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 = "2459822"
data_path = "/mnt/sn1/2459822"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H6C_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: 8-30-2022

# Per-season options
def ant_to_report_url(ant):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_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/sn1/2459822/zen.2459822.25304.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/sn1/2459822/zen.2459822.?????.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/sn1/2459822/zen.2459822.?????.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	2459822
Date	8-30-2022
LST Range	18.094 -- 20.094 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: 32 RF_ok: 11 digital_maintenance: 3 digital_ok: 95 not_connected: 3
Commanded Signal Source	None
Antennas in Commanded State	0 / 147 (0.0%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N02, N04, N10, N12
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	75 / 147 (51.0%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	105 / 147 (71.4%)
Redcal Done?	✅
Redcal Flagged Antennas	1 / 147 (0.7%)
Never Flagged Antennas	21 / 147 (14.3%)
A Priori Good Antennas Flagged	74 / 95 total a priori good antennas: 5, 7, 9, 10, 15, 19, 20, 21, 30, 31, 40, 41, 42, 51, 54, 55, 56, 68, 69, 71, 72, 73, 81, 83, 84, 86, 88, 91, 93, 94, 98, 100, 101, 103, 105, 106, 107, 108, 109, 111, 112, 117, 118, 121, 122, 123, 127, 128, 129, 130, 140, 141, 142, 144, 156, 157, 158, 160, 161, 165, 167, 169, 170, 176, 177, 178, 179, 181, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	0 / 52 total a priori bad antennas:

# 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/H6C_Notebooks/_rtp_summary_/array_health_table_2459822.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	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	2.740368	-0.884883	-0.690691	-0.577828	1.706936	1.331262	3.539642	3.375140	0.776742	0.559952	0.541284	1.818021	1.531939
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.685107	4.589902	-0.821319	1.924864	-0.851639	-0.496895	2.686573	-0.351011	0.790280	0.559128	0.540577	4.641249	4.459183
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.094686	0.537346	-0.759013	4.644489	-0.888348	1.874974	1.691586	-0.601154	0.794307	0.568256	0.545524	5.116554	4.622372
7	N02	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.284207	-1.412943	0.688733	0.107489	-0.268921	0.358208	-0.677828	0.682278	0.076165	0.075010	0.015102	1.225029	1.217156
8	N02	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.788493	14.424676	20.315212	20.779574	21.840470	21.829972	-0.140742	0.178231	0.096818	0.089932	0.015100	1.136877	1.136928
9	N02	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.392697	-0.147220	0.587905	-0.539438	6.263271	6.182956	4.309513	3.147102	0.068905	0.055747	0.006716	1.196612	1.193585
10	N02	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.359093	-0.883888	-0.783986	0.220872	-1.003378	1.028444	0.299527	-0.730583	0.070381	0.070358	0.009428	1.283442	1.279007
15	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.325873	1.426272	1.868166	1.285922	4.363138	3.879691	8.613853	4.754666	0.799793	0.572172	0.534722	4.583715	4.880186
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.161230	-0.760581	-0.493300	-0.486329	-0.975565	-1.072507	3.242218	2.107408	0.801966	0.581563	0.533025	1.874493	1.540542
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.083759	0.552005	0.730915	0.789203	0.325951	0.023437	2.990175	2.551375	0.800927	0.576264	0.546490	1.958907	1.552764
18	N01	RF_maintenance	100.00%	0.00%	81.18%	0.00%	100.00%	0.00%	5.889825	8.184575	3.083503	-0.084120	3.946958	5.209558	14.499218	17.283978	0.768828	0.348835	0.594402	4.379020	2.341813
19	N02	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.519999	7.187688	-0.838042	14.027157	3.615855	14.225717	3.507960	3.803382	0.048368	0.078472	0.008263	1.209665	1.199960
20	N02	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.519455	0.646254	-0.283350	-0.328655	-0.160043	0.793015	-0.558865	-0.929940	0.060746	0.056285	0.005500	1.250537	1.248835
21	N02	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.383876	-0.384121	1.033474	1.907408	-0.682520	0.926858	-0.095827	-0.677903	0.068882	0.066515	0.008251	1.272384	1.273256
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.567127	26.643823	43.328030	44.257446	30.886411	29.495082	1.628701	0.828723	0.039645	0.043517	0.002288	1.226936	1.227375
28	N01	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	16.516410	26.687773	1.818838	3.625804	26.798528	29.159129	3.535647	14.027200	0.449884	0.202805	0.299259	10.699155	2.894699
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.177077	-0.017193	0.400968	-0.130594	-1.079847	-0.722094	-0.322610	-0.566018	0.807323	0.586976	0.540344	2.016715	1.639668
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.879364	0.172485	0.098233	-0.442135	-0.478864	0.333374	4.556629	-0.719967	0.801402	0.581665	0.544596	4.493485	4.386979
31	N02	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.059623	-0.745309	-0.887356	0.557771	0.227452	0.867948	0.698734	0.467445	0.079957	0.086078	0.015565	1.204562	1.203597
32	N02	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	47.445175	9.417852	3.531708	1.718797	6.528212	14.033363	36.953200	149.813000	0.092686	0.087211	0.012807	1.205605	1.200640
33	N02	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	46.559323	46.141493	inf	inf	3894.459793	3894.106748	5147.013472	5146.487106	nan	nan	nan	0.000000	0.000000
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.703575	7.614487	0.353594	-0.142058	1.535267	-0.069257	-0.587927	-0.685150	0.791797	0.581209	0.513904	5.460983	4.691915
37	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.573105	1.118846	0.837192	0.729519	-0.925838	-1.008403	-0.197259	3.481286	0.802851	0.597341	0.510193	2.031126	1.626320
38	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.264367	-0.182592	-0.524840	-0.929635	-0.666462	-0.085250	1.640274	-0.080973	0.804039	0.608675	0.512071	1.956836	1.604652
40	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.048630	0.156524	0.270221	0.005662	1.487320	0.433718	-0.610309	-1.089531	0.095122	0.100886	0.022295	1.166033	1.164159
41	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.020205	-0.776545	2.939874	1.464651	3.069860	-1.132125	-0.760032	-0.314366	0.073179	0.088607	0.016297	1.184971	1.182510
42	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.800563	0.302593	2.205488	1.317262	0.113018	-0.463016	-0.194571	-0.900240	0.110514	0.107152	0.024930	1.191274	1.192155
45	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.137527	0.471937	-0.523516	0.292656	-0.705607	-0.632628	-0.475323	0.556394	0.790187	0.557979	0.555798	1.515803	1.285624
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.940806	-0.900503	0.429026	-0.734618	-0.887032	-0.740958	-0.523008	0.265657	0.787606	0.541299	0.570070	1.477637	1.290423
50	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.300483	27.239193	-0.897130	1.489416	-0.284254	6.138645	4.209085	10.346533	0.798297	0.515402	0.487033	4.931185	5.610839
51	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.558099	2.270638	-0.778862	-0.554454	1.305058	-0.942390	-0.284045	4.049469	0.806178	0.622254	0.492933	5.067919	4.846087
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.319739	7.560948	1.050507	0.138901	1.581811	-1.037183	0.712223	-0.647768	0.813876	0.628491	0.492773	5.456208	5.543786
53	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.805352	1.920953	-0.660248	0.155250	-1.067511	-1.061840	1.043453	1.939725	0.812316	0.638407	0.498991	1.958439	1.750861
54	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.914717	8.403356	1.392426	3.117768	-0.513406	5.749390	0.976564	7.216219	0.088927	0.094843	0.018165	1.199499	1.192283
55	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.570805	0.801800	-0.513871	0.972569	2.592200	-1.037868	5.578910	-0.621351	0.079201	0.067269	0.006505	1.164903	1.166528
56	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.354407	0.690551	1.351000	1.257669	1.040687	-0.479923	0.035956	8.937317	0.062976	0.071138	0.006922	1.175181	1.174852
57	N04	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	32.628819	-1.093990	14.026791	2.521759	11.282870	1.495692	3.690788	1.645296	0.123409	0.100696	0.023526	1.176799	1.177095
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.923386	0.603692	0.771584	1.394641	-0.544073	-0.048728	-0.513923	-0.498171	0.802570	0.598500	0.514218	2.008400	1.680546
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.996567	0.552257	-0.837092	0.703444	-0.120824	2.659143	0.157757	0.682587	0.807020	0.628678	0.484409	1.871528	1.522775
67	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.810798	-0.452839	-0.493532	0.491633	2.507078	1.419276	0.352538	2.288804	0.814663	0.646463	0.476932	2.073503	1.759036
68	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.637138	0.898933	0.425462	3.304682	1.375359	4.000856	0.501032	0.814695	0.814228	0.652818	0.477732	6.475642	7.760546
69	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.543591	-0.682302	-0.298436	-0.005662	-0.459596	1.031031	-0.142957	-0.965075	0.100749	0.113532	0.027201	1.189636	1.187931
70	N04	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.841712	-1.063345	0.474672	-1.021649	-0.656194	-1.243257	0.627963	-0.218445	0.089212	0.082952	0.009114	1.177698	1.177539
71	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.505317	-0.471364	-0.787439	-0.437131	0.419871	-0.532417	-0.385653	-0.630788	0.080672	0.083784	0.008953	1.174760	1.176978
72	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.573627	-0.841520	-0.621610	1.482194	1.386167	-0.341102	6.634668	4.188646	0.114714	0.101610	0.020170	1.154528	1.151649
73	N05	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	24.452221	0.281384	42.578167	2.451166	30.794583	0.993050	0.687979	-0.861425	0.032929	0.606993	0.257533	1.205376	3.293573
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.970423	3.151389	-0.159520	3.956960	0.318805	3.388112	1.447914	-0.335059	0.791538	0.584132	0.497712	5.209807	4.916065
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.113422	0.081588	3.213660	2.907046	0.393637	-0.301969	17.273789	5.298724	0.804015	0.610768	0.506382	7.025698	5.050796
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.883061	2.438365	2.298280	4.713053	0.868873	1.980782	-0.585487	-0.882791	0.809480	0.643804	0.479459	5.325270	5.058635
84	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.504014	11.856045	0.864122	1.138214	-0.241103	-0.080198	-0.563475	-0.982278	0.816581	0.654227	0.472600	4.916173	5.105727
85	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.577993	0.548634	-0.872719	-0.332280	-1.346205	-1.536986	-0.466625	-1.110454	0.816861	0.658393	0.496783	2.010686	1.512946
86	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	7.89%	1.878874	3.917192	-0.399122	-0.491651	0.350601	1.428900	-0.209532	-0.942052	0.811739	0.632638	0.498126	2.284802	1.632631
87	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	36.953223	12.662479	2.440386	1.527720	13.618307	0.881056	15.510699	-0.407926	0.722872	0.656908	0.413569	5.546224	4.673397
88	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.603648	15.232664	24.302949	21.539624	27.128348	21.686040	0.702102	0.209367	0.776040	0.626675	0.508950	3.219941	3.128166
90	N09	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.021352	0.120365	0.323283	4.066120	-0.694889	-0.614255	-0.012014	0.316390	0.811380	0.609073	0.524856	4.736874	3.300668
91	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.069208	16.998810	22.114816	22.752240	24.051159	23.968056	-0.264074	0.446877	0.780641	0.569645	0.538266	4.614238	3.379602
92	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	45.616581	61.826116	5.334336	7.019734	28.449720	34.011172	0.749934	3.303780	0.096401	0.090249	0.012246	0.000000	0.000000
93	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.573453	0.532475	4.059735	0.584369	4.069221	-0.879414	0.079832	-0.684840	0.081611	0.091361	0.014212	0.000000	0.000000
94	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.947257	-1.554103	-0.290975	0.106237	-0.137241	1.557493	4.916300	2.098078	0.080129	0.095518	0.016231	0.000000	0.000000
98	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.468511	5.831967	0.783605	3.158203	0.321509	-0.364252	3.863716	-0.085432	0.790726	0.560826	0.525654	5.435048	5.836832
99	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.866828	0.517642	3.451926	-0.255747	1.350173	-0.834869	-0.703420	0.597393	0.798791	0.593932	0.515125	2.046353	1.633659
100	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
101	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.567781	10.987811	3.413858	0.090584	0.832004	-1.025325	2.629229	2.133303	0.814113	0.641410	0.494745	5.507380	4.668896
102	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.948624	12.605552	16.612033	30.774484	59.691132	151.588393	84.019952	202.596213	0.775577	0.511224	0.530808	5.220735	3.115356
103	N08	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
104	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
105	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.712164	7.276575	8.820378	13.162964	6.486244	10.493096	-0.716106	-0.623006	0.824532	0.662346	0.499985	4.630051	3.759704
106	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.616581	15.886505	6.485772	21.789377	0.964834	22.122136	0.457046	0.005663	0.809849	0.631629	0.506716	3.805714	3.109539
107	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.253002	11.927030	21.095087	19.152617	22.422755	19.316358	0.851357	0.607342	0.807731	0.619920	0.495074	3.662304	2.973848
108	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.238544	4.983391	13.077987	0.419879	0.394274	-1.196191	0.475963	-0.604885	0.788811	0.617406	0.509919	3.807409	3.103822
109	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.415237	1.154458	0.333437	0.803908	-0.515048	-0.051364	4.132036	0.501268	0.082640	0.087727	0.015955	0.000000	0.000000
110	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	46.602376	20.586506	3.515580	0.884239	2.499541	11.805776	6.335611	41.834650	0.085133	0.069459	0.007830	0.000000	0.000000
111	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.227441	1.166884	-0.264870	0.994414	-0.026350	1.664755	0.685792	4.218245	0.064098	0.058179	0.005515	0.000000	0.000000
112	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.435493	0.047363	-0.786527	1.064943	0.214928	-0.204375	0.188030	-0.723731	0.069561	0.078144	0.013008	0.000000	0.000000
116	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.156753	2.271631	0.115025	-0.745070	-0.491010	-1.235569	0.920384	-1.015944	0.783269	0.558601	0.535326	2.041743	1.531185
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.840545	1.786963	5.677866	5.745628	4.272387	3.085362	-0.919780	-1.012424	0.793169	0.576062	0.531454	5.848053	5.642379
118	N07	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.686601	29.773009	2.296176	38.562312	-0.447120	29.723044	0.106859	-0.103968	0.803312	0.050275	0.412173	4.809486	1.273519
119	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
120	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
121	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.263409	6.736206	-0.532762	0.896097	-0.482849	0.734781	9.584038	5.944538	0.816608	0.653212	0.491660	4.223599	3.463624
122	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.859128	9.164591	1.217182	1.237439	-0.366396	0.074455	0.253659	0.777471	0.824169	0.659521	0.488126	6.194340	4.868198
123	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.523819	11.176847	-0.108959	0.248041	-0.871964	-1.100572	0.338930	0.662148	0.828078	0.666741	0.488943	7.164878	5.326635
125	N09	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.328486	12.739236	11.811631	15.832969	9.456835	14.126167	-0.778033	-0.637899	0.824575	0.644541	0.497652	5.601978	4.025309
126	N09	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	25.700442	3.268495	6.385784	6.794351	10.192293	4.793460	3.782579	-0.900118	0.710165	0.636191	0.389214	3.708507	3.257059
127	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.268611	0.322060	0.073263	-0.222422	0.082957	0.430985	-0.373287	-0.831647	0.095356	0.094156	0.024185	0.000000	0.000000
128	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.030554	2.233248	-0.155249	1.617265	0.749095	1.538213	0.671154	0.016499	0.083534	0.072255	0.014250	0.000000	0.000000
129	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.122944	-0.666679	-0.829629	-0.800829	0.337930	0.298699	-0.398176	-0.862934	0.055214	0.064668	0.007044	0.000000	0.000000
130	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.011612	0.192413	0.637086	1.734206	1.045194	-0.364482	-0.335003	0.950909	0.087463	0.077589	0.015307	0.000000	0.000000
135	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.096550	0.439456	-0.934749	-0.891535	-0.358732	-0.946555	2.799916	4.535869	0.095944	0.091113	0.026201	1.275869	1.278106
136	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.812003	12.845377	-0.339645	0.791994	2.952003	1.326112	0.951614	2.887273	0.091487	0.089287	0.023073	1.251889	1.247104
137	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.681755	13.972495	23.238947	20.146229	26.018190	18.959964	-0.279821	-0.394298	0.764898	0.546966	0.539162	3.687310	3.108239
138	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
140	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.534190	24.814004	42.357081	44.308469	30.818561	29.527975	0.521775	0.190929	0.039178	0.042251	0.001927	1.118128	1.113902
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.127647	3.606226	0.385708	9.691430	0.875133	3.567888	3.572939	34.649618	0.818351	0.623876	0.507011	8.961033	6.811908
142	N13	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	26.406267	30.592361	3.236734	44.529283	24.573683	29.531329	1.360510	0.587900	0.515756	0.043542	0.230718	17.511482	1.411585
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.122048	-1.281522	1.181881	1.118058	0.288319	-0.797625	0.790964	-0.016499	0.822468	0.663041	0.481141	1.054050	0.819413
144	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.209145	-0.462368	1.213202	0.663629	-0.560988	1.192412	3.077215	5.913821	0.824076	0.654262	0.491853	9.726073	7.677324
145	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.910411	26.400676	43.597711	44.878161	30.912088	29.573381	1.211334	1.433994	0.033702	0.034280	-0.000646	1.301422	1.320840
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.357952	29.113814	43.454197	45.755632	30.891378	29.539243	1.581160	1.264974	0.045125	0.046055	0.000681	0.000000	0.000000
155	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.289755	25.857331	42.455554	43.914117	30.794210	29.420539	1.415336	1.095268	0.035723	0.036207	0.000237	1.272979	1.267945
156	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.115596	1.582600	1.419967	-0.094285	-0.261056	-0.541950	5.310340	2.557242	0.065486	0.064997	0.008866	1.279868	1.282884
157	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.568871	0.033941	0.028105	3.229347	0.473909	0.002923	0.825862	0.717066	0.066029	0.061632	0.005544	0.905956	0.914355
158	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.314439	-1.224648	-0.426085	-0.383167	0.205642	0.349690	0.468424	-0.172805	0.072166	0.058403	0.005984	0.847640	0.852542
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.533778	24.735888	43.108786	44.392296	30.923641	29.592753	1.261705	1.303411	0.040327	0.042747	0.003839	1.187225	1.194239
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.443754	48.503298	-0.251031	3.697681	-0.210308	3.491791	0.844100	0.359348	0.807705	0.533420	0.469268	4.635795	7.219739
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.812159	0.688610	-0.398362	-0.879058	1.563692	0.403291	0.926490	2.824168	0.813734	0.628637	0.505807	2.256369	1.949037
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.363027	-0.119315	0.168969	-0.899812	-0.654998	-0.151487	0.640295	-0.023948	0.815646	0.646154	0.492013	2.147480	1.705858
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.990601	-1.283345	-0.725418	-0.753682	0.684958	0.749105	1.438153	0.788568	0.817903	0.646420	0.497973	2.080178	1.912494
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.975745	1.671056	6.566717	-0.799720	2.870501	-1.243203	0.720350	-0.170941	0.813683	0.641230	0.490595	5.222906	6.436415
166	N14	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.140299	25.815336	0.496458	1.969943	15.775012	5.396251	121.295732	24.334034	0.748108	0.538043	0.379091	0.000000	0.000000
167	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	37.050258	18.184126	15.796180	20.487084	27.040196	24.734654	58.675336	11.649535	0.646416	0.516589	0.317821	0.000000	0.000000
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.478196	17.231591	20.664758	23.083194	22.237704	24.312224	-0.611948	0.118157	0.777544	0.560703	0.517013	0.000000	0.000000
169	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.959956	16.158182	22.694879	21.784487	24.973400	23.077993	-0.251870	0.117565	0.767960	0.536782	0.535057	0.000000	0.000000
170	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.142712	14.344994	22.909067	20.854588	25.216518	21.150668	0.158114	-0.013606	0.758638	0.531095	0.540604	0.000000	0.000000
176	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.152784	-0.788094	-0.494150	0.637869	0.049549	-0.002923	-0.042345	-0.036795	0.052584	0.068551	0.010692	1.236582	1.237240
177	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.117376	1.626636	0.911036	2.778353	-0.368420	1.299445	0.830467	1.930488	0.065955	0.062105	0.006231	1.206533	1.202696
178	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.856879	-1.552102	1.084094	-0.500180	-0.485410	-0.332747	0.130164	-0.777212	0.068003	0.056034	0.005218	1.212774	1.212286
179	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.344974	0.823320	-0.940155	0.121739	4.012698	-0.962325	4.379445	-0.744692	0.065335	0.070210	0.012923	1.186536	1.189353
180	N13	RF_maintenance	100.00%	0.00%	78.49%	0.00%	100.00%	0.00%	0.941260	18.995992	0.579025	40.950142	-0.159650	21.752081	0.445364	0.299743	0.803339	0.337325	0.610056	15.619641	3.902542
181	N13	digital_ok	100.00%	100.00%	91.94%	0.00%	100.00%	0.00%	26.268310	46.629622	43.747996	6.490886	30.935693	41.910773	1.123709	6.942986	0.045474	0.329093	0.131009	1.181540	3.313841
182	N13	RF_maintenance	100.00%	0.00%	94.62%	0.00%	100.00%	0.00%	13.963081	27.434809	20.554135	43.474758	21.769330	27.984314	-0.644091	0.967505	0.791398	0.171709	0.552146	5.352238	1.854803
183	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.244827	-0.546189	0.230198	-0.936262	-1.137358	-1.545698	0.396058	1.996917	0.808666	0.619097	0.513787	1.955139	1.688442
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.331109	0.011612	-0.290589	0.835753	-0.055145	0.301803	0.247542	0.129258	0.811851	0.631545	0.501755	2.160582	1.696510
185	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.893616	0.253119	1.192622	1.193590	-0.559096	-0.262099	1.295101	4.339682	0.812381	0.629340	0.502248	4.313038	3.920356
186	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.579159	0.617293	3.651231	2.482255	2.518337	1.565668	5.263761	2.526106	0.801685	0.618922	0.495709	3.798236	3.383260
187	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.989075	1.076690	0.349241	0.841475	-0.008652	-0.991489	7.876410	0.684035	0.801391	0.619869	0.495670	0.000000	0.000000
189	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.674463	4.272980	0.727934	0.375975	6.383257	5.994941	4.652701	4.154288	0.786258	0.574025	0.528077	0.000000	0.000000
190	N15	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	33.510730	29.472632	2.131387	44.896870	9.692739	29.611515	63.453638	1.325346	0.699054	0.044367	0.391690	0.000000	0.000000
191	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.389913	0.340741	-0.805315	-0.577195	-0.073187	-0.493236	9.383397	3.364209	0.775914	0.536769	0.561273	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%	22.750156	21.492228	13.554893	0.856164	15.118304	6.057583	6.987464	8.400039	0.787762	0.553295	0.522168	5.086177	3.317121
206	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.664737	20.332316	1.245252	9.325556	5.019251	9.230195	8.871910	6.924499	0.766964	0.568469	0.510327	3.067055	2.684325
207	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	24.522487	22.064921	16.172459	13.162970	18.604357	13.762510	2.999254	2.291288	0.764209	0.559114	0.504291	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%	42.441276	42.272729	inf	inf	2816.406655	3323.502310	3132.475266	4312.541430	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.220042	19.438131	7.916030	5.977450	7.487719	4.304763	0.058736	2.913902	0.774234	0.543864	0.529414	4.310445	3.027539
224	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	30.544625	30.325371	24.517020	24.089162	28.604662	26.714691	-0.674297	-0.827856	0.727807	0.502604	0.508991	2.745647	2.364894
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%	42.270303	42.069562	inf	inf	3894.591245	3894.222900	5114.303393	5112.708796	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	31.461591	29.478167	30.744058	31.081040	30.769652	29.399065	2.290995	1.088015	0.053639	0.050240	0.003429	0.000000	0.000000
321	N02	not_connected	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	42.574037	42.371013	inf	inf	3894.529461	3894.148523	5149.357315	5148.737724	nan	nan	nan	0.000000	0.000000
323	N02	not_connected	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
324	N04	not_connected	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.125107	7.123065	17.739461	11.447926	18.553022	10.856704	0.628087	0.293613	0.114072	0.075442	0.049470	0.000000	0.000000
329	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.107736	5.856118	4.068322	11.833602	7.527418	11.189347	0.738870	-1.285942	0.095054	0.069644	0.039054	0.000000	0.000000
333	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.793476	5.700573	0.156849	10.859564	3.023169	8.612248	0.189417	-0.780880	0.094017	0.070407	0.041046	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: [4, 5, 7, 8, 9, 10, 15, 18, 19, 20, 21, 27, 28, 30, 31, 32, 33, 36, 40, 41, 42, 50, 51, 52, 54, 55, 56, 57, 68, 69, 70, 71, 72, 73, 81, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 130, 135, 136, 137, 138, 140, 141, 142, 144, 145, 150, 155, 156, 157, 158, 160, 161, 165, 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: [3, 16, 17, 29, 37, 38, 45, 46, 53, 65, 66, 67, 85, 99, 116, 143, 162, 163, 164, 183, 184]

golden_ants: [3, 16, 17, 29, 37, 38, 45, 46, 53, 65, 66, 67, 85, 99, 116, 143, 162, 163, 164, 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/H6C_Notebooks/_rtp_summary_/rtp_summary_table_2459822.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__)

2.0.3.dev44+g7d4aa18
3.1.4.dev9+gea58d1b