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_statuses = "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_statuses = "{good_statuses}"')

JD = "2459790"
data_path = "/mnt/sn1/2459790"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
good_statuses = "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: 7-29-2022

# 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/sn1/2459790/zen.2459790.25319.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 372 ant_metrics files matching glob /mnt/sn1/2459790/zen.2459790.?????.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 38 ant_metrics files matching glob /mnt/sn1/2459790/zen.2459790.?????.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 (np.max([dead_ant_frac[ap][ant] for ap in antpols]) + crossed_ant_frac[ant] == 1) 
                                                                                 and (crossed_ant_frac[ant] > .5)])

# 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
n_apriori_good = len([ant for ant in ants if a_priori_statuses[ant] in good_statuses.split(',')])
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'] = f'{len(apriori_good_flagged)} / {n_apriori_good} total a priori good antennas:<br>' + \
                                            comma_sep_paragraph(apriori_good_flagged)
to_show['A Priori Bad Antennas Not Flagged'] = f'{len(aprior_bad_unflagged)} / {len(ants) - n_apriori_good} total a priori bad antennas:<br>' + \
                                            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	2459790
Date	7-29-2022
LST Range	15.995 -- 17.995 hours
X-Engine Status	✅ ✅ ✅ ✅ ✅ ✅ ✅ ✅
Number of Files	372
Total Number of Antennas	147
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 42 RF_ok: 11 digital_maintenance: 1 digital_ok: 87 not_connected: 3
Commanded Signal Source	antenna
Antennas in Commanded State	108 / 147 (73.5%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N10
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	68 / 147 (46.3%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	114 / 147 (77.6%)
Redcal Done?	✅
Redcal Flagged Antennas	15 / 147 (10.2%)
Never Flagged Antennas	10 / 147 (6.8%)
A Priori Good Antennas Flagged	80 / 87 total a priori good antennas: 5, 7, 9, 10, 15, 16, 17, 19, 20, 21, 29, 30, 31, 37, 40, 41, 42, 45, 46, 50, 51, 53, 54, 55, 56, 57, 68, 69, 71, 72, 73, 81, 83, 84, 88, 91, 92, 93, 99, 101, 103, 105, 106, 107, 108, 109, 111, 117, 118, 121, 122, 123, 128, 129, 130, 135, 138, 140, 141, 142, 143, 144, 145, 160, 161, 162, 165, 167, 169, 170, 176, 177, 178, 179, 181, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	3 / 60 total a priori bad antennas: 3, 67, 100

# 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_2459790.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%	1.521002	-0.415874	-0.397819	-0.637493	-1.003110	-0.481391	-0.494977	-0.089300	0.646221	0.642047	0.415760	3.571465	3.775135
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.449901	6.171632	-0.342304	1.581410	-0.159998	0.636469	8.498932	2.453500	0.659029	0.648597	0.412256	5.674331	5.970091
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.238507	2.958880	1.375139	6.326471	1.355962	5.325416	3.858274	-1.335545	0.666664	0.659358	0.412287	7.110702	7.108611
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.884360	-1.480125	0.494291	0.987161	-0.421800	0.124028	-0.516814	16.879183	0.661227	0.654208	0.401917	4.746852	4.962232
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	22.625530	24.233279	22.428598	23.652003	33.377455	34.423809	1.073756	0.137819	0.637694	0.625895	0.397935	3.338468	3.240026
9	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.297379	-1.307755	0.018535	1.359296	1.449393	11.621406	-0.074049	14.647376	0.642364	0.636599	0.400180	3.114049	3.056996
10	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.817808	5.900215	13.267887	10.057039	17.327297	11.519051	13.475919	13.465459	0.616931	0.609220	0.402250	2.703770	2.737008
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	26.32%	0.684619	2.333641	1.805994	-0.152185	0.391524	0.755406	0.166633	1.783574	0.678038	0.670163	0.412306	2.342446	2.559868
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	13.16%	-0.798114	-0.252875	-0.700884	1.152372	-1.211257	0.270441	1.109652	-0.869004	0.685170	0.681261	0.407983	2.368784	2.301993
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	13.16%	0.981314	2.100930	0.177691	-0.023039	-0.270226	0.186530	0.734771	0.539853	0.685896	0.683049	0.397619	2.318964	2.750007
18	N01	RF_maintenance	100.00%	0.00%	48.39%	0.00%	100.00%	0.00%	9.016704	8.818083	1.052157	3.160028	3.683416	8.796172	90.544696	83.389980	0.617191	0.486893	0.409279	4.430803	3.190969
19	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.312050	9.613432	9.371180	11.541786	9.677225	5.354811	3.188264	1.506773	0.672347	0.666245	0.399428	4.019016	4.638637
20	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.835110	10.861989	1.685716	11.987232	1.794882	16.814390	-0.646132	-1.311130	0.656975	0.641689	0.395342	3.096751	3.460853
21	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.209222	0.975152	0.877425	0.940526	-0.852800	0.046592	0.570930	6.779951	0.636693	0.627216	0.399856	3.501438	3.385305
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	33.115640	35.780920	63.071017	63.541161	46.774927	46.620650	10.362973	8.895502	0.042116	0.048670	0.004716	1.230622	1.244993
28	N01	RF_maintenance	100.00%	40.32%	100.00%	0.00%	100.00%	0.00%	17.999876	25.151951	7.458779	12.484619	43.109654	49.287513	10.085097	52.347595	0.472482	0.251088	0.285037	24.866662	7.825443
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	10.53%	-1.520434	-0.961049	1.091667	-0.652337	0.084337	-0.461341	-0.722417	-0.493009	0.706674	0.703850	0.404679	1.960184	2.394433
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.581024	-0.107635	1.382941	-0.780116	1.397135	-0.313760	8.809668	0.022548	0.696087	0.694539	0.399432	4.473591	5.457960
31	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	15.79%	5.26%	0.177807	-0.558127	-0.768527	-0.462450	-1.155509	-0.382316	-0.504971	-0.746398	0.694373	0.686931	0.400936	1.459454	1.696615
32	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	46.747724	8.124520	3.576367	1.347410	15.215174	22.081389	42.608415	119.182392	0.607218	0.641983	0.327233	4.094904	3.597104
33	N02	RF_maintenance	100.00%	0.00%	48.39%	0.00%	100.00%	0.00%	1.095259	5.085199	1.808690	0.747986	-0.059067	3.483360	3.376527	16.809631	0.648373	0.487753	0.467565	2.938506	1.997294
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.704960	10.116021	0.327300	0.619525	0.823050	0.133149	0.199128	1.062015	0.643530	0.630088	0.395359	3.619539	3.450579
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.050798	1.256450	-0.036951	-0.435774	-0.230690	0.312474	0.075294	10.800952	0.671652	0.660955	0.402477	4.277091	4.697005
38	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.267515	0.438543	-0.617868	-0.240610	1.085891	0.410498	6.035896	1.762048	0.689897	0.683087	0.407677	5.205930	4.948485
40	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	42.11%	1.457951	0.106773	-0.037648	-0.743321	-0.974090	-0.670376	-0.290847	-0.901919	0.710092	0.708230	0.403652	2.119643	2.327384
41	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	28.95%	2.201074	-0.975850	3.468809	1.283226	3.138535	0.662854	-1.207984	3.716362	0.715041	0.711720	0.393765	2.147671	2.442175
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	52.63%	-0.090952	0.746400	3.130201	-0.008807	1.085819	-0.483778	-0.611446	-0.567861	0.721613	0.719781	0.410267	3.585332	3.564391
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.272790	1.316311	-0.444495	1.416001	-1.168352	0.204436	0.163275	25.464303	0.682824	0.671043	0.409848	3.387149	3.493563
46	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.255382	-0.647945	0.419183	-0.472936	-0.229747	-1.065610	-0.338325	4.852941	0.661490	0.653094	0.417278	2.762149	2.886119
50	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.691958	38.759384	-0.650421	2.501175	-0.216945	10.496608	1.262542	4.266574	0.650859	0.589431	0.359615	5.613371	7.414400
51	N03	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.231024	47.497338	-0.104133	78.978960	-0.430224	47.425983	1.629286	22.023689	0.678289	0.040669	0.428399	4.874480	1.238137
52	N03	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	16.372552	55.980260	0.981707	79.765238	3.492161	47.919470	11.799091	21.741822	0.659665	0.037667	0.411582	11.106672	1.205723
53	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	13.16%	1.388696	1.691642	-0.158140	-0.569970	-1.248053	-0.203483	1.735660	1.537441	0.713143	0.708414	0.397925	1.864203	2.033933
54	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.529705	1.055777	0.540893	0.467078	8.259194	12.554069	2.636152	16.122909	0.717896	0.713324	0.370961	21.088963	20.727157
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.504644	0.852192	0.079333	1.537243	1.484434	1.940851	8.703946	0.465484	0.722170	0.722841	0.397750	5.590464	6.582316
56	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.580366	1.444461	2.263383	2.606050	2.047872	3.500737	-0.055898	10.352251	0.729138	0.728046	0.407061	4.917789	4.953184
57	N04	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	32.722029	1.784847	62.648796	1.622141	46.653858	6.935595	7.605177	3.434984	0.049490	0.721476	0.463917	1.512759	4.444995
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.568003	0.162009	0.869442	0.328597	1.727290	1.367587	0.207324	1.030630	0.657270	0.647118	0.400932	1.385752	1.410226
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.624806	-0.016345	-0.704413	1.113404	0.308938	1.199409	-0.392073	3.124422	0.681265	0.671820	0.392982	1.529239	1.592531
67	N03	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.756547	-0.852337	0.141678	0.617324	0.550483	1.118390	0.999202	2.482634	0.701872	0.695038	0.388710	7.095329	9.388582
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	5.26%	0.542154	0.190804	-0.171746	0.633423	0.875716	2.195458	-0.131758	0.443062	0.711054	0.706192	0.383496	1.904907	2.023213
69	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	47.37%	-1.219308	-0.742504	0.008807	-0.347959	0.793091	0.578427	0.104652	-0.622644	0.720756	0.725447	0.385695	4.629634	5.246323
70	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.727594	-1.685143	0.214140	-0.671580	-0.469335	-0.318317	-0.015595	1.000834	0.731066	0.728309	0.410852	19.759453	14.395418
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	100.00%	0.021318	-1.171040	-0.677951	0.513706	0.197434	1.428146	-0.537709	-0.865489	0.728708	0.734966	0.406279	28.254659	40.442315
72	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.434747	-0.529161	-0.245733	2.880807	0.981366	2.825753	2.851920	-0.493792	0.717778	0.723628	0.412257	4.166777	4.193996
73	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.139821	1.276817	-0.132756	0.314171	-0.196619	-0.408068	6.038104	0.495494	0.707342	0.702161	0.429556	3.870312	3.878121
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.336113	6.395126	-0.424891	5.605101	0.382786	6.229377	10.051652	1.433882	0.642153	0.635825	0.390173	5.153980	5.922205
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.237629	-0.174513	-0.045997	0.713138	1.768566	-0.937490	0.054286	-0.152115	0.668912	0.663829	0.388594	7.183098	5.634430
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.476033	5.492073	17.037380	6.467092	23.618660	6.018879	-1.127536	-0.993124	0.693585	0.694955	0.381646	5.842233	6.668505
84	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	12.022500	11.824979	0.467348	1.313116	0.662379	3.623796	2.741784	6.386698	0.715827	0.713714	0.379357	6.213818	6.186056
85	N08	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
86	N08	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
87	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.133015	17.467764	7.978908	14.016753	26.190187	17.591398	199.064052	-1.458161	0.719966	0.731050	0.414832	2.941144	3.027425
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.978773	30.690534	55.240352	56.186738	46.939566	46.699850	14.866837	9.400276	0.045072	0.049674	0.001626	1.007305	1.006398
90	N09	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.567167	5.294811	1.491071	5.283809	-0.229229	5.872657	3.034547	-0.338455	0.679388	0.679375	0.428774	1.864863	1.833712
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.267564	31.588068	55.078388	57.024721	46.929817	46.602531	10.445736	9.602217	0.037813	0.041492	0.000304	1.141183	1.146197
92	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	48.456506	74.710902	9.957742	12.310009	50.977011	54.789464	10.601602	26.232621	0.095884	0.092257	0.012750	7.996896	9.797230
93	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.373787	0.695485	4.175789	-0.642237	4.750920	1.274292	-0.652630	0.289277	0.094554	0.094661	-0.014977	0.000000	0.000000
94	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.098668	-2.047438	-0.746163	-0.423713	-1.109927	1.514837	4.473951	19.597535	0.057288	0.068337	0.007092	0.000000	0.000000
98	N07	digital_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
99	N07	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
100	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.468897	0.530554	1.090099	-0.146008	0.678605	-0.855517	0.601359	0.165275	0.690816	0.685252	0.385927	4.192842	3.832074
101	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.591652	15.344648	12.677782	12.050613	15.080009	13.990731	0.767481	-0.591208	0.711970	0.707234	0.395036	4.347245	3.647847
102	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.373806	22.570403	97.027864	93.025494	2960.379662	2677.471808	18114.718507	15328.771731	nan	nan	nan	0.000000	0.000000
103	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.462543	12.407669	-0.310747	0.840436	-0.511519	-0.272091	0.028406	-0.473176	0.727055	0.724103	0.401852	3.034016	2.707351
104	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.172525	105.766458	1.558118	14.385794	1.025235	5.151840	1.847188	0.399960	0.722715	0.720831	0.413931	0.000000	0.000000
105	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	33.567568	39.897205	55.029433	56.873595	46.933151	46.947237	8.365327	8.146149	0.038267	0.040719	0.004228	0.877670	0.864233
106	N09	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
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.608553	30.693119	53.018209	53.840112	46.814304	46.364787	8.900733	9.582869	0.044876	0.049218	0.003704	1.156652	1.174236
108	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.399363	10.937748	10.682091	12.484501	12.635478	15.212015	-1.204227	-1.736195	0.665763	0.662998	0.429574	2.869952	2.940853
109	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.372426	0.321956	0.499029	0.232361	-0.961863	-0.214595	2.753593	0.962209	0.082327	0.081405	0.017620	0.000000	0.000000
110	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	52.225406	6.805543	4.561854	5.069005	13.716792	16.321071	14.063971	125.680284	0.076752	0.070925	0.008161	1.015971	1.018683
111	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.883118	1.567355	0.034051	2.536210	0.244111	2.317653	-0.389568	-0.035935	0.054040	0.061825	0.004276	0.979634	0.979546
112	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.463066	-0.532177	-0.031931	2.087322	-0.026905	1.266404	-0.057072	-1.342956	0.058071	0.064865	0.005705	0.905479	0.922792
116	N07	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
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.391695	6.369007	7.150999	7.585480	7.230270	6.835374	-1.296104	-1.687817	0.663033	0.654571	0.406871	3.937552	4.090767
118	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.539363	2.367631	3.226453	-0.248138	2.910259	0.560459	2.062531	1.960701	0.673023	0.668665	0.397414	2.415923	2.343932
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.382431	2.049845	10.255347	-0.337926	12.763538	-0.307941	-1.456602	-0.307192	0.695963	0.690754	0.408264	2.681277	2.511707
120	N08	RF_maintenance	100.00%	40.32%	100.00%	0.00%	100.00%	0.00%	26.174238	44.029724	9.157076	70.073934	43.826474	47.546170	5.732629	14.153574	0.477609	0.056016	0.376169	2.129066	0.939106
121	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.043563	7.243922	-0.307183	0.567895	-0.832618	0.181617	25.019310	18.588259	0.714110	0.713223	0.401180	3.321417	3.569296
122	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.106318	13.986559	10.811051	11.514149	26.606364	26.678601	20.797187	19.772800	0.711919	0.710859	0.409402	3.793008	4.328433
123	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.625923	11.611871	2.233201	0.409573	0.975144	-0.268182	-0.273211	1.108409	0.710705	0.709785	0.416022	5.396513	4.758870
125	N09	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
126	N09	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
127	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.628492	-1.033466	0.372668	-0.755486	0.144669	-0.195585	-0.324242	0.035935	0.082883	0.078027	0.018750	63.450681	55.872674
128	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.429020	2.572935	0.172433	3.117099	-0.796872	3.612961	0.426283	-0.720386	0.068753	0.067640	0.010826	103.064693	103.221100
129	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.467476	-1.936499	0.362865	-0.869592	-0.456404	-0.930465	-0.524995	-0.130874	0.058226	0.059764	0.005123	41.927628	39.872150
130	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.027820	0.107864	1.353633	0.665597	-0.528540	0.552767	0.299732	7.526018	0.064400	0.060289	0.006381	31.092876	32.909113
135	N12	digital_ok	0.00%	32.26%	34.95%	0.00%	34.21%	0.00%	-0.411519	-0.475517	-0.818986	-0.219073	-1.005636	-0.393252	2.600877	1.429385	0.546230	0.535375	0.355007	1.707253	1.623423
136	N12	RF_maintenance	100.00%	29.57%	34.95%	0.00%	100.00%	0.00%	4.519259	13.101290	-0.176630	0.727833	2.893123	2.562472	3.659086	10.304009	0.548203	0.531906	0.333999	7.373435	8.605739
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	31.767440	31.987415	54.392933	55.342264	46.853030	46.637741	8.620470	10.845228	0.038199	0.046229	0.003348	1.283704	1.282251
138	N07	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	38.240403	3.812654	54.009986	2.022215	47.067205	0.837559	9.687070	0.559549	0.048222	0.667804	0.454205	1.165153	3.807482
140	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.513973	5.804169	0.844731	1.473059	2.589736	3.486017	4.279509	4.262587	0.682919	0.678422	0.403670	4.313440	4.226118
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.267632	6.583836	1.033585	12.727839	1.435969	5.066403	1.104217	18.640390	0.681565	0.666435	0.405134	3.778542	4.197297
142	N13	digital_ok	100.00%	51.08%	100.00%	0.00%	100.00%	0.00%	31.309827	37.978388	8.069366	64.110075	41.011320	46.815368	8.476108	8.764875	0.437480	0.045681	0.255969	8.832036	1.361816
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	5.26%	0.217008	0.255586	1.814519	1.951467	2.178414	1.199588	2.652783	-1.222837	0.685636	0.681519	0.407222	1.907915	1.722581
144	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	2.63%	-0.057210	0.016345	1.420015	-0.703914	3.181915	-1.112040	3.428507	1.485672	0.684023	0.678438	0.417009	1.935046	1.815968
145	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	31.531275	31.367852	63.452347	64.619665	46.772555	46.817851	9.077385	10.669026	0.036251	0.037506	-0.000465	1.732537	1.923084
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	33.346001	35.592098	63.183305	65.662730	46.667880	46.485341	10.468514	11.332435	0.053262	0.056704	0.002222	0.000000	0.000000
155	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	31.385309	32.911445	62.038689	63.411817	46.802537	46.630629	10.193798	9.713919	0.043054	0.042676	-0.000513	1.340450	1.416795
156	N12	RF_maintenance	100.00%	29.57%	34.95%	0.00%	100.00%	0.00%	11.288275	11.074671	12.924789	12.301859	18.132934	16.737613	17.630558	20.250524	0.554693	0.539719	0.354400	6.713992	7.027486
157	N12	RF_maintenance	100.00%	29.57%	34.95%	0.00%	100.00%	0.00%	9.054577	25.811857	11.519631	24.522123	15.077424	36.126355	12.114271	0.030803	0.556264	0.527820	0.362368	2.170611	2.133414
158	N12	RF_maintenance	100.00%	29.57%	29.57%	0.00%	100.00%	0.00%	4.115096	6.857774	6.211775	10.222958	11.732932	16.136568	24.969987	21.529725	0.574469	0.567452	0.368690	2.046054	2.233566
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.327514	29.026795	62.805136	63.968108	46.720386	46.590015	9.848154	10.832424	0.042492	0.043087	0.003793	1.191973	1.190399
161	N13	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
162	N13	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
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	10.53%	0.00%	1.128610	-0.489846	0.496489	-0.671030	0.026905	-0.760500	1.845269	0.081999	0.677622	0.673052	0.404469	2.080230	1.971424
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	10.53%	0.00%	-1.044045	-0.672047	-0.806482	-0.756481	-0.819853	-0.592463	2.625457	1.704848	0.672438	0.666536	0.400948	2.087760	2.445616
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.233570	1.749940	7.547199	1.768592	7.962714	0.109890	1.082145	1.862221	0.668160	0.662293	0.400327	6.399207	8.720965
166	N14	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	22.999057	22.718127	3.655918	2.849431	10.233650	11.537541	27.008810	36.700080	0.606997	0.597460	0.291506	0.832106	0.823995
167	N15	digital_ok	100.00%	37.63%	24.19%	0.00%	100.00%	0.00%	29.266979	31.475138	18.305126	22.272781	41.849911	35.458534	28.970432	18.041848	0.527343	0.544084	0.203116	0.000000	0.000000
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	24.158550	28.241402	22.745088	25.825643	33.889870	38.132924	0.085161	0.359832	0.605457	0.590725	0.385995	0.000000	0.000000
169	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	26.948260	26.155980	24.890308	24.478286	37.480139	36.111871	0.617082	-0.047453	0.579551	0.572111	0.380769	38.026265	36.341137
170	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	27.071664	24.322679	25.113006	23.516792	38.027915	34.212746	0.878889	-0.184218	0.560393	0.561801	0.376016	53.621043	34.358759
176	N12	digital_ok	0.00%	32.26%	34.95%	0.00%	34.21%	5.26%	-0.024172	0.474408	-0.453755	1.679598	-0.438622	1.324173	-0.673617	-0.380983	0.536298	0.528195	0.346692	1.928179	1.933388
177	N12	digital_ok	100.00%	29.57%	34.95%	0.00%	100.00%	0.00%	2.019617	1.874894	1.670203	2.651604	0.166394	3.029905	2.322401	4.999487	0.548023	0.536440	0.354983	4.633764	6.605217
178	N12	digital_ok	0.00%	29.57%	34.95%	0.00%	39.47%	0.00%	-0.376969	-1.756645	1.678083	-0.748220	-0.233138	-0.926556	-0.149233	0.675365	0.553944	0.544781	0.358069	1.150282	1.122988
179	N12	digital_ok	0.00%	29.57%	34.95%	0.00%	39.47%	0.00%	0.039632	0.645627	-0.725579	-0.491859	0.778920	-0.125440	0.456110	-0.989763	0.560344	0.546851	0.368297	1.433443	1.461511
180	N13	RF_maintenance	100.00%	0.00%	56.45%	0.00%	100.00%	0.00%	1.342896	21.303195	1.782802	57.819826	1.133039	30.980744	-0.618664	7.602017	0.656857	0.431242	0.477611	16.492157	4.574938
181	N13	digital_ok	100.00%	100.00%	80.65%	0.00%	100.00%	0.00%	31.794138	72.036315	63.630336	6.435849	46.753056	50.130643	9.134681	13.535439	0.044583	0.288197	0.160126	1.234410	2.740933
182	N13	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
183	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.550787	0.619810	-0.097531	-0.712713	-0.433663	-0.709447	0.084576	2.885416	0.665463	0.653357	0.417236	1.931544	2.120513
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.028665	-0.818500	0.145778	1.168113	-0.117133	0.123597	-0.179643	-0.361377	0.667249	0.657220	0.400502	1.956281	2.081029
185	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.497798	0.676935	2.924705	1.259924	0.252164	-0.212107	-1.259923	-0.809781	0.664805	0.660613	0.392902	1.934540	1.872456
186	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.102441	0.825402	4.113183	1.954967	1.608893	0.740726	5.377829	-0.047210	0.652564	0.651397	0.385413	5.044009	5.422550
187	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.301644	11.384998	6.893244	12.911999	6.997750	15.675013	3.584812	1.902555	0.651244	0.649412	0.390060	0.940427	0.923577
189	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.849756	6.635938	1.025394	-0.149762	0.163844	0.444494	0.282625	0.531917	0.608935	0.605557	0.388350	0.000000	0.000000
190	N15	digital_ok	100.00%	45.70%	100.00%	0.00%	100.00%	0.00%	63.274389	36.059131	6.422338	64.547397	25.778351	46.707514	87.391553	10.085135	0.481425	0.050798	0.329065	0.000000	0.000000
191	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.083564	1.384680	-0.767504	-0.526806	-0.909011	-0.215582	2.680969	4.585037	0.578537	0.567913	0.390878	0.000000	0.000000
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	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.678323	8.299468	16.623564	0.766760	24.821731	8.727108	24.353057	41.851912	0.625127	0.611459	0.384467	4.262536	4.294999
206	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.166058	12.890685	2.730255	11.693203	8.566604	17.919689	29.912745	25.605432	0.594908	0.609501	0.372434	2.591773	3.071012
207	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.692002	17.516300	19.018280	16.724378	28.573640	24.404526	7.981260	6.728917	0.599558	0.604916	0.372818	0.000000	0.000000
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%	15.670918	12.728626	100.849362	103.513390	3210.224279	3779.154252	12034.025529	16896.763033	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.290733	18.932280	9.534865	18.386628	14.436727	26.067295	-0.965936	-1.324599	0.614372	0.606359	0.392648	2.443827	2.109423
224	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	30.730964	30.546976	28.020417	28.074435	42.559670	41.595107	1.241187	0.507024	0.567662	0.567869	0.372046	0.000000	0.000000
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%	9.641685	17.467494	inf	inf	4473.497856	4473.472752	20427.790071	20422.173257	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	37.027346	36.460655	46.864826	46.712247	47.097756	46.494700	13.943589	10.444804	0.064725	0.055749	-0.003143	0.000000	0.000000
321	N02	not_connected	100.00%	24.19%	67.20%	0.00%	100.00%	0.00%	15.448864	13.236282	15.448538	14.530726	24.312479	21.956440	34.612286	34.253538	0.476485	0.416315	0.308233	0.000000	0.000000
323	N02	not_connected	100.00%	45.70%	69.89%	0.00%	100.00%	0.00%	29.562644	18.207855	3.421136	18.296262	17.850331	26.006387	10.081508	1.065901	0.395723	0.389915	0.259863	0.000000	0.000000
324	N04	not_connected	100.00%	43.01%	67.20%	0.00%	100.00%	0.00%	21.117681	22.952703	20.807212	21.643162	31.225262	31.650519	10.770738	12.255990	0.456625	0.397960	0.307333	0.000000	0.000000
329	N12	dish_maintenance	100.00%	64.52%	72.58%	0.00%	100.00%	0.00%	3.559708	11.984021	2.101407	14.495535	9.087567	18.795945	13.914743	0.024299	0.379053	0.335344	0.246260	0.000000	0.000000
333	N12	dish_maintenance	100.00%	75.27%	100.00%	0.00%	100.00%	0.00%	6.754193	11.828295	10.455971	13.161853	7.285821	16.740085	3.250987	-0.222776	0.322062	0.306950	0.206678	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, 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, 45, 46, 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, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 130, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 150, 155, 156, 157, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 176, 177, 178, 179, 180, 181, 182, 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: [65, 66, 183, 184, 185]

golden_ants: [65, 66, 183, 184, 185]

# 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_2459790.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