Single File Calibration

by Josh Dillon, Aaron Parsons, Tyler Cox, and Zachary Martinot, last updated August 11, 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

still3.rtp.pvt

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

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

Parse inputs and outputs

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

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

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

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

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

SUM_FILE = '/mnt/sn1/data1/2461003/zen.2461003.43882.sum.uvh5'
DIFF_FILE = '/mnt/sn1/data1/2461003/zen.2461003.43882.diff.uvh5'
AM_FILE = '/mnt/sn1/data1/2461003/zen.2461003.43882.sum.ant_metrics.hdf5'
ANTCLASS_FILE = '/mnt/sn1/data1/2461003/zen.2461003.43882.sum.ant_class.csv'
OMNICAL_FILE = '/mnt/sn1/data1/2461003/zen.2461003.43882.sum.omni.calfits'
OMNIVIS_FILE = '/mnt/sn1/data1/2461003/zen.2461003.43882.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_MAXITER", 50))
OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE = float(os.environ.get("OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE", 1))
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_USE_GPU = os.environ.get("SAVE_RESULTS", "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", "FALSE").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', '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 = 50
OC_MAX_RERUN = 10
OC_RERUN_MAXITER = 50
OC_MAX_CHISQ_FLAGGING_DYNAMIC_RANGE = 1.5
OC_USE_PRIOR_SOL = True
OC_PRIOR_SOL_FLAG_THRESH = 0.95
OC_USE_GPU = False
RFI_DPSS_HALFWIDTH = 3e-07
RFI_NSIG = 4.0
ABSCAL_MODEL_FILES_GLOB = None
ABSCAL_MIN_BL_LEN = 1.0
ABSCAL_MAX_BL_LEN = 140.0
CALIBRATE_CROSS_POLS = True

Parse bounds

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

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

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

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

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

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

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

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

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

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

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

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

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

Load sum and diff data

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

Finished loading data in 1.15 minutes.

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

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

File: /mnt/sn1/data1/2461003/zen.2461003.43882.sum.uvh5
JDs: [2461003.43876498 2461003.43887683] (9.66368 s integrations)
LSTS: [4.16872184 4.17141355] 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']):
    '''Checks if an entire polarization is flagged. If it is, returns True and marks all antennas as bad in redcal_class.'''
    if np.logical_or(*[np.all([redcal_class[ant] == 'bad' for ant in redcal_class.ants if ant[1] == pol]) for pol in pols]):
        print('An entire polarization has been flagged. 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:
            # perform first stage of redundant calibration 
            meta, sol = redcal.redundantly_calibrate(data, reds, max_dims=None, **rc_settings)
            max_dly = np.max(np.abs(list(meta['fc_meta']['dlys'].values())))  # Needed for RFI delay-slope cal
            median_cspa = recheck_chisq(meta['chisq_per_ant'], sol, oc_cspa_suspect[1] * 5, np.nanmedian)
             # remove particularly bad antennas (5x the bound on median, not mean)
            cspa_class = ant_class.antenna_bounds_checker(median_cspa, good=(oc_cspa_good[0], oc_cspa_suspect[1] * 5), bad=[(-np.inf, np.inf)])
            redcal_class += cspa_class
            print(f'Removing {cspa_class.bad_ants} for >5x high median chi^2.')
            for ant in cspa_class.bad_ants:
                print(f'\t{ant}: {median_cspa[ant]:.3f}')
            
        malloc_trim()

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

if not all_flagged():
    # iteratively rerun redundant calibration
    redcal_done = False
    rc_settings['oc_maxiter'] = rc_settings['check_after'] = OC_RERUN_MAXITER
    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
        if check_if_whole_pol_flagged(redcal_class) or redcal_done or (i == OC_MAX_RERUN):
            break
    
        # re-run redundant calibration using previous solution, updating bad and suspicious antennas
        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
        mean_cspa = recheck_chisq(meta['chisq_per_ant'], sol, oc_cspa_suspect[1], np.nanmean)
        
        # remove bad antennas
        cspa_class = ant_class.antenna_bounds_checker(mean_cspa, good=oc_cspa_good, suspect=oc_cspa_suspect, bad=[(-np.inf, np.inf)])
        for ant in cspa_class.bad_ants:
            if mean_cspa[ant] < np.max(list(mean_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
        print(f'Removing {cspa_class.bad_ants} for high mean unflagged chi^2.')
        for ant in cspa_class.bad_ants:
            print(f'\t{ant}: {mean_cspa[ant]:.3f}')
    
        if len(cspa_class.bad_ants) == 0:
            redcal_done = True  # no new antennas to flag
    
    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
            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	0.023	0.0016	-39	0	0.69	0.52	INF	INF
3n	No	0.023	0.0016	-39	0	0.62	0.6	INF	INF
4e	No	0.54	0.45	-39	0	24	0.096	INF	INF	15
4n	No	0.03	0.45	-39	0	0.62	0.59	INF	INF
5e	No	0.52	0.35	-39	0	15	0.12	INF	INF	15
5n	No	0.52	0.35	-39	0	19	0.2	INF	INF	13
7e	No	0.28	0.19	-39	0	20	0.15	INF	INF
7n	No	0.27	0.19	-39	0	19	0.17	INF	INF
8e	No	0.26	0.17	-39	0	54	0.06	INF	INF
8n	No	0.25	0.17	-39	0	10	0.26	INF	INF
9e	No	0.27	0.18	-39	0	17	0.18	INF	INF
9n	No	0.25	0.18	-39	0	18	0.28	INF	INF
10e	No	0.36	0.26	-39	0	23	0.14	INF	INF	29
10n	No	0.35	0.26	-39	0	16	0.29	INF	INF	21
15e	No	0.57	0.37	-39	0	29	0.11	INF	INF	9
15n	No	0.56	0.37	-39	0	30	0.11	INF	INF	8
16e	No	0.57	0.38	-39	0	16	0.16	INF	INF	10
16n	No	0.56	0.38	-39	0	16	0.3	INF	INF	7
17e	No	0.57	0.38	-39	0	16	0.13	INF	INF	9
17n	No	0.55	0.38	-39	0	19	0.24	INF	INF	8
18e	No	0.5	0.38	-39	0	9.5	0.12	INF	INF	19
18n	No	0.34	0.38	-39	0	13	0.55	INF	INF
19e	No	0.38	0.27	-39	0	19	0.15	INF	INF	29
19n	No	0.37	0.27	-39	0	17	0.2	INF	INF	24
20e	No	0.38	0.27	-39	0	18	0.16	INF	INF	28
20n	No	0.37	0.27	-39	0	19	0.2	INF	INF	25
21e	No	0.33	0.23	-39	0	7.8	0.29	INF	INF
21n	No	0.32	0.23	-39	0	10	0.35	INF	INF
27e	No	0.11	-0.014	-39	0	9.9	0.64	INF	INF
27n	No	0.064	-0.014	-39	0	7.6	1	INF	INF
28e	No	0.024	0.0027	-39	0	0.72	0.52	INF	INF
28n	No	0.021	0.0027	-39	1	0.67	0.6	INF	INF
29e	No	0.5	0.28	-39	0	20	0.92	INF	INF
29n	No	0.41	0.28	-39	0	14	1.4	INF	INF
30e	No	0.57	0.39	-39	0	14	0.18	INF	INF	7
30n	No	0.53	0.39	-39	0	2.3	0.28	INF	INF	6
31e	No	0.37	0.25	-39	0	16	0.15	INF	INF	22
31n	No	0.35	0.25	-39	0	15	0.23	INF	INF
32e	No	0.35	0.23	-39	0	15	0.19	INF	INF	16
32n	No	0.22	0.23	-39	0	15	1.1	INF	INF
33e	No	0.41	0.31	-39	0	17	0.21	INF	INF	25
33n	No	0.25	0.31	-39	0	19	0.35	INF	INF
36e	No	0.56	0.32	-39	0	23	-0.18	INF	INF	11
36n	No	0.56	0.32	-39	0	18	-0.1	INF	INF	11
37e	No	0.61	0.38	-39	0	11	0.17	INF	INF	5
37n	No	0.61	0.38	-39	0	23	0.19	INF	INF	5
38e	No	0.61	0.38	-39	0	19	0.16	INF	INF	4
38n	No	0.61	0.38	-39	0	22	0.18	INF	INF	5
40e	No	0.56	0.37	-39	0	8.8	0.17	INF	INF	15
40n	No	0.52	0.37	-39	0	2.8	0.28	INF	INF	11
41e	No	0.41	0.27	-39	0	18	0.066	INF	INF	29
41n	No	0.39	0.27	-39	0	19	0.19	INF	INF	24
42e	No	0.46	0.32	-39	0	19	0.16	INF	INF	23
42n	No	0.4	0.32	-39	1	1.6	0.27	INF	INF	16
43e	No	0.5	0.33	-39	0	18	0.13	INF	INF	19
43n	No	0.48	0.33	-39	0	15	0.3	INF	INF	18
44e	No	0.024	0.0014	-39	0	0.67	0.52	INF	INF
44n	No	0.022	0.0014	-39	1	0.61	0.58	INF	INF
45e	No	0.49	0.34	-39	0	17	0.2	INF	INF	24
45n	No	0.47	0.34	-39	0	18	0.31	INF	INF	22
46e	No	0.46	0.33	-39	0	15	1.5	INF	INF
46n	No	0.46	0.33	-39	0	13	0.24	INF	INF	24
50e	No	0.54	0.32	-39	0	15	0.16	INF	INF	11
50n	No	0.54	0.32	-39	0	16	0.13	INF	INF	7
51e	Yes			-39	1.5E+03	0	0	INF	INF
51n	Yes			-39	1.5E+03	0	0	INF	INF
52e	No	0.55	0.32	-39	0	19	-0.12	INF	INF	11
52n	No	0.55	0.32	-39	0	16	-0.027	INF	INF	12
53e	Yes			-39	1.5E+03	0	0	INF	INF
53n	Yes			-39	1.5E+03	0	0	INF	INF
54e	No	0.46	0.29	-39	0	16	0.17	INF	INF	23
54n	No	0.45	0.29	-39	0	16	0.24	INF	INF	23
55e	No	0.47	0.28	-39	0	17	0.15	INF	INF	15
55n	No	0.47	0.28	-39	0	20	0.18	INF	INF	16
56e	No	0.58	0.36	-39	0	45	0.094	INF	INF	15
56n	No	0.58	0.36	-39	0	38	0.17	INF	INF	12
57e	No	0.4	0.28	-39	0	13	0.14	INF	INF	26
57n	No	0.38	0.28	-39	0	12	0.27	INF	INF	26
58e	No	0.55	0.37	-39	0	14	0.25	INF	INF	18
58n	No	0.53	0.37	-39	0	14	0.35	INF	INF	16
59e	No	0.55	0.38	-39	0	23	0.2	INF	INF	23
59n	No	0.54	0.38	-39	0	18	0.3	INF	INF	19
60e	No	0.48	0.33	-39	0	16	0.32	INF	INF	18
60n	No	0.45	0.33	-39	0	4.9	0.36	INF	INF	13
65e	No	0.62	0.39	-39	0	17	0.18	INF	INF	3
65n	No	0.63	0.39	-39	0	16	0.2	INF	INF	3
66e	No	0.62	0.38	-39	0	22	0.15	INF	INF	7
66n	No	0.62	0.38	-39	0	20	0.19	INF	INF	6
67e	No	0.64	0.39	-39	0	21	0.14	INF	INF	3
67n	No	0.64	0.39	-39	0	25	0.19	INF	INF	2
68e	No	0.64	0.37	-39	0	14	0.14	INF	INF	4
68n	No	0.64	0.37	-39	0	12	0.14	INF	INF	4
69e	No	0.58	0.35	-39	0	15	0.16	INF	INF	15
69n	No	0.57	0.35	-39	0	20	0.24	INF	INF	13
70e	No	0.48	0.29	-39	0	26	0.14	INF	INF	17
70n	No	0.46	0.29	-39	0	53	0.14	INF	INF	16
71e	No	0.48	0.31	-39	0	15	0.16	INF	INF	20
71n	No	0.42	0.31	-39	0	1.8	0.36	INF	INF	15
72e	No	0.46	0.29	-39	0	16	0.29	INF	INF	23
72n	No	0.44	0.29	-39	0	14	0.25	INF	INF	20
73e	No	0.51	0.35	-39	0	18	0.24	INF	INF	26
73n	No	0.49	0.35	-39	0	13	0.24	INF	INF	24
74e	No	0.5	0.33	-39	0	20	0.19	INF	INF	18
74n	No	0.49	0.33	-39	0	15	0.2	INF	INF	18
75e	No	0.45	0.32	-39	0	14	0.3	INF	INF	27
75n	No	0.44	0.32	-39	0	14	0.32	INF	INF	25
76e	No	0.023	0.0029	-39	0	3.2	0.56	INF	INF
76n	No	0.019	0.0029	-39	0	2.9	0.57	INF	INF
79e	No	0.23	0.15	-39	0	18	0.3	INF	INF
79n	No	0.23	0.15	-39	0	24	0.27	INF	INF
80e	No	0.3	0.21	-39	32	19	0.28	INF	INF
80n	No	0.27	0.21	-39	32	13	0.41	INF	INF
81e	No	0.59	0.38	-39	1	0.71	-0.17	INF	INF
81n	No	0.64	0.38	-39	0	2.5	-0.2	INF	INF	3
82e	No	0.65	0.39	-39	0	18	0.3	INF	INF	2
82n	No	0.64	0.39	-39	0	17	0.28	INF	INF	2
83e	No	0.63	0.38	-39	0	5.1	0.24	INF	INF	2
83n	No	0.61	0.38	-39	0	17	0.6	INF	INF
84e	No	0.63	0.37	-39	0	18	0.35	INF	INF	2
84n	No	0.64	0.37	-39	0	17	0.29	INF	INF	2
85e	No	0.52	0.3	-39	0	13	0.091	INF	INF	13
85n	No	0.51	0.3	-39	0	19	0.28	INF	INF	10
86e	No	0.028	0.0013	-39	0	1.2	0.54	INF	INF
86n	No	0.027	0.0013	-39	0	1.1	0.59	INF	INF
87e	No	0.42	0.27	-39	0	15	0.98	INF	INF
87n	No	0.5	0.27	-39	0	20	0.17	INF	INF	11
88e	Yes			-39	1.5E+03	0	0	INF	INF
88n	Yes			-39	1.5E+03	0	0	INF	INF
89e	No	0.53	0.33	-39	0	6	0.16	INF	INF	14
89n	No	0.53	0.33	-39	0	15	0.19	INF	INF	22
90e	Yes			-39	1.5E+03	0	0	INF	INF
90n	Yes			-39	1.5E+03	0	0	INF	INF
91e	No	0.36	0.24	-39	0	16	0.13	INF	INF	26
91n	No	0.33	0.24	-39	0	10	0.24	INF	INF
92e	No	0.29	0.19	-39	0	14	0.41	INF	INF
92n	No	0.29	0.19	-39	0	14	0.26	INF	INF
93e	No	0.24	0.2	-39	0	2.1	0.24	INF	INF
93n	No	0.28	0.2	-39	0	14	0.24	INF	INF
94e	No	0.31	0.21	-39	0	23	0.19	INF	INF
94n	No	0.3	0.21	-39	0	28	0.25	INF	INF
95e	No	0.24	0.16	-39	0	18	0.3	INF	INF
95n	No	0.23	0.16	-39	0	25	0.36	INF	INF
96e	No	0.28	0.2	-39	0	12	0.37	INF	INF
96n	No	0.26	0.2	-39	0	11	0.51	INF	INF
97e	No	0.26	0.19	-39	0	17	0.2	INF	INF
97n	No	0.17	0.19	-39	0	5.2	0.52	INF	INF
98e	No	0.63	0.36	-39	0	9.2	0.14	INF	INF	2
98n	No	0.63	0.36	-39	0	11	0.14	INF	INF	4
99e	No	0.026	0.37	-39	1	0.071	0.44	INF	INF
99n	No	0.52	0.37	-39	0	0.32	-0.11	INF	INF
100e	No	0.61	0.36	-39	0	23	0.2	INF	INF	4
100n	No	0.61	0.36	-39	0	18	0.29	INF	INF	2
101e	No	0.51	0.28	-39	0	16	0.12	INF	INF	13
101n	No	0.5	0.28	-39	0	15	0.17	INF	INF	9
102e	No	0.52	0.29	-39	0	23	0.18	INF	INF	12
102n	No	0.51	0.29	-39	0	21	0.3	INF	INF	12
103e	Yes			-39	1.5E+03	0	0	INF	INF
103n	Yes			-39	1.5E+03	0	0	INF	INF
104e	Yes			-39	1.5E+03	0	0	INF	INF
104n	Yes			-39	1.5E+03	0	0	INF	INF
105e	No	0.37	0.24	-39	0	14	0.19	INF	INF	24
105n	No	0.35	0.24	-39	0	14	0.19	INF	INF	18
106e	No	0.55	0.33	-39	0	16	0.15	INF	INF	25
106n	No	0.54	0.33	-39	0	15	0.17	INF	INF	11
107e	Yes			-39	1.5E+03	0	0	INF	INF
107n	Yes			-39	1.5E+03	0	0	INF	INF
108e	No	0.37	0.24	-39	0	15	0.28	INF	INF	27
108n	No	0.34	0.24	-39	0	11	0.37	INF	INF
109e	No	0.35	0.26	-39	0	18	0.18	INF	INF
109n	No	0.021	0.26	-39	1	0.67	0.58	INF	INF
110e	No	0.33	0.23	-39	0	15	0.29	INF	INF
110n	No	0.32	0.23	-39	0	14	0.31	INF	INF
111e	No	0.28	0.21	-39	0	19	0.84	INF	INF
111n	No	0.31	0.21	-39	0	16	0.21	INF	INF
112e	No	0.3	0.21	-39	0	22	0.19	INF	INF
112n	No	0.28	0.21	-39	0	14	0.21	INF	INF
113e	No	0.24	0.16	-39	0	21	0.27	INF	INF
113n	No	0.21	0.16	-39	0	20	0.56	INF	INF
114e	No	0.3	0.22	-39	0	20	0.29	INF	INF
114n	No	0.29	0.22	-39	0	19	0.31	INF	INF
115e	No	0.24	0.16	-39	0	16	0.2	INF	INF
115n	No	0.22	0.16	-39	0	12	0.37	INF	INF
116e	No	0.64	0.35	-39	0	15	0.12	INF	INF	5
116n	No	0.64	0.35	-39	0	32	0.24	INF	INF	2
117e	No	0.64	0.38	-39	0	18	0.24	INF	INF	4
117n	No	0.64	0.38	-39	0	15	0.25	INF	INF	4
118e	No	0.65	0.38	-39	0	18	0.2	INF	INF	2
118n	No	0.65	0.38	-39	0	25	0.23	INF	INF	1
119e	No	0.62	0.37	-39	0	27	0.091	INF	INF	4
119n	No	0.62	0.37	-39	0	21	0.19	INF	INF	4
120e	Yes			-39	1.5E+03	0	0	INF	INF
120n	Yes			-39	1.5E+03	0	0	INF	INF
121e	No	0.63	0.37	-39	0	10	0.46	INF	INF	4
121n	No	0.66	0.37	-39	0	13	0.16	INF	INF	3
122e	No	0.51	0.29	-39	0	16	0.09	INF	INF	13
122n	No	0.5	0.29	-39	0	14	0.18	INF	INF	10
123e	No	0.66	0.39	-39	0	15	0.097	INF	INF	4
123n	No	0.66	0.39	-39	0	15	0.17	INF	INF	2
124e	No	0.55	0.33	-39	0	32	0.19	INF	INF	25
124n	No	0.55	0.33	-39	0	15	0.29	INF	INF	23
125e	No	0.55	0.34	-39	0	19	0.1	INF	INF	16
125n	No	0.52	0.34	-39	0	17	0.28	INF	INF	13
126e	No	0.55	0.34	-39	0	14	0.14	INF	INF	14
126n	No	0.54	0.34	-39	0	14	0.12	INF	INF	10
127e	No	0.33	0.23	-39	0	18	0.17	INF	INF
127n	No	0.3	0.23	-39	0	15	0.23	INF	INF
128e	No	0.32	0.22	-39	0	17	0.16	INF	INF
128n	No	0.29	0.22	-39	0	7.6	0.33	INF	INF
129e	No	0.32	0.23	-39	0	18	0.17	INF	INF
129n	No	0.31	0.23	-39	0	17	0.21	INF	INF
130e	No	0.31	0.22	-39	0	21	0.16	INF	INF
130n	No	0.29	0.22	-39	0	16	0.3	INF	INF
131e	No	0.25	0.17	-39	0	14	0.28	INF	INF
131n	No	0.24	0.17	-39	0	12	0.36	INF	INF
132e	No	0.27	0.19	-39	0	20	0.24	INF	INF
132n	No	0.25	0.19	-39	0	16	0.32	INF	INF
133e	No	0.25	0.17	-39	0	17	0.22	INF	INF
133n	No	0.24	0.17	-39	0	16	0.3	INF	INF
134e	No	0.26	0.18	-39	0	34	0.15	INF	INF
134n	No	0.24	0.18	-39	0	19	0.39	INF	INF
135e	No	0.029	0.39	-39	0	2.9	2.1	INF	INF
135n	No	0.59	0.39	-39	0	4.5	0.27	INF	INF	5
136e	No	0.61	0.35	-39	0	18	0.088	INF	INF	7
136n	No	0.59	0.35	-39	0	15	0.48	INF	INF	2
137e	No	0.65	0.38	-39	0	25	0.16	INF	INF	2
137n	No	0.66	0.38	-39	0	33	0.18	INF	INF	4
138e	No	0.62	0.36	-39	0	18	0.17	INF	INF	4
138n	No	0.62	0.36	-39	0	14	0.28	INF	INF	4
139e	No	0.64	0.38	-39	0	32	0.16	INF	INF	2
139n	No	0.64	0.38	-39	0	24	0.24	INF	INF	1
140e	No	0.65	0.38	-39	0	15	0.17	INF	INF	3
140n	No	0.65	0.38	-39	0	16	0.27	INF	INF	1
141e	No	0.66	0.38	-39	0	17	0.16	INF	INF	4
141n	No	0.66	0.38	-39	0	16	0.22	INF	INF	2
142e	No	0.65	0.37	-39	0	19	0.24	INF	INF	2
142n	No	0.64	0.37	-39	0	16	0.3	INF	INF	3
143e	No	0.64	0.37	-39	0	24	0.2	INF	INF	8
143n	No	0.63	0.37	-39	0	26	0.2	INF	INF	6
144e	No	0.48	0.29	-39	0	14	0.18	INF	INF	24
144n	No	0.47	0.29	-39	0	14	0.23	INF	INF	19
145e	No	0.5	0.3	-39	0	17	0.13	INF	INF	23
145n	No	0.48	0.3	-39	0	17	0.29	INF	INF	17
146e	No	0.48	0.3	-39	0	18	0.27	INF	INF	17
146n	No	0.45	0.3	-39	0	13	0.37	INF	INF	11
147e	Yes			-39	1.5E+03	0	0	INF	INF
147n	Yes			-39	1.5E+03	0	0	INF	INF
148e	Yes			-39	1.5E+03	0	0	INF	INF
148n	Yes			-39	1.5E+03	0	0	INF	INF
149e	Yes			-39	1.5E+03	0	0	INF	INF
149n	Yes			-39	1.5E+03	0	0	INF	INF
150e	No	0.43	0.31	-39	0	17	0.16	INF	INF	21
150n	No	0.42	0.31	-39	0	15	0.25	INF	INF	20
151e	No	0.5	0.37	-39	0	29	0.19	INF	INF	8
151n	No	0.48	0.37	-39	0	16	0.28	INF	INF	8
152e	No	0.48	0.34	-39	0	16	0.22	INF	INF	19
152n	No	0.48	0.34	-39	0	17	0.29	INF	INF	17
153e	No	0.46	0.33	-39	0	16	0.25	INF	INF	12
153n	No	0.46	0.33	-39	0	17	0.38	INF	INF	12
154e	No	0.46	0.33	-39	0	18	0.2	INF	INF	11
154n	No	0.45	0.33	-39	0	17	0.33	INF	INF	9
155e	No	0.61	0.33	-39	0	16	0.1	INF	INF	10
155n	No	0.61	0.33	-39	0	17	0.19	INF	INF	10
156e	No	0.62	0.35	-39	0	18	0.17	INF	INF	5
156n	No	0.61	0.35	-39	0	14	0.29	INF	INF	4
157e	No	0.63	0.37	-39	0	15	0.12	INF	INF	4
157n	No	0.63	0.37	-39	0	15	0.26	INF	INF	3
158e	No	0.63	0.38	-39	0	16	0.24	INF	INF	4
158n	No	0.64	0.38	-39	1	16	0.24	INF	INF	2
159e	No	0.6	0.36	-39	0	12	0.32	INF	INF	1
159n	No	0.62	0.36	-39	0	18	0.31	INF	INF	1
160e	No	0.64	0.37	-39	0	15	0.15	INF	INF	2
160n	No	0.64	0.37	-39	0	17	0.28	INF	INF	1
161e	No	0.59	0.3	-39	0	18	0.29	INF	INF	5
161n	No	0.46	0.3	-39	0	14	1.1	INF	INF
162e	No	0.61	0.35	-39	0	19	0.22	INF	INF	6
162n	No	0.61	0.35	-39	0	16	0.27	INF	INF	5
163e	No	0.63	0.38	-39	0	10	0.12	INF	INF	7
163n	No	0.62	0.38	-39	0	16	0.17	INF	INF	8
164e	No	0.63	0.38	-39	0	12	0.2	INF	INF	7
164n	No	0.63	0.38	-39	0	12	0.23	INF	INF	6
165e	No	0.63	0.39	-39	0	11	0.11	INF	INF	8
165n	No	0.61	0.39	-39	0	8.1	0.32	INF	INF	5
166e	No	0.63	0.39	-39	0	11	0.21	INF	INF	4
166n	No	0.62	0.39	-39	0	16	0.43	INF	INF	4
167e	No	0.46	0.31	-39	0	14	0.13	INF	INF	31
167n	No	0.45	0.31	-39	0	37	0.16	INF	INF	29
168e	No	0.32	0.22	-39	0	23	0.16	INF	INF
168n	No	0.3	0.22	-39	0	17	0.21	INF	INF
169e	No	0.29	0.2	-39	0	7.6	0.28	INF	INF
169n	No	0.3	0.2	-39	0	16	0.2	INF	INF
170e	No	0.022	0.21	-39	0	0.65	0.57	INF	INF
170n	No	0.29	0.21	-39	0	12	0.32	INF	INF
171e	No	0.33	0.36	-39	0	4.7	0.45	INF	INF
171n	No	0.48	0.36	-39	0	15	0.29	INF	INF	8
172e	No	0.5	0.37	-39	0	21	0.22	INF	INF	10
172n	No	0.47	0.37	-39	0	14	0.49	INF	INF	4
173e	No	0.46	0.32	-39	0	15	0.28	INF	INF	18
173n	No	0.47	0.32	-39	0	16	0.33	INF	INF	18
174e	No	0.43	0.31	-39	0	19	0.25	INF	INF	28
174n	No	0.42	0.31	-39	0	31	0.33	INF	INF	22
175e	No	0.4	0.29	-39	0	18	0.27	INF	INF	16
175n	No	0.33	0.29	-39	0	6.3	0.47	INF	INF
176e	No	0.63	0.38	-39	0	43	0.056	INF	INF	3
176n	No	0.63	0.38	-39	0	14	0.19	INF	INF	3
177e	No	0.63	0.39	-39	0	11	0.14	INF	INF	2
177n	No	0.63	0.39	-39	0	14	0.21	INF	INF	1
178e	No	0.64	0.38	-39	0	18	0.3	INF	INF	4
178n	No	0.65	0.38	-39	0	25	0.17	INF	INF	1
179e	No	0.65	0.39	-39	0	15	0.24	INF	INF	3
179n	No	0.65	0.39	-39	0	15	0.29	INF	INF	1
180e	No	0.65	0.37	-39	0	17	0.23	INF	INF	2
180n	No	0.51	0.37	-39	0	16	0.95	INF	INF
181e	No	0.66	0.38	-39	0	17	0.15	INF	INF	4
181n	No	0.66	0.38	-39	0	12	0.3	INF	INF	1
182e	No	0.051	0.43	-39	1	0.69	0.54	INF	INF
182n	No	0.61	0.43	-39	0	11	0.23	INF	INF	6
183e	No	0.66	0.38	-39	0	22	0.17	INF	INF	4
183n	No	0.66	0.38	-39	0	23	0.22	INF	INF	8
184e	No	0.64	0.39	-39	0	11	0.15	INF	INF	4
184n	No	0.62	0.39	-39	0	15	0.34	INF	INF	3
185e	No	0.63	0.38	-39	0	15	0.19	INF	INF	8
185n	No	0.62	0.38	-39	0	27	0.16	INF	INF	6
186e	No	0.49	0.37	-39	0	35	0.99	INF	INF
186n	No	0.61	0.37	-39	0	22	0.25	INF	INF	7
187e	No	0.64	0.4	-39	0	19	0.2	INF	INF	4
187n	No	0.62	0.4	-39	0	11	0.31	INF	INF	4
188e	No	0.44	0.28	-39	0	17	0.23	INF	INF	22
188n	No	0.38	0.28	-39	0	36	0.69	INF	INF
189e	No	0.35	0.23	-39	0	15	0.2	INF	INF	21
189n	No	0.34	0.23	-39	0	20	0.21	INF	INF
190e	No	0.36	0.24	-39	0	17	0.27	INF	INF	23
190n	No	0.35	0.24	-39	0	14	0.23	INF	INF	20
191e	No	0.36	0.25	-39	0	19	0.18	INF	INF	21
191n	No	0.35	0.25	-39	0	27	0.27	INF	INF	18
192e	No	0.48	0.34	-39	0	21	0.23	INF	INF	20
192n	No	0.48	0.34	-39	0	26	0.31	INF	INF	19
193e	No	0.46	0.33	-39	0	19	0.39	INF	INF	19
193n	No	0.46	0.33	-39	0	15	0.34	INF	INF	15
194e	No	0.41	0.29	-39	0	15	0.22	INF	INF	19
194n	No	0.42	0.29	-39	0	20	0.23	INF	INF	20
195e	No	0.41	0.31	-39	0	21	0.19	INF	INF	15
195n	No	0.4	0.31	-39	0	20	0.35	INF	INF	15
196e	Yes			-39	1.5E+03	0	0	INF	INF
196n	Yes			-39	1.5E+03	0	0	INF	INF
197e	Yes			-39	1.5E+03	0	0	INF	INF
197n	Yes			-39	1.5E+03	0	0	INF	INF
198e	Yes			-39	1.5E+03	0	0	INF	INF
198n	Yes			-39	1.5E+03	0	0	INF	INF
200e	No	0.031	0.43	-39	0	3.1	0.58	INF	INF
200n	No	0.6	0.43	-39	0	23	0.25	INF	INF	8
201e	No	0.58	0.37	-39	0	54	0.24	INF	INF	6
201n	No	0.61	0.37	-39	0	42	0.15	INF	INF	4
202e	No	0.62	0.37	-39	0	32	0.2	INF	INF	10
202n	No	0.6	0.37	-39	0	19	0.33	INF	INF	5
203e	No	0.63	0.38	-39	0	15	0.21	INF	INF	6
203n	No	0.63	0.38	-39	0	15	0.21	INF	INF	6
204e	No	0.62	0.37	-39	0	12	-0.099	INF	INF	15
204n	No	0.61	0.37	-39	0	15	-0.13	INF	INF	14
205e	No	0.59	0.37	-39	0	22	0.18	INF	INF	17
205n	No	0.58	0.37	-39	0	20	0.24	INF	INF	10
206e	No	0.59	0.38	-39	0	20	0.25	INF	INF	17
206n	No	0.58	0.38	-39	0	19	0.29	INF	INF	10
207e	No	0.61	0.41	-39	0	23	0.24	INF	INF	17
207n	No	0.58	0.41	-39	0	18	0.49	INF	INF	12
208e	No	0.59	0.4	-39	0	26	0.15	INF	INF	17
208n	No	0.57	0.4	-39	0	38	0.27	INF	INF	13
209e	No	0.57	0.39	-39	0	25	0.18	INF	INF	12
209n	No	0.57	0.39	-39	0	29	0.33	INF	INF	11
210e	No	0.58	0.4	-39	0	19	-0.048	INF	INF	14
210n	No	0.57	0.4	-39	0	12	0.0071	INF	INF	16
211e	No	0.56	0.39	-39	0	29	0.22	INF	INF	13
211n	No	0.55	0.39	-39	0	20	0.29	INF	INF	14
212e	No	0.44	0.33	-39	0	24	0.22	INF	INF	14
212n	No	0.39	0.33	-39	0	9.8	0.39	INF	INF	11
213e	No	0.42	0.3	-39	0	17	0.24	INF	INF	21
213n	No	0.43	0.3	-39	0	18	0.27	INF	INF	19
214e	No	0.42	0.31	-39	0	19	0.23	INF	INF	24
214n	No	0.41	0.31	-39	0	19	0.33	INF	INF	17
215e	No	0.61	0.38	-39	0	33	0.14	INF	INF	3
215n	No	0.57	0.38	-39	0	16	0.46	INF	INF	1
216e	No	0.6	0.38	-39	0	20	0.24	INF	INF	2
216n	No	0.59	0.38	-39	0	13	0.37	INF	INF	1
217e	No	0.62	0.38	-39	0	25	0.26	INF	INF	6
217n	No	0.61	0.38	-39	0	17	0.32	INF	INF	2
218e	No	0.61	0.42	-39	0	27	0.17	INF	INF	6
218n	No	0.16	0.42	-39	1	0.54	0.24	INF	INF
219e	No	0.63	0.39	-39	0	20	0.17	INF	INF	11
219n	No	0.61	0.39	-39	0	16	0.3	INF	INF	10
220e	No	0.62	0.38	-39	0	19	0.24	INF	INF	13
220n	No	0.6	0.38	-39	0	15	0.27	INF	INF	12
221e	No	0.63	0.38	-39	0	18	0.28	INF	INF	9
221n	No	0.63	0.38	-39	0	15	0.34	INF	INF	8
222e	No	0.63	0.39	-39	0	18	0.3	INF	INF	10
222n	No	0.64	0.39	-39	0	24	0.26	INF	INF	8
223e	No	0.61	0.37	-39	0	16	0.2	INF	INF	17
223n	No	0.62	0.37	-39	0	21	0.2	INF	INF	15
224e	No	0.6	0.39	-39	0	23	0.4	INF	INF	14
224n	No	0.62	0.39	-39	0	20	0.28	INF	INF	15
225e	No	0.61	0.39	-39	0	25	0.19	INF	INF	8
225n	No	0.59	0.39	-39	0	20	0.29	INF	INF	8
226e	No	0.6	0.38	-39	0	23	0.18	INF	INF	8
226n	No	0.59	0.38	-39	0	29	0.28	INF	INF	8
227e	No	0.55	0.38	-39	0	19	0.24	INF	INF	22
227n	No	0.52	0.38	-39	0	12	0.39	INF	INF	16
228e	No	0.52	0.36	-39	0	18	0.32	INF	INF	18
228n	No	0.52	0.36	-39	0	17	0.41	INF	INF	20
229e	No	0.54	0.39	-39	0	17	0.26	INF	INF	22
229n	No	0.54	0.39	-39	0	15	0.27	INF	INF	22
231e	No	0.44	0.32	-39	0	37	0.12	INF	INF	16
231n	No	0.44	0.32	-39	0	28	0.16	INF	INF	14
232e	No	0.43	0.32	-39	0	44	0.11	INF	INF	16
232n	No	0.4	0.32	-39	0	14	0.35	INF	INF	15
233e	No	0.59	0.35	-39	0	18	0.2	INF	INF	11
233n	No	0.47	0.35	-39	0	35	0.92	INF	INF
234e	No	0.6	0.37	-39	0	26	0.22	INF	INF	11
234n	No	0.6	0.37	-39	0	28	0.25	INF	INF	6
237e	No	0.61	0.37	-39	0	18	0.27	INF	INF	11
237n	No	0.61	0.37	-39	0	19	0.3	INF	INF	10
238e	No	0.64	0.39	-39	0	28	0.16	INF	INF	11
238n	No	0.62	0.39	-39	0	18	0.32	INF	INF	10
239e	No	0.63	0.39	-39	0	22	0.2	INF	INF	11
239n	No	0.63	0.39	-39	0	27	0.23	INF	INF	10
240e	No	0.54	0.38	-39	0	7.9	0.43	INF	INF	15
240n	No	0.59	0.38	-39	0	15	0.38	INF	INF	10
241e	No	0.61	0.39	-39	0	19	0.19	INF	INF	8
241n	No	0.6	0.39	-39	0	16	0.35	INF	INF	6
242e	No	0.62	0.39	-39	32	24	0.17	INF	INF
242n	No	0.62	0.39	-39	32	28	0.19	INF	INF
243e	No	0.51	0.38	-39	0	33	0.86	INF	INF
243n	No	0.6	0.38	-39	0	17	0.28	INF	INF	8
244e	No	0.56	0.37	-39	0	16	0.19	INF	INF	14
244n	No	0.56	0.37	-39	0	17	0.32	INF	INF	22
245e	No	0.57	0.39	-39	0	27	0.21	INF	INF	22
245n	No	0.55	0.39	-39	0	20	0.36	INF	INF	20
246e	No	0.55	0.37	-39	0	18	0.31	INF	INF	10
246n	No	0.55	0.37	-39	0	24	0.32	INF	INF	10
250e	No	0.6	0.38	-39	0	22	0.19	INF	INF	9
250n	No	0.6	0.38	-39	0	18	0.3	INF	INF	8
251e	No	0.053	0.084	-39	0	5.7	0.66	INF	INF
251n	No	0.2	0.084	-39	0	21	0.21	INF	INF
252e	No	0.6	0.37	-39	0	17	0.27	INF	INF	6
252n	No	0.6	0.37	-39	0	16	0.3	INF	INF	6
253e	No	0.61	0.37	-39	0	18	0.29	INF	INF	9
253n	No	0.59	0.37	-39	0	11	0.4	INF	INF	2
254e	No	0.62	0.39	-39	0	33	0.18	INF	INF	4
254n	No	0.61	0.39	-39	0	28	0.35	INF	INF	7
255e	No	0.61	0.38	-39	0	15	0.31	INF	INF	2
255n	No	0.62	0.38	-39	0	42	0.25	INF	INF	3
256e	No	0.62	0.39	-39	0	26	0.3	INF	INF	9
256n	No	0.6	0.39	-39	0	11	0.43	INF	INF	2
257e	No	0.61	0.38	-39	0	38	0.17	INF	INF	13
257n	No	0.62	0.38	-39	0	23	0.21	INF	INF	12
261e	No	0.44	0.31	-39	0	5.8	0.4	INF	INF	6
261n	No	0.44	0.31	-39	0	5.3	0.48	INF	INF	4
262e	No	0.56	0.38	-39	0	13	0.013	INF	INF	13
262n	No	0.55	0.38	-39	0	15	0.11	INF	INF	14
266e	No	0.61	0.4	-39	0	8.9	-0.14	INF	INF	7
266n	No	0.61	0.4	-39	0	13	-0.19	INF	INF	7
267e	No	0.55	0.41	-39	0	8	0.32	INF	INF	3
267n	No	0.61	0.41	-39	0	14	0.25	INF	INF	8
268e	No	0.59	0.4	-39	0	10	0.31	INF	INF	9
268n	No	0.62	0.4	-39	0	18	0.31	INF	INF	8
269e	No	0.61	0.38	-39	0	18	0.27	INF	INF	11
269n	No	0.6	0.38	-39	0	15	0.36	INF	INF	8
270e	No	0.21	-0.34	-39	0	25	0.36	INF	INF
270n	No	0.22	-0.34	-39	0	23	0.17	INF	INF
271e	No	0.039	0.5	-39	0	3.3	0.56	INF	INF
271n	No	0.63	0.5	-39	0	45	0.11	INF	INF	5
272e	No	0.6	0.39	-39	0	17	0.35	INF	INF	8
272n	No	0.62	0.39	-39	0	25	0.29	INF	INF	9
273e	No	0.06	0.47	-39	0	3.3	0.58	INF	INF
273n	No	0.62	0.47	-39	0	33	0.21	INF	INF	4
277e	No	0.29	0.29	-39	0	21	0.94	INF	INF
277n	No	0.43	0.29	-39	0	47	0.16	INF	INF	18
278e	No	0.28	0.25	-39	0	6.5	0.46	INF	INF
278n	No	0.36	0.25	-39	0	9.6	0.39	INF	INF	15
281e	No	0.58	0.38	-39	0	17	0.28	INF	INF	9
281n	No	0.59	0.38	-39	0	15	0.32	INF	INF	2
282e	No	0.61	0.4	-39	0	18	0.25	INF	INF	9
282n	No	0.62	0.4	-39	0	32	0.18	INF	INF	8
283e	No	0.62	0.39	-39	0	21	0.16	INF	INF	6
283n	No	0.61	0.39	-39	0	18	0.27	INF	INF	10
284e	No	0.21	-0.34	-39	0	35	0.21	INF	INF
284n	No	0.22	-0.34	-39	0	28	0.15	INF	INF
285e	No	0.61	0.38	-39	0	22	0.26	INF	INF	9
285n	No	0.59	0.38	-39	0	15	0.33	INF	INF	8
286e	No	0.2	0.12	-39	0	26	0.31	INF	INF
286n	No	0.033	0.12	-39	0	2.8	0.6	INF	INF
287e	No	0.64	0.5	-39	0	31	0.076	INF	INF	2
287n	No	0.039	0.5	-39	0	3.2	0.63	INF	INF
290e	No	0.55	0.37	-39	0	75	0.059	INF	INF
290n	No	0.56	0.37	-39	0	73	0.074	INF	INF
291e	No	0.56	0.46	-39	0	69	0.058	INF	INF
291n	No	0.049	0.46	-39	0	0.68	-0.0016	INF	INF
292e	No	0.41	0.3	-39	0	17	0.27	INF	INF	18
292n	No	0.42	0.3	-39	0	34	0.22	INF	INF	18
293e	No	0.35	0.32	-39	0	25	0.97	INF	INF
293n	No	0.47	0.32	-39	0	12	0.31	INF	INF	20
294e	No	0.4	0.33	-39	0	23	0.66	INF	INF
294n	No	0.47	0.33	-39	0	19	0.24	INF	INF	15
295e	No	0.61	0.48	-39	0	4.3	-0.27	INF	INF	4
295n	No	0.03	0.48	-39	0	0.79	0.089	INF	INF
299e	No	0.51	0.35	-39	0	23	0.24	INF	INF	22
299n	No	0.48	0.35	-39	0	11	0.33	INF	INF	18
300e	No	0.52	0.34	-39	0	24	0.14	INF	INF	19
300n	No	0.38	0.34	-39	0	22	1.1	INF	INF
301e	No	0.51	0.35	-39	0	29	0.25	INF	INF	18
301n	No	0.49	0.35	-39	0	12	0.32	INF	INF	21
302e	No	0.45	0.45	-39	0	4.8	0.46	INF	INF	4
302n	No	0.6	0.45	-39	0	18	0.26	INF	INF	7
306e	No	0.47	0.33	-39	0	13	0.23	INF	INF	10
306n	No	0.33	0.33	-39	0	18	1	INF	INF
307e	No	0.3	0.1	-39	0	17	1	INF	INF
307n	No	0.3	0.1	-39	0	15	1.1	INF	INF
311e	No	0.58	0.39	-39	0	15	0.24	INF	INF	9
311n	No	0.55	0.39	-39	0	9.4	0.48	INF	INF	6
312e	No	0.18	0.13	-39	1	17	0.36	INF	INF
312n	No	0.028	0.13	-39	0	2.9	0.59	INF	INF
313e	No	0.44	0.39	-39	0	33	0.79	INF	INF
313n	No	0.56	0.39	-39	0	40	0.17	INF	INF	15
314e	No	0.56	0.4	-39	0	65	0.068	INF	INF
314n	No	0.57	0.4	-39	0	62	0.098	INF	INF
315e	No	0.53	0.38	-39	0	17	0.21	INF	INF	12
315n	No	0.52	0.38	-39	0	16	0.36	INF	INF	12
316e	No	0.54	0.38	-39	0	22	0.21	INF	INF	11
316n	No	0.54	0.38	-39	0	22	0.24	INF	INF	12
317e	No	0.54	0.38	-39	0	30	0.16	INF	INF	12
317n	No	0.51	0.38	-39	0	13	0.32	INF	INF	10
318e	No	0.58	0.42	-39	0	61	0.023	INF	INF
318n	No	0.55	0.42	-39	0	76	0.037	INF	INF
319e	No	0.46	0.34	-39	0	23	0.25	INF	INF	13
319n	No	0.46	0.34	-39	0	25	0.26	INF	INF	12
320e	Yes			-39	1.5E+03	0	0	INF	INF
320n	Yes			-39	1.5E+03	0	0	INF	INF
321e	No	0.31	0.24	-39	0	12	0.1	INF	INF
321n	No	0.32	0.24	-39	0	14	0.15	INF	INF
322e	No	0.34	0.25	-39	0	51	0.093	INF	INF
322n	No	0.27	0.25	-39	0	7.5	0.44	INF	INF
323e	No	0.25	0.21	-39	0	12	0.33	INF	INF
323n	No	0.29	0.21	-39	0	23	0.17	INF	INF
324e	No	0.25	0.18	-39	0	28	0.16	INF	INF
324n	No	0.25	0.18	-39	0	29	0.18	INF	INF
325e	No	0.47	0.34	-39	0	26	0.16	INF	INF	9
325n	No	0.46	0.34	-39	0	19	0.29	INF	INF	4
326e	No	0.021	-0.00024	-39	0	3.5	0.56	INF	INF
326n	No	0.021	-0.00024	-39	0	3.1	0.58	INF	INF
327e	No	0.29	0.19	-39	0	25	0.22	INF	INF
327n	No	0.28	0.19	-39	0	24	0.27	INF	INF
328e	No	0.021	0.16	-39	1	3.4	0.56	INF	INF
328n	No	0.21	0.16	-39	0	19	0.27	INF	INF
329e	No	0.5	0.38	-39	0	21	0.2	INF	INF	1
329n	No	0.52	0.38	-39	0	26	0.16	INF	INF	1
331e	No	0.3	0.22	-39	0	29	0.18	INF	INF
331n	No	0.29	0.22	-39	0	22	0.37	INF	INF
332e	No	0.32	0.23	-39	0	29	0.17	INF	INF
332n	No	0.29	0.23	-39	0	13	0.36	INF	INF
333e	No	0.47	0.38	-39	0	8.9	0.23	INF	INF	1
333n	No	0.5	0.38	-39	0	12	0.24	INF	INF	1
336e	No	0.27	0.2	-39	0	23	0.29	INF	INF
336n	No	0.28	0.2	-39	0	48	0.14	INF	INF
339e	Yes			-39	1.5E+03	0	0	INF	INF
339n	Yes			-39	1.5E+03	0	0	INF	INF
340e	No	0.31	0.23	-39	0	34	0.042	INF	INF
340n	No	0.31	0.23	-39	0	24	0.15	INF	INF
342e	No	0.49	0.38	-39	0	14	0.27	INF	INF	8
342n	No	0.5	0.38	-39	32	25	0.28	INF	INF
343e	No	0.53	0.41	-39	0	21	0.25	INF	INF	10
343n	No	0.51	0.41	-39	0	14	0.42	INF	INF	7
344e	No	0.11	-0.3	-39	0	69	0.075	INF	INF
344n	No	0.12	-0.3	-39	0	53	0.08	INF	INF
345e	Yes			-39	1.5E+03	0	0	INF	INF
345n	Yes			-39	1.5E+03	0	0	INF	INF
346e	No	0.025	0.0017	-39	0	3.3	0.56	INF	INF
346n	No	0.023	0.0017	-39	0	2.7	0.6	INF	INF
347e	No	0.49	0.38	-39	0	29	0.2	INF	INF	8
347n	No	0.5	0.38	-39	0	49	0.16	INF	INF	9
348e	No	0.035	0.38	-39	0	3.4	0.61	INF	INF
348n	No	0.48	0.38	-39	0	62	0.1	INF	INF
349e	No	0.42	0.33	-39	0	55	0.058	INF	INF	4
349n	No	0.42	0.33	-39	0	73	0.052	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/data1/2461003/zen.2461003.43882.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.dev68+g3286222d3
hera_filters: 0.1.7
hera_qm: 2.2.1.dev4+gf6d02113b
hera_notebook_templates: 0.1.dev989+gee0995d

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

Finished execution in 3.57 minutes.