Single File Calibration

by Josh Dillon, Aaron Parsons, Tyler Cox, and Zachary Martinot, last updated December 15, 2025

This notebook is designed to infer as much information about the array from a single file, including pushing the calibration and RFI mitigation as far as possible. Calibration includes redundant-baseline calibration, RFI-based calibration of delay slopes, model-based calibration of overall amplitudes, and a full per-frequency phase gradient absolute calibration if abscal model files are available.

Here's a set of links to skip to particular figures and tables:

• Figure 1: RFI Flagging

• Figure 2: Plot of autocorrelations with classifications

• Figure 3: Summary of antenna classifications prior to calibration

• Figure 4: Redundant calibration of a single baseline group

• Figure 5: Absolute calibration of redcal degeneracies

• Figure 6: Relative Phase Calibration

• Figure 7: chi^2 per antenna across the array

• Figure 8: Summary of antenna classifications after redundant calibration

• Table 1: Complete summary of per antenna classifications

import time
tstart = time.time()
!hostname

gpu6.rtp.pvt

import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import h5py
import hdf5plugin  # REQUIRED to have the compression plugins available
import numpy as np
from scipy import constants, interpolate
import copy
import glob
import re
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.max_rows', 1000)
from uvtools.plot import plot_antpos, plot_antclass
from hera_qm import ant_metrics, ant_class, xrfi
from hera_cal import io, utils, redcal, apply_cal, datacontainer, abscal
from hera_filters import dspec
from hera_notebook_templates.data import DATA_PATH as HNBT_DATA
from IPython.display import display, HTML
import linsolve
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore')  # get rid of red warnings
%config InlineBackend.figure_format = 'retina'

# this enables better memory management on linux
import ctypes
def malloc_trim():
    try:
        ctypes.CDLL('libc.so.6').malloc_trim(0) 
    except OSError:
        pass

Parse inputs and outputs

To use this notebook interactively, you will have to provide a sum filename path if none exists as an environment variable. All other parameters have reasonable default values.

# figure out whether to save results
SAVE_RESULTS = os.environ.get("SAVE_RESULTS", "TRUE").upper() == "TRUE"
SAVE_OMNIVIS_FILE = os.environ.get("SAVE_OMNIVIS_FILE", "FALSE").upper() == "TRUE"

# get infile names
SUM_FILE = os.environ.get("SUM_FILE", None)
# SUM_FILE = '/lustre/aoc/projects/hera/h6c-analysis/IDR2/2459867/zen.2459867.46002.sum.uvh5' # If sum_file is not defined in the environment variables, define it here.
USE_DIFF = os.environ.get("USE_DIFF", "TRUE").upper() == "TRUE"
if USE_DIFF:
    DIFF_FILE = SUM_FILE.replace('sum', 'diff')
else:
    DIFF_FILE = None
    RTP_ANTCLASS = SUM_FILE.replace('.uvh5', '.rtp_ant_class.csv')
VALIDATION = os.environ.get("VALIDATION", "FALSE").upper() == "TRUE"

# get outfilenames
AM_FILE = (SUM_FILE.replace('.uvh5', '.ant_metrics.hdf5') if SAVE_RESULTS else None)
ANTCLASS_FILE = (SUM_FILE.replace('.uvh5', '.ant_class.csv') if SAVE_RESULTS else None)
OMNICAL_FILE = (SUM_FILE.replace('.uvh5', '.omni.calfits') if SAVE_RESULTS else None)
OMNIVIS_FILE = (SUM_FILE.replace('.uvh5', '.omni_vis.uvh5') if SAVE_RESULTS else None)

for fname in ['SUM_FILE', 'DIFF_FILE', 'AM_FILE', 'ANTCLASS_FILE', 'OMNICAL_FILE', 'OMNIVIS_FILE', 
              'SAVE_RESULTS', 'SAVE_OMNIVIS_FILE', 'USE_DIFF', 'VALIDATION']:
    print(f"{fname} = '{eval(fname)}'")

SUM_FILE = '/mnt/sn1/data1/2461087/zen.2461087.45941.sum.uvh5'
DIFF_FILE = '/mnt/sn1/data1/2461087/zen.2461087.45941.diff.uvh5'
AM_FILE = '/mnt/sn1/data1/2461087/zen.2461087.45941.sum.ant_metrics.hdf5'
ANTCLASS_FILE = '/mnt/sn1/data1/2461087/zen.2461087.45941.sum.ant_class.csv'
OMNICAL_FILE = '/mnt/sn1/data1/2461087/zen.2461087.45941.sum.omni.calfits'
OMNIVIS_FILE = '/mnt/sn1/data1/2461087/zen.2461087.45941.sum.omni_vis.uvh5'
SAVE_RESULTS = 'True'
SAVE_OMNIVIS_FILE = 'False'
USE_DIFF = 'True'
VALIDATION = 'False'

Parse settings

Load settings relating to the operation of the notebook, then print what was loaded (or default).

# parse plotting settings
PLOT = os.environ.get("PLOT", "TRUE").upper() == "TRUE"
if PLOT:
    %matplotlib inline

# parse omnical settings
OC_MAX_DIMS = int(os.environ.get("OC_MAX_DIMS", 4))
OC_MIN_DIM_SIZE = int(os.environ.get("OC_MIN_DIM_SIZE", 8))
OC_SKIP_OUTRIGGERS = os.environ.get("OC_SKIP_OUTRIGGERS", "TRUE").upper() == "TRUE"
OC_MIN_BL_LEN = float(os.environ.get("OC_MIN_BL_LEN", 1))
OC_MAX_BL_LEN = float(os.environ.get("OC_MAX_BL_LEN", 1e100))
OC_MAXITER = int(os.environ.get("OC_MAXITER", 50))
OC_MAX_RERUN = int(os.environ.get("OC_MAX_RERUN", 10))
OC_RERUN_MAXITER = int(os.environ.get("OC_RERUN_MAXITER", 250))
OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE = float(os.environ.get("OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE", 1.5))
OC_USE_PRIOR_SOL = os.environ.get("OC_USE_PRIOR_SOL", "FALSE").upper() == "TRUE"
OC_PRIOR_SOL_FLAG_THRESH = float(os.environ.get("OC_PRIOR_SOL_FLAG_THRESH", .95))
OC_RESTART_FROM_FC_EVERY_ITER = os.environ.get("OC_RESTART_FROM_FC_EVERY_ITER", "FALSE").upper() == "TRUE"
OC_USE_GPU = os.environ.get("OC_USE_GPU", "FALSE").upper() == "TRUE"

# parse RFI settings
RFI_DPSS_HALFWIDTH = float(os.environ.get("RFI_DPSS_HALFWIDTH", 300e-9))
RFI_NSIG = float(os.environ.get("RFI_NSIG", 4))

# parse abscal settings
ABSCAL_MODEL_FILES_GLOB = os.environ.get("ABSCAL_MODEL_FILES_GLOB", None)
ABSCAL_MIN_BL_LEN = float(os.environ.get("ABSCAL_MIN_BL_LEN", 1.0))
ABSCAL_MAX_BL_LEN = float(os.environ.get("ABSCAL_MAX_BL_LEN", 140.0))
CALIBRATE_CROSS_POLS = os.environ.get("CALIBRATE_CROSS_POLS", "TRUE").upper() == "TRUE"

# print settings
for setting in ['PLOT', 'OC_MAX_DIMS', 'OC_MIN_DIM_SIZE', 'OC_SKIP_OUTRIGGERS', 
                'OC_MIN_BL_LEN', 'OC_MAX_BL_LEN', 'OC_MAXITER', 'OC_MAX_RERUN', 'OC_RERUN_MAXITER', 
                'OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE', 'OC_USE_PRIOR_SOL', 'OC_PRIOR_SOL_FLAG_THRESH', 
                'OC_USE_GPU', 'OC_RESTART_FROM_FC_EVERY_ITER', 'RFI_DPSS_HALFWIDTH', 'RFI_NSIG', 
                'ABSCAL_MODEL_FILES_GLOB', 'ABSCAL_MIN_BL_LEN', 'ABSCAL_MAX_BL_LEN', "CALIBRATE_CROSS_POLS"]:
    print(f'{setting} = {eval(setting)}')

PLOT = True
OC_MAX_DIMS = 4
OC_MIN_DIM_SIZE = 8
OC_SKIP_OUTRIGGERS = True
OC_MIN_BL_LEN = 1.0
OC_MAX_BL_LEN = 140.0
OC_MAXITER = 25
OC_MAX_RERUN = 10
OC_RERUN_MAXITER = 125
OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE = 1.5
OC_USE_PRIOR_SOL = True
OC_PRIOR_SOL_FLAG_THRESH = 0.95
OC_USE_GPU = False
OC_RESTART_FROM_FC_EVERY_ITER = False
RFI_DPSS_HALFWIDTH = 3e-07
RFI_NSIG = 4.0
ABSCAL_MODEL_FILES_GLOB = None
ABSCAL_MIN_BL_LEN = 1.0
ABSCAL_MAX_BL_LEN = 140.0
CALIBRATE_CROSS_POLS = True

Parse bounds

Load settings related to classifying antennas as good, suspect, or bad, then print what was loaded (or default).

# ant_metrics bounds for low correlation / dead antennas
am_corr_bad = (0, float(os.environ.get("AM_CORR_BAD", 0.35)))
am_corr_suspect = (float(os.environ.get("AM_CORR_BAD", 0.35)), float(os.environ.get("AM_CORR_SUSPECT", 0.45)))

# ant_metrics bounds for cross-polarized antennas
am_xpol_bad = (-1, float(os.environ.get("AM_XPOL_BAD", -0.1)))
am_xpol_suspect = (float(os.environ.get("AM_XPOL_BAD", -0.1)), float(os.environ.get("AM_XPOL_SUSPECT", 0)))

# bounds on solar altitude (in degrees)
good_solar_altitude = (-90, float(os.environ.get("SUSPECT_SOLAR_ALTITUDE", 0)))
suspect_solar_altitude = (float(os.environ.get("SUSPECT_SOLAR_ALTITUDE", 0)), 90)

# bounds on zeros in spectra
good_zeros_per_eo_spectrum = (0, int(os.environ.get("MAX_ZEROS_PER_EO_SPEC_GOOD", 2)))
suspect_zeros_per_eo_spectrum = (0, int(os.environ.get("MAX_ZEROS_PER_EO_SPEC_SUSPECT", 8)))

# bounds on autocorrelation power
auto_power_good = (float(os.environ.get("AUTO_POWER_GOOD_LOW", 5)), float(os.environ.get("AUTO_POWER_GOOD_HIGH", 30)))
auto_power_suspect = (float(os.environ.get("AUTO_POWER_SUSPECT_LOW", 1)), float(os.environ.get("AUTO_POWER_SUSPECT_HIGH", 60)))

# bounds on autocorrelation slope
auto_slope_good = (float(os.environ.get("AUTO_SLOPE_GOOD_LOW", -0.4)), float(os.environ.get("AUTO_SLOPE_GOOD_HIGH", 0.4)))
auto_slope_suspect = (float(os.environ.get("AUTO_SLOPE_SUSPECT_LOW", -0.6)), float(os.environ.get("AUTO_SLOPE_SUSPECT_HIGH", 0.6)))

# bounds on autocorrelation RFI
auto_rfi_good = (0, float(os.environ.get("AUTO_RFI_GOOD", 1.5)))
auto_rfi_suspect = (0, float(os.environ.get("AUTO_RFI_SUSPECT", 2)))

# bounds on autocorrelation shape
auto_shape_good = (0, float(os.environ.get("AUTO_SHAPE_GOOD", 0.1)))
auto_shape_suspect = (0, float(os.environ.get("AUTO_SHAPE_SUSPECT", 0.2)))

# bound on per-xengine non-noiselike power in diff
bad_xengine_zcut = float(os.environ.get("BAD_XENGINE_ZCUT", 10.0))

# bounds on chi^2 per antenna in omnical
oc_cspa_good = (0, float(os.environ.get("OC_CSPA_GOOD", 2)))
oc_cspa_suspect = (0, float(os.environ.get("OC_CSPA_SUSPECT", 3)))

# print bounds
for bound in ['am_corr_bad', 'am_corr_suspect', 'am_xpol_bad', 'am_xpol_suspect', 
              'good_solar_altitude', 'suspect_solar_altitude',
              'good_zeros_per_eo_spectrum', 'suspect_zeros_per_eo_spectrum',
              'auto_power_good', 'auto_power_suspect', 'auto_slope_good', 'auto_slope_suspect',
              'auto_rfi_good', 'auto_rfi_suspect', 'auto_shape_good', 'auto_shape_suspect',
              'bad_xengine_zcut', 'oc_cspa_good', 'oc_cspa_suspect']:
    print(f'{bound} = {eval(bound)}')

am_corr_bad = (0, 0.35)
am_corr_suspect = (0.35, 0.45)
am_xpol_bad = (-1, -0.1)
am_xpol_suspect = (-0.1, 0.0)
good_solar_altitude = (-90, 0.0)
suspect_solar_altitude = (0.0, 90)
good_zeros_per_eo_spectrum = (0, 2)
suspect_zeros_per_eo_spectrum = (0, 8)
auto_power_good = (5.0, 30.0)
auto_power_suspect = (1.0, 60.0)
auto_slope_good = (-0.4, 0.4)
auto_slope_suspect = (-0.6, 0.6)
auto_rfi_good = (0, 1.5)
auto_rfi_suspect = (0, 2.0)
auto_shape_good = (0, 0.1)
auto_shape_suspect = (0, 0.2)
bad_xengine_zcut = 10.0
oc_cspa_good = (0, 2.0)
oc_cspa_suspect = (0, 3.0)

Load sum and diff data

read_start = time.time()
hd = io.HERADataFastReader(SUM_FILE)
data, _, _ = hd.read(read_flags=False, read_nsamples=False)
if USE_DIFF:
    hd_diff = io.HERADataFastReader(DIFF_FILE)
    diff_data, _, _ = hd_diff.read(read_flags=False, read_nsamples=False, dtype=np.complex64, fix_autos_func=np.real)
print(f'Finished loading data in {(time.time() - read_start) / 60:.2f} minutes.')

Finished loading data in 1.30 minutes.

ants = sorted(set([ant for bl in hd.bls for ant in utils.split_bl(bl)]))
auto_bls = [bl for bl in data if (bl[0] == bl[1]) and (utils.split_pol(bl[2])[0] == utils.split_pol(bl[2])[1])]
antpols = sorted(set([ant[1] for ant in ants]))

# print basic information about the file
print(f'File: {SUM_FILE}')
print(f'JDs: {hd.times} ({np.median(np.diff(hd.times)) * 24 * 3600:.5f} s integrations)')
print(f'LSTS: {hd.lsts * 12 / np.pi } hours')
print(f'Frequencies: {len(hd.freqs)} {np.median(np.diff(hd.freqs)) / 1e6:.5f} MHz channels from {hd.freqs[0] / 1e6:.5f} to {hd.freqs[-1] / 1e6:.5f} MHz')
print(f'Antennas: {len(hd.data_ants)}')
print(f'Polarizations: {hd.pols}')

File: /mnt/sn1/data1/2461087/zen.2461087.45941.sum.uvh5
JDs: [2461087.45935538 2461087.45946723] (9.66368 s integrations)
LSTS: [10.18393065 10.18662236] hours
Frequencies: 1536 0.12207 MHz channels from 46.92078 to 234.29871 MHz
Antennas: 290
Polarizations: ['nn', 'ee', 'ne', 'en']

Classify good, suspect, and bad antpols

ALL_FLAGGED = False
def all_flagged():
    if ALL_FLAGGED:
        print('All antennas are flagged, so this cell is being skipped.')
    return ALL_FLAGGED

# initialize classes to None to help make Table 1 when everything is flagged
overall_class = None
am_totally_dead = None
am_corr = None
am_xpol = None
solar_class = None
zeros_class = None
auto_power_class = None
auto_slope_class = None
auto_rfi_class = None
auto_shape_class = None
xengine_diff_class = None
meta = None
redcal_class = None 

Load classifications that use diffs if diffs are not available

if not USE_DIFF:
    def read_antenna_classification(df, category):
        ac = ant_class.AntennaClassification()
        ac._data = {}
        for antname, class_data, antclass in zip(df['Antenna'], df[category], df[f'{category} Class']):
            try:        
                class_data = float(class_data)
            except:
                pass
            if isinstance(class_data, str) or np.isfinite(class_data):
                ant = (int(antname[:-1]), utils._comply_antpol(antname[-1]))
                ac[ant] = antclass
                ac._data[ant] = class_data
        return ac

    df = pd.read_csv(RTP_ANTCLASS)
    am_totally_dead = read_antenna_classification(df, 'Dead?')
    am_corr = read_antenna_classification(df, 'Low Correlation')
    am_xpol = read_antenna_classification(df, 'Cross-Polarized')
    zeros_class = read_antenna_classification(df, 'Even/Odd Zeros')
    xengine_diff_class = read_antenna_classification(df, 'Bad Diff X-Engines')

Run ant_metrics

This classifies antennas as cross-polarized, low-correlation, or dead. Such antennas are excluded from any calibration.

if USE_DIFF:
    am = ant_metrics.AntennaMetrics(SUM_FILE, DIFF_FILE, sum_data=data, diff_data=diff_data)
    am.iterative_antenna_metrics_and_flagging(crossCut=am_xpol_bad[1], deadCut=am_corr_bad[1])
    am.all_metrics = {}  # this saves time and disk by getting rid of per-iteration information we never use
    if SAVE_RESULTS:
        am.save_antenna_metrics(AM_FILE, overwrite=True)

if USE_DIFF:
    # Turn ant metrics into classifications
    totally_dead_ants = [ant for ant, i in am.xants.items() if i == -1]
    am_totally_dead = ant_class.AntennaClassification(good=[ant for ant in ants if ant not in totally_dead_ants], bad=totally_dead_ants)
    am_corr = ant_class.antenna_bounds_checker(am.final_metrics['corr'], bad=[am_corr_bad], suspect=[am_corr_suspect], good=[(0, 1)])
    am_xpol = ant_class.antenna_bounds_checker(am.final_metrics['corrXPol'], bad=[am_xpol_bad], suspect=[am_xpol_suspect], good=[(-1, 1)])
ant_metrics_class = am_totally_dead + am_corr + am_xpol
if np.all([ant_metrics_class[utils.split_bl(bl)[0]] == 'bad' for bl in auto_bls]):
    ALL_FLAGGED = True
    print('All antennas are flagged for ant_metrics.')

Mark sun-up (or high solar altitude) data as suspect

min_sun_alt = np.min(utils.get_sun_alt(hd.times))
solar_class = ant_class.antenna_bounds_checker({ant: min_sun_alt for ant in ants}, good=[good_solar_altitude], suspect=[suspect_solar_altitude])

Classify antennas responsible for 0s in visibilities as bad:

This classifier looks for X-engine failure or packet loss specific to an antenna which causes either the even visibilities (or the odd ones, or both) to be 0s.

if USE_DIFF:
    zeros_class = ant_class.even_odd_zeros_checker(data, diff_data, good=good_zeros_per_eo_spectrum, suspect=suspect_zeros_per_eo_spectrum)
if np.all([zeros_class[utils.split_bl(bl)[0]] == 'bad' for bl in auto_bls]):
    ALL_FLAGGED = True
    print('All antennas are flagged for too many even/odd zeros.')

All antennas are flagged for too many even/odd zeros.

Examine and classify autocorrelation power and slope

These classifiers look for antennas with too high or low power or to steep a slope.

auto_power_class = ant_class.auto_power_checker(data, good=auto_power_good, suspect=auto_power_suspect)
auto_slope_class = ant_class.auto_slope_checker(data, good=auto_slope_good, suspect=auto_slope_suspect, edge_cut=100, filt_size=17)
if np.all([(auto_power_class + auto_slope_class)[utils.split_bl(bl)[0]] == 'bad' for bl in auto_bls]):
    ALL_FLAGGED = True
    print('All antennas are flagged for bad autocorrelation power/slope.')
overall_class = auto_power_class + auto_slope_class + zeros_class + ant_metrics_class + solar_class

Find starting set of array flags

if not all_flagged():
    antenna_flags, array_flags = xrfi.flag_autos(data, flag_method="channel_diff_flagger", nsig=RFI_NSIG * 5, 
                                                 antenna_class=overall_class, flag_broadcast_thresh=.5)
    for key in antenna_flags:
        antenna_flags[key] = array_flags
    cache = {}
    _, array_flags = xrfi.flag_autos(data, freqs=data.freqs, flag_method="dpss_flagger",
                                     nsig=RFI_NSIG, antenna_class=overall_class,
                                     filter_centers=[0], filter_half_widths=[RFI_DPSS_HALFWIDTH],
                                     eigenval_cutoff=[1e-9], flags=antenna_flags, mode='dpss_matrix', 
                                     cache=cache, flag_broadcast_thresh=.5)

All antennas are flagged, so this cell is being skipped.

Classify antennas based on non-noiselike diffs

if not all_flagged():
    if USE_DIFF:
        xengine_diff_class = ant_class.non_noiselike_diff_by_xengine_checker(data, diff_data, flag_waterfall=array_flags, 
                                                                             antenna_class=overall_class, 
                                                                             xengine_chans=96, bad_xengine_zcut=bad_xengine_zcut)
        
        if np.all([overall_class[utils.split_bl(bl)[0]] == 'bad' for bl in auto_bls]):
            ALL_FLAGGED = True
            print('All antennas are flagged after flagging non-noiselike diffs.')
    overall_class += xengine_diff_class

All antennas are flagged, so this cell is being skipped.

Examine and classify autocorrelation excess RFI and shape, finding consensus RFI mask along the way

This classifier iteratively identifies antennas for excess RFI (characterized by RMS of DPSS-filtered autocorrelations after RFI flagging) and bad shape, as determined by a discrepancy with the mean good normalized autocorrelation's shape. Along the way, it iteratively discovers a conensus array-wide RFI mask.

def auto_bl_zscores(data, flag_array, cache={}):
    '''This function computes z-score arrays for each delay-filtered autocorrelation, normalized by the expected noise. 
    Flagged times/channels for the whole array are given 0 weight in filtering and are np.nan in the z-score.'''
    zscores = {}
    for bl in auto_bls:
        wgts = np.array(np.logical_not(flag_array), dtype=np.float64)
        model, _, _ = dspec.fourier_filter(hd.freqs, data[bl], wgts, filter_centers=[0], filter_half_widths=[RFI_DPSS_HALFWIDTH], mode='dpss_solve',
                                            suppression_factors=[1e-9], eigenval_cutoff=[1e-9], cache=cache)
        res = data[bl] - model
        int_time = 24 * 3600 * np.median(np.diff(data.times))
        chan_res = np.median(np.diff(data.freqs))
        int_count = int(int_time * chan_res)
        sigma = np.abs(model) / np.sqrt(int_count / 2)
        zscores[bl] = res / sigma    
        zscores[bl][flag_array] = np.nan

    return zscores

def rfi_from_avg_autos(data, auto_bls_to_use, prior_flags=None, nsig=RFI_NSIG):
    '''Average together all baselines in auto_bls_to_use, then find an RFI mask by looking for outliers after DPSS filtering.'''
    
    # If there are no good autos, return 100% flagged
    if len(auto_bls_to_use) == 0:
        return np.ones(data[next(iter(data))].shape, dtype=bool)
    
    # Compute int_count for all unflagged autocorrelations averaged together
    int_time = 24 * 3600 * np.median(np.diff(data.times_by_bl[auto_bls[0][0:2]]))
    chan_res = np.median(np.diff(data.freqs))
    int_count = int(int_time * chan_res) * len(auto_bls_to_use)
    avg_auto = {(-1, -1, 'ee'): np.mean([data[bl] for bl in auto_bls_to_use], axis=0)}
    
    # Flag RFI first with channel differences and then with DPSS
    antenna_flags, _ = xrfi.flag_autos(avg_auto, int_count=int_count, nsig=(nsig * 5))
    if prior_flags is not None:
        antenna_flags[(-1, -1, 'ee')] = prior_flags
    _, rfi_flags = xrfi.flag_autos(avg_auto, int_count=int_count, flag_method='dpss_flagger',
                                   flags=antenna_flags, freqs=data.freqs, filter_centers=[0],
                                   filter_half_widths=[RFI_DPSS_HALFWIDTH], eigenval_cutoff=[1e-9], nsig=nsig)

    return rfi_flags

# Iteratively develop RFI mask, excess RFI classification, and autocorrelation shape classification
if not all_flagged():
    stage = 1
    rfi_flags = np.array(array_flags)
    prior_end_states = set()
    while True:
        # compute DPSS-filtered z-scores with current array-wide RFI mask
        zscores = auto_bl_zscores(data, rfi_flags)
        rms = {bl: np.nanmean(zscores[bl]**2)**.5 if np.any(np.isfinite(zscores[bl])) else np.inf for bl in zscores}
        
        # figure out which autos to use for finding new set of flags
        candidate_autos = [bl for bl in auto_bls if overall_class[utils.split_bl(bl)[0]] != 'bad']
        if stage == 1:
            # use best half of the unflagged antennas
            med_rms = np.nanmedian([rms[bl] for bl in candidate_autos])
            autos_to_use = [bl for bl in candidate_autos if rms[bl] <= med_rms]
        elif stage == 2:
            # use all unflagged antennas which are auto RFI good, or the best half, whichever is larger
            med_rms = np.nanmedian([rms[bl] for bl in candidate_autos])
            best_half_autos = [bl for bl in candidate_autos if rms[bl] <= med_rms]
            good_autos = [bl for bl in candidate_autos if (overall_class[utils.split_bl(bl)[0]] != 'bad')
                          and (auto_rfi_class[utils.split_bl(bl)[0]] == 'good')]
            autos_to_use = (best_half_autos if len(best_half_autos) > len(good_autos) else good_autos)
        elif stage == 3:
            # use all unflagged antennas which are auto RFI good or suspect
            autos_to_use = [bl for bl in candidate_autos if (overall_class[utils.split_bl(bl)[0]] != 'bad')]
    
        # compute new RFI flags
        rfi_flags = rfi_from_avg_autos(data, autos_to_use)
    
        # perform auto shape and RFI classification
        overall_class = auto_power_class + auto_slope_class + zeros_class + ant_metrics_class + solar_class + xengine_diff_class
        auto_rfi_class = ant_class.antenna_bounds_checker(rms, good=auto_rfi_good, suspect=auto_rfi_suspect, bad=(0, np.inf))
        overall_class += auto_rfi_class
        auto_shape_class = ant_class.auto_shape_checker(data, good=auto_shape_good, suspect=auto_shape_suspect,
                                                        flag_spectrum=np.sum(rfi_flags, axis=0).astype(bool), 
                                                        antenna_class=overall_class)
        overall_class += auto_shape_class
        
        # check for convergence by seeing whether we've previously gotten to this number of flagged antennas and channels
        if stage == 3:
            if (len(overall_class.bad_ants), np.sum(rfi_flags)) in prior_end_states:
                break
            prior_end_states.add((len(overall_class.bad_ants), np.sum(rfi_flags)))
        else:
            stage += 1

All antennas are flagged, so this cell is being skipped.

auto_class = auto_power_class + auto_slope_class
if auto_rfi_class is not None:
    auto_class += auto_rfi_class
if auto_shape_class is not None:
    auto_class += auto_rfi_class
if np.all([overall_class[utils.split_bl(bl)[0]] == 'bad' for bl in auto_bls]):
    ALL_FLAGGED = True
    print('All antennas are flagged after flagging for bad autos power/slope/rfi/shape.')

All antennas are flagged after flagging for bad autos power/slope/rfi/shape.

if not all_flagged():
    def rfi_plot(cls, flags=rfi_flags):
        avg_auto = {(-1, -1, 'ee'): np.mean([data[bl] for bl in auto_bls if not cls[utils.split_bl(bl)[0]] == 'bad'], axis=0)}
        plt.figure(figsize=(12, 5), dpi=100)
        plt.semilogy(hd.freqs / 1e6, np.where(flags, np.nan, avg_auto[(-1, -1, 'ee')])[0], label = 'Average Good or Suspect Autocorrelation', zorder=100)
        plt.semilogy(hd.freqs / 1e6, np.where(False, np.nan, avg_auto[(-1, -1, 'ee')])[0], 'r', lw=.5, label=f'{np.sum(flags[0])} Channels Flagged for RFI')
        plt.legend()
        plt.xlabel('Frequency (MHz)')
        plt.ylabel('Uncalibrated Autocorrelation')
        plt.tight_layout()

All antennas are flagged, so this cell is being skipped.

Figure 1: RFI Flagging

This figure shows RFI identified using the average of all autocorrelations---excluding bad antennas---for the first integration in the file.

if PLOT and not all_flagged(): rfi_plot(overall_class)

All antennas are flagged, so this cell is being skipped.

def autocorr_plot(cls):    
    fig, axes = plt.subplots(1, 2, figsize=(14, 5), dpi=100, sharey=True, gridspec_kw={'wspace': 0})
    labels = []
    colors = ['darkgreen', 'goldenrod', 'maroon']
    for ax, pol in zip(axes, antpols):
        for ant in cls.ants:
            if ant[1] == pol:
                color = colors[cls.quality_classes.index(cls[ant])]
                ax.semilogy(np.mean(data[utils.join_bl(ant, ant)], axis=0), color=color, lw=.5)
        ax.set_xlabel('Channel', fontsize=12)
        ax.set_title(f'{utils.join_pol(pol, pol)}-Polarized Autos')

    axes[0].set_ylabel('Raw Autocorrelation', fontsize=12)
    axes[1].legend([matplotlib.lines.Line2D([0], [0], color=color) for color in colors], 
                   [cl.capitalize() for cl in cls.quality_classes], ncol=1, fontsize=12, loc='upper right', framealpha=1)
    plt.tight_layout()

Figure 2: Plot of autocorrelations with classifications

This figure shows a plot of all autocorrelations in the array, split by polarization. Antennas are classified based on their autocorrelations into good, suspect, and bad, by examining power, slope, and RFI-occupancy.

if PLOT and not all_flagged(): autocorr_plot(auto_class)

All antennas are flagged, so this cell is being skipped.

Summarize antenna classification prior to redundant-baseline calibration

def array_class_plot(cls, extra_label=""):
    outriggers = [ant for ant in hd.data_ants if ant >= 320]

    if len(outriggers) > 0:
        fig, axes = plt.subplots(1, 2, figsize=(14, 6), dpi=100, gridspec_kw={'width_ratios': [2, 1]})
        plot_antclass(hd.antpos, cls, ax=axes[0], ants=[ant for ant in hd.data_ants if ant < 320], legend=False, title=f'HERA Core{extra_label}')
        plot_antclass(hd.antpos, cls, ax=axes[1], ants=outriggers, radius=50, title='Outriggers')
    else:
        fig, axes = plt.subplots(1, 1, figsize=(9, 6), dpi=100)
        plot_antclass(hd.antpos, cls, ax=axes, ants=[ant for ant in hd.data_ants if ant < 320], legend=False, title=f'HERA Core{extra_label}')

Figure 3: Summary of antenna classifications prior to calibration

This figure shows the location and classification of all antennas prior to calibration. Antennas are split along the diagonal, with ee-polarized antpols represented by the southeast half of each antenna and nn-polarized antpols represented by the northwest half. Outriggers are split from the core and shown at exaggerated size in the right-hand panel. This classification includes ant_metrics, a count of the zeros in the even or odd visibilities, and autocorrelation power, slope, and RFI occupancy. An antenna classified as bad in any classification will be considered bad. An antenna marked as suspect any in any classification will be considered suspect unless it is also classified as bad elsewhere.

if PLOT and not all_flagged(): array_class_plot(overall_class)

All antennas are flagged, so this cell is being skipped.

# delete diffs to save memory
if USE_DIFF:
    del diff_data, hd_diff
try:
    del cache
except NameError:
    pass
malloc_trim()

Perform redundant-baseline calibration

def classify_off_grid(reds, all_ants):
    '''Returns AntennaClassification of all_ants where good ants are in reds while bad ants are not.'''
    ants_in_reds = set([ant for red in reds for bl in red for ant in utils.split_bl(bl)])
    on_grid = [ant for ant in all_ants if ant in ants_in_reds]
    off_grid = [ant for ant in all_ants if ant not in ants_in_reds]
    return ant_class.AntennaClassification(good=on_grid, bad=off_grid)

def per_pol_filter_reds(reds, pols=['nn', 'ee'], **kwargs):
    '''Performs redcal filtering separately on polarizations (which might have different min_dim_size issues).'''
    return [red for pol in pols for red in redcal.filter_reds(copy.deepcopy(reds), pols=[pol], **kwargs)]

def check_if_whole_pol_flagged(redcal_class, pols=['Jee', 'Jnn'], thresh=.75):
    '''Checks if nearly an entire polarization is flagged (depending on thresh). 
    If it is, returns True and marks all antennas as bad in redcal_class.'''
    flag_fracs = np.array([np.mean([redcal_class[ant] == 'bad' for ant in redcal_class.ants if ant[1] == pol]) for pol in pols])
    if np.any(flag_fracs > thresh):
        for pol, frac in zip(pols, flag_fracs):
            if frac > thresh:
                print(f'Polarization {pol} is {frac:.3%} flagged > {thresh:.3%} threshold. Stopping redcal.')
        for ant in redcal_class:
            redcal_class[ant] = 'bad'
        return True
    return False

def recheck_chisq(cspa, sol, cutoff, avg_alg):
    '''Recompute chisq per ant without apparently bad antennas to see if any antennas get better.'''
    avg_cspa = {ant: avg_alg(np.where(rfi_flags, np.nan, cspa[ant])) for ant in cspa}
    sol2 = redcal.RedSol(sol.reds, gains={ant: sol[ant] for ant in avg_cspa if avg_cspa[ant] <= cutoff}, vis=sol.vis)
    new_chisq_per_ant = {ant: np.array(cspa[ant]) for ant in sol2.gains}
    if len(set([bl[2] for red in per_pol_filter_reds(sol2.reds, ants=sol2.gains.keys(), antpos=hd.data_antpos, **fr_settings) for bl in red])) >= 2:
        redcal.expand_omni_gains(sol2, sol2.reds, data, chisq_per_ant=new_chisq_per_ant)
    for ant in avg_cspa:
        if ant in new_chisq_per_ant:
            if np.any(np.isfinite(new_chisq_per_ant[ant])):
                if not np.all(np.isclose(new_chisq_per_ant[ant], 0)):
                    new_avg_cspa = avg_alg(np.where(rfi_flags, np.nan, cspa[ant]))
                    if new_avg_cspa > 0:
                        avg_cspa[ant] = np.min([avg_cspa[ant], new_avg_cspa])
    return avg_cspa

Perform iterative redcal

# figure out and filter reds and classify antennas based on whether or not they are on the main grid
if not all_flagged():
    fr_settings = {'max_dims': OC_MAX_DIMS, 'min_dim_size': OC_MIN_DIM_SIZE, 'min_bl_cut': OC_MIN_BL_LEN, 'max_bl_cut': OC_MAX_BL_LEN}
    reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol', bl_error_tol=2.0)
    reds = per_pol_filter_reds(reds, ex_ants=overall_class.bad_ants, antpos=hd.data_antpos, **fr_settings)
    if OC_SKIP_OUTRIGGERS:
        reds = redcal.filter_reds(reds, ex_ants=[ant for ant in ants if ant[0] >= 320])
    redcal_class = classify_off_grid(reds, ants)

All antennas are flagged, so this cell is being skipped.

if OC_USE_PRIOR_SOL and not all_flagged():
    # Find closest omnical file
    omnical_files = sorted(glob.glob('.'.join(OMNICAL_FILE.split('.')[:-5]) + '.*.' + '.'.join(OMNICAL_FILE.split('.')[-3:])))
    if len(omnical_files) == 0:
        OC_USE_PRIOR_SOL = False
    else:
        omnical_jds = np.array([float(re.findall("\d+\.\d+", ocf)[-1]) for ocf in omnical_files])
        closest_omnical = omnical_files[np.argmin(np.abs(omnical_jds - data.times[0]))]

        # Load closest omnical file and use it if the antenna flagging is not too dissimilar
        hc = io.HERACal(closest_omnical)
        prior_gains, prior_flags, _, _ = hc.read()
        not_bad_not_prior_flagged = [ant for ant in overall_class if not ant in redcal_class.bad_ants and not np.all(prior_flags[ant])]
        if (len(redcal_class.bad_ants) == len(redcal_class.ants)):
            OC_USE_PRIOR_SOL = False  # all antennas flagged
        elif (len(not_bad_not_prior_flagged) / (len(redcal_class.ants) - len(redcal_class.bad_ants))) < OC_PRIOR_SOL_FLAG_THRESH:
            OC_USE_PRIOR_SOL = False  # too many antennas unflaged that were flagged in the prior sol
        else:
            print(f'Using {closest_omnical} as a starting point for redcal.')

All antennas are flagged, so this cell is being skipped.

if not all_flagged():
    redcal_start = time.time()
    rc_settings = {'oc_conv_crit': 1e-10, 'gain': 0.4, 'run_logcal': False,
                   'oc_maxiter': OC_MAXITER, 'check_after': OC_MAXITER, 'use_gpu': OC_USE_GPU}
    
    if check_if_whole_pol_flagged(redcal_class):
        # skip redcal, initialize empty sol and meta 
        sol = redcal.RedSol(reds)
        meta = {'chisq': None, 'chisq_per_ant': None}
    else:    
        if OC_USE_PRIOR_SOL:
            # use prior unflagged gains and data to create starting point for next step
            sol = redcal.RedSol(reds=reds, gains={ant: prior_gains[ant] for ant in not_bad_not_prior_flagged})
            reds_to_update = [[bl for bl in red if (utils.split_bl(bl)[0] in sol.gains) and (utils.split_bl(bl)[1] in sol.gains)] for red in reds]
            reds_to_update = [red for red in reds_to_update if len(red) > 0]
            sol.update_vis_from_data(data, reds_to_update=reds_to_update)
            redcal.expand_omni_gains(sol, reds, data)
            sol.update_vis_from_data(data)
        else:
            sol = None
            
        malloc_trim()

All antennas are flagged, so this cell is being skipped.

if not all_flagged():
    all_high_chisq_found = False
    metric = 'median'
    
    # iteratively rerun redundant calibration
    for i in range(OC_MAX_RERUN + 1):
        # refilter reds and update classification to reflect new off-grid ants, if any
        reds = per_pol_filter_reds(reds, ex_ants=(overall_class + redcal_class).bad_ants, antpos=hd.data_antpos, **fr_settings)
        reds = sorted(reds, key=len, reverse=True)
        redcal_class = classify_off_grid(reds, ants)
        
        # check to see whether we're done because an entire pol is flagged
        if check_if_whole_pol_flagged(redcal_class):
            break
    
        # change settings for final run
        if all_high_chisq_found or (i == OC_MAX_RERUN):
            sol = None  # start from scratch
            rc_settings['oc_maxiter'] = rc_settings['check_after'] = OC_RERUN_MAXITER
        
        # optionally redo firstcal and start from there every iteration
        if OC_RESTART_FROM_FC_EVERY_ITER:
            sol = None

        # re-run redundant calibration using previous solution (if it's not the final run and if not OC_RESTART_FROM_FC_EVERY_ITER)
        meta, sol = redcal.redundantly_calibrate(data, reds, sol0=sol, max_dims=None, **rc_settings)
        malloc_trim()
        
        # recompute chi^2 for bad antennas without bad antennas to make sure they are actually bad
        avg_cspa = recheck_chisq(meta['chisq_per_ant'], sol, oc_cspa_suspect[1], (np.nanmean if metric == 'mean' else np.nanmedian))

        # flag bad antennas
        cspa_class = ant_class.antenna_bounds_checker(avg_cspa, good=oc_cspa_good, suspect=oc_cspa_suspect, bad=[(-np.inf, np.inf)])
        for ant in cspa_class.bad_ants:
            if avg_cspa[ant] < np.max(list(avg_cspa.values())) / OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE:
                cspa_class[ant] = 'suspect'  # reclassify as suspect if they are much better than the worst antennas
        redcal_class += cspa_class

        if len(cspa_class.bad_ants) > 0:
            print(f'Removing {cspa_class.bad_ants} for high {metric} unflagged chi^2.')
            for ant in cspa_class.bad_ants:
                print(f'\t{ant}: {avg_cspa[ant]:.3f}')
    
        if check_if_whole_pol_flagged(redcal_class) or all_high_chisq_found or (i == OC_MAX_RERUN):
            break

        if len(cspa_class.bad_ants) == 0:
            if metric == 'median':
                metric = 'mean'  # switch to mean if no new median offenders found
            else:
                all_high_chisq_found = True  # no new antennas to flag, the next iteration will be the final one
    
    print(f'Finished redcal in {(time.time() - redcal_start) / 60:.2f} minutes.')
    overall_class += redcal_class

All antennas are flagged, so this cell is being skipped.

Expand solution to include calibratable baselines excluded from redcal (e.g. because they were too long)

if not all_flagged():
    expanded_reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol', bl_error_tol=2.0)
    expanded_reds = per_pol_filter_reds(expanded_reds, ex_ants=(ant_metrics_class + solar_class + zeros_class + auto_class + xengine_diff_class).bad_ants,
                                        max_dims=OC_MAX_DIMS, min_dim_size=OC_MIN_DIM_SIZE)
    if OC_SKIP_OUTRIGGERS:
        expanded_reds = redcal.filter_reds(expanded_reds, ex_ants=[ant for ant in ants if ant[0] >= 320])
    if len(sol.gains) > 0:
        redcal.expand_omni_vis(sol, expanded_reds, data, chisq=meta['chisq'], chisq_per_ant=meta['chisq_per_ant'])

All antennas are flagged, so this cell is being skipped.

if not all_flagged():
    # now figure out flags, nsamples etc.
    omni_flags = {ant: (~np.isfinite(g)) | (ant in overall_class.bad_ants) for ant, g in sol.gains.items()}
    vissol_flags = datacontainer.RedDataContainer({bl: ~np.isfinite(v) for bl, v in sol.vis.items()}, reds=sol.vis.reds)
    single_nsamples_array = np.ones((len(hd.times), len(hd.freqs)), dtype=float)
    nsamples = datacontainer.DataContainer({bl: single_nsamples_array for bl in data})
    vissol_nsamples = redcal.count_redundant_nsamples(nsamples, [red for red in expanded_reds if red[0] in vissol_flags], 
                                                      good_ants=[ant for ant in overall_class if ant not in overall_class.bad_ants])
    for bl in vissol_flags:
        vissol_flags[bl][vissol_nsamples[bl] == 0] = True
    sol.make_sol_finite()

All antennas are flagged, so this cell is being skipped.

Fix the firstcal delay slope degeneracy using RFI transmitters

if not OC_USE_PRIOR_SOL and not all_flagged():
    # find channels clearly contaminated by RFI
    not_bad_ants = [ant for ant in overall_class.ants if (overall_class[ant] != 'bad') and (utils.join_bl(ant, ant) in data)]
    if len(not_bad_ants) > 0:
        chan_flags = np.mean([xrfi.detrend_medfilt(data[utils.join_bl(ant, ant)], Kf=8, Kt=2) for ant in not_bad_ants], axis=(0, 1)) > 5

        # hardcoded RFI transmitters and their headings
        # channel: frequency (Hz), heading (rad), chi^2
        phs_sol = {359: ( 90744018.5546875, 0.7853981, 23.3),
                   360: ( 90866088.8671875, 0.7853981, 10.8),
                   385: ( 93917846.6796875, 0.7853981, 27.3),
                   386: ( 94039916.9921875, 0.7853981, 18.1),
                   400: ( 95748901.3671875, 6.0632738, 24.0),
                   441: (100753784.1796875, 0.7853981, 21.7),
                   442: (100875854.4921875, 0.7853981, 19.4),
                   455: (102462768.5546875, 6.0632738, 18.8),
                   456: (102584838.8671875, 6.0632738,  8.8),
                   471: (104415893.5546875, 0.7853981, 13.3),
                   484: (106002807.6171875, 6.0632738, 21.2),
                   485: (106124877.9296875, 6.0632738,  4.0),
                  1181: (191085815.4296875, 0.7853981, 26.3),
                  1182: (191207885.7421875, 0.7853981, 27.0),
                  1183: (191329956.0546875, 0.7853981, 25.6),
                  1448: (223678588.8671875, 2.6075219, 25.7),
                  1449: (223800659.1796875, 2.6075219, 22.6),
                  1450: (223922729.4921875, 2.6075219, 11.6),
                  1451: (224044799.8046875, 2.6075219,  5.9),
                  1452: (224166870.1171875, 2.6075219, 22.6),
                  1510: (231246948.2421875, 0.1068141, 23.9)}

        if not np.isclose(hd.freqs[0], 46920776.3671875, atol=0.001) or len(hd.freqs) != 1536:
            # We have less frequencies than usual (maybe testing)
            phs_sol = {np.argmin(np.abs(hd.freqs - freq)): (freq, heading, chisq) for chan, (freq, heading, chisq) in phs_sol.items() if hd.freqs[0] <= freq <= hd.freqs[-1]}


        rfi_chans = [chan for chan in phs_sol if chan_flags[chan]]
        if len(rfi_chans) >= 2:
            print('Channels used for delay-slope calibration with RFI:', rfi_chans)
            rfi_angles = np.array([phs_sol[chan][1] for chan in rfi_chans])
            rfi_headings = np.array([np.cos(rfi_angles), np.sin(rfi_angles), np.zeros_like(rfi_angles)])
            rfi_chisqs = np.array([phs_sol[chan][2] for chan in rfi_chans])

            # resolve firstcal degeneracy with delay slopes set by RFI transmitters, update cal
            max_dly = np.max(np.abs(list(meta['fc_meta']['dlys'].values())))
            RFI_dly_slope_gains = abscal.RFI_delay_slope_cal([red for red in expanded_reds if red[0] in sol.vis], hd.antpos, sol.vis, hd.freqs, rfi_chans, rfi_headings, rfi_wgts=rfi_chisqs**-1,
                                                             min_tau=-max_dly, max_tau=max_dly, delta_tau=0.1e-9, return_gains=True, gain_ants=sol.gains.keys())
            sol.gains = {ant: g * RFI_dly_slope_gains[ant] for ant, g in sol.gains.items()}
            apply_cal.calibrate_in_place(sol.vis, RFI_dly_slope_gains)
            malloc_trim()
        else:
            print(f"Only {len(rfi_chans)} RFI channels with known headings were flagged for RFI, so RFI-firstcal is being skipped.")

Perform absolute amplitude calibration using a model of autocorrelations

# Load simulated and then downsampled model of autocorrelations that includes receiver noise, then interpolate to upsample
if VALIDATION:
    hd_auto_model = io.HERAData('/lustre/aoc/projects/hera/Validation/H6C_IDR2/sim_data/h6c_validation_autos_for_amp_abscal_with_Trx_100K.uvh5')
else:
    hd_auto_model = io.HERAData(f'{HNBT_DATA}/SSM_autocorrelations_downsampled_sum_pol_convention.uvh5')
if not all_flagged():
    model, _, _ = hd_auto_model.read()
    per_pol_interpolated_model = {}
    for bl in model:
        sorted_lsts, lst_indices = np.unique(model.lsts, return_index=True)
        periodic_model = np.vstack([model[bl][lst_indices, :], model[bl][lst_indices[0], :]])
        periodic_lsts = np.append(sorted_lsts, sorted_lsts[0] + 2 * np.pi)
        lst_interpolated = interpolate.CubicSpline(periodic_lsts, periodic_model, axis=0, bc_type='periodic')(data.lsts)
        per_pol_interpolated_model[bl[2]] = interpolate.CubicSpline(model.freqs, lst_interpolated, axis=1)(data.freqs)
    model = {bl: per_pol_interpolated_model[bl[2]] for bl in auto_bls if utils.split_bl(bl)[0] not in overall_class.bad_ants}

All antennas are flagged, so this cell is being skipped.

if not all_flagged():
    # Run abscal and update omnical gains with abscal gains
    if len(model) > 0:
        redcaled_autos = {bl: sol.calibrate_bl(bl, data[bl]) for bl in auto_bls if utils.split_bl(bl)[0] not in overall_class.bad_ants}
        g_abscal = abscal.abs_amp_logcal(model, redcaled_autos, verbose=False, return_gains=True, gain_ants=sol.gains)
        sol.gains = {ant: g * g_abscal[ant] for ant, g in sol.gains.items()}
        apply_cal.calibrate_in_place(sol.vis, g_abscal)
        del redcaled_autos, g_abscal

All antennas are flagged, so this cell is being skipped.

Full absolute calibration of phase gradients

If an ABSCAL_MODEL_FILES_GLOB is provided, try to perform a full absolute calibration of tip-tilt phase gradients across the array using that those model files. Specifically, this step calibrates omnical visbility solutions using unique baselines simulated with a model of the sky and HERA's beam.

if not all_flagged():
    if ABSCAL_MODEL_FILES_GLOB is not None:
        abscal_model_files = sorted(glob.glob(ABSCAL_MODEL_FILES_GLOB))
    elif VALIDATION:
        abscal_model_files = sorted(glob.glob('/lustre/aoc/projects/hera/Validation/H6C_IDR2/sim_data/foregrounds/zen.LST.*.foregrounds.uvh5'))
    else:
        # try to find files on site
        abscal_model_files = sorted(glob.glob('/mnt/sn1/data1/abscal_models/H6C/zen.2458894.?????.uvh5'))
        if len(abscal_model_files) == 0:
            # try to find files at NRAO
            abscal_model_files = sorted(glob.glob('/lustre/aoc/projects/hera/h6c-analysis/abscal_models/h6c_abscal_files_unique_baselines/zen.2458894.?????.uvh5'))
    print(f'Found {len(abscal_model_files)} abscal model files{" in " + os.path.dirname(abscal_model_files[0]) if len(abscal_model_files) > 0 else ""}.')

All antennas are flagged, so this cell is being skipped.

if not all_flagged():
    # Try to perform a full abscal of phase
    if len(abscal_model_files) == 0:
        DO_FULL_ABSCAL = False
        print('No model files found... not performing full absolute calibration of phase gradients.')
    elif np.all([ant in overall_class.bad_ants for ant in ants]):
        DO_FULL_ABSCAL = False
        print('All antennas classified as bad... skipping absolute calibration of phase gradients.')
    else:
        abscal_start = time.time()
        # figure out which model files match the LSTs of the data
        matched_model_files = sorted(set(abscal.match_times(SUM_FILE, abscal_model_files, filetype='uvh5')))
        if len(matched_model_files) == 0:
            DO_FULL_ABSCAL = False
            print(f'No model files found matching the LSTs of this file after searching for {(time.time() - abscal_start) / 60:.2f} minutes. '
                  'Not performing full absolute calibration of phase gradients.')
        else:
            DO_FULL_ABSCAL = True
            # figure out appropriate model times to load
            hdm = io.HERAData(matched_model_files)
            all_model_times, all_model_lsts = abscal.get_all_times_and_lsts(hdm, unwrap=True)
            d2m_time_map = abscal.get_d2m_time_map(data.times, np.unwrap(data.lsts), all_model_times, all_model_lsts, extrap_limit=.5)

All antennas are flagged, so this cell is being skipped.

if not all_flagged():
    if DO_FULL_ABSCAL:
        abscal_meta = {}
        for pol in ['ee', 'nn']:
            print(f'Performing absolute phase gradient calibration of {pol}-polarized visibility solutions...')
            
            # load matching times and baselines
            unflagged_data_bls = [bl for bl in vissol_flags if not np.all(vissol_flags[bl]) and bl[2] == pol]
            model_bls = copy.deepcopy(hdm.bls)
            model_antpos = hdm.data_antpos
            if len(matched_model_files) > 1:  # in this case, it's a dictionary
                model_bls = list(set([bl for bls in list(hdm.bls.values()) for bl in bls]))
                model_antpos = {ant: pos for antpos in hdm.data_antpos.values() for ant, pos in antpos.items()}
            data_bls, model_bls, data_to_model_bl_map = abscal.match_baselines(unflagged_data_bls, model_bls, data.antpos, model_antpos=model_antpos, 
                                                                             pols=[pol], data_is_redsol=True, model_is_redundant=True, tol=1.0,
                                                                             min_bl_cut=ABSCAL_MIN_BL_LEN, max_bl_cut=ABSCAL_MAX_BL_LEN, verbose=True)
            model, model_flags, _ = io.partial_time_io(hdm, np.unique([d2m_time_map[time] for time in data.times]), bls=model_bls)
            model_bls = [data_to_model_bl_map[bl] for bl in data_bls]
            
            # rephase model to match in lsts
            model_blvecs = {bl: model.antpos[bl[0]] - model.antpos[bl[1]] for bl in model.keys()}
            utils.lst_rephase(model, model_blvecs, model.freqs, data.lsts - model.lsts,
                              lat=hdm.telescope.location.lat.deg, inplace=True)
    
            # run abscal and apply 
            abscal_meta[pol], delta_gains = abscal.complex_phase_abscal(sol.vis, model, sol.reds, data_bls, model_bls)
            
            # apply gains
            sol.gains = {antpol : g * delta_gains.get(antpol, 1) for antpol, g in sol.gains.items()}
            apply_cal.calibrate_in_place(sol.vis, delta_gains)            
         
        del model, model_flags, delta_gains
        malloc_trim()    
        
        print(f'Finished absolute calibration of tip-tilt phase slopes in {(time.time() - abscal_start) / 60:.2f} minutes.')

All antennas are flagged, so this cell is being skipped.

if not all_flagged() and DO_FULL_ABSCAL and CALIBRATE_CROSS_POLS:
    cross_pol_cal_start = time.time()

    # Compute reds for good antennas 
    cross_reds = redcal.get_reds(hd.data_antpos, pols=['en', 'ne'], bl_error_tol=2.0)        
    cross_reds = redcal.filter_reds(cross_reds, ex_ants=overall_class.bad_ants, pols=['en', 'ne'], antpos=hd.antpos, **fr_settings)    
    unflagged_data_bls = [red[0] for red in cross_reds]

    # Get cross-polarized model visibilities
    model_bls = copy.deepcopy(hdm.bls)
    model_antpos = hdm.data_antpos
    if len(matched_model_files) > 1:  # in this case, it's a dictionary
        model_bls = list(set([bl for bls in list(hdm.bls.values()) for bl in bls]))
        model_antpos = {ant: pos for antpos in hdm.data_antpos.values() for ant, pos in antpos.items()}

    data_bls, model_bls, data_to_model_bl_map = abscal.match_baselines(unflagged_data_bls, model_bls, data.antpos, model_antpos=model_antpos, 
                                                                     pols=['en', 'ne'], data_is_redsol=False, model_is_redundant=True, tol=1.0,
                                                                     min_bl_cut=ABSCAL_MIN_BL_LEN, max_bl_cut=ABSCAL_MAX_BL_LEN, verbose=True)
    
    model, model_flags, _ = io.partial_time_io(hdm, np.unique([d2m_time_map[time] for time in data.times]), 
                                               bls=list(set([bl[0:2] for bl in model_bls])), polarizations=['en', 'ne'])
    model_bls = [data_to_model_bl_map[bl] for bl in data_bls]

    # rephase model to match in lsts
    model_blvecs = {bl: model.antpos[bl[0]] - model.antpos[bl[1]] for bl in model.keys()}
    utils.lst_rephase(model, model_blvecs, model.freqs, data.lsts - model.lsts, lat=hdm.telescope.location.lat.deg, inplace=True)

    wgts_here = {}
    data_here = {}
    
    for red in cross_reds:
        data_bl = red[0]
        if data_bl in data_to_model_bl_map:

            wgts_here[data_bl] = np.sum([
                np.logical_not(omni_flags[utils.split_bl(bl)[0]] | omni_flags[utils.split_bl(bl)[1]])
                for bl in red
            ], axis=0)
            data_here[data_bl] = np.nanmean([
                np.where(
                    omni_flags[utils.split_bl(bl)[0]] | omni_flags[utils.split_bl(bl)[1]],
                    np.nan, sol.calibrate_bl(bl, data[bl])
                ) 
                for bl in red
            ], axis=0)
    
    # Run cross-polarized phase calibration
    delta = abscal.cross_pol_phase_cal(
        model=model, data=data_here, wgts=wgts_here, data_bls=data_bls, model_bls=model_bls, return_gains=False, 
        refpol='Jee', gain_ants=sol.gains.keys()
    )
    delta_gains = {antpol: (np.ones_like(delta) if antpol[1] == 'Jee' else np.exp(1j * delta)) for antpol in sol.gains.keys()}
    
    # apply gains
    # \Delta = \phi_e - \phi_n, where V_{en}^{cal} = V_{en}^{uncal} * e^{i \Delta} 
    # and V_{ne}^{cal} = V_{ne}^{uncal} * e^{-i \Delta}
    sol.gains = {antpol: g * delta_gains[antpol] for antpol, g in sol.gains.items()}
    apply_cal.calibrate_in_place(sol.vis, delta_gains)
    del hdm, model, model_flags, delta_gains
    print(f'Finished relative polarized phase calibration in {(time.time() - cross_pol_cal_start) / 60:.2f} minutes.')

All antennas are flagged, so this cell is being skipped.

Plotting

def redundant_group_plot():
    if np.all([ant in overall_class.bad_ants for ant in ants]):
        print('All antennas classified as bad. Nothing to plot.')
        return
    
    fig, axes = plt.subplots(2, 2, figsize=(14, 6), dpi=100, sharex='col', sharey='row', gridspec_kw={'hspace': 0, 'wspace': 0})
    for i, pol in enumerate(['ee', 'nn']):
        reds_here = redcal.get_reds(hd.data_antpos, pols=[pol], pol_mode='1pol', bl_error_tol=2.0)
        red = sorted(redcal.filter_reds(reds_here, ex_ants=overall_class.bad_ants), key=len, reverse=True)[0]
        rc_data = {bl: sol.calibrate_bl(bl, data[bl]) for bl in red}
        for bl in red:
            axes[0, i].plot(hd.freqs/1e6, np.angle(rc_data[bl][0]), alpha=.5, lw=.5)
            axes[1, i].semilogy(hd.freqs/1e6, np.abs(rc_data[bl][0]), alpha=.5, lw=.5)
        axes[0, i].plot(hd.freqs / 1e6, np.angle(sol.vis[red[0]][0]), lw=1, c='k')
        axes[1, i].semilogy(hd.freqs / 1e6, np.abs(sol.vis[red[0]][0]), lw=1, c='k', label=f'Baseline Group:\n{(int(red[0][0]), int(red[0][1]), red[0][2])}')
        axes[1, i].set_xlabel('Frequency (MHz)')
        axes[1, i].legend(loc='upper right')
    axes[0, 0].set_ylabel('Visibility Phase (radians)')
    axes[1, 0].set_ylabel('Visibility Amplitude (Jy)')
    plt.tight_layout()

def abscal_degen_plot():
    if DO_FULL_ABSCAL:
        fig, axes = plt.subplots(3, 1, figsize=(14, 6), dpi=100, sharex=True, gridspec_kw={'hspace': .05})

        for ax, pol in zip(axes[:2], ['ee', 'nn']):
            for kk in range(abscal_meta[pol]['Lambda_sol'].shape[-1]):
                ax.plot(hd.freqs[~rfi_flags[0]] * 1e-6, abscal_meta[pol]['Lambda_sol'][0, ~rfi_flags[0], kk], '.', ms=1, label=f"Component {kk}")

            ax.set_ylim(-np.pi-0.5, np.pi+0.5)
            ax.set_xlabel('Frequency (MHz)')
            ax.set_ylabel('Phase Gradient\nVector Component')
            ax.legend(markerscale=20, title=f'{pol}-polarization', loc='lower right')
            ax.grid()
            
        for pol, color in zip(['ee', 'nn'], ['b', 'r']):
            axes[2].plot(hd.freqs[~rfi_flags[0]]*1e-6, abscal_meta[pol]['Z_sol'].real[0, ~rfi_flags[0]], '.', ms=1, label=pol, color=color)
        axes[2].set_ylim(-.25, 1.05)
        axes[2].set_ylabel('Re[Z($\\nu$)]')
        axes[2].legend(markerscale=20, loc='lower right')
        axes[2].grid()            
        plt.tight_layout()

def polarized_gain_phase_plot():
    if CALIBRATE_CROSS_POLS and DO_FULL_ABSCAL:
        plt.figure(figsize=(14, 4), dpi=100)
        for i, time in enumerate(data.times):
            plt.plot(data.freqs / 1e6, np.where(rfi_flags[i], np.nan, delta[i, :]), '.', ms=1.5, label=f'{time:.6f}')
        plt.ylim(-np.pi-0.5, np.pi+0.5)
        plt.xlabel('Frequency (MHz)')
        plt.ylabel('Relative Phase $\Delta \ (\phi_{ee} - \phi_{nn})$')
        plt.grid()
        plt.legend()

Figure 4: Redundant calibration of a single baseline group

The results of a redundant-baseline calibration of a single integration and a single group, the one with the highest redundancy in each polarization after antenna classification and excision based on the above, plus the removal of antennas with high chi^2 per antenna. The black line is the redundant visibility solution. Each thin colored line is a different baseline group. Phases are shown in the top row, amplitudes in the bottom, ee-polarized visibilities in the left column, and nn-polarized visibilities in the right.

if PLOT and not all_flagged(): redundant_group_plot()

All antennas are flagged, so this cell is being skipped.

Figure 5: Absolute calibration of redcal degeneracies

This figure shows the per-frequency phase gradient solutions across the array for both polarizations and all components of the degenerate subspace of redundant-baseline calibraton. While full HERA only has two such tip-tilt degeneracies, a subset of HERA can have up to OC_MAX_DIMS (depending on antenna flagging). In addition to the absolute amplitude, this is the full set of the calibration degrees of freedom not constrainted by redcal. This figure also includes a plot of $Re[Z(\nu)]$, the complex objective function which varies from -1 to 1 and indicates how well the data and the absolute calibration model have been made to agree. Perfect agreement is 1.0 and good agreement is anything above $\sim$0.5 Decorrelation yields values closer to 0, where anything below $\sim$0.3 is suspect.

if PLOT and not all_flagged(): abscal_degen_plot()

All antennas are flagged, so this cell is being skipped.

Figure 6: Relative Phase Calibration

This figure shows the relative phase calibration between the ee vs. nn polarizations.

if PLOT and not all_flagged(): polarized_gain_phase_plot()

All antennas are flagged, so this cell is being skipped.

Attempt to calibrate some flagged antennas

This attempts to calibrate bad antennas using information from good or suspect antennas without allowing bad antennas to affect their calibration. However, introducing 0s in gains or infs/nans in gains or visibilities can create problems down the line, so those are removed.

if not all_flagged():
    expand_start = time.time()
    expanded_reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn'], pol_mode='2pol', bl_error_tol=2.0)
    sol.vis.build_red_keys(expanded_reds)
    redcal.expand_omni_gains(sol, expanded_reds, data, chisq_per_ant=meta['chisq_per_ant'])
    if not np.all([ant in overall_class.bad_ants for ant in ants]):
        redcal.expand_omni_vis(sol, expanded_reds, data)
    
    # Replace near-zeros in gains and infs/nans in gains/sols
    for ant in sol.gains:
        zeros_in_gains = np.isclose(sol.gains[ant], 0)
        if ant in omni_flags:
            omni_flags[ant][zeros_in_gains] = True
        sol.gains[ant][zeros_in_gains] = 1.0 + 0.0j
    sol.make_sol_finite()
    malloc_trim()
    print(f'Finished expanding gain solution in {(time.time() - expand_start) / 60:.2f} minutes.')

All antennas are flagged, so this cell is being skipped.

def array_chisq_plot(include_outriggers=True):
    if np.all([ant in overall_class.bad_ants for ant in ants]):
        print('All antennas classified as bad. Nothing to plot.')
        return    
    
    def _chisq_subplot(ants, size=250):
        fig, axes = plt.subplots(1, 2, figsize=(14, 5), dpi=100)
        for ax, pol in zip(axes, ['ee', 'nn']):
            ants_to_plot = set([ant for ant in meta['chisq_per_ant'] if utils.join_pol(ant[1], ant[1]) == pol and (ant[0] in ants)])
            cspas = np.array([np.nanmean(np.where(rfi_flags, np.nan, meta['chisq_per_ant'][ant])) for ant in ants_to_plot])
            xpos = [hd.antpos[ant[0]][0] for ant in ants_to_plot]
            ypos = [hd.antpos[ant[0]][1] for ant in ants_to_plot]
            scatter = ax.scatter(xpos, ypos, s=size, c=cspas, lw=.25, edgecolors=np.where(np.isfinite(cspas) & (cspas > 0), 'none', 'k'), 
                                 norm=matplotlib.colors.LogNorm(vmin=1, vmax=oc_cspa_suspect[1]))
            for ant in ants_to_plot:
                ax.text(hd.antpos[ant[0]][0], hd.antpos[ant[0]][1], ant[0], va='center', ha='center', fontsize=8,
                        c=('r' if ant in overall_class.bad_ants else 'w'))
            plt.colorbar(scatter, ax=ax, extend='both')
            ax.axis('equal')
            ax.set_xlabel('East-West Position (meters)')
            ax.set_ylabel('North-South Position (meters)')
            ax.set_title(f'{pol}-pol $\\chi^2$ / Antenna (Red is Flagged)')
        plt.tight_layout()    
    
    _chisq_subplot([ant for ant in hd.data_ants if ant < 320])
    outriggers = [ant for ant in hd.data_ants if ant >= 320]    
    if include_outriggers & (len(outriggers) > 0):
        _chisq_subplot([ant for ant in hd.data_ants if ant >= 320], size=400)

Figure 7: chi^2 per antenna across the array

This plot shows median (taken over time and frequency) of the normalized chi^2 per antenna. The expectation value for this quantity when the array is perfectly redundant is 1.0. Antennas that are classified as bad for any reason have their numbers shown in red. Some of those antennas were classified as bad during redundant calibration for high chi^2. Some of those antennas were originally excluded from redundant calibration because they were classified as bad earlier for some reason. See here for more details. Note that the color scale saturates at below 1 and above 10.

if PLOT and not all_flagged(): array_chisq_plot(include_outriggers=(not OC_SKIP_OUTRIGGERS))

All antennas are flagged, so this cell is being skipped.

Figure 8: Summary of antenna classifications after redundant calibration

This figure is the same as Figure 2, except that it now includes additional suspect or bad antennas based on redundant calibration. This can include antennas with high chi^2, but it can also include antennas classified as "bad" because they would add extra degeneracies to calibration.

if PLOT and not all_flagged(): array_class_plot(overall_class, extra_label=", Post-Redcal")

All antennas are flagged, so this cell is being skipped.

to_show = {'Antenna': [f'{ant[0]}{ant[1][-1]}' for ant in ants]}
classes = {'Antenna': [overall_class[ant] if ant in overall_class else '-' for ant in ants]}
to_show['Dead?'] = [{'good': 'No', 'bad': 'Yes'}[am_totally_dead[ant]] if (ant in am_totally_dead) else '' for ant in ants]
classes['Dead?'] = [am_totally_dead[ant] if (ant in am_totally_dead) else '' for ant in ants]
for title, ac in [('Low Correlation', am_corr),
                  ('Cross-Polarized', am_xpol),
                  ('Solar Alt', solar_class),
                  ('Even/Odd Zeros', zeros_class),
                  ('Autocorr Power', auto_power_class),
                  ('Autocorr Slope', auto_slope_class),
                  ('Auto RFI RMS', auto_rfi_class),
                  ('Autocorr Shape', auto_shape_class),
                  ('Bad Diff X-Engines', xengine_diff_class)]:
    to_show[title] = [f'{ac._data[ant]:.2G}' if (ac is not None and ant in ac._data) else '' for ant in ants]
    classes[title] = [ac[ant] if (ac is not None and ant in ac) else 'bad' for ant in ants]
    
to_show['Redcal chi^2'] = [f'{np.nanmean(np.where(rfi_flags, np.nan, meta["chisq_per_ant"][ant])):.3G}' \
                           if (meta is not None and meta['chisq_per_ant'] is not None and ant in meta['chisq_per_ant']) else '' for ant in ants]
classes['Redcal chi^2'] = [redcal_class[ant] if redcal_class is not None and ant in redcal_class else 'bad' for ant in ants]

df = pd.DataFrame(to_show)
df_classes = pd.DataFrame(classes)
colors = {'good': 'darkgreen', 'suspect': 'goldenrod', 'bad': 'maroon'}
df_colors = df_classes.applymap(lambda x: f'background-color: {colors.get(x, None)}')

table = df.style.hide() \
                .apply(lambda x: pd.DataFrame(df_colors.values, columns=x.columns), axis=None) \
                .set_properties(subset=['Antenna'], **{'font-weight': 'bold', 'border-right': "3pt solid black"}) \
                .set_properties(subset=df.columns[1:], **{'border-left': "1pt solid black"}) \
                .set_properties(**{'text-align': 'center', 'color': 'white'})

Table 1: Complete summary of per-antenna classifications

This table summarizes the results of the various classifications schemes detailed above. As before, green is good, yellow is suspect, and red is bad. The color for each antenna (first column) is the final summary of all other classifications. Antennas missing from redcal $\chi^2$ were excluded redundant-baseline calibration, either because they were flagged by ant_metrics or the even/odd zeros check, or because they would add unwanted extra degeneracies.

HTML(table.to_html())

Antenna	Dead?	Low Correlation	Cross-Polarized	Solar Alt	Even/Odd Zeros	Autocorr Power	Autocorr Slope	Auto RFI RMS	Autocorr Shape	Bad Diff X-Engines	Redcal chi^2
3e	No	0.55	0.36	-47	96	11	0.12
3n	No	0.55	0.36	-47	96	16	0.016
4e	No	0.32	0.14	-47	96	14	0.17
4n	No	0.31	0.14	-47	96	16	0.33
5e	No	0.58	0.37	-47	96	15	0.036
5n	No	0.58	0.37	-47	96	20	-0.057
7e	No	0.58	0.37	-47	96	21	0.084
7n	No	0.58	0.37	-47	96	22	-0.025
8e	No	0.59	0.38	-47	96	25	0.068
8n	No	0.58	0.38	-47	96	12	0.058
9e	No	0.58	0.38	-47	96	19	0.09
9n	No	0.58	0.38	-47	96	20	-0.00023
10e	No	0.57	0.38	-47	96	16	0.12
10n	No	0.57	0.38	-47	96	14	0.09
15e	No	0.58	0.38	-47	96	32	0.045
15n	No	0.58	0.38	-47	96	34	-0.077
16e	No	0.58	0.37	-47	96	19	0.095
16n	No	0.58	0.37	-47	96	17	0.088
17e	No	0.59	0.38	-47	96	18	0.055
17n	No	0.59	0.38	-47	96	19	0.019
18e	No	0.028	0.0035	-47	96	0.67	0.46
18n	No	0.031	0.0035	-47	96	0.6	0.53
19e	No	0.59	0.38	-47	96	20	0.078
19n	No	0.58	0.38	-47	96	19	0.0056
20e	No	0.58	0.38	-47	96	19	0.13
20n	No	0.59	0.38	-47	96	22	-0.019
21e	No	0.54	0.36	-47	96	8.5	0.32
21n	No	0.56	0.36	-47	96	6	0.083
27e	No	0.13	-0.0027	-47	96	11	0.39
27n	No	0.087	-0.0027	-47	96	7.2	0.62
28e	No	0.11	0.042	-47	96	6.5	0.67
28n	No	0.18	0.042	-47	96	2.5	0.21
29e	No	0.58	0.37	-47	96	22	0.13
29n	No	0.49	0.37	-47	96	16	0.91
30e	No	0.58	0.38	-47	96	15	0.1
30n	No	0.53	0.38	-47	96	2.6	0.088
31e	No	0.59	0.36	-47	96	17	0.12
31n	No	0.57	0.36	-47	96	18	0.12
32e	No	0.57	0.35	-47	96	6.5	0.13
32n	No	0.46	0.35	-47	96	14	0.93
33e	No	0.58	0.43	-47	96	18	0.11
33n	No	0.38	0.43	-47	96	19	0.086
36e	No	0.56	0.37	-47	96	25	-0.23
36n	No	0.54	0.37	-47	96	22	-0.28
37e	No	0.59	0.4	-47	96	10	0.095
37n	No	0.59	0.4	-47	96	23	-0.037
38e	No	0.6	0.39	-47	96	21	0.11
38n	No	0.59	0.39	-47	96	31	-0.038
40e	No	0.6	0.4	-47	96	13	0.074
40n	No	0.56	0.4	-47	96	3.2	0.033
41e	No	0.61	0.38	-47	96	20	-0.0024
41n	No	0.6	0.38	-47	96	28	-0.039
42e	No	0.6	0.39	-47	96	19	0.059
42n	No	0.52	0.39	-47	96	1.8	0.09
43e	No	0.6	0.37	-47	96	20	0.038
43n	No	0.6	0.37	-47	96	17	0.056
44e	No	0.048	0.017	-47	96	0.65	0.43
44n	No	0.073	0.017	-47	97	0.61	0.5
45e	No	0.6	0.37	-47	96	19	0.047
45n	No	0.59	0.37	-47	96	20	0.025
46e	No	0.56	0.37	-47	96	12	2.2
46n	No	0.59	0.37	-47	96	14	0.01
50e	No	0.59	0.4	-47	96	18	0.085
50n	No	0.58	0.4	-47	96	19	-0.051
51e	No	0.59	0.39	-47	96	19	0.11
51n	No	0.59	0.39	-47	96	17	-0.075
52e	No	0.6	0.39	-47	96	21	-0.19
52n	No	0.6	0.39	-47	96	19	-0.27
53e	No	0.6	0.38	-47	96	16	0.033
53n	No	0.61	0.38	-47	96	23	-0.13
54e	No	0.6	0.37	-47	96	17	0.11
54n	No	0.6	0.37	-47	96	19	0.026
55e	No	0.59	0.37	-47	96	15	0.1
55n	No	0.59	0.37	-47	96	15	-0.02
56e	No	0.61	0.37	-47	96	25	0.048
56n	No	0.61	0.37	-47	96	23	0.02
57e	No	0.51	0.39	-47	96	1.5	0.19
57n	No	0.036	0.39	-47	96	0.58	0.53
58e	No	0.6	0.37	-47	96	15	0.14
58n	No	0.6	0.37	-47	96	17	0.11
59e	No	0.6	0.37	-47	96	27	0.11
59n	No	0.6	0.37	-47	96	21	0.068
60e	No	0.6	0.38	-47	96	14	0.16
60n	No	0.58	0.38	-47	97	6.3	0.13
65e	No	0.6	0.4	-47	96	19	0.052
65n	No	0.6	0.4	-47	96	18	-0.037
66e	No	0.6	0.39	-47	96	22	0.048
66n	No	0.6	0.39	-47	96	23	-0.027
67e	No	0.61	0.39	-47	96	18	0.059
67n	No	0.61	0.39	-47	96	25	-0.023
68e	No	0.61	0.38	-47	96	16	0.045
68n	No	0.61	0.38	-47	96	15	-0.067
69e	No	0.61	0.37	-47	96	18	0.067
69n	No	0.61	0.37	-47	96	22	0.021
70e	No	0.61	0.37	-47	96	16	0.059
70n	No	0.61	0.37	-47	96	23	0.058
71e	No	0.62	0.38	-47	96	15	0.048
71n	No	0.61	0.38	-47	96	9.3	0.035
72e	No	0.62	0.37	-47	96	19	0.21
72n	No	0.61	0.37	-47	96	11	-0.0032
73e	No	0.62	0.38	-47	96	19	0.15
73n	No	0.61	0.38	-47	96	15	0.027
74e	No	0.6	0.37	-47	96	13	0.095
74n	No	0.61	0.37	-47	96	13	0.0024
75e	No	0.14	-0.049	-47	97	0.88	0.33
75n	No	0.086	-0.049	-47	96	0.64	0.46
76e	No	0.034	0.0027	-47	96	3.1	0.48
76n	No	0.033	0.0027	-47	96	2.7	0.48
79e	No	0.56	0.37	-47	96	19	0.21
79n	No	0.58	0.37	-47	96	26	0.063
80e	No	0.53	0.33	-47	96	12	0.29
80n	No	0.53	0.33	-47	96	13	0.21
81e	No	0.52	0.4	-47	96	0.71	-0.23
81n	No	0.58	0.4	-47	96	3	-0.4
82e	No	0.6	0.39	-47	96	15	0.2
82n	No	0.6	0.39	-47	96	15	0.0027
83e	No	0.59	0.37	-47	96	5.6	0.13
83n	No	0.55	0.37	-47	96	17	0.58
84e	No	0.59	0.37	-47	96	18	0.21
84n	No	0.6	0.37	-47	96	19	0.065
85e	No	0.61	0.37	-47	96	14	-0.0082
85n	No	0.61	0.37	-47	96	19	0.05
86e	No	0.25	-0.33	-47	96	20	0.084
86n	No	0.25	-0.33	-47	96	27	-0.01
87e	No	0.48	0.37	-47	96	16	0.95
87n	No	0.62	0.37	-47	96	22	-0.049
88e	Yes			-47	1.5E+03	0	0
88n	Yes			-47	1.5E+03	0	0
89e	No	0.041	0.0011	-47	96	0.97	0.45
89n	No	0.039	0.0011	-47	96	0.92	0.49
90e	Yes			-47	1.5E+03	0	0
90n	Yes			-47	1.5E+03	0	0
91e	No	0.61	0.38	-47	96	18	-0.0012
91n	No	0.6	0.38	-47	96	12	-0.033
92e	No	0.59	0.36	-47	96	14	0.12
92n	No	0.59	0.36	-47	96	14	0.049
93e	No	0.53	0.39	-47	96	2.2	0.14
93n	No	0.6	0.39	-47	96	17	0.012
94e	No	0.58	0.36	-47	96	25	0.11
94n	No	0.58	0.36	-47	96	31	0.026
95e	No	0.56	0.37	-47	96	18	0.22
95n	No	0.58	0.37	-47	96	27	0.056
96e	No	0.56	0.36	-47	96	15	0.23
96n	No	0.56	0.36	-47	96	13	0.15
97e	No	0.57	0.38	-47	96	18	0.13
97n	No	0.53	0.38	-47	96	9.2	0.24
98e	No	0.59	0.39	-47	96	13	0.054
98n	No	0.58	0.39	-47	96	13	-0.12
99e	No	0.15	0.43	-47	96	1.1	0.53
99n	No	0.58	0.43	-47	96	62	-0.064
100e	No	0.6	0.38	-47	96	23	0.2
100n	No	0.61	0.38	-47	96	20	0.042
101e	No	0.61	0.37	-47	96	18	0.0065
101n	No	0.61	0.37	-47	96	16	-0.077
102e	No	0.62	0.37	-47	96	20	0.051
102n	No	0.61	0.37	-47	96	20	0.059
103e	No	0.58	0.37	-47	96	4.8	0.18
103n	No	0.61	0.37	-47	96	20	0.036
104e	No	0.61	0.46	-47	96	15	0.14
104n	No	0.021	0.46	-47	97	0.048	0.26
105e	No	0.62	0.37	-47	96	14	0.03
105n	No	0.62	0.37	-47	96	16	-0.065
106e	No	0.62	0.37	-47	96	19	-0.035
106n	No	0.62	0.37	-47	96	17	-0.14
107e	Yes			-47	1.5E+03	0	0
107n	Yes			-47	1.5E+03	0	0
108e	No	0.61	0.37	-47	96	17	0.18
108n	No	0.61	0.37	-47	96	13	0.11
109e	No	0.6	0.37	-47	96	14	0.13
109n	No	0.6	0.37	-47	96	17	0.036
110e	No	0.61	0.38	-47	96	15	0.15
110n	No	0.61	0.38	-47	96	17	0.062
111e	No	0.61	0.38	-47	96	20	0.046
111n	No	0.61	0.38	-47	96	18	-0.0062
112e	No	0.6	0.37	-47	96	24	0.072
112n	No	0.6	0.37	-47	96	16	-0.042
113e	No	0.57	0.35	-47	96	22	0.15
113n	No	0.57	0.35	-47	96	21	0.12
114e	No	0.57	0.36	-47	96	20	0.19
114n	No	0.57	0.36	-47	96	20	0.06
115e	No	0.56	0.37	-47	96	19	0.1
115n	No	0.55	0.37	-47	96	13	0.14
116e	No	0.58	0.38	-47	96	11	0.2
116n	No	0.58	0.38	-47	96	15	0.13
117e	No	0.59	0.39	-47	96	16	0.23
117n	No	0.6	0.39	-47	96	17	0.044
118e	No	0.61	0.39	-47	96	21	0.097
118n	No	0.61	0.39	-47	96	43	-0.029
119e	No	0.61	0.38	-47	96	27	-0.014
119n	No	0.61	0.38	-47	96	26	-0.037
120e	No	0.61	0.37	-47	96	29	-0.047
120n	No	0.6	0.37	-47	96	49	-0.045
121e	No	0.61	0.37	-47	96	15	0.1
121n	No	0.62	0.37	-47	96	17	-0.082
122e	No	0.62	0.37	-47	96	18	-0.0058
122n	No	0.61	0.37	-47	96	17	-0.033
123e	No	0.61	0.37	-47	96	16	0.0014
123n	No	0.62	0.37	-47	96	18	-0.086
124e	No	0.62	0.38	-47	96	14	0.092
124n	No	0.63	0.38	-47	96	16	0.096
125e	No	0.61	0.37	-47	96	22	0.1
125n	No	0.61	0.37	-47	96	17	0.018
126e	No	0.61	0.37	-47	96	18	0.027
126n	No	0.62	0.37	-47	96	17	-0.11
127e	No	0.61	0.37	-47	96	13	0.083
127n	No	0.61	0.37	-47	96	14	0.011
128e	No	0.61	0.37	-47	96	19	0.083
128n	No	0.61	0.37	-47	96	8.6	0.11
129e	No	0.61	0.37	-47	96	18	0.099
129n	No	0.61	0.37	-47	96	18	-0.0025
130e	No	0.61	0.37	-47	96	23	0.062
130n	No	0.61	0.37	-47	96	18	0.07
131e	No	0.57	0.36	-47	96	14	0.17
131n	No	0.58	0.36	-47	96	14	0.15
132e	No	0.58	0.36	-47	96	20	0.13
132n	No	0.58	0.36	-47	96	18	0.085
133e	No	0.57	0.37	-47	96	18	0.12
133n	No	0.58	0.37	-47	96	19	0.086
134e	No	0.57	0.38	-47	96	30	0.047
134n	No	0.56	0.38	-47	96	18	0.14
135e	No	0.043	0.43	-47	96	7.3	2.2
135n	No	0.58	0.43	-47	96	18	0.045
136e	No	0.59	0.38	-47	96	20	0.022
136n	No	0.58	0.38	-47	96	16	0.27
137e	No	0.6	0.38	-47	96	17	0.13
137n	No	0.6	0.38	-47	96	34	0.00094
138e	No	0.61	0.38	-47	96	21	0.056
138n	No	0.61	0.38	-47	96	18	0.04
139e	No	0.59	0.36	-47	96	31	0.068
139n	No	0.6	0.36	-47	96	28	0.041
140e	No	0.6	0.35	-47	96	18	0.063
140n	No	0.6	0.35	-47	96	20	0.038
141e	No	0.61	0.36	-47	96	18	0.061
141n	No	0.61	0.36	-47	96	19	0.0038
142e	No	0.6	0.36	-47	96	20	0.15
142n	No	0.58	0.36	-47	96	15	1.5
143e	No	0.62	0.37	-47	96	25	0.064
143n	No	0.62	0.37	-47	96	28	0.013
144e	No	0.62	0.37	-47	96	16	0.056
144n	No	0.62	0.37	-47	96	13	-0.0035
145e	No	0.62	0.37	-47	96	18	0.038
145n	No	0.62	0.37	-47	96	23	0.023
146e	No	0.59	0.36	-47	96	20	0.19
146n	No	0.58	0.36	-47	96	15	0.16
147e	No	0.62	0.37	-47	96	18	0.09
147n	No	0.62	0.37	-47	96	18	0.013
148e	No	0.61	0.37	-47	96	21	0.16
148n	No	0.61	0.37	-47	96	20	0.048
149e	No	0.61	0.38	-47	96	18	0.11
149n	No	0.62	0.38	-47	96	19	0.0071
150e	No	0.61	0.38	-47	96	22	0.037
150n	No	0.62	0.38	-47	96	18	0.026
151e	Yes			-47	1.5E+03	0	0
151n	Yes			-47	1.5E+03	0	0
152e	No	0.56	0.36	-47	96	18	0.13
152n	No	0.57	0.36	-47	96	19	0.084
153e	No	0.56	0.36	-47	96	17	0.15
153n	No	0.57	0.36	-47	96	18	0.16
154e	No	0.55	0.37	-47	96	18	0.15
154n	No	0.56	0.37	-47	96	18	0.082
155e	No	0.58	0.38	-47	96	17	0.084
155n	No	0.58	0.38	-47	96	21	0.014
156e	No	0.3	0.12	-47	96	16	0.6
156n	No	0.27	0.12	-47	96	12	0.91
157e	No	0.59	0.37	-47	96	17	0.056
157n	No	0.6	0.37	-47	96	18	0.028
158e	No	0.6	0.38	-47	96	16	0.16
158n	No	0.61	0.38	-47	96	18	0.036
159e	No	0.51	0.36	-47	96	8.2	0.26
159n	No	0.58	0.36	-47	96	20	0.11
160e	No	0.6	0.37	-47	96	17	0.054
160n	No	0.62	0.37	-47	96	18	0.025
161e	No	0.6	0.35	-47	96	20	0.18
161n	No	0.45	0.35	-47	96	13	1.1
162e	No	0.61	0.37	-47	96	19	0.13
162n	No	0.62	0.37	-47	96	9.7	0.067
163e	No	0.62	0.37	-47	96	9.8	0.027
163n	No	0.62	0.37	-47	96	18	-0.024
164e	No	0.62	0.37	-47	96	12	0.12
164n	No	0.62	0.37	-47	96	13	-0.015
165e	No	0.61	0.37	-47	96	13	0.022
165n	No	0.61	0.37	-47	96	9.2	-0.021
166e	No	0.6	0.35	-47	96	25	0.028
166n	No	0.61	0.35	-47	96	31	-0.039
167e	No	0.63	0.38	-47	96	17	0.048
167n	No	0.62	0.38	-47	96	43	-0.018
168e	No	0.62	0.38	-47	96	24	0.068
168n	No	0.63	0.38	-47	96	19	-0.0017
169e	No	0.62	0.38	-47	96	13	0.082
169n	No	0.62	0.38	-47	96	16	0.013
170e	No	0.041	0.36	-47	96	0.61	0.48
170n	No	0.54	0.36	-47	96	34	0.058
171e	Yes			-47	1.5E+03	0	0
171n	Yes			-47	1.5E+03	0	0
172e	Yes			-47	1.5E+03	0	0
172n	Yes			-47	1.5E+03	0	0
173e	No	0.57	0.37	-47	96	17	0.16
173n	No	0.57	0.37	-47	96	18	0.15
174e	No	0.56	0.38	-47	96	20	0.15
174n	No	0.57	0.38	-47	96	32	0.00046
175e	No	0.54	0.38	-47	96	16	0.19
175n	No	0.55	0.38	-47	96	25	0.09
176e	No	0.57	0.38	-47	96	47	-0.023
176n	No	0.59	0.38	-47	96	16	-0.016
177e	No	0.59	0.38	-47	96	13	0.053
177n	No	0.6	0.38	-47	96	16	-0.02
178e	No	0.6	0.38	-47	96	20	0.13
178n	No	0.6	0.38	-47	96	27	-0.053
179e	No	0.6	0.37	-47	96	17	0.15
179n	No	0.61	0.37	-47	96	19	0.063
180e	No	0.61	0.37	-47	96	18	0.14
180n	No	0.48	0.37	-47	96	15	0.83
181e	No	0.62	0.38	-47	96	20	0.064
181n	No	0.61	0.38	-47	96	19	0.062
182e	No	0.61	0.38	-47	96	13	0.2
182n	No	0.62	0.38	-47	96	15	-0.0081
183e	No	0.62	0.37	-47	96	29	0.026
183n	No	0.62	0.37	-47	96	38	-0.0063
184e	No	0.57	0.38	-47	96	2.9	0.22
184n	No	0.62	0.38	-47	96	14	0.074
185e	No	0.61	0.37	-47	96	12	0.21
185n	No	0.62	0.37	-47	96	14	0.085
186e	No	0.61	0.37	-47	96	32	0.029
186n	No	0.61	0.37	-47	96	21	-0.0042
187e	No	0.62	0.37	-47	96	20	0.12
187n	No	0.61	0.37	-47	96	10	0.082
188e	No	0.59	0.36	-47	96	21	0.11
188n	No	0.59	0.36	-47	96	37	0.048
189e	No	0.6	0.36	-47	96	18	0.097
189n	No	0.6	0.36	-47	96	22	-0.03
190e	No	0.62	0.38	-47	96	18	0.12
190n	No	0.62	0.38	-47	96	17	0.025
191e	No	0.61	0.38	-47	96	20	0.085
191n	No	0.62	0.38	-47	96	30	0.087
192e	No	0.57	0.37	-47	96	22	0.13
192n	No	0.59	0.37	-47	96	28	0.038
193e	No	0.56	0.36	-47	96	18	0.2
193n	No	0.56	0.36	-47	96	15	0.13
194e	No	0.58	0.38	-47	96	33	0.0024
194n	No	0.57	0.38	-47	96	27	-0.0041
195e	No	0.56	0.39	-47	96	23	0.087
195n	No	0.56	0.39	-47	96	23	0.082
196e	No	0.036	0.43	-47	96	3.1	0.49
196n	No	0.57	0.43	-47	96	20	0.032
197e	No	0.55	0.35	-47	96	15	0.21
197n	No	0.54	0.35	-47	96	19	0.22
198e	No	0.6	0.38	-47	96	29	0.05
198n	No	0.6	0.38	-47	96	35	-0.027
200e	No	0.044	0.46	-47	96	3	0.49
200n	No	0.59	0.46	-47	96	27	0.032
201e	No	0.59	0.37	-47	96	54	0.0083
201n	No	0.61	0.37	-47	96	44	-0.025
202e	No	0.6	0.36	-47	96	33	0.1
202n	No	0.59	0.36	-47	96	19	0.1
203e	No	0.6	0.35	-47	96	19	0.1
203n	No	0.6	0.35	-47	96	18	-0.00086
204e	No	0.61	0.37	-47	96	12	-0.2
204n	No	0.61	0.37	-47	96	15	-0.37
205e	No	0.6	0.37	-47	96	21	0.093
205n	No	0.61	0.37	-47	96	19	0.026
206e	No	0.59	0.35	-47	96	29	0.095
206n	No	0.58	0.35	-47	96	18	0.053
207e	No	0.58	0.44	-47	96	25	0.14
207n	No	0.042	0.44	-47	96	2.9	0.52
208e	No	0.56	0.36	-47	96	17	0.12
208n	No	0.58	0.36	-47	96	39	0.14
209e	No	0.58	0.36	-47	96	26	0.086
209n	No	0.45	0.36	-47	96	24	0.78
210e	No	0.6	0.38	-47	96	22	-0.23
210n	No	0.6	0.38	-47	96	15	-0.07
211e	No	0.58	0.37	-47	96	29	0.1
211n	No	0.58	0.37	-47	96	21	0.076
212e	No	0.57	0.37	-47	96	26	0.11
212n	No	0.54	0.37	-47	96	13	0.14
213e	No	0.55	0.38	-47	96	17	0.23
213n	No	0.57	0.38	-47	96	19	0.041
214e	No	0.56	0.38	-47	96	23	0.15
214n	No	0.55	0.38	-47	96	20	0.12
215e	No	0.58	0.38	-47	96	36	0.068
215n	No	0.55	0.38	-47	96	17	0.26
216e	No	0.58	0.38	-47	96	20	0.13
216n	No	0.57	0.38	-47	96	13	0.18
217e	No	0.58	0.37	-47	96	27	0.16
217n	No	0.58	0.37	-47	96	18	0.11
218e	No	0.58	0.45	-47	96	29	0.076
218n	No	0.22	0.45	-47	96	0.66	0.29
219e	No	0.59	0.37	-47	96	22	0.061
219n	No	0.59	0.37	-47	96	18	0.035
220e	No	0.59	0.36	-47	96	20	0.15
220n	No	0.59	0.36	-47	96	18	0.053
221e	No	0.59	0.35	-47	96	18	0.17
221n	No	0.59	0.35	-47	96	18	0.15
222e	No	0.58	0.37	-47	96	18	0.21
222n	No	0.61	0.37	-47	96	27	0.046
223e	No	0.58	0.35	-47	96	18	0.12
223n	No	0.59	0.35	-47	96	19	0.033
224e	No	0.59	0.37	-47	96	23	0.14
224n	No	0.6	0.37	-47	96	22	0.077
225e	No	0.6	0.37	-47	96	25	0.11
225n	No	0.59	0.37	-47	96	22	0.034
226e	No	0.59	0.36	-47	96	25	0.053
226n	No	0.52	0.36	-47	96	32	0.47
227e	No	0.58	0.37	-47	96	20	0.12
227n	No	0.57	0.37	-47	96	15	0.1
228e	No	0.56	0.36	-47	96	19	0.19
228n	No	0.57	0.36	-47	96	18	0.11
229e	No	0.57	0.36	-47	96	18	0.2
229n	No	0.57	0.36	-47	96	18	0.063
231e	No	0.57	0.38	-47	96	39	0.014
231n	No	0.57	0.38	-47	96	31	-0.021
232e	No	0.56	0.39	-47	96	48	0.0012
232n	No	0.54	0.39	-47	96	14	0.13
233e	No	0.55	0.36	-47	96	18	0.13
233n	No	0.46	0.36	-47	96	30	0.67
234e	No	0.58	0.38	-47	96	28	0.13
234n	No	0.53	0.38	-47	96	9.1	0.21
237e	No	0.57	0.36	-47	96	18	0.18
237n	No	0.59	0.36	-47	96	20	0.098
238e	No	0.59	0.36	-47	96	27	0.089
238n	No	0.59	0.36	-47	96	19	0.12
239e	No	0.59	0.36	-47	96	23	0.1
239n	No	0.6	0.36	-47	96	29	0.028
240e	No	0.58	0.36	-47	96	19	0.15
240n	No	0.58	0.36	-47	96	19	0.13
241e	No	0.59	0.37	-47	96	19	0.09
241n	No	0.59	0.37	-47	96	18	0.057
242e	No	0.59	0.46	-47	96	26	0.053
242n	No	0.052	0.46	-47	96	2.9	0.55
243e	No	0.59	0.37	-47	96	36	0.095
243n	No	0.58	0.37	-47	96	19	0.068
244e	No	0.57	0.36	-47	96	18	0.097
244n	No	0.57	0.36	-47	96	18	0.098
245e	No	0.59	0.38	-47	96	28	0.1
245n	No	0.58	0.38	-47	96	21	0.13
246e	No	0.56	0.37	-47	96	19	0.28
246n	No	0.57	0.37	-47	96	26	0.1
250e	No	0.56	0.36	-47	96	23	0.14
250n	No	0.56	0.36	-47	96	21	0.11
251e	No	0.061	0.089	-47	96	5.3	0.59
251n	No	0.21	0.089	-47	96	23	0.11
252e	No	0.56	0.36	-47	96	18	0.17
252n	No	0.56	0.36	-47	96	18	0.095
253e	No	0.56	0.36	-47	96	16	0.2
253n	No	0.55	0.36	-47	96	11	0.2
254e	No	0.59	0.37	-47	96	35	0.078
254n	No	0.59	0.37	-47	96	26	0.1
255e	No	0.57	0.37	-47	96	15	0.23
255n	No	0.58	0.37	-47	96	20	0.11
256e	No	0.57	0.36	-47	96	26	0.15
256n	No	0.55	0.36	-47	96	12	0.16
257e	No	0.54	0.37	-47	96	36	0.32
257n	No	0.59	0.37	-47	96	26	-0.022
261e	No	0.52	0.33	-47	96	11	0.22
261n	No	0.52	0.33	-47	96	9.8	0.19
262e	No	0.59	0.38	-47	96	15	-0.13
262n	No	0.59	0.38	-47	96	22	-0.32
266e	No	0.58	0.38	-47	96	9.8	-0.21
266n	No	0.59	0.38	-47	96	14	-0.38
267e	No	0.54	0.36	-47	96	14	0.13
267n	No	0.56	0.36	-47	96	16	0.045
268e	No	0.53	0.38	-47	96	11	0.22
268n	No	0.57	0.38	-47	96	20	0.073
269e	No	0.57	0.37	-47	96	21	0.11
269n	No	0.53	0.37	-47	96	11	0.16
270e	No	0.57	0.36	-47	96	25	0.14
270n	No	0.57	0.36	-47	96	24	0.082
271e	No	0.041	0.48	-47	96	3.1	0.47
271n	No	0.57	0.48	-47	96	19	0.037
272e	No	0.55	0.36	-47	96	19	0.25
272n	No	0.56	0.36	-47	96	27	0.09
273e	No	0.2	-0.32	-47	96	78	-0.061
273n	No	0.21	-0.32	-47	96	29	0.04
277e	No	0.4	0.37	-47	96	11	0.84
277n	No	0.58	0.37	-47	96	25	0.083
278e	No	0.27	0.38	-47	96	3.9	0.45
278n	No	0.5	0.38	-47	96	6.7	0.22
281e	No	0.55	0.36	-47	96	18	0.2
281n	No	0.56	0.36	-47	96	17	0.089
282e	No	0.57	0.38	-47	96	19	0.15
282n	No	0.57	0.38	-47	96	35	-0.01
283e	No	0.57	0.37	-47	96	23	0.068
283n	No	0.54	0.37	-47	96	12	0.0079
284e	No	0.59	0.38	-47	96	36	0.041
284n	No	0.58	0.38	-47	96	38	-0.0057
285e	No	0.045	0.0024	-47	96	3.1	0.5
285n	No	0.047	0.0024	-47	96	2.7	0.54
286e	No	0.21	-0.33	-47	96	32	0.072
286n	No	0.2	-0.33	-47	19	78	-0.064
287e	No	0.57	0.34	-47	96	41	0.035
287n	No	0.22	0.34	-47	96	28	-0.026
290e	No	0.52	0.35	-47	96	75	-0.032
290n	No	0.52	0.35	-47	96	74	-0.05
291e	No	0.55	0.47	-47	96	71	-0.039
291n	No	0.054	0.47	-47	96	0.57	-0.11
292e	No	0.56	0.39	-47	96	17	0.17
292n	No	0.57	0.39	-47	96	37	0.028
293e	No	0.41	0.38	-47	96	23	0.78
293n	No	0.55	0.38	-47	96	16	0.097
294e	No	0.48	0.38	-47	96	23	0.51
294n	No	0.54	0.38	-47	96	23	0.0027
295e	No	0.57	0.46	-47	96	4.7	-0.33
295n	No	0.039	0.46	-47	96	0.75	-0.0078
299e	No	0.56	0.38	-47	96	25	0.14
299n	No	0.54	0.38	-47	96	16	0.082
300e	No	0.57	0.38	-47	96	27	0.037
300n	No	0.41	0.38	-47	96	22	0.86
301e	No	0.56	0.38	-47	96	31	0.058
301n	No	0.52	0.38	-47	96	13	0.092
302e	No	0.43	0.41	-47	96	5.6	0.33
302n	No	0.56	0.41	-47	96	22	0.048
306e	No	0.54	0.37	-47	96	14	0.15
306n	No	0.41	0.37	-47	96	19	0.77
307e	No	0.38	0.37	-47	96	17	0.85
307n	No	0.53	0.37	-47	96	18	0.029
311e	No	0.54	0.4	-47	96	16	0.18
311n	No	0.27	0.4	-47	96	4.2	0.7
312e	No	0.19	-0.21	-47	96	18	0.81
312n	No	0.17	-0.21	-47	96	7.6	0.22
313e	No	0.4	0.37	-47	96	10	0.83
313n	No	0.55	0.37	-47	96	43	-0.0048
314e	No	0.56	0.46	-47	96	39	0.058
314n	No	0.043	0.46	-47	96	3.2	0.52
315e	No	0.55	0.38	-47	96	20	0.094
315n	No	0.53	0.38	-47	96	20	0.14
316e	No	0.53	0.37	-47	96	14	0.18
316n	No	0.55	0.37	-47	96	24	0.022
317e	No	0.55	0.38	-47	96	30	0.031
317n	No	0.53	0.38	-47	96	14	0.12
318e	No	0.55	0.4	-47	96	64	-0.072
318n	No	0.53	0.4	-47	96	78	-0.086
319e	No	0.56	0.39	-47	96	24	0.084
319n	No	0.56	0.39	-47	96	30	0.032
320e	No	0.05	0.0042	-47	96	1.5	0.58
320n	No	0.046	0.0042	-47	96	1.5	0.5
321e	No	0.38	0.32	-47	96	5.3	0.14
321n	No	0.44	0.32	-47	96	6.9	-0.048
322e	No	0.036	0.0032	-47	96	3.2	0.47
322n	No	0.031	0.0032	-47	96	3	0.51
323e	No	0.039	0.0022	-47	96	3	0.52
323n	No	0.035	0.0022	-47	96	3	0.53
324e	No	0.49	0.36	-47	96	29	0.066
324n	No	0.49	0.36	-47	96	31	-0.016
325e	No	0.52	0.39	-47	96	27	0.076
325n	No	0.52	0.39	-47	96	21	0.048
326e	No	0.041	-0.00074	-47	96	3.3	0.48
326n	No	0.041	-0.00074	-47	96	3	0.49
327e	No	0.49	0.34	-47	96	30	0.085
327n	No	0.5	0.34	-47	96	30	-0.008
328e	No	0.044	0.39	-47	96	3.2	0.47
328n	No	0.5	0.39	-47	96	21	0.014
329e	No	0.36	0.25	-47	96	11	0.84
329n	No	0.04	0.25	-47	96	2.6	0.5
331e	No	0.36	0.24	-47	96	18	0.99
331n	No	0.38	0.24	-47	96	13	0.87
332e	No	0.49	0.37	-47	96	25	0.17
332n	No	0.47	0.37	-47	96	11	0.051
333e	No	0.43	0.36	-47	96	8.8	0.19
333n	No	0.48	0.36	-47	96	14	-0.013
336e	No	0.13	-0.33	-47	96	50	-0.0084
336n	No	0.13	-0.33	-47	96	21	0.13
339e	Yes			-47	1.5E+03	0	0
339n	Yes			-47	1.5E+03	0	0
340e	No	0.5	0.37	-47	96	26	-0.067
340n	No	0.5	0.37	-47	96	19	-0.12
342e	No	0.51	0.38	-47	96	15	0.21
342n	No	0.53	0.38	-47	96	27	0.047
343e	No	0.05	0.41	-47	96	3.2	0.5
343n	No	0.5	0.41	-47	96	73	-0.054
344e	No	0.034	0.004	-47	96	3	0.5
344n	No	0.037	0.004	-47	96	2.7	0.52
345e	Yes			-47	1.5E+03	0	0
345n	Yes			-47	1.5E+03	0	0
346e	No	0.36	0.27	-47	96	5.2	0.36
346n	No	0.17	0.27	-47	96	2.9	0.47
347e	No	0.033	0.0025	-47	96	3	0.47
347n	No	0.035	0.0025	-47	96	2.7	0.49
348e	No	0.039	0.085	-47	96	3.3	0.53
348n	No	0.13	0.085	-47	96	3	0.5
349e	No	0.5	0.39	-47	96	53	-0.069
349n	No	0.48	0.39	-47	96	71	-0.086

# Save antenna classification table as a csv
if SAVE_RESULTS:
    for ind, col in zip(np.arange(len(df.columns), 0, -1), df_classes.columns[::-1]):
        df.insert(int(ind), col + ' Class', df_classes[col])
    df.to_csv(ANTCLASS_FILE)    

print('Final Ant-Pol Classification:\n\n', overall_class)

Final Ant-Pol Classification:

 Jee:
----------
bad (290 antpols):
3, 4, 5, 7, 8, 9, 10, 15, 16, 17, 18, 19, 20, 21, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 250, 251, 252, 253, 254, 255, 256, 257, 261, 262, 266, 267, 268, 269, 270, 271, 272, 273, 277, 278, 281, 282, 283, 284, 285, 286, 287, 290, 291, 292, 293, 294, 295, 299, 300, 301, 302, 306, 307, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 331, 332, 333, 336, 339, 340, 342, 343, 344, 345, 346, 347, 348, 349


Jnn:
----------
bad (290 antpols):
3, 4, 5, 7, 8, 9, 10, 15, 16, 17, 18, 19, 20, 21, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 250, 251, 252, 253, 254, 255, 256, 257, 261, 262, 266, 267, 268, 269, 270, 271, 272, 273, 277, 278, 281, 282, 283, 284, 285, 286, 287, 290, 291, 292, 293, 294, 295, 299, 300, 301, 302, 306, 307, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 331, 332, 333, 336, 339, 340, 342, 343, 344, 345, 346, 347, 348, 349

Save calibration solutions

if not all_flagged():
    # update flags in omnical gains and visibility solutions
    for ant in omni_flags:
        omni_flags[ant] |= rfi_flags
    for bl in vissol_flags:
        vissol_flags[bl] |= rfi_flags

All antennas are flagged, so this cell is being skipped.

if SAVE_RESULTS:
    add_to_history = 'Produced by file_calibration notebook with the following environment:\n' + '=' * 65 + '\n' + os.popen('conda env export').read() + '=' * 65    
    
    if not all_flagged():
        hd_vissol = io.HERAData(SUM_FILE)
        hc_omni = hd_vissol.init_HERACal(gain_convention='divide', cal_style='redundant')
        hc_omni.pol_convention = hd_auto_model.pol_convention
        hc_omni.gain_scale = hd_auto_model.vis_units
        hc_omni.update(gains=sol.gains, flags=omni_flags, quals=meta['chisq_per_ant'], total_qual=meta['chisq'])
        hc_omni.history += add_to_history
        hc_omni.write_calfits(OMNICAL_FILE, clobber=True)
        del hc_omni
        malloc_trim()
        
        if SAVE_OMNIVIS_FILE:
            # output results, harmonizing keys over polarizations
            all_reds = redcal.get_reds(hd.data_antpos, pols=['ee', 'nn', 'en', 'ne'], pol_mode='4pol', bl_error_tol=2.0)
            bl_to_red_map = {bl: red[0] for red in all_reds for bl in red}
            hd_vissol.read(bls=[bl_to_red_map[bl] for bl in sol.vis], return_data=False)
            hd_vissol.empty_arrays()
            hd_vissol.history += add_to_history
            hd_vissol.update(data={bl_to_red_map[bl]: sol.vis[bl] for bl in sol.vis}, 
                             flags={bl_to_red_map[bl]: vissol_flags[bl] for bl in vissol_flags}, 
                             nsamples={bl_to_red_map[bl]: vissol_nsamples[bl] for bl in vissol_nsamples})
            hd_vissol.pol_convention = hd_auto_model.pol_convention
            hd_vissol.vis_units = hd_auto_model.vis_units
            hd_vissol.write_uvh5(OMNIVIS_FILE, clobber=True)
    
        del hd_vissol
        malloc_trim()        

All antennas are flagged, so this cell is being skipped.

Output fully flagged calibration file if OMNICAL_FILE is not written

if SAVE_RESULTS and not os.path.exists(OMNICAL_FILE):
    print(f'WARNING: No calibration file produced at {OMNICAL_FILE}. Creating a fully-flagged placeholder calibration file.')
    hd_writer = io.HERAData(SUM_FILE)
    # create fully flagged unit gains with chi^2 = 0
    hc_omni = hd_writer.init_HERACal(gain_convention='divide', cal_style='redundant')
    hc_omni.history += add_to_history
    hc_omni.pol_convention = hd_auto_model.pol_convention
    hc_omni.gain_scale = hd_auto_model.vis_units
    hc_omni.write_calfits(OMNICAL_FILE, clobber=True)
    del hc_omni

WARNING: No calibration file produced at /mnt/sn1/data1/2461087/zen.2461087.45941.sum.omni.calfits. Creating a fully-flagged placeholder calibration file.

Output empty visibility file if OMNIVIS_FILE is not written

if SAVE_RESULTS and SAVE_OMNIVIS_FILE and not os.path.exists(OMNIVIS_FILE):
    print(f'WARNING: No omnivis file produced at {OMNIVIS_FILE}. Creating an empty visibility solution file.')
    hd_writer = io.HERAData(SUM_FILE)
    hd_writer.initialize_uvh5_file(OMNIVIS_FILE, clobber=True)

Metadata

for repo in ['pyuvdata', 'hera_cal', 'hera_filters', 'hera_qm', 'hera_notebook_templates']:
    exec(f'from {repo} import __version__')
    print(f'{repo}: {__version__}')

pyuvdata: 3.2.5.dev1+g5a985ae31
hera_cal: 3.7.7.dev97+gc2668d3f7
hera_filters: 0.1.7
hera_qm: 2.2.1.dev4+gf6d02113b
hera_notebook_templates: 0.0.1.dev1313+g92178a09c

print(f'Finished execution in {(time.time() - tstart) / 60:.2f} minutes.')

Finished execution in 4.24 minutes.