# Update filename parameter
this.filename = uvutils._combine_filenames(this.filename, other.filename)
if this.filename is not None:
this._filename.form = (len(this.filename),)
# Simplify axis referencing
axis = axis.lower()
type_nums = {"waterfall": 0, "baseline": 1, "antenna": 2}
if self.future_array_shapes:
axis_nums = {
"time": [0, 0, 2],
"baseline": [None, 0, None],
"antenna": [None, None, 0],
"frequency": [1, 1, 1],
"polarization": [2, 2, 3],
"pol": [2, 2, 3],
"jones": [2, 2, 3],
}
else:
axis_nums = {
"time": [0, 0, 3],
"baseline": [None, 0, None],
"antenna": [None, None, 0],
"frequency": [1, 2, 2],
"polarization": [2, 3, 4],
"pol": [2, 3, 4],
"jones": [2, 3, 4],
}
if axis not in axis_nums.keys():
raise ValueError(f"Axis not recognized, must be one of {axis_nums.keys()}")
ax = axis_nums[axis][type_nums[self.type]]
warn_compatibility_params = [
"telescope_name",
"telescope_location",
"antenna_names",
"antenna_numbers",
"antenna_positions",
]
if axis != "frequency":
warn_compatibility_params.extend(
["freq_array", "channel_width", "spw_array"]
)
if self.Nspws > 1:
warn_compatibility_params.append("flex_spw_id_array")
if axis not in ["polarization", "pol", "jones"]:
warn_compatibility_params.extend(["polarization_array"])
if axis != "time":
warn_compatibility_params.extend(["time_array", "lst_array"])
if axis != "antenna" and self.type == "antenna":
warn_compatibility_params.extend(["ant_array"])
if axis != "baseline" and self.type == "baseline":
warn_compatibility_params.extend(
["baseline_array", "ant_1_array", "ant_2_array"]
)
nants_telescope_unmatched = []
for param in warn_compatibility_params:
# compare the UVParameter objects to properly handle tolerances
this_param = getattr(self, "_" + param)
other_param = getattr(other, "_" + param)
if this_param.value is not None and this_param != other_param:
warnings.warn(
f"{param} is not the same the two objects. The value on this "
f"object is {this_param.value}; the value on the other object is "
f"{other_param.value}. This will become an error in version 2.4.",
DeprecationWarning,
)
if param in ["antenna_numbers", "antenna_names", "antenna_positions"]:
nants_telescope_unmatched.append(param)
if axis == "time":
this.time_array = np.concatenate([this.time_array, other.time_array])
this.lst_array = np.concatenate([this.lst_array, other.lst_array])
if this.type == "baseline":
this.baseline_array = np.concatenate(
[this.baseline_array, other.baseline_array]
)
this.ant_1_array = np.concatenate([this.ant_1_array, other.ant_1_array])
this.ant_2_array = np.concatenate([this.ant_2_array, other.ant_2_array])
this.Nants_data = int(
np.union1d(this.ant_1_array, this.ant_2_array).size
)
this.Ntimes = np.unique(this.time_array).size
this.Nblts = len(this.time_array)
elif axis == "baseline":
if self.type != "baseline":
raise ValueError(
"Flag object of type " + self.type + " cannot be "
"concatenated along baseline axis."
)
this.time_array = np.concatenate([this.time_array, other.time_array])
this.lst_array = np.concatenate([this.lst_array, other.lst_array])
this.baseline_array = np.concatenate(
[this.baseline_array, other.baseline_array]
)
this.ant_1_array = np.concatenate([this.ant_1_array, other.ant_1_array])
this.ant_2_array = np.concatenate([this.ant_2_array, other.ant_2_array])
this.Nants_data = int(np.union1d(this.ant_1_array, this.ant_2_array).size)
this.Nbls = np.unique(this.baseline_array).size
this.Nblts = len(this.baseline_array)
elif axis == "antenna":
if self.type != "antenna":
raise ValueError(
"Flag object of type " + self.type + " cannot be "
"concatenated along antenna axis."
)
this.ant_array = np.concatenate([this.ant_array, other.ant_array])
this.Nants_data = len(this.ant_array)
elif axis == "frequency":
this.freq_array = np.concatenate(
[this.freq_array, other.freq_array], axis=-1
)
if this.channel_width is not None and other.channel_width is not None:
this.channel_width = np.concatenate(
[this.channel_width, other.channel_width]
)
elif this.channel_width is not None or other.channel_width is not None:
warnings.warn(
"channel_width is None on one object and an array on the other. "
"It will be set to None on the combined object. This will become "
"an error in version 2.4",
DeprecationWarning,
)
this.channel_width = None
this.Nfreqs = np.unique(this.freq_array.flatten()).size
elif axis in ["polarization", "pol", "jones"]:
if this.pol_collapsed:
raise NotImplementedError(
"Two UVFlag objects with their "
"polarizations collapsed cannot be "
"added along the polarization axis "
"at this time."
)
this.polarization_array = np.concatenate(
[this.polarization_array, other.polarization_array]
)
this.Npols = len(this.polarization_array)
if len(nants_telescope_unmatched) > 0 and (
axis in ["baseline", "antenna"]
or (axis == "time" and self.type == "baseline", "antenna")
):
# this handles the case where antenna_numbers/names/positions do not match
# but we added objects across related axes. The following code ensures we
# at least have all the unique values so that the check doesn't error
warnings.warn(
f"Parameters {nants_telescope_unmatched} are different on the two "
"objects but are related to the axis they are being combined along. "
"We will keep the unique antenna_numbers, but if there are different "
"names or positions for those numbers on the two objects we will keep "
"the values from the first object. This will become an error in "
"version 2.4",
DeprecationWarning,
)
this.antenna_numbers = np.concatenate(
(this.antenna_numbers, other.antenna_numbers)
)
this.antenna_names = np.concatenate(
(this.antenna_names, other.antenna_names)
)
this.antenna_positions = np.concatenate(
(this.antenna_positions, other.antenna_positions), axis=0
)
this.antenna_numbers, ri = np.unique(
this.antenna_numbers, return_index=True
)
this.antenna_names = this.antenna_names[ri]
this.antenna_positions = this.antenna_positions[ri]
this.Nants_telescope = this.antenna_numbers.size
for attr in this._data_params:
# Check that 'other' also has the attribute filled
if getattr(other, attr) is not None:
setattr(
this,
attr,
np.concatenate(
[getattr(this, attr), getattr(other, attr)], axis=ax
),
)
# May 21, 2020 - should only happen for weights_square_array attr
else:
raise ValueError(
f"{attr} optional parameter is missing from second UVFlag"
f" object. To concatenate two {this.mode} objects, they"
" must both contain the same optional parameters set."
)
this.history += "Data combined along " + axis + " axis. "
if not uvutils._check_history_version(this.history, this.pyuvdata_version_str):
this.history += this.pyuvdata_version_str
this.Ntimes = np.unique(this.time_array).size
if run_check:
this.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return this
def __iadd__(
self,
other,
axis="time",
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""In place add.
Parameters
----------
other : UVFlag
object to combine with self.
axis : str
Axis along which to combine UVFlag objects.
run_check : bool
Option to check for the existence and proper shapes of parameters
after combining two 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 two objects.
"""
self.__add__(
other,
inplace=True,
axis=axis,
run_check=run_check,
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
return self
def __or__(
self,
other,
inplace=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Combine two UVFlag objects in "flag" mode by "OR"-ing their flags.
Parameters
----------
other : UVFlag
object to combine with self.
run_check : bool
Option to check for the existence and proper shapes of parameters
after combining two 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 two objects.
inplace : bool
Option to perform the select directly on self or return a new UVData
object with just the selected data.
Returns
-------
uvf : UVFlag
If inplace==False, return new UVFlag object.
"""
if (self.mode != "flag") or (other.mode != "flag"):
raise ValueError(
'UVFlag object must be in "flag" mode to use "or" function.'
)
# Handle in place
if inplace:
this = self
else:
this = self.copy()
this.flag_array += other.flag_array
if other.history not in this.history:
this.history += "Flags OR'd with: " + other.history
if not uvutils._check_history_version(this.history, this.pyuvdata_version_str):
this.history += this.pyuvdata_version_str
if run_check:
this.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return this
def __ior__(
self, other, run_check=True, check_extra=True, run_check_acceptability=True
):
"""Perform an inplace logical or.
Parameters
----------
other : UVFlag
object to combine with self.
run_check : bool
Option to check for the existence and proper shapes of parameters
after combining two 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 two objects.
"""
self.__or__(
other,
inplace=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
)
return self
def combine_metrics(
self,
others,
method="quadmean",
inplace=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Combine metric arrays between different UVFlag objects together.
Parameters
----------
others : UVFlag or list of UVFlags
Other UVFlag objects to combine metrics with this one.
method : str, {"quadmean", "absmean", "mean", "or", "and"}
Method to combine metrics.
inplace : bool, optional
Perform combination in place.
Returns
-------
uvf : UVFlag
If inplace==False, return new UVFlag object with combined metrics.
"""
# Ensure others is iterable (in case of single UVFlag object)
# cannot use uvutils._get_iterable because the object itself is iterable
if not isinstance(others, (list, tuple, np.ndarray)):
others = [others]
if np.any([not isinstance(other, UVFlag) for other in others]):
raise ValueError('"others" must be UVFlag or list of UVFlag objects')
if (self.mode != "metric") or np.any(
[other.mode != "metric" for other in others]
):
raise ValueError(
'UVFlag object and "others" must be in "metric" mode '
'to use "add_metrics" function.'
)
if inplace:
this = self
else:
this = self.copy()
method = method.lower()
darray = np.expand_dims(this.metric_array, 0)
warray = np.expand_dims(this.weights_array, 0)
for other in others:
if this.metric_array.shape != other.metric_array.shape:
raise ValueError("UVFlag metric array shapes do not match.")
darray = np.vstack([darray, np.expand_dims(other.metric_array, 0)])
warray = np.vstack([warray, np.expand_dims(other.weights_array, 0)])
darray, warray = uvutils.collapse(
darray, method, weights=warray, axis=0, return_weights=True
)
this.metric_array = darray
this.weights_array = warray
this.history += "Combined metric arrays. "
if not uvutils._check_history_version(this.history, this.pyuvdata_version_str):
this.history += this.pyuvdata_version_str
if run_check:
this.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return this
def _select_preprocess(
self,
antenna_nums,
ant_str,
bls,
frequencies,
freq_chans,
times,
polarizations,
blt_inds,
ant_inds,
):
"""Build up blt_inds, freq_inds, pol_inds and history_update_string for select.
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 unless
`keep_all_metadata` is False).
bls : list of tuple, optional
A list of antenna number tuples (e.g. [(0,1), (3,2)]) or a list of
baseline 3-tuples (e.g. [(0,1,'xx'), (2,3,'yy')]) specifying baselines
to keep in the object. For length-2 tuples, the ordering of the numbers
within the tuple does not matter. For length-3 tuples, the polarization
string is in the order of the two antennas. If length-3 tuples are
provided, `polarizations` must be None.
ant_str : str, optional
A string containing information about what antenna numbers
and polarizations to keep in the object. Can be 'auto', 'cross', 'all',
or combinations of antenna numbers and polarizations (e.g. '1',
'1_2', '1x_2y'). See tutorial for more examples of valid strings and
the behavior of different forms for ant_str.
If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
be kept for both baselines (1, 2) and (2, 3) to return a valid
pyuvdata object.
An ant_str cannot be passed in addition to any of `antenna_nums`,
`bls` args or the `polarizations` parameters,
if it is a ValueError will be raised.
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.
polarizations : array_like of int or str, optional
The polarizations numbers to keep in the object, each value passed
here should exist in the polarization_array. If passing strings, the
canonical polarization strings (e.g. "xx", "rr") are supported and if the
`x_orientation` attribute is set, the physical dipole strings
(e.g. "nn", "ee") are also supported.
blt_inds : array_like of int, optional
The baseline-time indices to keep in the object. This is
not commonly used.
ant_inds : array_like of int, optional
The antenna indices to keep in the object. This is
not commonly used.
Returns
-------
blt_inds : list of int
list of baseline-time indices to keep. Can be None (to keep everything).
ant_inds : list of int
list of antenna number indices to keep. Can be None
(keep all; only valid for "antenna" mode).
freq_inds : list of int
list of frequency indices to keep. Can be None (to keep everything).
pol_inds : list of int
list of polarization indices to keep. Can be None (to keep everything).
history_update_string : str
string to append to the end of the history.
"""
# build up history string as we go
history_update_string = " Downselected to specific "
n_selects = 0
if self.type == "waterfall":
if antenna_nums is not None:
raise ValueError(
"Cannot select on antenna_nums with waterfall type "
"UVFlag objects."
)
if bls is not None:
raise ValueError(
"Cannot select on bls with waterfall type UVFlag objects."
)
if ant_str is not None:
if not (antenna_nums is None and bls is None and polarizations is None):
raise ValueError(
"Cannot provide ant_str with antenna_nums, bls, or polarizations."
)
else:
bls, polarizations = self.parse_ants(ant_str)
if bls is not None and len(bls) == 0:
raise ValueError(
f"There is no data matching ant_str={ant_str} in this object."
)
# Antennas, times and blt_inds all need to be combined into a set of
# blts indices to keep.
# test for blt_inds presence before adding inds from antennas & times
if blt_inds is not None:
blt_inds = uvutils._get_iterable(blt_inds)
if np.array(blt_inds).ndim > 1:
blt_inds = np.array(blt_inds).flatten()
if self.type == "baseline":
history_update_string += "baseline-times"
else:
history_update_string += "times"
n_selects += 1
if antenna_nums is not None:
antenna_nums = uvutils._get_iterable(antenna_nums)
if np.array(antenna_nums).ndim > 1:
antenna_nums = np.array(antenna_nums).flatten()
if n_selects > 0:
history_update_string += ", antennas"
else:
history_update_string += "antennas"
n_selects += 1
if self.type == "baseline":
inds1 = np.zeros(0, dtype=np.int64)
inds2 = np.zeros(0, dtype=np.int64)
for ant in antenna_nums:
if ant in self.ant_1_array or ant in self.ant_2_array:
wh1 = np.where(self.ant_1_array == ant)[0]
wh2 = np.where(self.ant_2_array == ant)[0]
if len(wh1) > 0:
inds1 = np.append(inds1, list(wh1))
if len(wh2) > 0:
inds2 = np.append(inds2, list(wh2))
else:
raise ValueError(
"Antenna number {a} is not present in the "
"ant_1_array or ant_2_array".format(a=ant)
)
ant_blt_inds = set(inds1).intersection(inds2)
if self.type == "antenna":
ant_blt_inds = None
ant_inds = np.zeros(0, dtype=np.int64)
for ant in antenna_nums:
if ant in self.ant_array:
wh = np.nonzero(self.ant_array == ant)[0]
if len(wh) > 0:
ant_inds = np.append(ant_inds, list(wh))
else:
raise ValueError(
"Antenna number {a} is not present in the "
"ant_array".format(a=ant)
)
else:
ant_blt_inds = None
if bls is not None:
if self.type != "baseline":
raise ValueError(
'Only "baseline" mode UVFlag objects may select'
" along the baseline axis"
)
if isinstance(bls, tuple) and (len(bls) == 2 or len(bls) == 3):
bls = [bls]
if not all(isinstance(item, tuple) for item in bls):
raise ValueError(
"bls must be a list of tuples of antenna numbers "
"(optionally with polarization)."
)
if not all(
[isinstance(item[0], (int, np.integer)) for item in bls]
+ [isinstance(item[1], (int, np.integer)) for item in bls]
):
raise ValueError(
"bls must be a list of tuples of integer antenna numbers "
"(optionally with polarization)."
)
if all(len(item) == 3 for item in bls):
if polarizations is not None:
raise ValueError(
"Cannot provide length-3 tuples and also specify polarizations."
)
if not all(isinstance(item[2], str) for item in bls):
raise ValueError(
"The third element in each bl must be a polarization string"
)
if n_selects > 0:
history_update_string += ", baselines"
else:
history_update_string += "baselines"
n_selects += 1
bls_blt_inds = np.zeros(0, dtype=np.int64)
bl_pols = set()
for bl in bls:
if not (bl[0] in self.ant_1_array or bl[0] in self.ant_2_array):
raise ValueError(
"Antenna number {a} is not present in the "
"ant_1_array or ant_2_array".format(a=bl[0])
)
if not (bl[1] in self.ant_1_array or bl[1] in self.ant_2_array):
raise ValueError(
"Antenna number {a} is not present in the "
"ant_1_array or ant_2_array".format(a=bl[1])
)
wh1 = np.where(
np.logical_and(self.ant_1_array == bl[0], self.ant_2_array == bl[1])
)[0]
wh2 = np.where(
np.logical_and(self.ant_1_array == bl[1], self.ant_2_array == bl[0])
)[0]
if len(wh1) > 0:
bls_blt_inds = np.append(bls_blt_inds, list(wh1))
if len(bl) == 3:
bl_pols.add(bl[2])
elif len(wh2) > 0:
bls_blt_inds = np.append(bls_blt_inds, list(wh2))
if len(bl) == 3:
bl_pols.add(uvutils.conj_pol(bl[2]))
else:
raise ValueError(
"Antenna pair {p} does not have any data "
"associated with it.".format(p=bl)
)
if len(bl_pols) > 0:
polarizations = list(bl_pols)
if ant_blt_inds is not None:
# Use intersection (and) to join antenna_names/nums & ant_pairs_nums
ant_blt_inds = set(ant_blt_inds).intersection(bls_blt_inds)
else:
ant_blt_inds = bls_blt_inds
if ant_blt_inds is not None:
if blt_inds is not None:
# Use intersection (and) to join
# antenna_names/nums/ant_pairs_nums with blt_inds
blt_inds = set(blt_inds).intersection(ant_blt_inds)
else:
blt_inds = ant_blt_inds
if times is not None:
times = uvutils._get_iterable(times)
if np.array(times).ndim > 1:
times = np.array(times).flatten()
if n_selects > 0:
if (
self.type != "baseline" and "times" not in history_update_string
) or self.type == "baseline":
history_update_string += ", times"
else:
history_update_string += "times"
n_selects += 1
time_blt_inds = np.zeros(0, dtype=np.int64)
for jd in times:
if jd in self.time_array:
time_blt_inds = np.append(
time_blt_inds, np.where(self.time_array == jd)[0]
)
else:
raise ValueError(
"Time {t} is not present in the time_array".format(t=jd)
)
if blt_inds is not None:
# Use intesection (and) to join
# antenna_names/nums/ant_pairs_nums/blt_inds with times
blt_inds = set(blt_inds).intersection(time_blt_inds)
else:
blt_inds = time_blt_inds
if blt_inds is not None:
if len(blt_inds) == 0:
raise ValueError("No baseline-times were found that match criteria")
if self.type == "baseline":
compare_length = self.Nblts
else:
compare_length = self.Ntimes
if max(blt_inds) >= compare_length:
raise ValueError("blt_inds contains indices that are too large")
if min(blt_inds) < 0:
raise ValueError("blt_inds contains indices that are negative")
blt_inds = sorted(set(blt_inds))
if freq_chans is not None:
freq_chans = uvutils._get_iterable(freq_chans)
if np.array(freq_chans).ndim > 1:
freq_chans = np.array(freq_chans).flatten()
if frequencies is None:
if self.type != "waterfall" and not self.future_array_shapes:
frequencies = self.freq_array[0, freq_chans]
else:
frequencies = self.freq_array[freq_chans]
else:
frequencies = uvutils._get_iterable(frequencies)
if self.type != "waterfall" and not self.future_array_shapes:
frequencies = np.sort(
list(set(frequencies) | set(self.freq_array[0, freq_chans]))
)
else:
frequencies = np.sort(
list(set(frequencies) | set(self.freq_array[freq_chans]))
)
if frequencies is not None:
frequencies = uvutils._get_iterable(frequencies)
if np.array(frequencies).ndim > 1:
frequencies = np.array(frequencies).flatten()
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.
if self.type != "waterfall" and not self.future_array_shapes:
freq_arr_use = self.freq_array[0, :]
else:
freq_arr_use = self.freq_array
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))
else:
freq_inds = None
if polarizations is not None:
polarizations = uvutils._get_iterable(polarizations)
if np.array(polarizations).ndim > 1:
polarizations = np.array(polarizations).flatten()
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 isinstance(p, str):
p_num = uvutils.polstr2num(p, x_orientation=self.x_orientation)
else:
p_num = p
if p_num in self.polarization_array:
pol_inds = np.append(
pol_inds, np.where(self.polarization_array == p_num)[0]
)
else:
raise ValueError(
"Polarization {p} is not present in the "
"polarization_array".format(p=p)
)
pol_inds = sorted(set(pol_inds))
else:
pol_inds = None
history_update_string += " using pyuvdata."
return blt_inds, ant_inds, freq_inds, pol_inds, history_update_string
def _select_metadata(
self, blt_inds, ant_inds, freq_inds, pol_inds, history_update_string
):
"""Perform select on everything except the data-sized arrays.
Parameters
----------
blt_inds : list of int
list of baseline-time indices to keep. Can be None (to keep everything).
freq_inds : list of int
list of frequency indices to keep. Can be None (to keep everything).
pol_inds : list of int
list of polarization indices to keep. Can be None (to keep everything).
history_update_string : str
string to append to the end of the history.
keep_all_metadata : bool
Option to keep metadata for antennas that are no longer in the dataset.
"""
if blt_inds is not None:
if self.type == "baseline":
self.Nblts = len(blt_inds)
self.baseline_array = self.baseline_array[blt_inds]
self.Nbls = len(np.unique(self.baseline_array))
self.ant_1_array = self.ant_1_array[blt_inds]
self.ant_2_array = self.ant_2_array[blt_inds]
self.Nants_data = int(
np.union1d(self.ant_1_array, self.ant_2_array).size
)
self.time_array = self.time_array[blt_inds]
self.lst_array = self.lst_array[blt_inds]
self.Ntimes = len(np.unique(self.time_array))
if self.type == "antenna":
if ant_inds is not None:
self.ant_array = self.ant_array[ant_inds]
self.Nants_data = int(len(self.ant_array))
if freq_inds is not None:
self.Nfreqs = len(freq_inds)
if self.type != "waterfall" and not self.future_array_shapes:
self.freq_array = self.freq_array[:, freq_inds]
else:
self.freq_array = self.freq_array[freq_inds]
self.channel_width = self.channel_width[freq_inds]
if pol_inds is not None:
self.Npols = len(pol_inds)
self.polarization_array = self.polarization_array[pol_inds]
self.history = self.history + history_update_string
def select(
self,
antenna_nums=None,
ant_inds=None,
bls=None,
ant_str=None,
frequencies=None,
freq_chans=None,
times=None,
polarizations=None,
blt_inds=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 depend on the current type of the object.
However some axis may always be selected upon, these include frequencies,
times and polarizations.
In "baseline" and "antenna" modes, antenna numbers may be selected.
In "baseline" mode, antenna pairs may be selected.
Specific baseline-time indices can also be selected in "baseline" mode,
but this is not commonly used.
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 unless
`keep_all_metadata` is False). This cannot be provided if
`antenna_names` is also provided.
bls : list of tuple, optional
A list of antenna number tuples (e.g. [(0,1), (3,2)]) or a list of
baseline 3-tuples (e.g. [(0,1,'xx'), (2,3,'yy')]) specifying baselines
to keep in the object. For length-2 tuples, the ordering of the numbers
within the tuple does not matter. For length-3 tuples, the polarization
string is in the order of the two antennas. If length-3 tuples are
provided, `polarizations` must be None.
ant_str : str, optional
A string containing information about what antenna numbers
and polarizations to keep in the object. Can be 'auto', 'cross', 'all',
or combinations of antenna numbers and polarizations (e.g. '1',
'1_2', '1x_2y'). See tutorial for more examples of valid strings and
the behavior of different forms for ant_str.
If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
be kept for both baselines (1, 2) and (2, 3) to return a valid
pyuvdata object.
An ant_str cannot be passed in addition to any of `antenna_nums`,
`antenna_names`, `bls` args or the `polarizations` parameters,
if it is a ValueError will be raised.
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.
polarizations : array_like of int or str, optional
The polarizations numbers to keep in the object, each value passed
here should exist in the polarization_array. If passing strings, the
canonical polarization strings (e.g. "xx", "rr") are supported and if the
`x_orientation` attribute is set, the physical dipole strings
(e.g. "nn", "ee") are also supported.
blt_inds : array_like of int, optional
The baseline-time indices to keep in the object. This is
not commonly used.
ant_inds : array_like of int, optional
The antenna indices to keep in the object. This is
not commonly used.
run_check : bool
Option to check for the existence and proper shapes of parameters
after downselecting data on this object.
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.
inplace : bool
Option to perform the select directly on self or return a new UVData
object with just the selected data.
Returns
-------
UVData object or None
None is returned if inplace is True, otherwise a new UVData object
with just the selected data is returned
Raises
------
ValueError
If any of the parameters are set to inappropriate values.
"""
if inplace:
uv_object = self
else:
uv_object = self.copy()
(
blt_inds,
ant_inds,
freq_inds,
pol_inds,
history_update_string,
) = uv_object._select_preprocess(
antenna_nums=antenna_nums,
ant_str=ant_str,
bls=bls,
frequencies=frequencies,
freq_chans=freq_chans,
times=times,
polarizations=polarizations,
blt_inds=blt_inds,
ant_inds=ant_inds,
)
# do select operations on everything except data_array, flag_array
# and nsample_array
uv_object._select_metadata(
blt_inds, ant_inds, freq_inds, pol_inds, history_update_string
)
if blt_inds is not None:
if self.type == "baseline":
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[blt_inds])
if self.type == "waterfall":
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[blt_inds])
if self.type == "antenna":
if self.future_array_shapes:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, blt_inds, :])
else:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, :, blt_inds, :])
if ant_inds is not None and self.type == "antenna":
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[ant_inds])
if freq_inds is not None:
if self.type == "baseline":
if self.future_array_shapes:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, freq_inds, :])
else:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, freq_inds, :])
if self.type == "waterfall":
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, freq_inds, :])
if self.type == "antenna":
if self.future_array_shapes:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, freq_inds, :, :])
else:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, freq_inds, :, :])
if pol_inds is not None:
if self.type == "baseline":
if self.future_array_shapes:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, pol_inds])
else:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, :, pol_inds])
if self.type == "waterfall":
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, pol_inds])
if self.type == "antenna":
if self.future_array_shapes:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, :, pol_inds])
else:
for param_name, param in zip(
self._data_params, uv_object.data_like_parameters
):
setattr(uv_object, param_name, param[:, :, :, :, pol_inds])
# check if object is uv_object-consistent
if run_check:
uv_object.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
if not inplace:
return uv_object
def read(
self,
filename,
history="",
use_future_array_shapes=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Read in flag/metric data from a HDF5 file.
Parameters
----------
filename : str or pathlib.Path
The file name to read.
history : str
History string to append to UVFlag history attribute.
use_future_array_shapes : bool
Option to convert to the future planned array shapes before the changes go
into effect by removing the spectral window axis.
run_check : bool
Option to check for the existence and proper shapes of parameters
after reading data.
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 data.
"""
# make sure we have an empty object.
self.__init__()
if isinstance(filename, (tuple, list)):
self.read(filename[0])
if len(filename) > 1:
for f in filename[1:]:
f2 = UVFlag(f, history=history)
self += f2
del f2
else:
if not os.path.exists(filename):
raise IOError(filename + " not found.")
# update filename attribute
basename = os.path.basename(filename)
self.filename = [basename]
self._filename.form = (1,)
# Open file for reading
with h5py.File(filename, "r") as f:
header = f["/Header"]
self.type = header["type"][()].decode("utf8")
if self.type == "antenna":
self._set_type_antenna()
elif self.type == "baseline":
self._set_type_baseline()
elif self.type == "waterfall":
self._set_type_waterfall()
else:
raise ValueError(
"File cannot be read. Received type "
"parameter: {receive} but "
"must be within acceptable values: "
"{expect}".format(
receive=self.type,
expect=(", ").join(self._type.acceptable_vals),
)
)
self.mode = header["mode"][()].decode("utf8")
if self.mode == "metric":
self._set_mode_metric()
elif self.mode == "flag":
self._set_mode_flag()
else:
raise ValueError(
"File cannot be read. Received mode "
"parameter: {receive} but "
"must be within acceptable values: "
"{expect}".format(
receive=self.mode,
expect=(", ").join(self._mode.acceptable_vals),
)
)
if "x_orientation" in header.keys():
self.x_orientation = header["x_orientation"][()].decode("utf8")
self.time_array = header["time_array"][()]
if "Ntimes" in header.keys():
self.Ntimes = int(header["Ntimes"][()])
else:
self.Ntimes = np.unique(self.time_array).size
self.lst_array = header["lst_array"][()]
# read data arrays to figure out if the file has future shapes or not
future_shapes_ndim = {"antenna": 4, "baseline": 3}
dgrp = f["/Data"]
if self.mode == "metric":
self.metric_array = dgrp["metric_array"][()]
if self.type != "waterfall":
if self.metric_array.ndim == future_shapes_ndim[self.type]:
self._set_future_array_shapes()
self.weights_array = dgrp["weights_array"][()]
if "weights_square_array" in dgrp:
self.weights_square_array = dgrp["weights_square_array"][()]
elif self.mode == "flag":
self.flag_array = dgrp["flag_array"][()]
if self.type != "waterfall":
if self.flag_array.ndim == future_shapes_ndim[self.type]:
self._set_future_array_shapes()
self.freq_array = header["freq_array"][()]
# older save files will not have this spws axis
# at least_2d will preserve shape of 2d arrays and
# promote 1D to (1, Nfreqs)
if self.type != "waterfall" and not self.future_array_shapes:
self.freq_array = np.atleast_2d(self.freq_array)
if "Nfreqs" in header.keys():
self.Nfreqs = int(header["Nfreqs"][()])
else:
self.Nfreqs = np.unique(self.freq_array).size
if "channel_width" in header.keys():
self.channel_width = header["channel_width"][()]
else:
# older files do not have the channel_width parameter. Guess it from
# the freq array spacing.
msg = (
"channel_width not available in file, computing it from the "
"freq_array spacing."
)
freq_delta = np.diff(np.squeeze(self.freq_array))
if uvutils._test_array_constant_spacing(
self.freq_array, tols=self._freq_array.tols
):
self.channel_width = np.full(self.Nfreqs, freq_delta[0])
else:
msg += (
" The freq_array does not have equal spacing, so the last "
"channel_width is set equal to the channel width below it."
)
self.channel_width = np.concatenate(
(freq_delta, np.array([freq_delta[-1]]))
)
warnings.warn(msg)
if "spw_array" in header.keys():
self.spw_array = header["spw_array"][()]
else:
self.spw_array = np.array([0])
if "Nspws" in header.keys():
self.Nspws = int(header["Nspws"][()])
else:
self.Nspws = self.spw_array.size
if "flex_spw_id_array" in header.keys():
self.flex_spw_id_array = header["flex_spw_id_array"][()]
if "telescope_name" in header.keys():
self.telescope_name = header["telescope_name"][()].decode("utf8")
if "telescope_location" in header.keys():
self.telescope_location = header["telescope_location"][()]
self.history = header["history"][()].decode("utf8")
self.history += history
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
self.history += self.pyuvdata_version_str
# get extra_keywords
if "extra_keywords" in header.keys():
self.extra_keywords = {}
for key in header["extra_keywords"].keys():
if header["extra_keywords"][key].dtype.type in (
np.string_,
np.object_,
):
self.extra_keywords[key] = bytes(
header["extra_keywords"][key][()]
).decode("utf8")
else:
self.extra_keywords[key] = header["extra_keywords"][key][()]
else:
self.extra_keywords = {}
if "label" in header.keys():
self.label = header["label"][()].decode("utf8")
polarization_array = header["polarization_array"][()]
if isinstance(polarization_array[0], np.string_):
polarization_array = np.asarray(polarization_array, dtype=np.str_)
self.polarization_array = polarization_array
self._check_pol_state()
if "Npols" in header.keys():
self.Npols = int(header["Npols"][()])
else:
self.Npols = len(self.polarization_array)
if self.type == "baseline":
self.ant_1_array = header["ant_1_array"][()]
self.ant_2_array = header["ant_2_array"][()]
self.baseline_array = self.antnums_to_baseline(
self.ant_1_array, self.ant_2_array
)
if "Nblts" in header.keys():
self.Nblts = int(header["Nblts"][()])
else:
self.Nblts = len(self.baseline_array)
self.Nbls = np.unique(self.baseline_array).size
if "Nants_data" in header.keys():
self.Nants_data = int(header["Nants_data"][()])
n_ants_detected = int(
np.union1d(self.ant_1_array, self.ant_2_array).size
)
if self.Nants_data != n_ants_detected:
warnings.warn(
"Nants_data in file does not match number of antennas "
"with data. Resetting Nants_data."
)
self.Nants_data = n_ants_detected
else:
self.Nants_data = int(
np.union1d(self.ant_1_array, self.ant_2_array).size
)
elif self.type == "antenna":
self.ant_array = header["ant_array"][()]
if "Nants_data" in header.keys():
self.Nants_data = int(header["Nants_data"][()])
else:
self.Nants_data = len(self.ant_array)
if "Nants_telescope" in header.keys():
self.Nants_telescope = int(header["Nants_telescope"][()])
if "antenna_numbers" in header.keys():
self.antenna_numbers = header["antenna_numbers"][()]
if "antenna_names" in header.keys():
self.antenna_names = np.array(
[bytes(n).decode("utf8") for n in header["antenna_names"][:]]
)
if "antenna_positions" in header.keys():
self.antenna_positions = header["antenna_positions"][()]
if self.telescope_name is None:
warnings.warn(
"telescope_name not available in file, so telescope related "
"parameters cannot be set."
)
elif (
self.telescope_location is None
or self.antenna_numbers is None
or self.antenna_names is None
or self.antenna_positions is None
):
if (
self.antenna_numbers is None
and self.antenna_names is None
and self.antenna_positions is None
):
self.Nants_telescope = None
self.set_telescope_params()
if self.antenna_numbers is None and self.type in [
"baseline",
"antenna",
]:
msg = "antenna_numbers not in file"
if (
self.Nants_telescope is None
or self.Nants_telescope == self.Nants_data
):
if self.type == "baseline":
msg += ", setting based on ant_1_array and ant_2_array."
self.antenna_numbers = np.unique(
np.union1d(self.ant_1_array, self.ant_2_array)
)
else:
msg += ", setting based on ant_array."
self.antenna_numbers = np.unique(self.ant_array)
else:
if self.type == "baseline":
msg += (
", cannot be set based on ant_1_array and ant_2_array "
"because Nants_telescope is greater than Nants_data."
)
else:
msg += (
", cannot be set based on ant_array because "
"Nants_telescope is greater than Nants_data."
)
warnings.warn(msg)
if self.antenna_names is None and self.antenna_numbers is not None:
warnings.warn(
"antenna_names not in file, setting based on antenna_numbers"
)
self.antenna_names = self.antenna_numbers.astype(str)
if self.Nants_telescope is None:
if self.antenna_numbers is not None:
self.Nants_telescope = self.antenna_numbers.size
elif self.antenna_names is not None:
self.Nants_telescope = self.antenna_names.size
elif self.antenna_positions is not None:
self.Nants_telescope = (self.antenna_positions.shape)[0]
self.clear_unused_attributes()
if use_future_array_shapes != self.future_array_shapes:
if use_future_array_shapes:
self.use_future_array_shapes()
else:
self.use_current_array_shapes()
if run_check:
self.check(
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
)
def write(self, filename, clobber=False, data_compression="lzf"):
"""Write a UVFlag object to a hdf5 file.
Parameters
----------
filename : str
The file to write to.
clobber : bool
Option to overwrite the file if it already exists.
data_compression : str
HDF5 filter to apply when writing the data_array.
If no compression is wanted, set to None.
"""
if os.path.exists(filename):
if clobber:
print("File " + filename + " exists; clobbering")
else:
raise ValueError("File " + filename + " exists; skipping")
with h5py.File(filename, "w") as f:
header = f.create_group("Header")
# write out metadata
if self.future_array_shapes:
# this is Version 1.0
header["version"] = np.string_("1.0")
else:
header["version"] = np.string_("0.1")
header["type"] = np.string_(self.type)
header["mode"] = np.string_(self.mode)
if self.telescope_name is not None:
header["telescope_name"] = np.string_(self.telescope_name)
if self.telescope_location is not None:
header["telescope_location"] = self.telescope_location
header["Ntimes"] = self.Ntimes
header["time_array"] = self.time_array
header["lst_array"] = self.lst_array
header["freq_array"] = self.freq_array
header["Nfreqs"] = self.Nfreqs
header["channel_width"] = self.channel_width
header["Nspws"] = self.Nspws
header["spw_array"] = self.spw_array
if self.flex_spw_id_array is not None:
header["flex_spw_id_array"] = self.flex_spw_id_array
header["Npols"] = self.Npols
if self.x_orientation is not None:
header["x_orientation"] = np.string_(self.x_orientation)
if isinstance(self.polarization_array.item(0), str):
polarization_array = np.asarray(
self.polarization_array, dtype=np.string_
)
else:
polarization_array = self.polarization_array
header["polarization_array"] = polarization_array
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
self.history += self.pyuvdata_version_str
# write out extra keywords if it exists and has elements
if self.extra_keywords:
extra_keywords = header.create_group(
"extra_keywords"
) # create spot in header
for k in self.extra_keywords.keys():
if isinstance(self.extra_keywords[k], str):
extra_keywords[k] = np.string_(self.extra_keywords[k])
else:
extra_keywords[k] = self.extra_keywords[k]
header["history"] = np.string_(self.history)
header["label"] = np.string_(self.label)
if self.type == "baseline":
header["Nblts"] = self.Nblts
header["ant_1_array"] = self.ant_1_array
header["ant_2_array"] = self.ant_2_array
header["Nants_data"] = self.Nants_data
elif self.type == "antenna":
header["ant_array"] = self.ant_array
header["Nants_data"] = self.Nants_data
header["Nants_telescope"] = self.Nants_telescope
if self.antenna_names is not None:
header["antenna_names"] = np.asarray(self.antenna_names, dtype="bytes")
if self.antenna_numbers is not None:
header["antenna_numbers"] = self.antenna_numbers
if self.antenna_positions is not None:
header["antenna_positions"] = self.antenna_positions
dgrp = f.create_group("Data")
if self.mode == "metric":
dgrp.create_dataset(
"metric_array",
chunks=True,
data=self.metric_array,
compression=data_compression,
)
dgrp.create_dataset(
"weights_array",
chunks=True,
data=self.weights_array,
compression=data_compression,
)
if self.weights_square_array is not None:
dgrp.create_dataset(
"weights_square_array",
chunks=True,
data=self.weights_square_array,
compression=data_compression,
)
elif self.mode == "flag":
dgrp.create_dataset(
"flag_array",
chunks=True,
data=self.flag_array,
compression=data_compression,
)
def from_uvdata(
self,
indata,
mode="metric",
copy_flags=False,
waterfall=False,
history="",
label="",
use_future_array_shapes=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Construct a UVFlag object from a UVData object.
Parameters
----------
indata : UVData
Input to initialize UVFlag object.
mode : {"metric", "flag"}, optional
The mode determines whether the object has a floating point metric_array
or a boolean flag_array.
copy_flags : bool, optional
Whether to copy flags from indata to new UVFlag object
waterfall : bool, optional
Whether to immediately initialize as a waterfall object, with flag/metric
axes: time, frequency, polarization.
history : str, optional
History string to attach to object.
label: str, optional
String used for labeling the object (e.g. 'FM').
use_future_array_shapes : bool
Option to convert to the future planned array shapes before the changes go
into effect by removing the spectral window axis.
run_check : bool
Option to check for the existence and proper shapes of parameters
after creating UVFlag object.
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
creating UVFlag object.
"""
if not issubclass(indata.__class__, UVData):
raise ValueError(
"from_uvdata can only initialize a UVFlag object from an input "
"UVData object or a subclass of a UVData object."
)
if mode.lower() == "metric":
self._set_mode_metric()
elif mode.lower() == "flag":
self._set_mode_flag()
else:
raise ValueError(
"Input mode must be within acceptable values: "
"{}".format((", ").join(self._mode.acceptable_vals))
)
if use_future_array_shapes:
self._set_future_array_shapes()
self.Nfreqs = indata.Nfreqs
self.polarization_array = copy.deepcopy(indata.polarization_array)
self.Npols = indata.Npols
self.Nants_telescope = indata.Nants_telescope
self.Ntimes = indata.Ntimes
if indata.future_array_shapes or indata.flex_spw:
self.channel_width = copy.deepcopy(indata.channel_width)
else:
self.channel_width = np.full(self.Nfreqs, indata.channel_width)
self.telescope_name = indata.telescope_name
self.telescope_location = indata.telescope_location
self.antenna_names = copy.deepcopy(indata.antenna_names)
self.antenna_numbers = copy.deepcopy(indata.antenna_numbers)
self.antenna_positions = copy.deepcopy(indata.antenna_positions)
self.Nspws = indata.Nspws
self.spw_array = copy.deepcopy(indata.spw_array)
if indata.flex_spw_id_array is not None:
self.flex_spw_id_array = copy.deepcopy(indata.flex_spw_id_array)
if waterfall:
self._set_type_waterfall()
self.history += 'Flag object with type "waterfall" created. '
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
self.history += self.pyuvdata_version_str
self.time_array, ri = np.unique(indata.time_array, return_index=True)
if indata.future_array_shapes:
self.freq_array = copy.deepcopy(indata.freq_array)
else:
self.freq_array = indata.freq_array[0, :]
self.lst_array = indata.lst_array[ri]
if copy_flags:
raise NotImplementedError(
"Cannot copy flags when initializing waterfall UVFlag from "
"UVData or UVCal."
)
else:
if self.mode == "flag":
self.flag_array = np.zeros(
(self.Ntimes, self.Nfreqs, self.Npols), np.bool_
)
elif self.mode == "metric":
self.metric_array = np.zeros((self.Ntimes, self.Nfreqs, self.Npols))
else:
self._set_type_baseline()
self.history += 'Flag object with type "baseline" created. '
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
self.history += self.pyuvdata_version_str
self.baseline_array = copy.deepcopy(indata.baseline_array)
self.Nbls = indata.Nbls
self.Nblts = indata.Nblts
self.ant_1_array = copy.deepcopy(indata.ant_1_array)
self.ant_2_array = copy.deepcopy(indata.ant_2_array)
self.Nants_data = indata.Nants_data
self.time_array = copy.deepcopy(indata.time_array)
self.lst_array = copy.deepcopy(indata.lst_array)
if self.future_array_shapes == indata.future_array_shapes:
# match on future shape
self.freq_array = copy.deepcopy(indata.freq_array)
elif indata.future_array_shapes:
# input is future shaped, self is not
self.freq_array = indata.freq_array[np.newaxis, :]
else:
# input is not future shaped, self is
self.freq_array = indata.freq_array[0, :]
if copy_flags:
if self.future_array_shapes == indata.future_array_shapes:
self.flag_array = copy.deepcopy(indata.flag_array)
elif indata.future_array_shapes:
self.flag_array = indata.flag_array[:, np.newaxis, :, :]
else:
self.flag_array = indata.flag_array[:, 0, :, :]
self.history += (
" Flags copied from " + str(indata.__class__) + " object."
)
if self.mode == "metric":
warnings.warn(
'Copying flags to type=="baseline" results in mode=="flag".'
)
self._set_mode_flag()
else:
if self.future_array_shapes:
array_shape = (self.Nblts, self.Nfreqs, self.Npols)
else:
array_shape = (self.Nblts, 1, self.Nfreqs, self.Npols)
if self.mode == "flag":
self.flag_array = np.zeros(array_shape, dtype=np.bool_)
elif self.mode == "metric":
self.metric_array = np.zeros(array_shape, dtype=np.float64)
self.filename = indata.filename
self._filename.form = indata._filename.form
if indata.x_orientation is not None:
self.x_orientation = indata.x_orientation
if self.mode == "metric":
self.weights_array = np.ones(self.metric_array.shape)
if indata.extra_keywords is not None:
self.extra_keywords = copy.deepcopy(indata.extra_keywords)
if history not in self.history:
self.history += history
self.label += label
self.clear_unused_attributes()
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
return
def from_uvcal(
self,
indata,
mode="metric",
copy_flags=False,
waterfall=False,
history="",
label="",
use_future_array_shapes=False,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""Construct a UVFlag object from a UVCal object.
Parameters
----------
indata : UVData
Input to initialize UVFlag object.
mode : {"metric", "flag"}, optional
The mode determines whether the object has a floating point metric_array
or a boolean flag_array.
copy_flags : bool, optional
Whether to copy flags from indata to new UVFlag object
waterfall : bool, optional
Whether to immediately initialize as a waterfall object, with flag/metric
axes: time, frequency, polarization.
history : str, optional
History string to attach to object.
label: str, optional
String used for labeling the object (e.g. 'FM').
use_future_array_shapes : bool
Option to convert to the future planned array shapes before the changes go
into effect by removing the spectral window axis.
run_check : bool
Option to check for the existence and proper shapes of parameters
after creating UVFlag object.
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
creating UVFlag object.
"""
if not issubclass(indata.__class__, UVCal):
raise ValueError(
"from_uvcal can only initialize a UVFlag object from an input "
"UVCal object or a subclass of a UVCal object."
)
if mode.lower() == "metric":
self._set_mode_metric()
elif mode.lower() == "flag":
self._set_mode_flag()
else:
raise ValueError(
"Input mode must be within acceptable values: "
"{}".format((", ").join(self._mode.acceptable_vals))
)
if use_future_array_shapes:
self._set_future_array_shapes()
self.Nfreqs = indata.Nfreqs
self.polarization_array = copy.deepcopy(indata.jones_array)
self.Npols = indata.Njones
self.Nants_telescope = indata.Nants_telescope
self.Ntimes = indata.Ntimes
self.time_array = copy.deepcopy(indata.time_array)
self.lst_array = copy.deepcopy(indata.lst_array)
if indata.future_array_shapes or indata.flex_spw:
self.channel_width = copy.deepcopy(indata.channel_width)
else:
self.channel_width = np.full(self.Nfreqs, indata.channel_width)
self.telescope_name = indata.telescope_name
self.telescope_location = indata.telescope_location
self.antenna_names = copy.deepcopy(indata.antenna_names)
self.antenna_numbers = copy.deepcopy(indata.antenna_numbers)
self.antenna_positions = copy.deepcopy(indata.antenna_positions)
self.Nspws = indata.Nspws
self.spw_array = copy.deepcopy(indata.spw_array)
if indata.flex_spw_id_array is not None:
self.flex_spw_id_array = copy.deepcopy(indata.flex_spw_id_array)
if waterfall:
self._set_type_waterfall()
self.history += 'Flag object with type "waterfall" created. '
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
self.history += self.pyuvdata_version_str
if indata.future_array_shapes:
self.freq_array = copy.deepcopy(indata.freq_array)
else:
self.freq_array = indata.freq_array[0, :]
if copy_flags:
raise NotImplementedError(
"Cannot copy flags when "
"initializing waterfall UVFlag "
"from UVData or UVCal."
)
else:
if self.mode == "flag":
self.flag_array = np.zeros(
(self.Ntimes, self.Nfreqs, self.Npols), np.bool_
)
elif self.mode == "metric":
self.metric_array = np.zeros((self.Ntimes, self.Nfreqs, self.Npols))
else:
self._set_type_antenna()
self.history += 'Flag object with type "antenna" created. '
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
self.history += self.pyuvdata_version_str
self.ant_array = copy.deepcopy(indata.ant_array)
self.Nants_data = len(self.ant_array)
if self.future_array_shapes == indata.future_array_shapes:
# match on future shape
self.freq_array = copy.deepcopy(indata.freq_array)
elif indata.future_array_shapes:
# input is future shaped, self is not
self.freq_array = indata.freq_array[np.newaxis, :]
else:
# input is not future shaped, self is
self.freq_array = indata.freq_array[0, :]
if copy_flags:
if self.future_array_shapes == indata.future_array_shapes:
self.flag_array = copy.deepcopy(indata.flag_array)
elif indata.future_array_shapes:
self.flag_array = indata.flag_array[:, np.newaxis, :, :]
else:
self.flag_array = indata.flag_array[:, 0, :, :]
self.history += (
" Flags copied from " + str(indata.__class__) + " object."
)
if self.mode == "metric":
warnings.warn(
'Copying flags to type=="antenna" results in mode=="flag".'
)
self._set_mode_flag()
else:
if self.future_array_shapes:
array_shape = (
self.Nants_data,
self.Nfreqs,
self.Ntimes,
self.Npols,
)
else:
array_shape = (
self.Nants_data,
1,
self.Nfreqs,
self.Ntimes,
self.Npols,
)
if self.mode == "flag":
self.flag_array = np.zeros(array_shape, dtype=np.bool_)
elif self.mode == "metric":
self.metric_array = np.zeros(array_shape, dtype=np.float64)
if self.mode == "metric":
self.weights_array = np.ones(self.metric_array.shape)
self.filename = indata.filename
self._filename.form = indata._filename.form
if indata.x_orientation is not None:
self.x_orientation = indata.x_orientation
if history not in self.history:
self.history += history
self.label += label
self.clear_unused_attributes()
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
return
Computing file changes ...
