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 = "2459794"
data_path = "/mnt/sn1/2459794"
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: 8-2-2022

# 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/sn1/2459794/zen.2459794.25320.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/sn1/2459794/zen.2459794.?????.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/sn1/2459794/zen.2459794.?????.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	2459794
Date	8-2-2022
LST Range	16.258 -- 18.258 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	107 / 147 (72.8%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N09, N18
Nodes Not Correlating	N04, N08, N10
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	81 / 147 (55.1%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	116 / 147 (78.9%)
Redcal Done?	✅
Redcal Flagged Antennas	11 / 147 (7.5%)
Never Flagged Antennas	8 / 147 (5.4%)
A Priori Good Antennas Flagged	81 / 87 total a priori good antennas: 5, 7, 9, 10, 15, 16, 17, 19, 20, 21, 29, 30, 31, 37, 40, 41, 42, 45, 50, 51, 53, 54, 55, 56, 57, 66, 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, 163, 165, 167, 169, 170, 176, 177, 178, 179, 181, 183, 185, 186, 187, 190, 191
A Priori Bad Antennas Not Flagged	2 / 60 total a priori bad antennas: 3, 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_2459794.csv

Build DataFrame

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

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

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

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

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

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

Table 2: RTP Per-Antenna Metrics Summary Table

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

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

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

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

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

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

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
3	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.542359	-0.820805	-0.726882	-1.042336	-0.716104	-0.229038	-0.655759	-0.905684	0.675360	0.663781	0.437358	0.000000	0.000000
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.151300	5.396737	-0.593702	1.940160	-0.204068	0.326757	5.547578	2.049553	0.685798	0.666978	0.433327	0.000000	0.000000
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.672155	1.346830	0.650974	4.340934	0.575136	1.741411	1.269197	-2.618409	0.697847	0.679313	0.431545	0.000000	0.000000
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.098235	-1.176204	0.261295	-0.203123	0.206074	0.033572	-0.203283	12.420422	0.693654	0.673464	0.417832	0.000000	0.000000
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.852438	16.554474	18.913603	19.171849	22.804202	22.919912	6.327503	4.245205	0.666967	0.638175	0.408346	0.000000	0.000000
9	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.351621	-1.078695	-0.561040	0.289361	1.970030	2.008864	2.493281	9.583244	0.670305	0.648556	0.408405	0.000000	0.000000
10	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.727509	4.143752	-1.037037	8.197244	0.741359	6.788105	33.686573	31.711270	0.652332	0.625117	0.417904	0.000000	0.000000
15	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.072586	0.576446	1.098391	0.700377	0.606410	0.932523	2.911744	5.554690	0.704776	0.686426	0.438525	5.167148	5.061904
16	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.286804	-1.751055	-0.554140	-0.476796	-0.699201	-0.719240	5.628432	6.877466	0.708768	0.693405	0.431428	15.136862	14.709997
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	28.95%	21.05%	0.120973	0.562835	0.464529	0.483049	-0.263881	-0.169678	3.667298	1.992705	0.708044	0.693056	0.413582	0.771546	0.808167
18	N01	RF_maintenance	100.00%	0.00%	37.63%	0.00%	100.00%	0.00%	3.499850	2.595169	2.846203	-0.490648	5.060387	4.869955	73.938464	142.954828	0.688636	0.516102	0.454664	59.119464	14.707447
19	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.458152	-0.970153	5.148398	0.000336	3.325529	0.679283	8.494382	1.275046	0.697904	0.678073	0.407426	90.415451	56.374833
20	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	18.42%	36.84%	-1.442469	-0.114430	-1.028281	-0.443852	1.319831	1.963338	-0.586160	-0.640080	0.683027	0.655754	0.401810	1.039282	1.013944
21	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	10.53%	44.74%	0.019620	1.444523	0.397854	1.297659	-0.602564	1.035377	0.534835	1.410663	0.666640	0.641192	0.411638	0.835391	0.797926
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.219906	24.279531	54.396874	55.411429	32.558163	32.053087	29.631844	25.920747	0.044209	0.049498	0.004005	0.000000	0.000000
28	N01	RF_maintenance	100.00%	37.63%	100.00%	0.00%	100.00%	0.00%	13.022274	15.106034	7.995497	9.967421	29.473009	34.457671	32.119975	184.716692	0.468824	0.245850	0.292414	0.000000	0.000000
29	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.895283	-0.332874	0.781369	-0.372852	0.392281	-0.344746	-0.348796	6.982788	0.723268	0.706973	0.439289	0.000000	0.000000
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.372287	-0.621807	0.179769	-0.694306	0.245675	0.351669	36.049869	0.173384	0.715273	0.698638	0.420306	0.000000	0.000000
31	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.719027	-0.808148	-1.040898	-0.183706	-0.554493	-1.295174	6.543205	9.101299	0.712524	0.689204	0.413508	0.000000	0.000000
32	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	31.865396	18.912641	2.623787	4.588721	10.020674	14.086180	137.956789	519.680334	0.641556	0.642726	0.286866	0.000000	0.000000
33	N02	RF_maintenance	100.00%	0.00%	37.63%	0.00%	100.00%	0.00%	-0.644143	3.624012	-0.937938	0.266296	6.875663	9.024847	168.887027	250.971611	0.677793	0.508620	0.485326	0.000000	0.000000
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.978535	5.963292	-0.039121	0.631206	1.615169	0.813129	0.976219	4.004299	0.682786	0.665989	0.414198	0.000000	0.000000
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.504650	0.900310	-0.671101	-0.034309	0.231627	0.215077	0.355783	11.438705	0.703172	0.689595	0.416204	0.000000	0.000000
38	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.934288	0.018422	-0.839318	-0.759630	2.407867	0.372317	19.987763	10.819745	0.717073	0.705076	0.427860	0.000000	0.000000
40	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	63.394139	77.433828	118.105539	128.895039	30.078057	34.227784	1512.233588	3106.424089	0.016641	0.016337	0.000374	0.000000	0.000000
41	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	127.463888	94.907635	116.950177	130.295904	29.714894	49.627797	1828.963150	2914.185992	0.018783	0.017178	0.001089	0.000000	0.000000
42	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	63.527720	59.845274	120.633859	108.416927	21.811384	41.308851	1976.719843	1551.808999	0.017117	0.016870	0.000288	0.000000	0.000000
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.770605	0.151682	-1.062092	1.074781	-0.867570	-0.469281	0.900449	15.419019	0.704387	0.677753	0.426958	0.000000	0.000000
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.371182	-0.890242	0.708102	-0.864430	-0.308634	-1.114558	-0.939076	3.563592	0.689347	0.663736	0.433086	0.000000	0.000000
50	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.182408	2.070140	-0.505304	2.842981	1.016698	2.960709	6.328233	1.582728	0.690868	0.675199	0.391092	0.000000	0.000000
51	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.660060	17.779964	2.306929	19.645819	2.272376	23.376617	1.133126	4.311062	0.708068	0.671996	0.411484	0.000000	0.000000
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.102773	17.531826	2.812369	20.162065	1.081512	23.977497	2.425397	5.528403	0.721104	0.684815	0.424420	0.000000	0.000000
53	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.784009	1.313413	-0.535712	-0.167441	-0.734996	0.573692	8.389599	21.405730	0.731314	0.721328	0.435577	0.000000	0.000000
54	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	55.978946	110.136908	114.215835	144.695249	37.373093	37.398687	2662.387634	3276.394068	0.017171	0.016428	0.000624	0.000000	0.000000
55	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	81.633577	93.019432	138.926685	131.058521	89.730062	37.910748	4350.365614	3261.923667	0.017331	0.016524	0.000742	0.000000	0.000000
56	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	83.096079	65.771780	120.387011	123.829303	35.311465	39.453035	2629.443578	2451.481375	0.018166	0.016891	0.000848	0.000000	0.000000
57	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	49.314503	76.490294	103.475003	128.954882	39.616561	51.160818	1570.328596	3690.108450	0.018607	0.016515	0.002007	0.000000	0.000000
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.647930	0.150208	-0.433232	0.403819	1.290589	1.150212	0.227612	2.131959	0.699498	0.682104	0.405164	0.297734	0.315229
66	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.235933	-0.191853	-0.623421	0.973255	0.656085	1.505535	0.306316	9.172978	0.715692	0.703073	0.395180	13.616885	17.757199
67	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.463717	-0.271440	-0.584821	0.364464	1.770169	1.546127	3.655533	10.319877	0.727967	0.718069	0.401045	12.154456	17.977487
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	28.95%	0.305660	-0.113317	0.109521	0.099592	1.724044	2.571840	-0.322001	0.137800	0.729296	0.720561	0.419279	0.602993	0.601992
69	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	71.017565	84.972552	133.689813	118.336404	61.381609	41.815685	3314.126889	2743.191453	0.017073	0.016636	0.000432	1.305624	1.309861
70	N04	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	55.145856	120.265359	117.314040	154.781368	41.265695	56.184033	3143.347089	4161.980031	0.016569	0.016324	0.000474	1.012115	1.008793
71	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	50.224093	75.348594	108.417950	125.440008	32.570400	57.863881	1828.509010	3164.875137	0.016725	0.016386	0.000375	1.243649	1.223075
72	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	71.806428	57.160248	121.459316	109.085667	48.934637	30.987774	2540.681634	1689.430705	0.018067	0.017018	0.000594	1.112673	1.101660
73	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	18.42%	81.58%	1.304546	1.840346	-0.793288	0.578325	-0.365288	0.104099	3.399015	-0.387421	0.721615	0.699097	0.480851	5.172317	4.516755
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.337561	8.126299	-0.735999	10.016260	0.652694	10.274357	1.267843	-1.951699	0.688922	0.670949	0.387515	0.000000	0.000000
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.387941	-0.951367	-0.334602	1.501365	-0.613767	0.057516	0.157435	0.418724	0.711395	0.697370	0.394449	0.000000	0.000000
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.847189	3.672395	2.255300	4.964549	1.522134	3.144653	-1.029325	-2.213522	0.727252	0.719269	0.396790	0.000000	0.000000
84	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.328494	7.373871	-0.100029	1.672589	-0.362751	0.072529	-0.280949	0.560666	0.089567	0.101689	0.019138	0.000000	0.000000
85	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.796342	4.093023	-0.244109	6.448451	-0.891612	5.053049	-0.605561	-2.825494	0.083164	0.096253	0.014498	0.000000	0.000000
86	N08	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.729166	2.648740	1.072651	-0.723788	0.430938	1.143072	1.068593	3.639759	0.074809	0.070059	0.007000	0.000000	0.000000
87	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.456702	8.868072	7.040656	0.300506	5.459464	-0.017346	-2.578013	-0.909592	0.084258	0.076871	0.009392	0.000000	0.000000
88	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
90	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
91	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
92	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	32.301335	47.996895	10.143893	13.608414	35.948650	36.321995	25.326642	38.289587	0.107876	0.103208	0.015099	0.000000	0.000000
93	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.049662	-0.254925	3.104358	-0.421921	2.015354	-0.170949	-1.702247	0.077080	0.108193	0.106296	-0.013882	0.000000	0.000000
94	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.674268	0.140302	-0.870756	-0.892594	-0.293882	1.300173	16.860032	4.617563	0.072048	0.073655	0.006905	0.000000	0.000000
98	N07	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.092708	5.638895	10.048063	3.046118	9.472811	2.163242	0.647134	3.225975	0.688010	0.668843	0.390959	0.000000	0.000000
99	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.342283	5.673045	9.784700	7.920207	9.288823	6.247130	0.643310	-2.511561	0.703353	0.695126	0.384982	0.000000	0.000000
100	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.540353	0.066577	-0.265532	0.351377	-0.664864	-0.446472	-0.240413	0.190552	0.721457	0.710712	0.406680	0.000000	0.000000
101	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.551397	7.476346	3.757537	1.087700	2.406697	0.428182	3.237237	2.100790	0.108731	0.096186	0.018911	0.000000	0.000000
102	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.548348	2.515994	20.189866	11.926451	32.864175	19.306670	654.520876	379.422120	0.057972	0.066655	0.009167	0.000000	0.000000
103	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.079262	7.642636	-0.573981	0.105575	-0.103260	-0.370860	0.960764	1.150092	0.068671	0.069359	0.005363	0.000000	0.000000
104	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.197801	72.849623	0.366692	14.988177	0.240883	4.419568	1.059673	2.699522	0.083047	0.088960	0.009794	0.000000	0.000000
105	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
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%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
108	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
109	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.558246	-0.424282	0.300975	1.396125	-0.745323	1.312883	-0.731914	1.016247	0.095291	0.095491	0.018708	0.000000	0.000000
110	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	34.963878	3.612793	3.556745	3.406642	8.725316	10.367154	8.229413	48.731406	0.085444	0.088791	0.008341	0.000000	0.000000
111	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.268067	0.352501	-0.587460	1.224371	1.452952	1.697845	0.218351	0.600077	0.057034	0.070696	0.003948	0.000000	0.000000
112	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.672204	-0.676777	-0.890019	0.525859	-0.113622	-0.617436	-0.453823	-2.043530	0.061209	0.070512	0.005313	0.000000	0.000000
116	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.587843	6.881429	-0.901640	8.380024	-0.157416	8.104761	0.647318	-2.163637	0.681152	0.667338	0.397319	0.000000	0.000000
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.091472	3.523828	9.686090	5.392616	9.152015	2.835910	-2.498010	-2.977253	0.706130	0.694876	0.408963	0.000000	0.000000
118	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.092764	0.529182	2.560715	0.274739	1.324401	1.284787	6.273812	9.121623	0.709684	0.700055	0.410865	0.000000	0.000000
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.305981	0.264305	8.225507	0.174866	8.071437	-0.805336	-2.274875	-0.357845	0.723796	0.714496	0.431609	0.000000	0.000000
120	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.658206	29.308713	9.620333	61.288364	29.709514	32.768568	17.315901	37.218169	0.121301	0.042852	0.060843	0.000000	0.000000
121	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.642002	4.469590	4.793028	0.371134	1.910511	0.043233	74.112351	53.598315	0.080921	0.070354	0.006969	0.000000	0.000000
122	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.158297	6.526209	1.972132	1.446111	2.148728	0.708452	1.453078	1.269983	0.096982	0.087176	0.011327	0.000000	0.000000
123	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.965732	7.114590	1.225166	-0.060789	1.505226	0.537882	-0.058386	0.235982	0.124695	0.121535	0.029314	0.000000	0.000000
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%	-0.695788	-1.136581	0.337359	-0.090947	0.543476	-0.162903	0.127648	0.669445	0.093028	0.089406	0.019078	0.000000	0.000000
128	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.067398	0.552121	-0.634890	1.300180	-0.809820	0.499951	-0.407977	-1.274861	0.083843	0.082080	0.010225	0.000000	0.000000
129	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.441233	-1.616340	-0.804952	-1.133532	0.042783	-0.260173	-0.845211	-0.679536	0.068828	0.066257	0.004904	0.000000	0.000000
130	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.022036	-0.032377	0.798391	1.184313	0.164721	1.329963	1.226768	15.510998	0.073999	0.059716	0.004692	0.000000	0.000000
135	N12	digital_ok	0.00%	18.82%	18.82%	0.00%	18.42%	2.63%	-0.848435	-1.571859	-1.031706	-1.111168	-0.690540	-0.815125	0.742421	-0.430587	0.598991	0.582662	0.374814	-1.130860	-57.002539
136	N12	RF_maintenance	100.00%	18.82%	18.82%	0.00%	100.00%	0.00%	1.738025	10.289075	-0.621141	0.427887	4.235553	2.511981	6.312840	17.550124	0.598127	0.580458	0.355492	7.047756	7.999076
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.297995	21.784784	47.013425	48.433761	32.624725	32.055929	25.304313	30.787468	0.039851	0.050900	0.006030	1.159723	1.164709
138	N07	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	26.478287	2.366482	46.783903	2.045618	32.713745	0.837847	27.357820	1.446267	0.047687	0.702326	0.453606	1.318875	6.866210
140	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	81.58%	3.937798	3.098129	0.088280	1.007572	2.468523	3.124875	1.521989	0.804044	0.711256	0.703107	0.434115	0.743768	1.208967
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.600026	3.903983	-0.795280	12.105697	1.369750	3.929040	1.025776	19.830667	0.709723	0.692254	0.443197	1.080857	1.319973
142	N13	digital_ok	100.00%	40.32%	100.00%	0.00%	100.00%	0.00%	21.627438	25.629095	10.209064	55.927092	29.848730	32.242500	19.824287	25.641824	0.447884	0.045950	0.257299	9.639323	1.370106
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	31.58%	-0.419297	-0.713873	1.699948	0.489085	1.117393	-0.365886	0.619232	-2.057962	0.715742	0.705063	0.432639	0.000000	0.000000
144	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	81.58%	0.072696	0.452075	-0.647887	0.116513	-0.414036	-0.273203	2.747646	0.012544	0.713897	0.700456	0.441863	71.095219	25.779269
145	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.458473	22.242341	54.757421	56.306406	32.531765	31.987331	26.308099	30.826743	0.036245	0.038961	-0.000468	0.977884	0.932085
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.897197	24.028416	54.544680	57.247195	32.533293	31.869606	27.843562	29.476610	0.061239	0.063856	0.001486	0.000000	0.000000
155	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.347761	23.066788	53.433020	55.233385	32.450969	31.844360	28.268549	28.260348	0.042815	0.043484	-0.000641	1.143632	1.168252
156	N12	RF_maintenance	100.00%	18.82%	18.82%	0.00%	100.00%	0.00%	0.732544	2.753115	1.836691	5.602587	1.529443	3.765770	11.967046	27.515511	0.598931	0.583432	0.376002	3.238578	3.076756
157	N12	RF_maintenance	0.00%	18.82%	18.82%	0.00%	100.00%	0.00%	-0.725827	0.835585	-0.097434	3.030678	1.453324	1.689527	0.657118	-1.803400	0.603798	0.592339	0.380656	9.081305	9.403429
158	N12	RF_maintenance	0.00%	18.82%	18.82%	0.00%	100.00%	0.00%	-0.014313	-1.168699	-0.951607	-0.353429	-0.052324	0.717330	-0.658361	0.606509	0.616884	0.604966	0.387599	2.524686	2.686020
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.753557	19.883585	54.154069	55.748152	32.511602	32.047583	26.809122	30.710501	0.045378	0.045776	0.002708	1.378268	1.364830
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.893461	39.382630	-0.383048	3.662770	0.334025	7.586710	0.420171	3.067045	0.713428	0.631627	0.407130	46.272508	50.235816
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.385153	-0.043283	-0.078675	-0.885127	3.366323	1.831533	0.446525	-0.274012	0.710139	0.702778	0.417519	0.463407	0.439593
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	7.89%	0.463177	-0.359194	-0.017411	-1.006799	-0.014498	-1.509385	-0.230862	-0.937756	0.715287	0.706182	0.416163	0.751514	0.721362
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.454548	-0.930908	-0.987038	-0.519348	-0.827085	-0.631985	-0.662744	1.098471	0.710643	0.698697	0.416305	0.483773	0.469944
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.354970	0.729627	6.711757	-0.911794	5.331480	-1.264350	-0.380042	-0.224583	0.708282	0.694292	0.414343	25.309778	22.135424
166	N14	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.323071	1.539789	-0.285035	1.159715	8.879907	1.274842	73.697924	4.266828	0.676360	0.684046	0.393536	0.000000	0.000000
167	N15	digital_ok	100.00%	21.51%	16.13%	0.00%	100.00%	0.00%	18.322147	21.828153	16.992465	18.269437	23.234319	24.515865	218.592814	37.487077	0.603570	0.589429	0.257766	0.000000	0.000000
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.690452	19.428029	19.053709	21.170147	22.829320	25.677255	3.636741	6.335595	0.651223	0.622230	0.412885	0.000000	0.000000
169	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.685097	17.770134	20.999028	19.912128	25.706351	24.129609	5.940798	4.299057	0.628263	0.603644	0.406129	0.000000	0.000000
170	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.784867	16.643715	21.117654	19.216303	25.859653	22.562740	6.587143	4.206118	0.611570	0.595158	0.405132	0.000000	0.000000
176	N12	digital_ok	0.00%	18.82%	18.82%	0.00%	68.42%	0.00%	-0.300878	-0.196423	-0.888959	0.575495	0.147297	0.087462	-0.718814	-1.451302	0.579917	0.565636	0.375373	0.000000	0.000000
177	N12	digital_ok	100.00%	18.82%	18.82%	0.00%	100.00%	0.00%	1.344990	1.009976	1.253857	6.356900	0.485774	7.511528	1.009564	47.456013	0.591307	0.572781	0.381408	0.000000	0.000000
178	N12	digital_ok	0.00%	18.82%	18.82%	0.00%	68.42%	0.00%	-0.602478	-1.198161	0.725363	-0.793166	-0.319608	-0.684118	1.461767	3.103301	0.604353	0.590869	0.386885	0.000000	0.000000
179	N12	digital_ok	100.00%	18.82%	18.82%	0.00%	100.00%	0.00%	0.114023	-0.345839	-0.539706	-0.002072	1.410296	0.152165	10.196677	0.280624	0.614489	0.599580	0.399831	0.000000	0.000000
180	N13	RF_maintenance	100.00%	0.00%	40.32%	0.00%	100.00%	0.00%	-0.300218	13.678310	0.336420	50.267929	-0.004852	20.705394	-0.639672	18.832343	0.705315	0.498293	0.511772	0.000000	0.000000
181	N13	digital_ok	100.00%	100.00%	61.83%	0.00%	100.00%	0.00%	22.764599	44.632454	54.936380	7.222127	32.566828	31.746710	26.242442	23.342333	0.047243	0.321719	0.167355	0.000000	0.000000
182	N13	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.983203	16.955287	19.087505	19.501316	22.681488	23.277294	3.137544	4.202707	0.687981	0.673136	0.435100	0.000000	0.000000
183	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.309403	-1.171391	-0.088724	-1.086045	-0.324118	-1.297384	0.247770	9.173369	0.709536	0.699187	0.422998	0.000000	0.000000
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.213853	0.014313	0.017411	0.701095	0.168959	0.529186	-0.012544	-0.733333	0.708206	0.696361	0.406049	0.000000	0.000000
185	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	68.42%	31.58%	0.732646	0.582821	2.524892	1.280299	0.979085	0.014498	-2.301743	-0.553840	0.704834	0.692020	0.404512	0.000000	0.000000
186	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.325159	0.569663	3.911318	2.075848	0.575761	0.130847	5.251697	0.586889	0.693909	0.682331	0.398250	0.000000	0.000000
187	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.202470	0.285747	0.014283	0.580933	0.376020	-0.419329	7.341968	2.211635	0.692153	0.680605	0.402690	0.000000	0.000000
189	N15	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.841021	3.206912	0.246505	-0.442401	-0.298729	-0.708172	0.397094	-0.513948	0.654574	0.637276	0.407434	0.000000	0.000000
190	N15	digital_ok	100.00%	32.26%	100.00%	0.00%	100.00%	0.00%	40.996664	24.507828	5.568268	56.342900	14.284826	32.135278	226.592081	29.185147	0.527751	0.050706	0.303428	0.000000	0.000000
191	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.293903	-0.394225	-0.834958	-0.973624	-0.463991	-0.729514	3.417740	9.379714	0.623287	0.596252	0.418874	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%	11.810461	4.890670	13.914259	1.572900	16.904859	4.726972	57.955438	82.886783	0.666797	0.643623	0.395888	0.000000	0.000000
206	N19	RF_ok	100.00%	0.00%	5.38%	0.00%	100.00%	0.00%	2.382250	8.014937	-0.068810	8.749335	5.323960	27.706854	58.207602	167.779389	0.645915	0.629474	0.384419	0.000000	0.000000
207	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.843184	11.040640	15.875565	13.224244	18.964142	15.225631	19.498215	17.572774	0.644226	0.635402	0.389937	1.190562	1.187761
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%	9.432693	6.090893	85.971646	88.682725	2435.928709	2858.216662	29204.766182	40398.015699	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.768655	12.711286	8.248792	15.114315	9.628099	16.895872	-0.973816	-0.263358	0.658614	0.637075	0.410056	0.000000	0.000000
224	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	21.585167	21.133065	23.728215	23.061676	29.504908	28.177359	7.902455	6.750152	0.609562	0.596912	0.389816	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%	7.286938	12.159095	inf	inf	3383.496372	3382.941891	48796.528391	48787.697222	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.971945	24.946497	40.288440	40.845216	32.808263	31.975688	38.118522	29.184309	0.060069	0.069995	0.003341	0.000000	0.000000
321	N02	not_connected	100.00%	18.82%	56.99%	0.00%	100.00%	0.00%	10.430057	8.188270	12.713985	11.396739	15.829016	13.418260	79.102650	79.447902	0.506204	0.426337	0.327919	0.000000	0.000000
323	N02	not_connected	100.00%	34.95%	62.37%	0.00%	100.00%	0.00%	19.727206	11.884725	2.257332	14.842089	11.903134	15.827533	21.815985	7.095634	0.430969	0.395324	0.271848	0.000000	0.000000
324	N04	not_connected	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.203620	29.255514	60.010451	60.526746	27.601623	28.916066	747.038984	779.758827	0.023938	0.023379	0.000473	0.000000	0.000000
329	N12	dish_maintenance	100.00%	53.76%	73.12%	0.00%	100.00%	0.00%	3.837385	6.989460	6.367700	11.068504	7.174679	12.271350	4.144884	-1.301761	0.413246	0.341273	0.270262	0.000000	0.000000
333	N12	dish_maintenance	100.00%	67.74%	100.00%	0.00%	100.00%	0.00%	2.028900	7.031442	3.866048	10.085348	2.817073	9.856052	4.546181	-1.313461	0.363687	0.310672	0.228136	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, 66, 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, 163, 165, 166, 167, 168, 169, 170, 176, 177, 178, 179, 180, 181, 182, 183, 184, 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: [65, 162, 164]

golden_ants: [65, 162, 164]

# 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_2459794.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