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

bigmem1.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/data2/2461100/zen.2461100.45939.sum.uvh5'
DIFF_FILE = '/mnt/sn1/data2/2461100/zen.2461100.45939.diff.uvh5'
AM_FILE = '/mnt/sn1/data2/2461100/zen.2461100.45939.sum.ant_metrics.hdf5'
ANTCLASS_FILE = '/mnt/sn1/data2/2461100/zen.2461100.45939.sum.ant_class.csv'
OMNICAL_FILE = '/mnt/sn1/data2/2461100/zen.2461100.45939.sum.omni.calfits'
OMNIVIS_FILE = '/mnt/sn1/data2/2461100/zen.2461100.45939.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 0.58 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/data2/2461100/zen.2461100.45939.sum.uvh5
JDs: [2461100.45933233 2461100.45944418] (9.66368 s integrations)
LSTS: [11.03760276 11.04029447] 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.')

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)

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

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

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	0.48	0.28	-51	0	14	0.11	INF	INF	2
3n	No	0.48	0.28	-51	0	17	-0.058	INF	INF	2
4e	No	0.3	0.11	-51	0	15	0.15	INF	INF
4n	No	0.3	0.11	-51	0	19	0.23	INF	INF
5e	No	0.51	0.28	-51	0	18	0.045	INF	INF	2
5n	No	0.51	0.28	-51	0	24	-0.086	INF	INF	2
7e	No	0.51	0.29	-51	0	24	0.087	INF	INF	2
7n	No	0.53	0.29	-51	0	24	-0.068	INF	INF	2
8e	No	0.51	0.28	-51	96	26	0.093	INF	INF
8n	No	0.52	0.28	-51	96	13	-0.023	INF	INF
9e	No	0.51	0.29	-51	96	19	0.13	INF	INF
9n	No	0.52	0.29	-51	96	21	-0.03	INF	INF
10e	Yes			-51	1.5E+03	0	0	INF	INF
10n	Yes			-51	1.5E+03	0	0	INF	INF
15e	No	0.5	0.29	-51	0	29	0.074	INF	INF	2
15n	No	0.51	0.29	-51	0	33	-0.071	INF	INF	2
16e	No	0.51	0.28	-51	96	20	0.095	INF	INF
16n	No	0.52	0.28	-51	96	19	0.036	INF	INF
17e	No	0.51	0.28	-51	96	19	0.072	INF	INF
17n	No	0.52	0.28	-51	96	21	-0.02	INF	INF
18e	No	0.029	0.0059	-51	96	0.7	0.54	INF	INF
18n	No	0.033	0.0059	-51	96	0.63	0.61	INF	INF
19e	Yes			-51	1.5E+03	0	0	INF	INF
19n	Yes			-51	1.5E+03	0	0	INF	INF
20e	Yes			-51	1.5E+03	0	0	INF	INF
20n	Yes			-51	1.5E+03	0	0	INF	INF
21e	No	0.5	0.36	-51	0	9.6	0.17	INF	INF	2
21n	No	0.22	0.36	-51	0	0.72	0.49	INF	INF
27e	No	0.15	0.036	-51	96	11	0.14	INF	INF
27n	No	0.09	0.036	-51	96	7.8	0.84	INF	INF
28e	No	0.041	0.14	-51	96	0.72	0.51	INF	INF
28n	No	0.19	0.14	-51	96	2.9	0.22	INF	INF
29e	No	0.51	0.27	-51	96	25	0.15	INF	INF
29n	No	0.44	0.27	-51	96	21	0.87	INF	INF
30e	No	0.52	0.28	-51	0	18	0.1	INF	INF	2
30n	No	0.49	0.28	-51	0	2.8	0.057	INF	INF	2
31e	No	0.5	0.3	-51	0	14	0.27	INF	INF	2
31n	No	0.53	0.3	-51	0	19	-0.018	INF	INF	2
32e	No	0.5	0.26	-51	0	7.8	0.13	INF	INF	2
32n	No	0.41	0.26	-51	0	20	0.95	INF	INF
33e	No	0.51	0.33	-51	0	20	0.12	INF	INF	2
33n	No	0.34	0.33	-51	0	27	-0.022	INF	INF
36e	No	0.48	0.28	-51	0	28	-0.2	INF	INF	2
36n	No	0.47	0.28	-51	0	23	-0.31	INF	INF	2
37e	No	0.49	0.3	-51	0	14	0.13	INF	INF	2
37n	No	0.5	0.3	-51	0	29	-0.023	INF	INF	2
38e	No	0.5	0.28	-51	0	22	0.1	INF	INF	2
38n	No	0.5	0.28	-51	0	26	-0.067	INF	INF	2
40e	No	0.52	0.28	-51	96	13	0.091	INF	INF
40n	No	0.5	0.28	-51	96	3.7	0.045	INF	INF
41e	No	0.53	0.28	-51	96	23	0.0094	INF	INF
41n	No	0.53	0.28	-51	96	24	-0.061	INF	INF
42e	No	0.53	0.29	-51	96	23	0.07	INF	INF
42n	No	0.48	0.29	-51	96	1.9	0.074	INF	INF
43e	No	0.53	0.28	-51	96	21	0.062	INF	INF
43n	No	0.53	0.28	-51	96	20	0.013	INF	INF
44e	No	0.13	0.036	-51	96	0.8	0.6	INF	INF
44n	No	0.077	0.036	-51	96	0.65	0.58	INF	INF
45e	No	0.52	0.28	-51	96	20	0.062	INF	INF
45n	No	0.52	0.28	-51	96	23	0.02	INF	INF
46e	No	0.49	0.29	-51	0	12	2.2	INF	INF
46n	No	0.52	0.29	-51	0	10	-0.032	INF	INF	2
50e	No	0.5	0.3	-51	96	20	0.09	INF	INF
50n	No	0.49	0.3	-51	96	20	-0.082	INF	INF
51e	No	0.5	0.29	-51	0	21	0.083	INF	INF	2
51n	No	0.51	0.29	-51	0	19	-0.12	INF	INF	2
52e	No	0.52	0.28	-51	96	23	-0.15	INF	INF
52n	No	0.52	0.28	-51	96	21	-0.29	INF	INF
53e	No	0.53	0.28	-51	0	16	0.029	INF	INF	2
53n	No	0.53	0.28	-51	0	27	-0.15	INF	INF	2
54e	No	0.53	0.28	-51	96	19	0.11	INF	INF
54n	No	0.54	0.28	-51	96	21	-0.025	INF	INF
55e	No	0.52	0.27	-51	0	17	0.098	INF	INF	2
55n	No	0.52	0.27	-51	0	18	-0.056	INF	INF	2
56e	No	0.54	0.28	-51	96	27	0.067	INF	INF
56n	No	0.54	0.28	-51	96	23	-0.039	INF	INF
57e	No	0.54	0.4	-51	96	14	0.077	INF	INF
57n	No	0.044	0.4	-51	96	0.61	0.61	INF	INF
58e	No	0.53	0.27	-51	96	17	0.16	INF	INF
58n	No	0.53	0.27	-51	96	17	0.056	INF	INF
59e	No	0.52	0.29	-51	96	25	0.11	INF	INF
59n	No	0.53	0.29	-51	96	20	0.039	INF	INF
60e	No	0.51	0.28	-51	96	15	0.18	INF	INF
60n	No	0.52	0.28	-51	96	7.6	0.079	INF	INF
65e	No	0.51	0.3	-51	0	19	0.047	INF	INF	2
65n	No	0.51	0.3	-51	0	20	-0.083	INF	INF	2
66e	No	0.52	0.29	-51	0	26	0.063	INF	INF	2
66n	No	0.52	0.29	-51	0	25	-0.059	INF	INF	2
67e	No	0.53	0.28	-51	96	22	0.06	INF	INF
67n	No	0.53	0.28	-51	96	31	-0.032	INF	INF
68e	No	0.53	0.27	-51	96	18	0.051	INF	INF
68n	No	0.54	0.27	-51	96	16	-0.1	INF	INF
69e	No	0.54	0.27	-51	96	18	0.093	INF	INF
69n	No	0.54	0.27	-51	96	26	-0.024	INF	INF
70e	No	0.54	0.27	-51	96	19	0.068	INF	INF
70n	No	0.54	0.27	-51	96	26	0.023	INF	INF
71e	No	0.42	0.27	-51	96	17	0.83	INF	INF
71n	No	0.54	0.27	-51	96	9.3	-0.059	INF	INF
72e	No	0.54	0.27	-51	96	19	0.22	INF	INF
72n	No	0.54	0.27	-51	96	12	-0.024	INF	INF
73e	No	0.54	0.28	-51	96	21	0.15	INF	INF
73n	No	0.54	0.28	-51	96	17	-0.0013	INF	INF
74e	No	0.53	0.28	-51	0	11	0.11	INF	INF	2
74n	No	0.54	0.28	-51	0	14	-0.037	INF	INF	2
75e	No	0.045	0.0026	-51	96	0.67	0.53	INF	INF
75n	No	0.04	0.0026	-51	96	0.62	0.58	INF	INF
76e	No	0.029	0.0005	-51	96	3.2	0.55	INF	INF
76n	No	0.031	0.0005	-51	96	2.9	0.56	INF	INF
79e	No	0.5	0.28	-51	96	21	0.2	INF	INF
79n	No	0.53	0.28	-51	96	28	0.021	INF	INF
80e	No	0.47	0.27	-51	96	12	0.25	INF	INF
80n	No	0.51	0.27	-51	96	15	0.13	INF	INF
81e	No	0.45	0.29	-51	0	0.81	-0.23	INF	INF
81n	No	0.5	0.29	-51	0	3.1	-0.47	INF	INF	2
82e	No	0.52	0.28	-51	96	19	0.19	INF	INF
82n	No	0.53	0.28	-51	96	18	-0.056	INF	INF
83e	No	0.51	0.26	-51	96	6	0.16	INF	INF
83n	No	0.48	0.26	-51	96	18	0.59	INF	INF
84e	No	0.53	0.26	-51	96	21	0.11	INF	INF
84n	No	0.53	0.26	-51	96	22	0.0083	INF	INF
85e	No	0.54	0.26	-51	96	15	-0.018	INF	INF
85n	No	0.54	0.26	-51	96	20	-0.0039	INF	INF
86e	No	0.28	-0.25	-51	96	22	0.035	INF	INF
86n	No	0.28	-0.25	-51	96	34	-0.015	INF	INF
87e	No	0.4	0.28	-51	96	18	1.1	INF	INF
87n	No	0.55	0.28	-51	96	24	-0.084	INF	INF
88e	Yes			-51	1.5E+03	0	0	INF	INF
88n	Yes			-51	1.5E+03	0	0	INF	INF
89e	No	0.52	0.34	-51	0	7.4	0.053	INF	INF	2
89n	No	0.094	0.34	-51	0	0.98	0.56	INF	INF
90e	No	0.32		-51	1.5E+03	0	0	INF	INF
90n	Yes			-51	1.5E+03	0	0	INF	INF
91e	No	0.52	0.27	-51	96	20	0.031	INF	INF
91n	No	0.53	0.27	-51	96	13	-0.062	INF	INF
92e	No	0.52	0.28	-51	96	14	0.12	INF	INF
92n	No	0.53	0.28	-51	96	14	-0.014	INF	INF
93e	No	0.48	0.3	-51	96	2.6	0.15	INF	INF
93n	No	0.54	0.3	-51	96	19	-0.014	INF	INF
94e	No	0.52	0.27	-51	96	28	0.13	INF	INF
94n	No	0.53	0.27	-51	96	34	0.0066	INF	INF
95e	No	0.5	0.28	-51	96	21	0.26	INF	INF
95n	No	0.53	0.28	-51	96	29	0.044	INF	INF
96e	No	0.49	0.26	-51	96	15	0.25	INF	INF
96n	No	0.51	0.26	-51	96	13	0.12	INF	INF
97e	No	0.51	0.28	-51	96	19	0.13	INF	INF
97n	No	0.49	0.28	-51	96	9.6	0.21	INF	INF
98e	No	0.52	0.29	-51	96	14	0.041	INF	INF
98n	No	0.52	0.29	-51	96	14	-0.28	INF	INF
99e	No	0.18	0.32	-51	96	1.2	0.59	INF	INF
99n	No	0.51	0.32	-51	96	60	-0.015	INF	INF
100e	No	0.53	0.28	-51	96	27	0.12	INF	INF
100n	No	0.54	0.28	-51	96	22	-0.012	INF	INF
101e	No	0.53	0.27	-51	96	19	0.025	INF	INF
101n	No	0.54	0.27	-51	96	18	-0.095	INF	INF
102e	No	0.55	0.27	-51	96	23	0.063	INF	INF
102n	No	0.55	0.27	-51	96	23	0.014	INF	INF
103e	No	0.51	0.27	-51	96	4.9	0.18	INF	INF
103n	No	0.54	0.27	-51	96	23	0.0011	INF	INF
104e	No	0.54	0.37	-51	96	16	0.12	INF	INF
104n	No	0.024	0.37	-51	96	0.053	0.35	INF	INF
105e	No	0.56	0.28	-51	96	17	0.062	INF	INF
105n	No	0.56	0.28	-51	96	18	-0.069	INF	INF
106e	No	0.53	0.26	-51	0	18	-0.015	INF	INF	2
106n	No	0.51	0.26	-51	0	16	0.079	INF	INF	2
107e	Yes			-51	1.5E+03	0	0	INF	INF
107n	No	0.12		-51	1.5E+03	0	0	INF	INF
108e	No	0.53	0.26	-51	0	18	0.16	INF	INF	2
108n	No	0.53	0.26	-51	0	13	0.15	INF	INF	2
109e	No	0.52	0.28	-51	96	15	0.13	INF	INF
109n	No	0.54	0.28	-51	96	17	0.00061	INF	INF
110e	No	0.54	0.28	-51	96	18	0.15	INF	INF
110n	No	0.55	0.28	-51	96	19	0.02	INF	INF
111e	No	0.54	0.28	-51	96	22	0.076	INF	INF
111n	No	0.55	0.28	-51	96	20	-0.019	INF	INF
112e	No	0.53	0.27	-51	96	27	0.087	INF	INF
112n	No	0.55	0.27	-51	96	19	-0.08	INF	INF
113e	No	0.51	0.25	-51	0	24	0.15	INF	INF	2
113n	No	0.5	0.25	-51	0	22	0.56	INF	INF	2
114e	No	0.51	0.27	-51	96	24	0.18	INF	INF
114n	No	0.53	0.27	-51	96	23	0.013	INF	INF
115e	No	0.5	0.27	-51	96	21	0.13	INF	INF
115n	No	0.5	0.27	-51	96	14	0.1	INF	INF
116e	No	0.5	0.28	-51	96	11	0.11	INF	INF
116n	No	0.51	0.28	-51	96	17	0.066	INF	INF
117e	No	0.51	0.28	-51	96	19	0.15	INF	INF
117n	No	0.53	0.28	-51	96	19	-0.026	INF	INF
118e	No	0.54	0.28	-51	96	21	0.1	INF	INF
118n	No	0.53	0.28	-51	96	45	-0.021	INF	INF
119e	No	0.53	0.28	-51	96	30	0.022	INF	INF
119n	No	0.54	0.28	-51	96	25	-0.06	INF	INF
120e	No	0.54	0.26	-51	96	30	0.00048	INF	INF
120n	No	0.53	0.26	-51	96	51	-0.015	INF	INF
121e	No	0.52	0.27	-51	96	11	0.23	INF	INF
121n	No	0.54	0.27	-51	96	16	-0.052	INF	INF
122e	No	0.54	0.27	-51	0	18	-0.015	INF	INF	2
122n	No	0.55	0.27	-51	0	19	-0.097	INF	INF	2
123e	No	0.51	0.25	-51	96	17	-0.17	INF	INF
123n	No	0.5	0.25	-51	96	16	-0.28	INF	INF
124e	No	0.54	0.28	-51	96	15	0.092	INF	INF
124n	No	0.55	0.28	-51	96	19	0.049	INF	INF
125e	No	0.52	0.26	-51	96	23	0.3	INF	INF
125n	No	0.54	0.26	-51	96	23	-0.29	INF	INF
126e	No	0.53	0.27	-51	0	15	0.042	INF	INF	2
126n	No	0.54	0.27	-51	0	23	-0.11	INF	INF	2
127e	No	0.53	0.27	-51	96	12	0.092	INF	INF
127n	No	0.55	0.27	-51	96	16	0.00026	INF	INF
128e	No	0.54	0.27	-51	96	20	0.094	INF	INF
128n	No	0.55	0.27	-51	96	11	0.055	INF	INF
129e	No	0.54	0.27	-51	96	18	0.12	INF	INF
129n	No	0.56	0.27	-51	96	19	-0.029	INF	INF
130e	No	0.54	0.27	-51	96	26	0.086	INF	INF
130n	No	0.55	0.27	-51	96	21	0.049	INF	INF
131e	No	0.51	0.27	-51	0	17	0.16	INF	INF	2
131n	No	0.52	0.27	-51	0	15	0.091	INF	INF	2
132e	No	0.5	0.27	-51	96	23	0.14	INF	INF
132n	No	0.52	0.27	-51	96	20	0.058	INF	INF
133e	No	0.51	0.28	-51	96	20	0.14	INF	INF
133n	No	0.52	0.28	-51	96	20	0.076	INF	INF
134e	No	0.51	0.27	-51	0	34	0.061	INF	INF	2
134n	No	0.5	0.27	-51	0	20	0.092	INF	INF	2
135e	No	0.053	0.38	-51	96	9.3	2.3	INF	INF
135n	No	0.54	0.38	-51	96	21	-0.0082	INF	INF
136e	No	0.53	0.28	-51	96	22	0.031	INF	INF
136n	No	0.53	0.28	-51	96	19	0.27	INF	INF
137e	No	0.52	0.28	-51	96	20	0.1	INF	INF
137n	No	0.53	0.28	-51	96	36	-0.011	INF	INF
138e	No	0.56	0.29	-51	96	25	0.059	INF	INF
138n	No	0.57	0.29	-51	96	18	-0.0016	INF	INF
139e	No	0.53	0.27	-51	96	33	0.088	INF	INF
139n	No	0.53	0.27	-51	96	30	0.0048	INF	INF
140e	No	0.53	0.26	-51	96	18	0.041	INF	INF
140n	No	0.52	0.26	-51	96	20	0.16	INF	INF
141e	No	0.53	0.27	-51	96	21	0.083	INF	INF
141n	No	0.54	0.27	-51	96	20	-0.033	INF	INF
142e	No	0.52	0.27	-51	96	18	0.16	INF	INF
142n	No	0.54	0.27	-51	96	19	0.049	INF	INF
143e	No	0.54	0.27	-51	96	29	0.083	INF	INF
143n	No	0.55	0.27	-51	96	32	-0.0063	INF	INF
144e	No	0.54	0.27	-51	96	17	0.063	INF	INF
144n	No	0.55	0.27	-51	96	12	-0.039	INF	INF
145e	No	0.54	0.27	-51	96	19	0.033	INF	INF
145n	No	0.56	0.27	-51	96	21	0.005	INF	INF
146e	No	0.52	0.25	-51	96	21	0.2	INF	INF
146n	No	0.52	0.25	-51	96	15	0.13	INF	INF
147e	No	0.54	0.27	-51	96	11	0.13	INF	INF
147n	No	0.56	0.27	-51	96	20	-0.018	INF	INF
148e	No	0.53	0.27	-51	96	23	0.18	INF	INF
148n	No	0.55	0.27	-51	96	23	0.016	INF	INF
149e	No	0.54	0.27	-51	96	19	0.12	INF	INF
149n	No	0.55	0.27	-51	96	22	-0.036	INF	INF
150e	No	0.53	0.28	-51	96	19	0.068	INF	INF
150n	No	0.55	0.28	-51	96	20	0.0026	INF	INF
151e	Yes			-51	1.5E+03	0	0	INF	INF
151n	Yes			-51	1.5E+03	0	0	INF	INF
152e	No	0.49	0.27	-51	96	18	0.14	INF	INF
152n	No	0.51	0.27	-51	96	21	0.042	INF	INF
153e	No	0.48	0.28	-51	96	13	0.19	INF	INF
153n	No	0.51	0.28	-51	96	21	0.085	INF	INF
154e	No	0.49	0.28	-51	96	21	0.13	INF	INF
154n	No	0.5	0.28	-51	96	20	0.038	INF	INF
155e	No	0.53	0.3	-51	96	20	0.079	INF	INF
155n	No	0.54	0.3	-51	96	23	-0.025	INF	INF
156e	No	0.5	0.27	-51	0	22	0.1	INF	INF	2
156n	No	0.52	0.27	-51	0	16	-0.035	INF	INF	2
157e	No	0.53	0.28	-51	96	18	0.06	INF	INF
157n	No	0.54	0.28	-51	96	20	-0.018	INF	INF
158e	No	0.54	0.28	-51	96	20	0.16	INF	INF
158n	No	0.55	0.28	-51	96	22	-0.018	INF	INF
159e	No	0.4	0.29	-51	96	6	0.36	INF	INF
159n	No	0.52	0.29	-51	96	22	0.058	INF	INF
160e	No	0.53	0.27	-51	96	11	0.069	INF	INF
160n	No	0.54	0.27	-51	96	21	-0.006	INF	INF
161e	No	0.52	0.25	-51	96	22	0.17	INF	INF
161n	No	0.4	0.25	-51	96	16	1.2	INF	INF
162e	No	0.53	0.27	-51	96	22	0.13	INF	INF
162n	No	0.55	0.27	-51	96	21	0.017	INF	INF
163e	No	0.53	0.28	-51	96	6.5	0.052	INF	INF
163n	No	0.56	0.28	-51	96	20	-0.11	INF	INF
164e	No	0.54	0.27	-51	96	14	0.14	INF	INF
164n	No	0.56	0.27	-51	96	17	-0.036	INF	INF
165e	No	0.54	0.26	-51	96	13	0.056	INF	INF
165n	No	0.54	0.26	-51	96	9.8	-0.0016	INF	INF
166e	No	0.52	0.26	-51	96	27	0.19	INF	INF
166n	No	0.55	0.26	-51	96	33	-0.042	INF	INF
167e	No	0.55	0.27	-51	96	20	0.048	INF	INF
167n	No	0.56	0.27	-51	96	43	-0.0085	INF	INF
168e	No	0.55	0.27	-51	96	27	0.08	INF	INF
168n	No	0.56	0.27	-51	96	21	-0.041	INF	INF
169e	No	0.55	0.27	-51	96	14	0.087	INF	INF
169n	No	0.56	0.27	-51	96	18	-0.043	INF	INF
170e	No	0.051	0.32	-51	96	0.65	0.55	INF	INF
170n	No	0.51	0.32	-51	96	30	0.047	INF	INF
171e	Yes			-51	1.5E+03	0	0	INF	INF
171n	Yes			-51	1.5E+03	0	0	INF	INF
172e	Yes			-51	1.5E+03	0	0	INF	INF
172n	Yes			-51	1.5E+03	0	0	INF	INF
173e	No	0.48	0.27	-51	96	74	0.025	INF	INF
173n	No	0.49	0.27	-51	96	76	0.0039	INF	INF
174e	No	0.49	0.28	-51	96	21	0.14	INF	INF
174n	No	0.49	0.28	-51	96	35	0.07	INF	INF
175e	No	0.47	0.28	-51	96	18	0.19	INF	INF
175n	No	0.45	0.28	-51	96	11	0.18	INF	INF
176e	No	0.49	0.29	-51	96	60	0.023	INF	INF
176n	No	0.53	0.29	-51	96	17	-0.045	INF	INF
177e	No	0.52	0.29	-51	96	11	0.063	INF	INF
177n	No	0.54	0.29	-51	96	18	-0.057	INF	INF
178e	No	0.53	0.29	-51	96	22	0.15	INF	INF
178n	No	0.54	0.29	-51	96	31	-0.063	INF	INF
179e	No	0.54	0.29	-51	96	15	0.15	INF	INF
179n	No	0.55	0.29	-51	96	20	0.014	INF	INF
180e	No	0.54	0.37	-51	96	19	0.15	INF	INF
180n	No	0.049	0.37	-51	96	0.62	0.57	INF	INF
181e	No	0.49	0.29	-51	96	2.4	0.18	INF	INF
181n	No	0.54	0.29	-51	96	23	0.053	INF	INF
182e	No	0.54	0.28	-51	96	14	0.18	INF	INF
182n	No	0.55	0.28	-51	96	17	-0.061	INF	INF
183e	No	0.55	0.27	-51	96	33	0.041	INF	INF
183n	No	0.56	0.27	-51	96	41	-0.0036	INF	INF
184e	No	0.35	0.29	-51	96	1.4	0.84	INF	INF
184n	No	0.54	0.29	-51	96	5.2	0.08	INF	INF
185e	No	0.54	0.27	-51	96	13	0.13	INF	INF
185n	No	0.56	0.27	-51	96	15	0.046	INF	INF
186e	No	0.55	0.26	-51	96	33	0.048	INF	INF
186n	No	0.56	0.26	-51	96	22	-0.043	INF	INF
187e	No	0.54	0.27	-51	96	23	0.084	INF	INF
187n	No	0.55	0.27	-51	96	13	0.03	INF	INF
188e	No	0.51	0.25	-51	96	23	0.15	INF	INF
188n	No	0.52	0.25	-51	96	39	0.16	INF	INF
189e	No	0.52	0.27	-51	96	19	0.11	INF	INF
189n	No	0.54	0.27	-51	96	26	-0.043	INF	INF
190e	No	0.54	0.28	-51	96	19	0.15	INF	INF
190n	No	0.55	0.28	-51	96	18	0.00078	INF	INF
191e	No	0.53	0.28	-51	96	22	0.085	INF	INF
191n	No	0.55	0.28	-51	96	30	0.061	INF	INF
192e	No	0.5	0.28	-51	96	24	0.14	INF	INF
192n	No	0.52	0.28	-51	96	32	0.014	INF	INF
193e	No	0.49	0.26	-51	96	21	0.22	INF	INF
193n	No	0.49	0.26	-51	96	18	0.13	INF	INF
194e	No	0.49	0.28	-51	96	35	0.035	INF	INF
194n	No	0.49	0.28	-51	96	30	-0.0099	INF	INF
195e	No	0.48	0.29	-51	96	24	0.1	INF	INF
195n	No	0.48	0.29	-51	96	23	0.063	INF	INF
196e	No	0.036	0.37	-51	96	3.2	0.57	INF	INF
196n	No	0.51	0.37	-51	96	22	-0.018	INF	INF
197e	No	0.49	0.26	-51	96	19	0.19	INF	INF
197n	No	0.39	0.26	-51	96	22	0.87	INF	INF
198e	No	0.51	0.29	-51	96	31	0.14	INF	INF
198n	No	0.53	0.29	-51	96	39	-0.013	INF	INF
200e	No	0.047	0.4	-51	96	3.1	0.57	INF	INF
200n	No	0.53	0.4	-51	96	29	0.0099	INF	INF
201e	No	0.52	0.28	-51	96	58	0.051	INF	INF
201n	No	0.54	0.28	-51	96	48	-0.013	INF	INF
202e	No	0.52	0.26	-51	96	35	0.12	INF	INF
202n	No	0.53	0.26	-51	96	23	0.088	INF	INF
203e	No	0.52	0.27	-51	96	17	0.14	INF	INF
203n	No	0.55	0.27	-51	96	19	-0.042	INF	INF
204e	No	0.54	0.27	-51	96	15	-0.21	INF	INF
204n	No	0.55	0.27	-51	96	19	-0.4	INF	INF
205e	No	0.53	0.27	-51	96	25	0.15	INF	INF
205n	No	0.55	0.27	-51	96	25	0.0072	INF	INF
206e	No	0.52	0.25	-51	96	27	0.14	INF	INF
206n	No	0.53	0.25	-51	96	20	0.044	INF	INF
207e	No	0.51	0.36	-51	96	27	0.15	INF	INF
207n	No	0.045	0.36	-51	96	3	0.59	INF	INF
208e	No	0.52	0.27	-51	96	29	0.078	INF	INF
208n	No	0.53	0.27	-51	96	42	0.087	INF	INF
209e	No	0.51	0.27	-51	96	29	0.097	INF	INF
209n	No	0.52	0.27	-51	96	36	0.12	INF	INF
210e	No	0.52	0.27	-51	96	22	-0.2	INF	INF
210n	No	0.52	0.27	-51	96	16	-0.27	INF	INF
211e	No	0.5	0.28	-51	96	33	0.11	INF	INF
211n	No	0.51	0.28	-51	96	24	0.029	INF	INF
212e	No	0.48	0.27	-51	96	28	0.12	INF	INF
212n	No	0.47	0.27	-51	96	14	0.1	INF	INF
213e	No	0.47	0.29	-51	96	19	0.21	INF	INF
213n	No	0.49	0.29	-51	96	22	0.0022	INF	INF
214e	No	0.48	0.29	-51	96	25	0.12	INF	INF
214n	No	0.48	0.29	-51	96	23	0.055	INF	INF
215e	No	0.5	0.28	-51	96	37	0.08	INF	INF
215n	No	0.5	0.28	-51	96	19	0.2	INF	INF
216e	No	0.5	0.27	-51	96	23	0.14	INF	INF
216n	No	0.5	0.27	-51	96	16	0.15	INF	INF
217e	No	0.51	0.28	-51	96	29	0.19	INF	INF
217n	No	0.52	0.28	-51	96	21	0.07	INF	INF
218e	No	0.51	0.34	-51	96	31	0.1	INF	INF
218n	No	0.18	0.34	-51	96	0.49	0.31	INF	INF
219e	No	0.51	0.27	-51	96	23	0.084	INF	INF
219n	No	0.52	0.27	-51	96	20	0.061	INF	INF
220e	No	0.52	0.26	-51	96	22	0.14	INF	INF
220n	No	0.53	0.26	-51	96	18	0.023	INF	INF
221e	No	0.52	0.26	-51	96	20	0.18	INF	INF
221n	No	0.54	0.26	-51	96	18	0.089	INF	INF
222e	No	0.52	0.27	-51	96	21	0.22	INF	INF
222n	No	0.55	0.27	-51	96	28	0.026	INF	INF
223e	No	0.51	0.26	-51	0	19	0.1	INF	INF	2
223n	No	0.54	0.26	-51	0	25	-0.036	INF	INF	2
224e	No	0.51	0.27	-51	96	26	0.23	INF	INF
224n	No	0.54	0.27	-51	96	25	0.037	INF	INF
225e	No	0.52	0.27	-51	96	29	0.12	INF	INF
225n	No	0.53	0.27	-51	96	23	0.021	INF	INF
226e	No	0.52	0.26	-51	96	27	0.076	INF	INF
226n	No	0.48	0.26	-51	96	34	0.34	INF	INF
227e	No	0.5	0.26	-51	96	22	0.11	INF	INF
227n	No	0.5	0.26	-51	96	13	0.12	INF	INF
228e	No	0.49	0.27	-51	96	20	0.2	INF	INF
228n	No	0.5	0.27	-51	96	20	0.048	INF	INF
229e	No	0.48	0.28	-51	96	20	0.23	INF	INF
229n	No	0.5	0.28	-51	96	19	0.037	INF	INF
231e	No	0.48	0.29	-51	96	41	0.061	INF	INF
231n	No	0.5	0.29	-51	96	34	-0.033	INF	INF
232e	No	0.47	0.28	-51	96	46	0.056	INF	INF
232n	No	0.47	0.28	-51	96	18	0.077	INF	INF
233e	No	0.48	0.27	-51	96	16	0.15	INF	INF
233n	No	0.46	0.27	-51	96	32	0.42	INF	INF
234e	No	0.5	0.31	-51	96	31	0.15	INF	INF
234n	No	0.39	0.31	-51	96	5.2	0.35	INF	INF
237e	No	0.51	0.27	-51	96	21	0.14	INF	INF
237n	No	0.54	0.27	-51	96	22	0.048	INF	INF
238e	No	0.53	0.27	-51	96	29	0.089	INF	INF
238n	No	0.54	0.27	-51	96	23	0.072	INF	INF
239e	No	0.52	0.27	-51	96	21	0.12	INF	INF
239n	No	0.54	0.27	-51	96	30	-0.0027	INF	INF
240e	No	0.49	0.27	-51	96	12	0.23	INF	INF
240n	No	0.52	0.27	-51	96	19	0.23	INF	INF
241e	No	0.51	0.27	-51	96	22	0.11	INF	INF
241n	No	0.53	0.27	-51	96	20	0.028	INF	INF
242e	No	0.51	0.28	-51	96	32	0.072	INF	INF
242n	No	0.53	0.28	-51	96	37	-0.0068	INF	INF
243e	No	0.5	0.27	-51	96	38	0.13	INF	INF
243n	No	0.51	0.27	-51	96	20	0.05	INF	INF
244e	No	0.49	0.28	-51	96	20	0.1	INF	INF
244n	No	0.51	0.28	-51	96	21	0.065	INF	INF
245e	No	0.5	0.28	-51	96	30	0.12	INF	INF
245n	No	0.42	0.28	-51	96	23	0.71	INF	INF
246e	Yes			-51	1.5E+03	0	0	INF	INF
246n	No	0.17		-51	1.5E+03	0	0	INF	INF
250e	No	0.48	0.29	-51	96	18	0.17	INF	INF
250n	No	0.5	0.29	-51	96	24	0.055	INF	INF
251e	No	0.073	0.11	-51	96	5.7	0.66	INF	INF
251n	No	0.23	0.11	-51	96	24	0.11	INF	INF
252e	No	0.5	0.28	-51	96	21	0.17	INF	INF
252n	No	0.51	0.28	-51	96	20	0.045	INF	INF
253e	No	0.5	0.27	-51	96	18	0.19	INF	INF
253n	No	0.5	0.27	-51	96	13	0.15	INF	INF
254e	No	0.52	0.28	-51	96	38	0.1	INF	INF
254n	No	0.54	0.28	-51	96	32	0.04	INF	INF
255e	No	0.5	0.27	-51	96	15	0.21	INF	INF
255n	No	0.52	0.27	-51	96	22	0.14	INF	INF
256e	No	0.5	0.26	-51	96	30	0.28	INF	INF
256n	No	0.5	0.26	-51	96	14	0.12	INF	INF
257e	No	0.44	0.28	-51	96	41	0.4	INF	INF
257n	No	0.53	0.28	-51	96	29	-0.026	INF	INF
261e	Yes			-51	1.5E+03	0	0	INF	INF
261n	Yes			-51	1.5E+03	0	0	INF	INF
262e	Yes			-51	1.5E+03	0	0	INF	INF
262n	Yes			-51	1.5E+03	0	0	INF	INF
266e	No	0.035	0.0066	-51	96	0.82	0.12	INF	INF
266n	No	0.029	0.0066	-51	96	0.69	0.08	INF	INF
267e	No	0.48	0.29	-51	96	14	0.15	INF	INF
267n	No	0.51	0.29	-51	96	18	0.028	INF	INF
268e	No	0.036	0.0011	-51	96	3.3	0.55	INF	INF
268n	No	0.037	0.0011	-51	96	3	0.59	INF	INF
269e	No	0.5	0.27	-51	96	21	0.14	INF	INF
269n	No	0.51	0.27	-51	96	20	0.073	INF	INF
270e	No	0.51	0.28	-51	96	27	0.12	INF	INF
270n	No	0.53	0.28	-51	96	35	0.026	INF	INF
271e	No	0.047	0.43	-51	96	3.2	0.55	INF	INF
271n	No	0.52	0.43	-51	96	21	0.017	INF	INF
272e	No	0.48	0.28	-51	96	20	0.25	INF	INF
272n	No	0.51	0.28	-51	96	31	0.049	INF	INF
273e	No	0.22	-0.24	-51	96	80	0.0076	INF	INF
273n	No	0.23	-0.24	-51	96	34	0.057	INF	INF
277e	No	0.35	0.29	-51	96	19	0.85	INF	INF
277n	No	0.5	0.29	-51	96	27	0.056	INF	INF
278e	No	0.26	0.32	-51	96	4.9	0.49	INF	INF
278n	No	0.45	0.32	-51	96	12	0.13	INF	INF
281e	No	0.48	0.29	-51	96	19	0.24	INF	INF
281n	No	0.51	0.29	-51	96	19	0.058	INF	INF
282e	No	0.49	0.3	-51	96	22	0.13	INF	INF
282n	No	0.52	0.3	-51	96	37	-0.013	INF	INF
283e	No	0.51	0.3	-51	96	25	0.08	INF	INF
283n	No	0.53	0.3	-51	96	27	0.006	INF	INF
284e	No	0.5	0.29	-51	96	23	0.14	INF	INF
284n	No	0.52	0.29	-51	96	30	0.012	INF	INF
285e	No	0.048	0.0013	-51	96	3.4	0.58	INF	INF
285n	No	0.052	0.0013	-51	96	3	0.62	INF	INF
286e	No	0.23	-0.24	-51	96	33	0.065	INF	INF
286n	No	0.21	-0.24	-51	96	79	0.013	INF	INF
287e	No	0.48	0.25	-51	96	44	0.066	INF	INF
287n	No	0.25	0.25	-51	96	29	0.01	INF	INF
290e	No	0.44	0.26	-51	96	77	0.029	INF	INF
290n	No	0.44	0.26	-51	96	76	0.03	INF	INF
291e	No	0.45	0.35	-51	0	73	0.029	INF	INF
291n	No	0.068	0.35	-51	0	0.6	-0.022	INF	INF
292e	No	0.47	0.3	-51	96	19	0.19	INF	INF
292n	No	0.49	0.3	-51	96	34	0.035	INF	INF
293e	No	0.33	0.28	-51	96	27	0.79	INF	INF
293n	No	0.45	0.28	-51	96	16	0.14	INF	INF
294e	No	0.38	0.28	-51	96	25	0.54	INF	INF
294n	No	0.46	0.28	-51	96	23	0.0017	INF	INF
295e	No	0.036	0.0045	-51	96	0.79	0.11	INF	INF
295n	No	0.032	0.0045	-51	96	0.79	0.079	INF	INF
299e	No	0.48	0.29	-51	96	28	0.16	INF	INF
299n	No	0.49	0.29	-51	96	19	0.034	INF	INF
300e	No	0.49	0.29	-51	96	28	0.043	INF	INF
300n	No	0.37	0.29	-51	96	24	0.84	INF	INF
301e	No	0.47	0.28	-51	96	32	0.09	INF	INF
301n	No	0.46	0.28	-51	96	15	0.061	INF	INF
302e	No	0.37	0.32	-51	96	5.9	0.37	INF	INF
302n	No	0.49	0.32	-51	96	24	0.013	INF	INF
306e	No	0.45	0.28	-51	96	15	0.15	INF	INF
306n	No	0.34	0.28	-51	96	23	0.75	INF	INF
307e	No	0.31	0.28	-51	96	19	0.88	INF	INF
307n	No	0.45	0.28	-51	96	18	-0.02	INF	INF
311e	No	0.46	0.31	-51	96	18	0.17	INF	INF
311n	No	0.23	0.31	-51	96	4.4	0.79	INF	INF
312e	No	0.21	-0.14	-51	96	22	0.85	INF	INF
312n	No	0.19	-0.14	-51	96	8.2	0.24	INF	INF
313e	No	0.34	0.29	-51	96	18	0.88	INF	INF
313n	No	0.48	0.29	-51	96	49	0.011	INF	INF
314e	No	0.46	0.36	-51	96	43	0.11	INF	INF
314n	No	0.051	0.36	-51	96	3.4	0.59	INF	INF
315e	No	0.46	0.29	-51	96	21	0.11	INF	INF
315n	No	0.44	0.29	-51	96	21	0.15	INF	INF
316e	No	0.44	0.3	-51	96	15	0.24	INF	INF
316n	No	0.47	0.3	-51	96	27	-0.0021	INF	INF
317e	No	0.46	0.29	-51	96	34	0.051	INF	INF
317n	No	0.45	0.29	-51	96	17	0.037	INF	INF
318e	No	0.46	0.3	-51	96	65	-0.0037	INF	INF
318n	No	0.43	0.3	-51	96	79	-0.014	INF	INF
319e	No	0.47	0.31	-51	96	28	0.085	INF	INF
319n	No	0.46	0.31	-51	96	33	0.013	INF	INF
320e	No	0.059	0.0054	-51	0	1.6	0.67	INF	INF
320n	No	0.054	0.0054	-51	0	1.6	0.59	INF	INF
321e	No	0.32	0.24	-51	0	5.5	0.18	INF	INF
321n	No	0.36	0.24	-51	0	7	-0.06	INF	INF	2
322e	No	0.028	0.0004	-51	96	3.3	0.55	INF	INF
322n	No	0.03	0.0004	-51	96	3.1	0.59	INF	INF
323e	No	0.041	3.2E-05	-51	0	3.1	0.6	INF	INF
323n	No	0.04	3.2E-05	-51	0	3.1	0.6	INF	INF
324e	No	0.41	0.28	-51	96	31	0.075	INF	INF
324n	No	0.4	0.28	-51	96	34	-0.021	INF	INF
325e	No	0.44	0.3	-51	96	29	0.098	INF	INF
325n	No	0.43	0.3	-51	96	23	0.015	INF	INF
326e	No	0.043	0.0041	-51	96	3.4	0.56	INF	INF
326n	No	0.048	0.0041	-51	96	3.1	0.57	INF	INF
327e	No	0.42	0.27	-51	96	29	0.14	INF	INF
327n	No	0.44	0.27	-51	96	30	0.0081	INF	INF
328e	No	0.05	0.31	-51	96	3.3	0.55	INF	INF
328n	No	0.43	0.31	-51	96	22	-0.0025	INF	INF
329e	No	0.12	0.051	-51	96	3.9	0.58	INF	INF
329n	No	0.042	0.051	-51	96	3.2	0.59	INF	INF
331e	No	0.35	0.17	-51	0	31	0.55	INF	INF
331n	No	0.35	0.17	-51	0	26	0.57	INF	INF
332e	No	0.046	0.0013	-51	96	3.3	0.57	INF	INF
332n	No	0.047	0.0013	-51	96	2.9	0.6	INF	INF
333e	No	0.39	0.28	-51	96	10	0.16	INF	INF
333n	No	0.41	0.28	-51	96	15	-0.057	INF	INF
336e	No	0.13	-0.24	-51	96	54	0.028	INF	INF
336n	No	0.13	-0.24	-51	96	21	0.19	INF	INF
339e	Yes			-51	1.5E+03	0	0	INF	INF
339n	Yes			-51	1.5E+03	0	0	INF	INF
340e	No	0.046	0.0038	-51	96	3.6	0.56	INF	INF
340n	No	0.044	0.0038	-51	96	3	0.58	INF	INF
342e	No	0.42	0.31	-51	96	11	0.27	INF	INF
342n	No	0.47	0.31	-51	96	30	0.024	INF	INF
343e	No	0.061	0.32	-51	96	3.4	0.58	INF	INF
343n	No	0.41	0.32	-51	96	74	0.014	INF	INF
344e	No	0.037	0.0025	-51	96	3.1	0.58	INF	INF
344n	No	0.04	0.0025	-51	96	2.8	0.59	INF	INF
345e	Yes			-51	1.5E+03	0	0	INF	INF
345n	Yes			-51	1.5E+03	0	0	INF	INF
346e	No	0.3	0.2	-51	96	5.4	0.4	INF	INF
346n	No	0.16	0.2	-51	96	3.1	0.54	INF	INF
347e	No	0.032	0.0047	-51	96	3.2	0.54	INF	INF
347n	No	0.037	0.0047	-51	96	2.8	0.57	INF	INF
348e	No	0.045	0.028	-51	96	3.4	0.61	INF	INF
348n	No	0.078	0.028	-51	96	3.2	0.64	INF	INF
349e	No	0.42	0.3	-51	96	58	0.011	INF	INF
349n	No	0.4	0.3	-51	96	75	-0.018	INF	INF

# 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/data2/2461100/zen.2461100.45939.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 1.53 minutes.