Nightly Per-Antenna Quality Summary Notebook

Josh Dillon, Last Revised February 2021

This notebooks brings together as much information as possible from ant_metrics, auto_metrics and redcal to help figure out which antennas are working properly and summarizes it in a single giant table. It is meant to be lightweight and re-run as often as necessary over the night, so it can be run when any of those is done and then be updated when another one completes.

Contents:

• Table 1: Overall Array Health
• Table 2: RTP Per-Antenna Metrics Summary Table
• Figure 1: Array Plot of Flags and A Priori Statuses

import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
pd.set_option('display.max_rows', 1000)
from hera_qm.metrics_io import load_metric_file
from hera_cal import utils, io, redcal
import glob
import h5py
from copy import deepcopy
from IPython.display import display, HTML
from hera_notebook_templates.utils import status_colors
from hera_mc import mc
from pyuvdata import UVData

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
display(HTML("<style>.container { width:100% !important; }</style>"))

# If you want to run this notebook locally, copy the output of the next cell into the first few lines of this cell.

# JD = "2459122"
# data_path = '/lustre/aoc/projects/hera/H4C/2459122'
# ant_metrics_ext = ".ant_metrics.hdf5"
# redcal_ext = ".maybe_good.omni.calfits"
# nb_outdir = '/lustre/aoc/projects/hera/H4C/h4c_software/H4C_Notebooks/_rtp_summary_'
# good_statues = "digital_ok,calibration_maintenance,calibration_triage,calibration_ok"
# os.environ["JULIANDATE"] = JD
# os.environ["DATA_PATH"] = data_path
# os.environ["ANT_METRICS_EXT"] = ant_metrics_ext
# os.environ["REDCAL_EXT"] = redcal_ext
# os.environ["NB_OUTDIR"] = nb_outdir
# os.environ["GOOD_STATUSES"] = good_statuses

# Use environment variables to figure out path to data
JD = os.environ['JULIANDATE']
data_path = os.environ['DATA_PATH']
ant_metrics_ext = os.environ['ANT_METRICS_EXT']
redcal_ext = os.environ['REDCAL_EXT']
nb_outdir = os.environ['NB_OUTDIR']
good_statuses = os.environ['GOOD_STATUSES']
print(f'JD = "{JD}"')
print(f'data_path = "{data_path}"')
print(f'ant_metrics_ext = "{ant_metrics_ext}"')
print(f'redcal_ext = "{redcal_ext}"')
print(f'nb_outdir = "{nb_outdir}"')
print(f'good_statues = "{good_statuses}"')

JD = "2459781"
data_path = "/mnt/sn1/2459781"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
good_statues = "digital_ok,calibration_maintenance,calibration_triage,calibration_ok"

from astropy.time import Time
utc = Time(JD, format='jd').datetime
print(f'Date: {utc.month}-{utc.day}-{utc.year}')

Date: 7-20-2022

# Per-season options
def ant_to_report_url(ant):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/antenna_report/antenna_{ant}_report.html'

Load Auto Metrics

use_auto_metrics = False

# find the auto_metrics file
glob_str = os.path.join(data_path, f'zen.{JD}*.auto_metrics.h5')
auto_metrics_file = sorted(glob.glob(glob_str))

# if it exists, load and extract relevant information
if len(auto_metrics_file) > 0:
    auto_metrics_file = auto_metrics_file[0]
    print(f'Found auto_metrics results file at {auto_metrics_file}.')
    
    auto_metrics = load_metric_file(auto_metrics_file)
    mean_round_modz_cut = auto_metrics['parameters']['mean_round_modz_cut']
    auto_ex_ants = auto_metrics['ex_ants']['r2_ex_ants']
    
    use_auto_metrics = True
else:
    print(f'No files found matching glob {glob_str}. Skipping auto_metrics.')

Found auto_metrics results file at /mnt/sn1/2459781/zen.2459781.25322.sum.auto_metrics.h5.

Load Ant Metrics

use_ant_metrics = False

# get a list of all ant_metrics files
glob_str = os.path.join(data_path, f'zen.{JD}.?????.sum{ant_metrics_ext}')
ant_metrics_files = sorted(glob.glob(glob_str))

# if they exist, load as many of them as possible
if len(ant_metrics_files) > 0:
    print(f'Found {len(ant_metrics_files)} ant_metrics files matching glob {glob_str}')
    ant_metrics_apriori_exants = {}
    ant_metrics_xants_dict = {}
    ant_metrics_dead_ants_dict = {}
    ant_metrics_crossed_ants_dict = {}
    ant_metrics_dead_metrics = {}
    ant_metrics_crossed_metrics = {}
    dead_cuts = {}
    crossed_cuts = {}
    for amf in ant_metrics_files:
        with h5py.File(amf, "r") as infile: # use h5py directly since it's much faster than load_metric_file
            # get out results for this file
            dead_cuts[amf] = infile['Metrics']['dead_ant_cut'][()]
            crossed_cuts[amf] = infile['Metrics']['cross_pol_cut'][()]
            xants = infile['Metrics']['xants'][:]
            dead_ants = infile['Metrics']['dead_ants'][:]
            crossed_ants = infile['Metrics']['crossed_ants'][:]        
            try:
                # look for ex_ants in history
                ex_ants_string = infile['Header']['history'][()].decode()
                ex_ants_string = ex_ants_string.split('--apriori_xants')[1]
                ex_ants_string = ex_ants_string.split('--')[0].strip()
            except:
                ex_ants_string = ''
                    
            # This only works for the new correlation-matrix-based ant_metrics
            if 'corr' in infile['Metrics']['final_metrics'] and 'corrXPol' in infile['Metrics']['final_metrics']:
                ant_metrics_dead_metrics[amf] = {eval(ant): infile['Metrics']['final_metrics']['corr'][ant][()]
                                                 for ant in infile['Metrics']['final_metrics']['corr']}
                ant_metrics_crossed_metrics[amf] = {eval(ant): infile['Metrics']['final_metrics']['corrXPol'][ant][()]
                                                    for ant in infile['Metrics']['final_metrics']['corrXPol']}                       
            else:
                raise(KeywordError)
        
        # organize results by file
        ant_metrics_xants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in xants]
        ant_metrics_dead_ants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in dead_ants]
        ant_metrics_crossed_ants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in crossed_ants]
        ant_metrics_apriori_exants[amf] = [int(ant) for ant in ex_ants_string.split()]
    
    dead_cut = np.median(list(dead_cuts.values()))
    crossed_cut = np.median(list(crossed_cuts.values()))
        
    use_ant_metrics = True
else:
    print(f'No files found matching glob {glob_str}. Skipping ant_metrics.')

Found 767 ant_metrics files matching glob /mnt/sn1/2459781/zen.2459781.?????.sum.ant_metrics.hdf5

Load chi^2 info from redcal

use_redcal = False
glob_str = os.path.join(data_path, f'zen.{JD}.?????.sum{redcal_ext}')

redcal_files = sorted(glob.glob(glob_str))
if len(redcal_files) > 0:
    print(f'Found {len(redcal_files)} ant_metrics files matching glob {glob_str}')
    post_redcal_ant_flags_dict = {}
    flagged_by_redcal_dict = {}
    cspa_med_dict = {}
    for cal in redcal_files:
        hc = io.HERACal(cal)
        _, flags, cspa, chisq = hc.read()
        cspa_med_dict[cal] = {ant: np.nanmedian(cspa[ant], axis=1) for ant in cspa}

        post_redcal_ant_flags_dict[cal] = {ant: np.all(flags[ant]) for ant in flags}
        # check history to distinguish antennas flagged going into redcal from ones flagged during redcal
        tossed_antenna_lines =  hc.history.replace('\n','').split('Throwing out antenna ')[1:]
        flagged_by_redcal_dict[cal] = sorted([int(line.split(' ')[0]) for line in tossed_antenna_lines])
        
    use_redcal = True
else:
    print(f'No files found matching glob {glob_str}. Skipping redcal chisq.')

Found 77 ant_metrics files matching glob /mnt/sn1/2459781/zen.2459781.?????.sum.known_good.omni.calfits

Figure out some general properties

# Parse some general array properties, taking into account the fact that we might be missing some of the metrics
ants = []
pols = []
antpol_pairs = []

if use_auto_metrics:
    ants = sorted(set(bl[0] for bl in auto_metrics['modzs']['r2_shape_modzs']))
    pols = sorted(set(bl[2] for bl in auto_metrics['modzs']['r2_shape_modzs']))
if use_ant_metrics:
    antpol_pairs = sorted(set([antpol for dms in ant_metrics_dead_metrics.values() for antpol in dms.keys()]))
    antpols = sorted(set(antpol[1] for antpol in antpol_pairs))
    ants = sorted(set(antpol[0] for antpol in antpol_pairs) | set(ants))
    pols = sorted(set(utils.join_pol(ap, ap) for ap in antpols) | set(pols))
if use_redcal:
    antpol_pairs = sorted(set([ant for cspa in cspa_med_dict.values() for ant in cspa.keys()]) | set(antpol_pairs))
    antpols = sorted(set(antpol[1] for antpol in antpol_pairs))
    ants = sorted(set(antpol[0] for antpol in antpol_pairs) | set(ants))
    pols = sorted(set(utils.join_pol(ap, ap) for ap in antpols) | set(pols))

# Figure out remaining antennas not in data and also LST range
data_files = sorted(glob.glob(os.path.join(data_path, 'zen.*.sum.uvh5')))
hd = io.HERAData(data_files[0])
unused_ants = [ant for ant in hd.antpos if ant not in ants]    
hd_last = io.HERAData(data_files[-1])

Load a priori antenna statuses and node numbers

# try to load a priori antenna statusesm but fail gracefully if this doesn't work.
a_priori_statuses = {ant: 'Not Found' for ant in ants}
nodes = {ant: np.nan for ant in ants + unused_ants}
try:
    from hera_mc import cm_hookup

    # get node numbers
    hookup = cm_hookup.get_hookup('default')
    for ant_name in hookup:
        ant = int("".join(filter(str.isdigit, ant_name)))
        if ant in nodes:
            if hookup[ant_name].get_part_from_type('node')['E<ground'] is not None:
                nodes[ant] = int(hookup[ant_name].get_part_from_type('node')['E<ground'][1:])
    
    # get apriori antenna status
    for ant_name, data in hookup.items():
        ant = int("".join(filter(str.isdigit, ant_name)))
        if ant in a_priori_statuses:
            a_priori_statuses[ant] = data.apriori

except Exception as err:
    print(f'Could not load node numbers and a priori antenna statuses.\nEncountered {type(err)} with message: {err}')

Summarize auto metrics

if use_auto_metrics:
    # Parse modzs
    modzs_to_check = {'Shape': 'r2_shape_modzs', 'Power': 'r2_power_modzs', 
                      'Temporal Variability': 'r2_temp_var_modzs', 'Temporal Discontinuties': 'r2_temp_diff_modzs'}
    worst_metrics = []
    worst_zs = []
    all_modzs = {}
    binary_flags = {rationale: [] for rationale in modzs_to_check}

    for ant in ants:
        # parse modzs and figure out flag counts
        modzs = {f'{pol} {rationale}': auto_metrics['modzs'][dict_name][(ant, ant, pol)] 
                 for rationale, dict_name in modzs_to_check.items() for pol in pols}
        for pol in pols:
            for rationale, dict_name in modzs_to_check.items():
                binary_flags[rationale].append(auto_metrics['modzs'][dict_name][(ant, ant, pol)] > mean_round_modz_cut)

        # parse out all metrics for dataframe
        for k in modzs:
            col_label = k + ' Modified Z-Score'
            if col_label in all_modzs:
                all_modzs[col_label].append(modzs[k])
            else:
                all_modzs[col_label] = [modzs[k]]
                
    mean_round_modz_cut = auto_metrics['parameters']['mean_round_modz_cut']
else:
    mean_round_modz_cut = 0

Summarize ant metrics

if use_ant_metrics:
    a_priori_flag_frac = {ant: np.mean([ant in apxa for apxa in ant_metrics_apriori_exants.values()]) for ant in ants}
    dead_ant_frac = {ap: {ant: np.mean([(ant, ap) in das for das in ant_metrics_dead_ants_dict.values()])
                                 for ant in ants} for ap in antpols}
    crossed_ant_frac = {ant: np.mean([np.any([(ant, ap) in cas for ap in antpols])
                                      for cas in ant_metrics_crossed_ants_dict.values()]) for ant in ants}
    ant_metrics_xants_frac_by_antpol = {antpol: np.mean([antpol in amx for amx in ant_metrics_xants_dict.values()]) for antpol in antpol_pairs}
    ant_metrics_xants_frac_by_ant = {ant: np.mean([np.any([(ant, ap) in amx for ap in antpols])
                                     for amx in ant_metrics_xants_dict.values()]) for ant in ants}
    average_dead_metrics = {ap: {ant: np.nanmean([dm.get((ant, ap), np.nan) for dm in ant_metrics_dead_metrics.values()]) 
                                 for ant in ants} for ap in antpols}
    average_crossed_metrics = {ant: np.nanmean([cm.get((ant, ap), np.nan) for ap in antpols 
                                                for cm in ant_metrics_crossed_metrics.values()]) for ant in ants}
else:
    dead_cut = 0.4
    crossed_cut = 0.0

Summarize redcal chi^2 metrics

if use_redcal:
    cspa = {ant: np.nanmedian(np.hstack([cspa_med_dict[cal][ant] for cal in redcal_files])) for ant in antpol_pairs}
    redcal_prior_flag_frac = {ant: np.mean([np.any([afd[ant, ap] and not ant in flagged_by_redcal_dict[cal] for ap in antpols])
                                            for cal, afd in post_redcal_ant_flags_dict.items()]) for ant in ants}
    redcal_flagged_frac = {ant: np.mean([ant in fbr for fbr in flagged_by_redcal_dict.values()]) for ant in ants}

Get fem switch states

HHautos = sorted(glob.glob(f"{data_path}/zen.{JD}.*.sum.autos.uvh5"))
diffautos = sorted(glob.glob(f"{data_path}/zen.{JD}.*.diff.autos.uvh5"))

try:
    db = mc.connect_to_mc_db(None)
    session = db.sessionmaker()
    startJD = float(HHautos[0].split('zen.')[1].split('.sum')[0])
    stopJD = float(HHautos[-1].split('zen.')[1].split('.sum')[0])
    startTime = Time(startJD,format='jd')
    stopTime = Time(stopJD,format='jd')
    res = session.get_antenna_status(starttime=startTime, stoptime=stopTime)
    fem_switches = {}
    if len(res) == 0:
        femState = None
    else:
        for antpol in res:
            fem_switches[(antpol.antenna_number, antpol.antenna_feed_pol)] = antpol.fem_switch
    femState = (max(set(list(fem_switches.values())), key = list(fem_switches.values()).count)) 
except Exception as e:
    print(e)
    femState = None

Find X-engine Failures

read_inds = [1, len(HHautos)//2, -2]
x_status = [1,1,1,1,1,1,1,1]
s = UVData()
s.read(HHautos[1])

nants = len(s.get_ants())
freqs = s.freq_array[0]*1e-6
nfreqs = len(freqs)

antCon = {a: None for a in ants}
rightAnts = []
for i in read_inds:
    s = UVData()
    d = UVData()
    s.read(HHautos[i])
    d.read(diffautos[i])
    for pol in [0,1]:
        sm = np.abs(s.data_array[:,0,:,pol])
        df = np.abs(d.data_array[:,0,:,pol])
        sm = np.r_[sm, np.nan + np.zeros((-len(sm) % nants,len(freqs)))]
        sm = np.nanmean(sm.reshape(-1,nants,nfreqs),axis=1)
        df = np.r_[df, np.nan + np.zeros((-len(df) % nants,len(freqs)))]
        df = np.nanmean(df.reshape(-1,nants,nfreqs),axis=1)

        evens = (sm + df)/2
        odds = (sm - df)/2
        rat = np.divide(evens,odds)
        rat = np.nan_to_num(rat)
        for xbox in range(0,8):
            xavg = np.nanmean(rat[:,xbox*192:(xbox+1)*192],axis=1)
            if np.nanmax(xavg)>1.5 or np.nanmin(xavg)<0.5:
                x_status[xbox] = 0
    for ant in ants:
        for pol in ["xx", "yy"]:
            if antCon[ant] is False:
                continue
            spectrum = s.get_data(ant, ant, pol)
            stdev = np.std(spectrum)
            med = np.median(np.abs(spectrum))
            if (femState == "load" or femState == 'noise') and 80000 < stdev <= 4000000 and antCon[ant] is not False:
                antCon[ant] = True
            elif femState == "antenna" and stdev > 500000 and med > 950000 and antCon[ant] is not False:
                antCon[ant] = True
            else:
                antCon[ant] = False
            if np.min(np.abs(spectrum)) < 100000:
                antCon[ant] = False
for ant in ants:
    if antCon[ant] is True:
        rightAnts.append(ant)
            
x_status_str = ''
for i,x in enumerate(x_status):
    if x==0:
        x_status_str += '\u274C '
    else:
        x_status_str += '\u2705 '

Build Overall Health DataFrame

def comma_sep_paragraph(vals, chars_per_line=40):
    outstrs = []
    for val in vals:
        if (len(outstrs) == 0) or (len(outstrs[-1]) > chars_per_line):
            outstrs.append(str(val))
        else:
            outstrs[-1] += ', ' + str(val)
    return ',<br>'.join(outstrs)

# Time data
to_show = {'JD': [JD]}
to_show['Date'] = f'{utc.month}-{utc.day}-{utc.year}'
to_show['LST Range'] = f'{hd.lsts[0] * 12 / np.pi:.3f} -- {hd_last.lsts[-1] * 12 / np.pi:.3f} hours'

# X-engine status
to_show['X-Engine Status'] = x_status_str

# Files
to_show['Number of Files'] = len(data_files)

# Antenna Calculations
to_show['Total Number of Antennas'] = len(ants)

to_show[' '] = ''
to_show['OPERATIONAL STATUS SUMMARY'] = ''

status_count = {status: 0 for status in status_colors}
for ant, status in a_priori_statuses.items():
    if status in status_count:
        status_count[status] = status_count[status] + 1
    else:
        status_count[status] = 1
to_show['Antenna A Priori Status Count'] = '<br>'.join([f'{status}: {status_count[status]}' for status in status_colors if status in status_count and status_count[status] > 0])

to_show['Commanded Signal Source'] = femState
to_show['Antennas in Commanded State'] = f'{len(rightAnts)} / {len(ants)} ({len(rightAnts) / len(ants):.1%})'

if use_ant_metrics:
    to_show['Cross-Polarized Antennas'] = ', '.join([str(ant) for ant in ants if crossed_ant_frac[ant] == 1])

# Node calculations
nodes_used = set([nodes[ant] for ant in ants if np.isfinite(nodes[ant])])
to_show['Total Number of Nodes'] = len(nodes_used)
if use_ant_metrics:
    node_off = {node: True for node in nodes_used}
    not_correlating = {node: True for node in nodes_used}
    for ant in ants:
        for ap in antpols:
            if np.isfinite(nodes[ant]):
                if np.isfinite(average_dead_metrics[ap][ant]):
                    node_off[nodes[ant]] = False
                if dead_ant_frac[ap][ant] < 1:
                    not_correlating[nodes[ant]] = False
    to_show['Nodes Registering 0s'] = ', '.join([f'N{n:02}' for n in sorted([node for node in node_off if node_off[node]])])
    to_show['Nodes Not Correlating'] = ', '.join([f'N{n:02}' for n in sorted([node for node in not_correlating if not_correlating[node] and not node_off[node]])])

# Pipeline calculations    
to_show['  '] = ''
to_show['NIGHTLY ANALYSIS SUMMARY'] = ''
    
all_flagged_ants = []
if use_ant_metrics:
    to_show['Ant Metrics Done?'] = '\u2705'
    ant_metrics_flagged_ants = [ant for ant in ants if ant_metrics_xants_frac_by_ant[ant] > 0]
    all_flagged_ants.extend(ant_metrics_flagged_ants)
    to_show['Ant Metrics Flagged Antennas'] = f'{len(ant_metrics_flagged_ants)} / {len(ants)} ({len(ant_metrics_flagged_ants) / len(ants):.1%})' 
else:
    to_show['Ant Metrics Done?'] = '\u274C'
if use_auto_metrics:
    to_show['Auto Metrics Done?'] = '\u2705'
    auto_metrics_flagged_ants = [ant for ant in ants if ant in auto_ex_ants]
    all_flagged_ants.extend(auto_metrics_flagged_ants)    
    to_show['Auto Metrics Flagged Antennas'] = f'{len(auto_metrics_flagged_ants)} / {len(ants)} ({len(auto_metrics_flagged_ants) / len(ants):.1%})' 
else:
    to_show['Auto Metrics Done?'] = '\u274C'
if use_redcal:
    to_show['Redcal Done?'] = '\u2705'    
    redcal_flagged_ants = [ant for ant in ants if redcal_flagged_frac[ant] > 0]
    all_flagged_ants.extend(redcal_flagged_ants)    
    to_show['Redcal Flagged Antennas'] = f'{len(redcal_flagged_ants)} / {len(ants)} ({len(redcal_flagged_ants) / len(ants):.1%})' 
else:
    to_show['Redcal Done?'] = '\u274C' 
to_show['Never Flagged Antennas'] = f'{len(ants) - len(set(all_flagged_ants))} / {len(ants)} ({(len(ants) - len(set(all_flagged_ants))) / len(ants):.1%})'

# Count bad antennas with good statuses and vice versa
apriori_good_flagged = []
aprior_bad_unflagged = []
for ant in ants:
    if ant in set(all_flagged_ants) and a_priori_statuses[ant] in good_statuses.split(','):
        apriori_good_flagged.append(ant)
    elif ant not in set(all_flagged_ants) and a_priori_statuses[ant] not in good_statuses.split(','):
        aprior_bad_unflagged.append(ant)
to_show['A Priori Good Antennas Flagged'] = comma_sep_paragraph(apriori_good_flagged)
to_show['A Priori Bad Antennas Not Flagged'] = comma_sep_paragraph(aprior_bad_unflagged)


# Apply Styling
df = pd.DataFrame(to_show)
divider_cols = [df.columns.get_loc(col) for col in ['NIGHTLY ANALYSIS SUMMARY', 'OPERATIONAL STATUS SUMMARY']]
try:
    to_red_columns = [df.columns.get_loc(col) for col in ['Cross-Polarized Antennas', 'Nodes Registering 0s', 
                                                          'Nodes Not Correlating', 'A Priori Good Antennas Flagged']]
except:
    to_red_columns = []
def red_specific_cells(x):
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    for col in to_red_columns:
        df1.iloc[col] = 'color: red'
    return df1

df = df.T
table = df.style.hide_columns().apply(red_specific_cells, axis=None)
for col in divider_cols:
    table = table.set_table_styles([{"selector":f"tr:nth-child({col+1})", "props": [("background-color", "black"), ("color", "white")]}], overwrite=False)

Table 1: Overall Array Health

HTML(table.render())

JD	2459781
Date	7-20-2022
LST Range	15.404 -- 23.804 hours
X-Engine Status	✅ ✅ ✅ ✅ ✅ ✅ ✅ ✅
Number of Files	813
Total Number of Antennas	147
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 46 RF_ok: 11 digital_maintenance: 2 digital_ok: 82 not_connected: 3
Commanded Signal Source	antenna
Antennas in Commanded State	86 / 147 (58.5%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N12
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	90 / 147 (61.2%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	79 / 147 (53.7%)
Redcal Done?	✅
Redcal Flagged Antennas	15 / 147 (10.2%)
Never Flagged Antennas	34 / 147 (23.1%)
A Priori Good Antennas Flagged	7, 10, 21, 29, 31, 37, 40, 41, 42, 50, 51, 53, 54, 55, 56, 57, 68, 69, 71, 72, 81, 83, 84, 88, 91, 92, 101, 103, 105, 106, 107, 108, 111, 117, 118, 128, 129, 135, 138, 141, 142, 145, 160, 161, 167, 169, 170, 176, 177, 178, 179, 181, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	3, 4, 8, 36, 38, 70, 73, 82, 100, 119

# write to csv
outpath = os.path.join(nb_outdir, f'array_health_table_{JD}.csv')
print(f'Now saving Table 2 to a csv at {outpath}')
df.replace({'\u2705': 'Y'}, regex=True).replace({'\u274C': 'N'}, regex=True).replace({'<br>': ' '}, regex=True).to_csv(outpath)

Now saving Table 2 to a csv at /home/obs/src/H5C_Notebooks/_rtp_summary_/array_health_table_2459781.csv

Build DataFrame

# build dataframe
to_show = {'Ant': [f'<a href="{ant_to_report_url(ant)}" target="_blank">{ant}</a>' for ant in ants],
           'Node': [f'N{nodes[ant]:02}' for ant in ants], 
           'A Priori Status': [a_priori_statuses[ant] for ant in ants]}
           #'Worst Metric': worst_metrics, 'Worst Modified Z-Score': worst_zs}
df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
if use_auto_metrics:
    bar_cols['Auto Metrics Flags'] = [float(ant in auto_ex_ants) for ant in ants]
if use_ant_metrics:
    if np.sum(list(a_priori_flag_frac.values())) > 0:  # only include this col if there are any a priori flags
        bar_cols['A Priori Flag Fraction in Ant Metrics'] = [a_priori_flag_frac[ant] for ant in ants]
    for ap in antpols:
        bar_cols[f'Dead Fraction in Ant Metrics ({ap})'] = [dead_ant_frac[ap][ant] for ant in ants]
    bar_cols['Crossed Fraction in Ant Metrics'] = [crossed_ant_frac[ant] for ant in ants]
if use_redcal:
    bar_cols['Flag Fraction Before Redcal'] = [redcal_prior_flag_frac[ant] for ant in ants]
    bar_cols['Flagged By Redcal chi^2 Fraction'] = [redcal_flagged_frac[ant] for ant in ants]  
for col in bar_cols:
    df[col] = bar_cols[col]

# add auto_metrics
if use_auto_metrics:
    for label, modz in all_modzs.items():
        df[label] = modz
z_score_cols = [col for col in df.columns if 'Modified Z-Score' in col]        
        
# add ant_metrics
ant_metrics_cols = {}
if use_ant_metrics:
    for ap in antpols:
        ant_metrics_cols[f'Average Dead Ant Metric ({ap})'] = [average_dead_metrics[ap][ant] for ant in ants]
    ant_metrics_cols['Average Crossed Ant Metric'] = [average_crossed_metrics[ant] for ant in ants]
    for col in ant_metrics_cols:
        df[col] = ant_metrics_cols[col]   

# add redcal chisq
redcal_cols = []
if use_redcal:
    for ap in antpols:
        col_title = f'Median chi^2 Per Antenna ({ap})'
        df[col_title] = [cspa[ant, ap] for ant in ants]
        redcal_cols.append(col_title)

# sort by node number and then by antenna number within nodes
df.sort_values(['Node', 'Ant'], ascending=True)

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=z_score_cols) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=redcal_cols) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=z_score_cols) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=z_score_cols) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format({col: '{:,.2%}'.format for col in bar_cols}) \
          .applymap(lambda val: 'font-weight: bold', subset=['Ant']) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])]) \

Table 2: RTP Per-Antenna Metrics Summary Table

This admittedly very busy table incorporates summary information about all antennas in the array. Its columns depend on what information is available when the notebook is run (i.e. whether auto_metrics, ant_metrics, and/or redcal is done). These can be divided into 5 sections:

Basic Antenna Info: antenna number, node, and its a priori status.

Flag Fractions: Fraction of the night that an antenna was flagged for various reasons. Note that auto_metrics flags antennas for the whole night, so it'll be 0% or 100%.

auto_metrics Details: If auto_metrics is included, this section shows the modified Z-score signifying how much of an outlier each antenna and polarization is in each of four categories: bandpass shape, overall power, temporal variability, and temporal discontinuities. Bold red text indicates that this is a reason for flagging the antenna. It is reproduced from the auto_metrics_inspect.ipynb nightly notebook, so check that out for more details on the precise metrics.

ant_metrics Details: If ant_metrics is included, this section shows the average correlation-based metrics for antennas over the whole night. Low "dead ant" metrics (nominally below 0.4) indicate antennas not correlating with the rest of the array. Negative "crossed ant" metrics indicate antennas that show stronger correlations in their cross-pols than their same-pols, indicating that the two polarizations are probably swapped. Bold text indicates that the average is below the threshold for flagging.

redcal chi^2 Details: If redcal is included, this shows the median chi^2 per antenna. This would be 1 in an ideal array. Antennas are thrown out when they they are outliers in their median chi^2, usually greater than 4-sigma outliers in modified Z-score.

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
3	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.061252	-0.840749	0.132118	-0.709711	0.186246	-0.097647	-0.009702	0.019053	0.715195	0.667605	0.360532	3.957168	3.652405
4	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.338803	0.345204	0.402007	-0.347592	0.870878	0.554578	2.626680	0.351972	0.730494	0.668130	0.361168	5.227682	4.201041
5	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.213971	-0.909254	-0.064076	-1.199698	0.458325	-1.970952	0.190367	-1.226525	0.738511	0.679718	0.358318	2.204118	2.261917
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.385246	-0.236091	-0.135059	-0.143344	0.513781	0.740723	-0.197948	7.694379	0.727850	0.673319	0.356127	3.360384	3.751826
8	N02	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.401105	1.443518	0.574770	0.701023	-1.082572	-0.977026	-1.097633	-1.916165	0.717517	0.650937	0.359280	3.791084	3.897396
9	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.142531	-1.007255	0.665038	-0.878442	1.333777	-0.039592	0.132952	-0.379105	0.710909	0.662472	0.359400	1.902558	1.704570
10	N02	digital_ok	0.00%	6.52%	1.30%	0.00%	6.49%	1.30%	-0.068829	0.341809	-0.011990	0.987572	0.681600	0.980095	0.443622	0.773769	0.700387	0.633851	0.368527	1.912135	1.931601
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.044184	-0.085052	0.867489	0.272438	0.916721	0.830202	0.172798	0.688658	0.736875	0.678297	0.354862	2.306370	2.462291
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.375612	-1.204917	-0.343269	-0.895131	-0.057022	-0.939667	0.996685	1.194903	0.753082	0.697218	0.352779	2.256963	2.332889
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.447418	-0.427785	-0.134444	-0.512471	0.353848	0.014261	-0.099862	-0.248836	0.745364	0.694632	0.350049	2.275537	2.312511
18	N01	RF_maintenance	100.00%	49.15%	64.80%	0.00%	100.00%	0.00%	0.381959	2.467448	-0.033140	4.175485	15.272660	15.383676	46.221043	25.320550	0.694929	0.516125	0.480072	2.240609	1.612923
19	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.133890	-0.570969	-1.007275	-0.148791	-0.638423	0.239009	-0.511939	0.330041	0.738928	0.685205	0.351515	1.844071	1.964551
20	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.282631	-0.471051	3.142204	0.009219	-0.079656	0.780624	0.252694	0.077227	0.696864	0.662397	0.351586	2.059069	1.977580
21	N02	digital_ok	100.00%	58.28%	49.15%	0.00%	100.00%	0.00%	1.414380	1.909725	3.490655	2.335019	15.448195	15.683628	0.313642	3.818779	0.653358	0.650585	0.426775	2.143881	2.378520
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.399862	3.781633	7.386381	7.522124	16.121629	16.166929	1.480404	1.412973	0.039461	0.041269	0.001673	1.159977	1.177097
28	N01	RF_maintenance	100.00%	60.89%	100.00%	0.00%	100.00%	0.00%	0.841489	2.776487	1.413753	6.463127	15.746812	16.220575	2.230026	9.791441	0.505678	0.201298	0.338240	8.423132	2.406612
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	1.30%	-1.132956	-0.377296	-0.911700	-0.248803	-0.605554	0.405543	-0.696411	0.021273	0.760625	0.706348	0.345859	2.318371	2.493608
30	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.056260	0.371540	1.100408	1.741147	0.382848	0.474645	2.490789	0.417518	0.737883	0.684358	0.345762	2.167838	2.242908
31	N02	digital_ok	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	1.353464	1.768270	3.702223	3.052975	15.677030	15.662079	0.679960	0.583859	0.715086	0.701685	0.424248	2.133491	2.179171
32	N02	RF_maintenance	100.00%	59.58%	59.58%	0.00%	100.00%	0.00%	9.144815	9.379213	2.081702	1.859568	15.760966	15.959597	46.331245	47.482253	0.645806	0.638752	0.228237	3.491413	2.724901
33	N02	RF_maintenance	100.00%	0.00%	16.95%	0.00%	100.00%	0.00%	-0.002606	2.063470	0.169376	1.418841	0.788280	-0.483757	0.627608	10.287418	0.720966	0.487345	0.475381	3.618823	1.970056
36	N03	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.977580	0.528676	-0.741383	0.277467	0.113591	0.695055	-0.295498	0.499280	0.733644	0.668803	0.351324	3.988189	3.418992
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.427408	-0.577099	0.724589	-0.580902	0.853006	0.137688	0.591226	4.232800	0.742192	0.693021	0.345973	3.900797	3.746001
38	N03	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.661358	-0.944769	0.998703	-0.810767	1.718914	0.241055	2.504962	0.264792	0.753849	0.709262	0.350006	3.643737	3.502485
40	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	24.68%	0.208030	0.570076	0.603021	1.765664	0.962718	0.381404	0.176446	0.148214	0.761425	0.701406	0.351968	2.070480	2.044129
41	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	5.19%	-0.950951	-0.522653	-1.224387	-0.412266	-1.571511	0.111939	-0.918736	-0.249220	0.772141	0.722254	0.340159	2.541020	2.046398
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	16.88%	-1.081552	0.324786	-1.129218	0.277803	-1.035298	0.868240	-0.695728	-0.045798	0.770154	0.720335	0.350128	2.220832	2.099640
45	N05	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.043373	0.263291	0.556360	1.985333	1.117969	-0.444234	0.102951	6.776197	0.738036	0.658759	0.367134	3.372093	2.979351
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.976665	-0.159675	-0.820438	0.046181	-0.007648	0.625017	-0.517887	0.361509	0.730595	0.677128	0.363293	1.826147	1.729769
50	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.763746	0.860558	-0.335939	-0.674885	0.682150	-1.228811	1.053745	32.523428	0.736243	0.630886	0.335204	4.895875	4.163206
51	N03	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.048841	7.372118	-0.290377	7.860824	-3.333447	1.067691	-1.234789	4.067093	0.749430	0.050243	0.463581	3.590190	1.216142
52	N03	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.533954	8.138126	0.957829	7.954982	0.828318	1.119136	5.439577	3.149015	0.733462	0.040409	0.451799	7.166528	1.159983
53	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	14.29%	0.772393	0.765536	0.043443	-0.022358	-2.643848	-2.907701	-1.265452	-0.693476	0.772790	0.725975	0.340188	2.211753	2.075968
54	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	94.81%	0.251124	0.133954	0.033034	0.342000	0.007648	0.919281	1.040739	0.327071	0.767192	0.721174	0.311208	4.068035	4.567711
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.605872	-0.889427	2.549550	-1.003594	0.966922	-0.843292	4.535369	-0.450427	0.743045	0.734896	0.345639	2.752058	3.400862
56	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	36.36%	-1.098192	-0.908530	-1.039078	-1.027173	-0.539222	-0.281890	-0.630118	1.755331	0.779098	0.738641	0.341375	2.686603	2.228268
57	N04	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	3.263594	0.668652	5.663463	0.400753	1.663354	5.743242	0.178178	1.296898	0.049775	0.715672	0.454521	1.325760	3.462819
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.035786	-0.327632	-0.058014	0.286915	1.363436	1.143975	0.067662	0.576299	0.733468	0.679220	0.347521	1.995996	1.875643
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.694515	-0.197429	-0.710389	0.775107	-0.371030	1.290251	-0.400469	1.419025	0.753929	0.701362	0.337551	1.957498	1.878375
67	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.471704	0.050877	-0.177374	4.695227	1.099736	-1.564913	0.287766	0.441212	0.766805	0.654651	0.361349	4.049105	3.115260
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	12.99%	0.074492	0.168763	-0.096224	1.072880	0.880309	1.451496	-0.056190	0.283508	0.772814	0.726461	0.329462	2.427876	2.328513
69	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	98.70%	-0.854057	-0.720557	-0.776308	-0.580607	0.309145	-0.248027	-0.298402	0.134569	0.779704	0.740603	0.326013	4.629319	4.251655
70	N04	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.101899	-0.900651	1.376149	-0.698778	3.270536	0.184448	1.331917	0.073838	0.774928	0.740932	0.347409	18.089443	11.735558
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	100.00%	-0.751275	-0.938043	-0.674986	-0.806986	-0.704743	0.206447	-0.507686	-0.268809	0.778665	0.744967	0.338824	16.001745	13.844806
72	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	88.31%	0.767713	-0.939972	0.047678	-1.082244	0.446075	-0.405173	1.828852	-0.811709	0.772484	0.739471	0.348994	4.150568	3.416127
73	N05	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.194056	-0.030222	0.515192	2.323132	0.821712	-0.218591	1.424049	0.792465	0.756397	0.685975	0.374732	3.513512	2.795858
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.693035	-0.592093	0.852770	-1.275840	-0.180978	-1.920628	4.876562	0.009527	0.708409	0.673452	0.345467	3.430387	3.818491
82	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.103083	-0.460004	0.494929	0.795420	0.530188	0.303915	0.200693	0.083639	0.725239	0.685543	0.344822	3.695644	3.572510
83	N07	digital_ok	100.00%	64.80%	0.00%	0.00%	100.00%	0.00%	2.646367	-0.846898	4.988843	-1.289093	0.462680	-1.575129	0.049474	-0.968223	0.318944	0.721336	0.526019	1.308100	4.396810
84	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.60%	1.432247	1.356698	-0.373540	-0.427795	-3.415355	-3.036931	-1.723602	-1.695237	0.698540	0.652366	0.314057	2.703125	2.587525
85	N08	RF_maintenance	100.00%	49.15%	50.46%	0.00%	100.00%	0.00%	-0.404792	5.374477	-1.256422	-0.084229	15.161522	15.278786	-1.881330	2.641406	0.694034	0.637396	0.391902	2.543823	3.279252
86	N08	RF_maintenance	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	-0.675579	1.203384	-0.976425	-0.584279	15.168059	15.525540	-1.370183	-1.391713	0.692330	0.661824	0.396665	2.749552	2.546218
87	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.953326	1.595227	0.104371	0.682230	-2.190312	-1.134416	8.449881	-2.084053	0.698056	0.646714	0.336454	3.366856	3.093990
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.435439	3.839686	5.140996	5.317083	1.717429	1.323164	0.509618	0.191667	0.038360	0.038697	0.000268	1.154739	1.150823
90	N09	RF_maintenance	100.00%	10.43%	0.00%	0.00%	100.00%	0.00%	-0.051746	-0.400183	3.156583	-1.260654	-2.687992	-1.822032	0.640739	3.634241	0.678299	0.709494	0.385619	2.588678	3.054978
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.206910	3.487085	6.517159	6.804947	16.114893	16.159068	1.252455	1.210389	0.033984	0.038660	0.001465	1.155728	1.151928
92	N10	digital_ok	100.00%	79.14%	100.00%	0.00%	100.00%	0.00%	6.461609	9.805749	2.448681	4.744797	16.211044	16.385882	1.741192	4.767011	0.355782	0.258881	0.161719	2.662643	2.295693
93	N10	RF_maintenance	100.00%	59.58%	59.58%	40.42%	100.00%	0.00%	-0.519411	1.797682	-0.521392	3.672548	15.253525	15.572084	-1.151289	0.406218	0.234255	0.234627	-0.330550	2.334306	2.085418
94	N10	RF_maintenance	100.00%	55.67%	49.15%	0.00%	100.00%	0.00%	1.172523	1.334828	0.765205	0.740356	15.611195	15.622480	1.030340	0.600110	0.656091	0.635722	0.436922	2.475377	2.546575
98	N07	digital_maintenance	0.00%	0.00%	10.43%	0.00%	100.00%	0.00%	-0.687222	0.193657	-0.126988	2.526469	0.087740	-1.796223	-0.169404	0.460535	0.711250	0.599164	0.372622	3.567481	3.464532
99	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.400108	-0.169763	-1.226324	0.657023	-1.305477	1.061627	0.247582	0.190861	0.731123	0.670788	0.345721	1.866005	2.028297
100	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.020849	-0.509904	-0.894702	-0.197479	-1.084952	0.155181	-0.280045	-0.214733	0.754103	0.707159	0.341869	4.058245	4.532968
101	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	16.88%	1.314658	1.284191	-0.291071	-0.624568	-3.213746	-3.056882	-0.030017	-1.563155	0.690616	0.642855	0.324623	2.477853	2.334917
102	N08	RF_maintenance	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	1.211969	0.574604	-0.605342	0.491539	15.356693	15.814396	16.611723	73.419182	0.665361	0.668976	0.394456	2.416957	2.691735
103	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	1.30%	0.495105	1.293478	-0.574309	-0.623044	-2.582061	-2.512957	-1.540221	-1.616718	0.700958	0.649721	0.326063	2.635222	2.342142
104	N08	RF_maintenance	100.00%	10.43%	10.43%	89.57%	100.00%	0.00%	1.282981	2.053392	-0.850090	1.378885	-2.731725	32.037002	-0.947344	23.768956	0.362559	0.329425	-0.225477	5.904000	5.208054
105	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.555724	4.317346	6.212145	6.505624	16.112306	16.160876	1.109700	1.083226	0.034256	0.039046	0.007200	1.147587	1.146686
106	N09	digital_ok	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	-0.050082	0.055443	0.420521	0.223734	15.445684	15.463439	1.180200	-0.189579	0.737763	0.730190	0.445730	2.487907	2.295680
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.070433	3.176365	5.198819	5.379920	1.730201	1.304947	0.198113	0.305745	0.038321	0.042649	0.005113	1.186526	1.186827
108	N09	digital_ok	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	-0.253436	0.015806	-0.064166	-0.537906	15.379960	15.326530	-0.596639	-1.263922	0.712015	0.705344	0.453278	2.111058	2.219477
109	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.093615	-0.325875	-0.842797	-0.108971	-0.229248	0.211656	1.373545	0.833944	0.736573	0.683391	0.360149	1.571194	1.505462
110	N10	RF_maintenance	100.00%	7.82%	0.00%	0.00%	100.00%	0.00%	8.151316	-0.765822	-0.243648	-1.115572	-0.770377	-0.821024	3.743385	-0.930664	0.646289	0.680719	0.294122	5.905529	5.194617
111	N10	digital_ok	0.00%	6.52%	0.00%	0.00%	6.49%	0.00%	-0.329436	-0.887135	-0.173554	-1.046976	0.635370	-0.888111	0.067806	1.002771	0.709098	0.656443	0.356010	1.708517	1.496187
112	N10	RF_maintenance	100.00%	50.85%	52.15%	0.00%	100.00%	0.00%	0.806875	1.428128	2.041535	1.419550	16.302356	16.378765	-0.496083	-0.699018	0.739655	0.648413	0.272397	0.000000	0.000000
116	N07	RF_maintenance	0.00%	6.52%	0.00%	0.00%	100.00%	0.00%	-0.159123	-0.666500	1.308929	-0.738604	-0.074431	-0.869981	0.009702	-0.615013	0.689037	0.657572	0.356799	3.350407	3.946159
117	N07	digital_ok	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	-0.993625	-0.871464	-0.796719	-0.798767	15.216788	15.279742	-1.379195	-1.576006	0.696664	0.677937	0.435554	2.308937	2.374587
118	N07	digital_ok	100.00%	49.15%	49.15%	0.00%	100.00%	0.00%	-0.575675	0.446567	-0.461975	0.181251	15.292783	15.536031	0.068251	0.228706	0.711107	0.696479	0.428069	2.202730	2.294811
119	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.012712	-0.846158	-0.568407	-0.521720	-3.362717	-0.477903	-1.568997	-0.511571	0.754225	0.703926	0.349948	3.853307	3.632402
120	N08	RF_maintenance	100.00%	64.80%	100.00%	0.00%	100.00%	0.00%	2.427220	6.451404	-1.181919	6.890328	-0.399019	1.316835	-0.258492	2.320694	0.387456	0.050008	0.288712	4.602723	1.356964
121	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.114664	0.295986	-0.476678	-1.171412	-3.159380	-0.768169	6.326338	5.784658	0.695035	0.641010	0.328693	5.480649	4.980413
122	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.242442	1.047641	-0.766473	-0.418322	-2.280711	-3.328589	-1.219327	-1.529208	0.693806	0.636648	0.331675	2.522378	2.252901
123	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.370970	1.251432	-0.009219	0.190181	-2.727524	-2.372497	-1.763980	-1.993208	0.691548	0.637242	0.332212	2.757290	2.552648
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.377364	3.492181	6.604281	6.837188	16.113346	16.156805	1.091367	1.328093	0.028337	0.031189	0.002080	1.166821	1.163408
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.959998	3.513787	6.535442	6.889002	16.115922	16.160914	1.131660	1.251018	0.030807	0.032992	-0.000298	1.216003	1.217194
127	N10	RF_maintenance	100.00%	50.85%	52.15%	0.00%	100.00%	0.00%	0.605143	1.224199	3.778423	2.778751	16.304419	16.382511	-0.047385	0.788952	0.742314	0.670404	0.248047	0.000000	0.000000
128	N10	digital_ok	100.00%	50.85%	52.15%	0.00%	100.00%	0.00%	0.407713	1.695136	2.246340	1.225630	16.305409	16.376713	0.082161	-0.225521	0.760337	0.662100	0.256452	0.000000	0.000000
129	N10	digital_ok	100.00%	55.67%	49.15%	0.00%	100.00%	0.00%	0.926493	0.538588	0.552070	0.119928	15.548762	15.496059	-0.129689	-0.127748	0.665458	0.653292	0.421631	2.583579	2.665317
130	N10	digital_maintenance	100.00%	56.98%	55.67%	0.00%	100.00%	0.00%	1.051708	1.074965	1.071546	0.277166	15.510889	15.501480	0.702296	1.683418	0.644808	0.628584	0.411967	2.440169	2.449535
135	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.863640	1.467177	2.862639	1.805226	16.303935	16.381011	-0.218687	-0.326015	0.084352	0.089060	0.008546	0.000000	0.000000
136	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.830751	2.100590	2.019011	1.742182	16.307763	16.379668	0.581137	1.549909	0.085072	0.095525	0.010323	0.000000	0.000000
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.262453	3.547004	6.437916	6.625634	16.118140	16.162880	1.185535	1.264598	0.032694	0.042576	0.004639	1.153447	1.155018
138	N07	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	3.899081	-0.090285	4.723770	0.062726	1.732546	0.308314	0.238409	-0.143527	0.045357	0.669092	0.464821	1.216081	3.756839
140	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.202553	-0.237492	-0.502017	-0.419347	0.326654	0.501340	0.799587	1.499521	0.741973	0.689656	0.347987	2.495985	2.365116
141	N13	digital_ok	100.00%	0.00%	54.76%	0.00%	100.00%	0.00%	0.133793	1.751771	0.719992	5.471881	0.154425	-1.419908	2.210269	0.701955	0.736997	0.469543	0.431072	4.846405	2.706289
142	N13	digital_ok	100.00%	71.32%	100.00%	0.00%	100.00%	0.00%	3.416737	3.972349	4.461273	6.223919	0.009795	1.207993	0.499849	0.461272	0.366077	0.041026	0.208548	5.227498	1.266632
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.070922	-1.167469	0.407239	-1.042683	0.352338	-0.927940	-0.053981	-0.834788	0.743946	0.709114	0.342797	2.654296	2.914605
144	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.108093	-0.178528	3.418458	3.180119	-1.239716	-1.605278	0.203265	0.317884	0.688819	0.651843	0.351996	2.333862	2.883515
145	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.051410	3.279435	6.054028	6.263641	1.610455	1.186162	0.455302	0.675623	0.030064	0.031732	0.001502	1.351188	1.402942
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.422586	4.058317	6.074504	6.416113	1.605095	1.226632	1.611298	1.967346	0.048805	0.053894	0.002600	1.327556	1.335626
155	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.218464	3.336022	7.410835	7.634297	16.304297	16.382037	-0.167958	-0.173644	0.031715	0.034230	0.001578	0.000000	0.000000
156	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.252320	-0.704833	0.093408	-0.559492	0.750071	-0.448773	1.211928	5.540868	0.054132	0.055730	0.004061	1.187263	1.188657
157	N12	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.036266	-1.065859	0.264319	-1.270888	0.873030	-1.265814	0.114238	-0.190282	0.069467	0.063091	0.004676	1.250567	1.248627
158	N12	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.093793	0.047999	1.683681	0.685425	-0.152167	0.403263	2.042196	0.920148	0.079212	0.064400	0.008749	1.275622	1.280207
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.014799	3.364759	5.987684	6.186439	1.597020	1.145504	0.457357	0.601398	0.045471	0.044460	0.000090	1.221155	1.232356
161	N13	digital_ok	100.00%	0.00%	10.43%	0.00%	100.00%	0.00%	-0.139700	10.237917	-0.014098	2.043711	0.682151	-1.102156	0.272922	-0.068853	0.743247	0.554015	0.339416	5.479247	7.030397
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.413249	-0.775264	-0.488854	-0.825513	-0.341156	-0.667382	0.660323	-0.433030	0.740886	0.689997	0.342132	3.146964	2.558074
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.207850	0.116777	0.675569	1.275097	0.843359	0.350710	0.441614	0.530555	0.743425	0.681877	0.345634	2.473211	2.368035
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.133761	0.181120	0.032696	0.894336	0.660731	0.583783	-0.082522	1.023060	0.742150	0.682713	0.349415	2.368422	2.143705
165	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.848291	0.221607	-1.093330	0.824475	-1.486931	0.455153	-0.107307	0.735023	0.742296	0.677020	0.352421	2.184485	2.452791
166	N14	RF_maintenance	100.00%	10.43%	2.61%	0.00%	100.00%	0.00%	4.841873	2.434224	-0.260838	-0.621442	-2.445358	-0.878468	6.801395	37.499243	0.661440	0.608427	0.260517	3.375191	3.117673
167	N15	digital_ok	100.00%	10.43%	10.43%	0.00%	100.00%	0.00%	4.327875	2.474508	0.248791	0.715140	0.198658	-0.589533	29.062020	9.160660	0.573322	0.579078	0.246914	2.417700	3.187172
168	N15	RF_maintenance	0.00%	7.82%	9.13%	0.00%	100.00%	0.00%	1.750877	2.054095	0.642821	1.015873	-0.843917	-0.167012	-1.869875	-2.070388	0.700709	0.634832	0.352564	4.276536	4.716191
169	N15	digital_ok	0.00%	9.13%	9.13%	0.00%	9.09%	0.00%	2.134744	1.779943	0.937542	0.819256	-0.145457	-0.646234	-2.101618	-1.776040	0.686027	0.622058	0.349032	1.509315	1.499173
170	N15	digital_ok	0.00%	10.43%	10.43%	0.00%	10.39%	0.00%	2.125253	1.479477	0.978507	0.691952	0.018025	-0.974979	-2.061312	-2.094310	0.671366	0.623124	0.346761	1.506505	1.388808
176	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.789670	-1.132236	-0.774894	-0.971114	-0.279159	-0.442764	-0.531227	-0.824988	0.052816	0.066755	0.005541	1.211791	1.210702
177	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.832397	0.255735	-0.943635	1.546540	-0.369753	1.427137	1.694951	2.345347	0.064000	0.063155	0.004807	1.231050	1.232021
178	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.682481	-0.963370	-0.400668	-0.655157	0.373032	0.200034	-0.085681	-0.385980	0.066294	0.062887	0.005478	1.239995	1.240591
179	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.340948	1.367375	1.132793	0.846644	15.505970	15.690443	0.713670	0.517809	0.053136	0.065065	0.008594	1.213963	1.217845
180	N13	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.703647	3.582659	-0.930574	6.244044	-0.499964	1.094788	-0.602190	0.484115	0.732202	0.132424	0.485603	13.890577	1.388490
181	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.060282	8.526733	6.075857	4.395933	1.593637	0.789727	0.444013	1.019234	0.048273	0.204186	0.100483	1.205524	1.688978
182	N13	RF_maintenance	100.00%	0.00%	6.52%	0.00%	100.00%	0.00%	1.463688	-0.615913	0.594022	-1.175374	-1.167986	7.799806	-1.923505	24.226684	0.724504	0.628471	0.377259	4.516739	3.824908
183	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.609673	0.000864	-0.186952	0.508489	0.279713	0.763060	0.083291	3.291930	0.737843	0.661905	0.359372	2.413725	2.235549
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.601291	0.362789	1.006246	1.175220	0.358280	0.610392	0.497146	0.209710	0.735248	0.666290	0.348399	2.963192	2.472719
185	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
186	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
187	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
189	N15	digital_ok	100.00%	58.28%	58.28%	0.00%	100.00%	0.00%	1.323178	1.076472	0.928557	0.418899	15.615326	15.563443	0.254823	0.182449	0.641014	0.623832	0.412253	1.849260	1.868387
190	N15	digital_ok	100.00%	60.89%	100.00%	0.00%	100.00%	0.00%	7.749193	3.967148	1.688194	7.640890	15.582637	16.165603	39.114978	1.575664	0.570406	0.045168	0.419091	1.481292	1.067483
191	N15	digital_ok	100.00%	59.58%	59.58%	0.00%	100.00%	0.00%	0.912533	0.973188	0.747871	0.317214	15.614795	15.567810	2.142807	2.897043	0.611266	0.587730	0.413743	1.895304	2.014033
203	N18	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
205	N19	RF_ok	0.00%	7.82%	7.82%	0.00%	100.00%	0.00%	2.021094	0.113797	0.832856	-0.673995	-0.476099	-3.144575	-2.061123	2.627723	0.693755	0.641766	0.348024	3.988064	3.567108
206	N19	RF_ok	0.00%	10.43%	9.13%	0.00%	100.00%	0.00%	-0.000864	1.571030	0.175106	0.531244	-0.698860	-1.578728	-0.071505	-1.621939	0.651753	0.631447	0.352305	2.824054	3.025223
207	N19	RF_ok	0.00%	10.43%	9.13%	0.00%	100.00%	0.00%	2.255449	1.642361	0.998111	0.719582	0.112547	0.035707	-2.068591	-1.952926	0.665115	0.620086	0.341370	7.334525	7.585301
220	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
221	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
222	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	0.00%	7.82%	9.13%	0.00%	100.00%	0.00%	1.395588	1.158582	0.381350	0.426777	-1.803530	2.334512	-1.846811	-1.539845	0.691527	0.620122	0.356080	3.757014	2.901746
224	N19	RF_ok	0.00%	10.43%	10.43%	0.00%	100.00%	0.00%	2.626864	2.299944	1.407315	1.327550	1.169909	0.518147	-2.305114	-2.312605	0.657005	0.591932	0.346843	3.992818	3.233101
241	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
242	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
243	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.486881	1.368820	12.011190	11.884024	535.545115	559.216531	6700.225726	6699.748213	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.459066	4.232907	4.276850	4.328773	1.539814	1.143545	1.796487	1.014999	0.076384	0.066658	-0.009126	0.000000	0.000000
321	N02	not_connected	0.00%	11.73%	29.99%	0.00%	100.00%	0.00%	0.657125	0.222996	-0.107524	-0.257490	-2.595293	-3.052330	1.159765	1.041105	0.603871	0.494663	0.310302	0.000000	0.000000
323	N02	not_connected	0.00%	17.86%	32.59%	0.00%	100.00%	0.00%	3.672483	0.885452	0.229405	0.210761	-2.403385	-2.113717	0.926653	-1.163884	0.460981	0.471042	0.236950	0.000000	0.000000
324	N04	not_connected	0.00%	11.73%	32.59%	0.00%	100.00%	0.00%	1.456946	1.492041	0.493525	0.559527	-1.220847	-1.269867	-1.911722	-2.096701	0.592766	0.480203	0.304067	0.000000	0.000000
329	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.162790	-0.624897	1.356893	-1.277434	15.322708	15.255860	3.317044	-1.679973	0.066600	0.063782	0.022110	0.000000	0.000000
333	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.862412	-0.623927	3.332578	-1.299558	15.331463	15.241477	0.155648	-1.422098	0.059366	0.063826	0.018840	0.000000	0.000000

# print ex_ants for easy copy-pasting to YAML file
proposed_ex_ants = [ant for i, ant in enumerate(ants) if np.any([col[i] > 0 for col in bar_cols.values()])]
print('ex_ants: [' + ", ".join(str(ant) for ant in proposed_ex_ants) + ']')
print(f'\nunflagged_ants: [{", ".join([str(ant) for ant in ants if ant not in proposed_ex_ants])}]')
# "golden" means no flags and good a priori status
golden_ants = ", ".join([str(ant) for ant in ants if ((ant not in proposed_ex_ants) and (a_priori_statuses[ant] in good_statuses.split(',')))])
print(f'\ngolden_ants: [{golden_ants}]')

ex_ants: [3, 4, 7, 8, 10, 18, 21, 27, 28, 29, 31, 32, 33, 36, 37, 38, 40, 41, 42, 45, 50, 51, 52, 53, 54, 55, 56, 57, 67, 68, 69, 70, 71, 72, 73, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 110, 111, 112, 116, 117, 118, 119, 120, 121, 125, 126, 127, 128, 129, 130, 135, 136, 137, 138, 141, 142, 145, 150, 155, 156, 157, 158, 160, 161, 166, 167, 168, 169, 170, 176, 177, 178, 179, 180, 181, 182, 185, 186, 187, 189, 190, 191, 203, 205, 206, 207, 220, 221, 222, 223, 224, 241, 242, 243, 320, 321, 323, 324, 329, 333]

unflagged_ants: [5, 9, 15, 16, 17, 19, 20, 30, 46, 65, 66, 99, 109, 122, 123, 140, 143, 144, 162, 163, 164, 165, 183, 184]

golden_ants: [5, 9, 15, 16, 17, 19, 20, 30, 46, 65, 66, 99, 109, 122, 123, 140, 143, 144, 162, 163, 164, 165, 183, 184]

# write to csv
outpath = os.path.join(nb_outdir, f'rtp_summary_table_{JD}.csv')
print(f'Now saving Table 2 to a csv at {outpath}')
df.to_csv(outpath)

Now saving Table 2 to a csv at /home/obs/src/H5C_Notebooks/_rtp_summary_/rtp_summary_table_2459781.csv

# Load antenna positions
data_list = sorted(glob.glob(os.path.join(data_path, f'zen.{JD}.?????.sum.uvh5')))
hd = io.HERAData(data_list[len(data_list) // 2])

# Figure out where to draw the nodes
node_centers = {}
for node in sorted(set(list(nodes.values()))):
    if np.isfinite(node):
        this_node_ants = [ant for ant in ants + unused_ants if nodes[ant] == node]
        if len(this_node_ants) == 1:
            # put the node label just to the west of the lone antenna 
            node_centers[node] = hd.antpos[ant][node] + np.array([-14.6 / 2, 0, 0])
        else:
            # put the node label between the two antennas closest to the node center
            node_centers[node] = np.mean([hd.antpos[ant] for ant in this_node_ants], axis=0)
            closest_two_pos = sorted([hd.antpos[ant] for ant in this_node_ants], 
                                     key=lambda pos: np.linalg.norm(pos - node_centers[node]))[0:2]
            node_centers[node] = np.mean(closest_two_pos, axis=0)

def Plot_Array(ants, unused_ants, outriggers):
    plt.figure(figsize=(16,16))
    
    plt.scatter(np.array([hd.antpos[ant][0] for ant in hd.data_ants if ant in ants]), 
                np.array([hd.antpos[ant][1] for ant in hd.data_ants if ant in ants]), c='w', s=0)

    # connect every antenna to their node
    for ant in ants:
        if nodes[ant] in node_centers:
            plt.plot([hd.antpos[ant][0], node_centers[nodes[ant]][0]], 
                     [hd.antpos[ant][1], node_centers[nodes[ant]][1]], 'k', zorder=0)

    rc_color = '#0000ff'
    antm_color = '#ffa500'
    autom_color = '#ff1493'

    # Plot 
    unflagged_ants = []
    for i, ant in enumerate(ants):
        ant_has_flag = False
        # plot large blue annuli for redcal flags
        if use_redcal:
            if redcal_flagged_frac[ant] > 0:
                ant_has_flag = True
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=7 * (2 - 1 * float(not outriggers)), fill=True, lw=0,
                                                color=rc_color, alpha=redcal_flagged_frac[ant]))
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=6 * (2 - 1 * float(not outriggers)), fill=True, color='w'))
        
        # plot medium green annuli for ant_metrics flags
        if use_ant_metrics: 
            if ant_metrics_xants_frac_by_ant[ant] > 0:
                ant_has_flag = True
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=6 * (2 - 1 * float(not outriggers)), fill=True, lw=0,
                                                color=antm_color, alpha=ant_metrics_xants_frac_by_ant[ant]))
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=5 * (2 - 1 * float(not outriggers)), fill=True, color='w'))
        
        # plot small red annuli for auto_metrics
        if use_auto_metrics:
            if ant in auto_ex_ants:
                ant_has_flag = True                
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=5 * (2 - 1 * float(not outriggers)), fill=True, lw=0, color=autom_color)) 
        
        # plot black/white circles with black outlines for antennas
        plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=4 * (2 - 1 * float(not outriggers)), fill=True, color=['w', 'k'][ant_has_flag], ec='k'))
        if not ant_has_flag:
            unflagged_ants.append(ant)

        # label antennas, using apriori statuses if available
        try:
            bgc = matplotlib.colors.to_rgb(status_colors[a_priori_statuses[ant]])
            c = 'black' if (bgc[0]*0.299 + bgc[1]*0.587 + bgc[2]*0.114) > 186 / 256 else 'white'
        except:
            c = 'k'
            bgc='white'
        plt.text(hd.antpos[ant][0], hd.antpos[ant][1], str(ant), va='center', ha='center', color=c, backgroundcolor=bgc)

    # label nodes
    for node in sorted(set(list(nodes.values()))):
        if not np.isnan(node) and not np.all(np.isnan(node_centers[node])):
            plt.text(node_centers[node][0], node_centers[node][1], str(node), va='center', ha='center', bbox={'color': 'w', 'ec': 'k'})
    
    # build legend 
    legend_objs = []
    legend_labels = []
    
    # use circles for annuli 
    legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgecolor='k', markerfacecolor='w', markersize=13))
    legend_labels.append(f'{len(unflagged_ants)} / {len(ants)} Total {["Core", "Outrigger"][outriggers]} Antennas Never Flagged')
    legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markerfacecolor='k', markersize=15))
    legend_labels.append(f'{len(ants) - len(unflagged_ants)} Antennas {["Core", "Outrigger"][outriggers]} Flagged for Any Reason')

    if use_auto_metrics:
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgewidth=2, markeredgecolor=autom_color, markersize=15))
        legend_labels.append(f'{len([ant for ant in auto_ex_ants if ant in ants])} {["Core", "Outrigger"][outriggers]} Antennas Flagged by Auto Metrics')
    if use_ant_metrics: 
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgewidth=2, markeredgecolor=antm_color, markersize=15))
        legend_labels.append(f'{np.round(np.sum([frac for ant, frac in ant_metrics_xants_frac_by_ant.items() if ant in ants]), 2)} Antenna-Nights on' 
                             f'\n{np.sum([frac > 0 for ant, frac in ant_metrics_xants_frac_by_ant.items() if ant in ants])} {["Core", "Outrigger"][outriggers]} Antennas '
                             'Flagged by Ant Metrics\n(alpha indicates fraction of time)')        
    if use_redcal:
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgewidth=2, markeredgecolor=rc_color, markersize=15))
        legend_labels.append(f'{np.round(np.sum(list(redcal_flagged_frac.values())), 2)} Antenna-Nights on' 
                             f'\n{np.sum([frac > 0 for ant, frac in redcal_flagged_frac.items() if ant in ants])} {["Core", "Outrigger"][outriggers]} Antennas '
                             'Flagged by Redcal\n(alpha indicates fraction of time)')

    # use rectangular patches for a priori statuses that appear in the array
    for aps in sorted(list(set(list(a_priori_statuses.values())))):
        if aps != 'Not Found':
            legend_objs.append(plt.Circle((0, 0), radius=7, fill=True, color=status_colors[aps]))
            legend_labels.append(f'A Priori Status:\n{aps} ({[status for ant, status in a_priori_statuses.items() if ant in ants].count(aps)} {["Core", "Outrigger"][outriggers]} Antennas)')

    # label nodes as a white box with black outline
    if len(node_centers) > 0:
        legend_objs.append(matplotlib.patches.Patch(facecolor='w', edgecolor='k'))
        legend_labels.append('Node Number')

    if len(unused_ants) > 0:
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markerfacecolor='grey', markersize=15, alpha=.2))
        legend_labels.append(f'Anntenna Not In Data')
        
    
    plt.legend(legend_objs, legend_labels, ncol=2, fontsize='large', framealpha=1)
    
    if outriggers:
        pass
    else:
        plt.xlim([-200, 150])
        plt.ylim([-150, 150])        
       
    # set axis equal and label everything
    plt.axis('equal')
    plt.tight_layout()
    plt.title(f'Summary of {["Core", "Outrigger"][outriggers]} Antenna Statuses and Metrics on {JD}', size=20)    
    plt.xlabel("Antenna East-West Position (meters)", size=12)
    plt.ylabel("Antenna North-South Position (meters)", size=12)
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)
    xlim = plt.gca().get_xlim()
    ylim = plt.gca().get_ylim()    
        
    # plot unused antennas
    plt.autoscale(False)    
    for ant in unused_ants:
        if nodes[ant] in node_centers:
            plt.plot([hd.antpos[ant][0], node_centers[nodes[ant]][0]], 
                     [hd.antpos[ant][1], node_centers[nodes[ant]][1]], 'k', alpha=.2, zorder=0)
        
        plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=4, fill=True, color='w', ec=None, alpha=1, zorder=0))
        plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=4, fill=True, color='grey', ec=None, alpha=.2, zorder=0))
        if hd.antpos[ant][0] < xlim[1] and hd.antpos[ant][0] > xlim[0]:
            if hd.antpos[ant][1] < ylim[1] and hd.antpos[ant][1] > ylim[0]:
                plt.text(hd.antpos[ant][0], hd.antpos[ant][1], str(ant), va='center', ha='center', color='k', alpha=.2) 

Figure 1: Array Plot of Flags and A Priori Statuses

This plot shows all antennas, which nodes they are connected to, and their a priori statuses (as the highlight text of their antenna numbers). It may also show (depending on what is finished running):

• Whether they were flagged by auto_metrics (red circle) for bandpass shape, overall power, temporal variability, or temporal discontinuities. This is done in a binary fashion for the whole night.
• Whether they were flagged by ant_metrics (green circle) as either dead (on either polarization) or crossed, with the transparency indicating the fraction of the night (i.e. number of files) that were flagged.
• Whether they were flagged by redcal (blue circle) for high chi^2, with the transparency indicating the fraction of the night (i.e. number of files) that were flagged.

Note that the last fraction does not include antennas that were flagged before going into redcal due to their a priori status, for example.

core_ants = [ant for ant in ants if ant < 320]
outrigger_ants = [ant for ant in ants if ant >= 320]
Plot_Array(ants=core_ants, unused_ants=unused_ants, outriggers=False)
if len(outrigger_ants) > 0:
    Plot_Array(ants=outrigger_ants, unused_ants=sorted(set(unused_ants + core_ants)), outriggers=True)

Metadata

from hera_qm import __version__
print(__version__)
from hera_cal import __version__
print(__version__)

2.0.2
3.1.1.dev2+g1b5039f