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 = "2459786"
data_path = "/mnt/sn1/2459786"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
good_statuses = "digital_ok,calibration_maintenance,calibration_triage,calibration_ok"

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

Date: 7-25-2022

# 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/sn1/2459786/zen.2459786.25318.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/sn1/2459786/zen.2459786.?????.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/sn1/2459786/zen.2459786.?????.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	2459786
Date	7-25-2022
LST Range	15.732 -- 17.732 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: 43 RF_ok: 11 digital_maintenance: 2 digital_ok: 85 not_connected: 3
Commanded Signal Source	antenna
Antennas in Commanded State	104 / 147 (70.7%)
Cross-Polarized Antennas	104
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N10, N12
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	82 / 147 (55.8%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	117 / 147 (79.6%)
Redcal Done?	✅
Redcal Flagged Antennas	10 / 147 (6.8%)
Never Flagged Antennas	12 / 147 (8.2%)
A Priori Good Antennas Flagged	78 / 85 total a priori good antennas: 5, 7, 9, 10, 15, 16, 17, 19, 20, 21, 30, 37, 40, 41, 45, 46, 50, 51, 53, 54, 55, 56, 57, 65, 66, 68, 69, 71, 72, 81, 83, 84, 88, 91, 92, 99, 101, 103, 105, 107, 108, 109, 111, 117, 118, 121, 122, 123, 128, 129, 135, 138, 140, 141, 142, 143, 144, 145, 160, 161, 162, 163, 165, 167, 169, 170, 176, 177, 178, 179, 181, 183, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	5 / 62 total a priori bad antennas: 3, 67, 98, 100, 116

# 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_2459786.csv

Build DataFrame

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

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

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

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

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

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

Table 2: RTP Per-Antenna Metrics Summary Table

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

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

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

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

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

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

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
3	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.175814	-0.198339	-0.219839	-0.971215	-0.531983	0.236517	-0.436856	-0.021300	0.611638	0.607944	0.379143	11.301788	6.463827
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.580880	5.065150	-0.738568	-0.097512	-0.444858	-0.319397	3.047853	0.474690	0.624994	0.615091	0.376886	34.040179	40.807122
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.158440	1.823798	-0.549317	6.835890	-1.047555	5.575700	0.369744	-1.480588	0.631631	0.621670	0.376721	3.854121	3.360004
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.982109	5.989918	13.134945	13.575553	16.998666	16.050501	-1.706355	7.039193	0.612922	0.605340	0.369311	3.819297	3.612314
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.842617	19.266081	25.693704	27.112977	37.421596	37.108365	1.482308	-0.202577	0.585707	0.572027	0.363955	7.907515	8.127515
9	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.237077	5.714344	0.477839	13.816555	2.433890	14.873901	0.746362	-0.072232	0.587343	0.579644	0.369990	0.000000	0.000000
10	N02	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
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	5.26%	0.451549	1.932213	-0.879300	0.006550	0.136460	0.006539	-0.308169	3.062976	0.647860	0.637930	0.379969	5.767899	5.452482
16	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.630197	-0.976003	-1.004358	0.813427	-0.677471	-0.367935	7.256681	5.071857	0.650750	0.644792	0.371002	23.788904	44.140275
17	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.067027	1.089328	0.454714	0.327787	0.409414	0.041971	16.045875	21.268368	0.644157	0.639635	0.355976	24.074718	24.278161
18	N01	RF_maintenance	100.00%	0.00%	59.14%	0.00%	100.00%	0.00%	10.666899	9.729634	1.813390	3.002916	4.716794	10.275005	87.914924	125.208330	0.560739	0.431427	0.364437	7.001414	3.778347
19	N02	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
20	N02	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
21	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.129793	-0.604467	-0.052109	0.935375	0.244440	0.056857	0.177501	17.577428	0.580526	0.568696	0.363401	6.456648	6.335716
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.086710	29.350068	68.044752	68.550378	52.255495	50.090095	15.542269	11.535780	0.041563	0.046090	0.003401	1.314354	1.315442
28	N01	RF_maintenance	100.00%	45.70%	100.00%	0.00%	100.00%	0.00%	14.781161	26.222638	6.556497	12.082029	48.099620	51.884460	11.629859	74.807373	0.464170	0.247739	0.272942	15.635067	3.993894
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	0.00%	-1.613717	-0.812560	0.658581	-0.132214	0.474093	-0.510037	-0.901876	1.320963	0.664542	0.662260	0.361644	2.528773	2.070649
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.895154	-0.025859	-0.443693	-0.609918	-0.292841	-0.784351	7.319438	1.096618	0.650180	0.648405	0.362455	12.934338	23.861798
31	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	0.00%	-0.516672	-0.529220	-0.759137	-0.891498	-0.280830	0.112102	2.341774	2.392694	0.642274	0.632519	0.365278	2.040716	2.383457
32	N02	RF_maintenance	100.00%	16.13%	0.00%	0.00%	100.00%	0.00%	36.603012	41.150197	3.074559	3.556101	17.368001	16.316046	52.761276	94.277355	0.538838	0.552054	0.194613	4.870372	4.484673
33	N02	RF_maintenance	100.00%	0.00%	61.83%	0.00%	100.00%	0.00%	-0.349928	7.432906	0.344146	1.628029	17.580266	22.756808	156.965678	208.137754	0.592832	0.423304	0.428442	5.967897	3.878739
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.387770	9.671994	0.273623	1.114422	-0.007000	-0.545353	0.556798	1.891365	0.613894	0.599555	0.359826	8.815380	13.649064
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.612657	0.953563	0.655123	-0.117812	0.224496	0.699592	-0.371771	12.624334	0.646048	0.636768	0.369004	8.485097	7.958684
38	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.321107	0.069620	-0.509920	-0.482723	1.301268	0.498814	12.629475	2.080364	0.667243	0.659877	0.379119	6.108474	8.224110
40	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	0.981262	0.244509	0.185915	-0.886036	-0.661709	-0.923558	0.021300	-0.551367	0.681804	0.681962	0.369757	2.602208	3.135254
41	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.914236	-0.419266	3.616176	-0.852326	4.683647	-0.706783	-0.791025	1.127015	0.678841	0.678040	0.355425	19.999393	41.759825
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	2.63%	0.00%	-0.827649	0.576573	3.015294	0.632458	1.327077	0.318477	-1.317094	-0.404973	0.679865	0.679899	0.369840	5.017200	4.268365
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.107429	0.353721	0.048068	1.422955	0.435852	0.455074	0.525747	27.117412	0.629528	0.618967	0.371928	4.823773	5.046436
46	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.351193	-0.808803	0.774196	-1.045240	0.233470	-1.097067	0.079957	5.407342	0.609216	0.602044	0.378726	4.982602	5.587332
50	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.208501	3.619116	-0.812457	1.113307	0.403263	2.539488	14.602642	15.659090	0.621744	0.614040	0.343047	0.000000	0.000000
51	N03	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.153960	45.032564	4.664832	85.618471	3.281223	50.340129	0.160226	33.887391	0.653975	0.046680	0.395355	8.255682	1.096412
52	N03	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	14.401159	50.747694	0.853090	86.498236	7.896085	50.854205	68.665318	35.964035	0.631845	0.045030	0.372326	7.267560	1.127230
53	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.427715	3.452531	6.387844	5.442845	5.156133	3.608037	2.594004	8.437289	0.689674	0.688442	0.368025	0.000000	0.000000
54	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.292642	2.393418	0.363375	0.947954	6.055326	6.688967	2.096256	1.857817	0.686864	0.686426	0.337620	36.920840	35.904819
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.145117	-0.057213	0.724064	0.916277	1.073732	2.637590	19.602460	1.094993	0.684542	0.688067	0.355999	166.158951	96.576155
56	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.639190	4.775924	14.630902	11.951497	18.034542	13.208564	-1.669017	13.852071	0.687382	0.691304	0.368112	38.627012	38.345251
57	N04	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	28.559707	2.595983	64.406897	1.963144	52.118953	8.823564	10.483061	4.152530	0.051846	0.678258	0.406170	1.246269	34.935536
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	55.26%	1.061735	0.620465	-0.025948	0.736383	1.054921	0.943439	-0.738611	1.448302	0.630487	0.625210	0.360474	5.826158	4.708789
66	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.850309	1.175191	-0.835222	-0.313049	-0.228607	-1.026668	0.803972	7.549018	0.656654	0.652637	0.353623	18.481031	23.838783
67	N03	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.499859	-1.117557	0.850441	1.046533	0.633462	0.931612	0.967527	1.761218	0.677229	0.676694	0.353121	2.167368	2.146729
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	86.84%	0.115734	0.258965	-0.663714	0.960202	0.691432	0.323096	-0.093185	0.135059	0.684485	0.685063	0.353815	0.000000	0.000000
69	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.571181	4.603611	12.601937	11.712416	16.002824	15.954080	-0.601309	-1.141893	0.690935	0.698539	0.347545	20.930130	81.455015
70	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.611194	-1.505116	0.555327	-0.889655	-0.044378	-0.610243	0.580596	2.468254	0.691487	0.689871	0.374510	23.531930	31.398530
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	18.42%	0.329801	-1.244505	-0.761881	-0.006550	0.594703	0.034415	1.013232	2.467684	0.690454	0.699569	0.361728	10.406154	7.608157
72	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.049174	-0.938306	0.074907	2.699085	-0.313025	1.888655	1.107294	-0.964316	0.675679	0.684255	0.380702	21.373164	50.082155
73	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.849084	0.078602	-0.069404	1.243664	1.218124	-0.569835	0.515595	0.696663	0.662359	0.657109	0.392153	3.966253	4.406594
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.862080	4.593321	-0.894696	6.297140	0.029973	5.784165	6.925759	0.534850	0.614561	0.611729	0.341576	8.511189	13.997436
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.055871	0.791083	0.101509	-0.704233	0.263622	-1.673320	0.505173	-0.042042	0.644880	0.644161	0.345208	12.178551	15.384229
83	N07	digital_ok	100.00%	34.95%	0.00%	0.00%	100.00%	0.00%	14.141633	2.731877	48.333211	6.068942	23.379179	4.730845	10.869098	4.021589	0.528868	0.671622	0.403374	3.446025	8.560362
84	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.542883	11.216088	3.293800	2.830162	2.256859	1.442517	-0.791094	-0.605758	0.689052	0.691883	0.346139	0.000000	0.000000
85	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.019869	36.216796	0.023014	3.212243	-1.010947	13.752777	-0.082538	6.829253	0.696335	0.637034	0.353170	9.964239	14.086806
86	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.939863	5.236214	-0.535885	-0.273626	-0.884262	1.591079	1.956710	4.160747	0.690908	0.685540	0.348156	0.000000	0.000000
87	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
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.042921	27.799402	59.271407	60.435768	52.375964	50.086186	22.085550	13.617890	0.043090	0.044049	-0.000125	1.202828	1.204486
90	N09	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.867919	4.030837	-0.558934	5.538036	0.459793	5.961658	2.947674	0.170518	0.634272	0.635214	0.387049	19.164324	20.180510
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%	43.396074	67.949436	8.920015	11.591281	54.931139	58.775750	10.071593	19.767939	0.092871	0.090767	0.009996	0.000000	0.000000
93	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.415923	0.594925	4.043070	-0.305809	6.220864	0.706256	-0.111194	1.214098	0.092123	0.091701	-0.011548	0.000000	0.000000
94	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.153343	-1.641092	0.280339	-0.850906	1.405459	0.999305	9.552871	0.374114	0.066070	0.063361	0.008659	0.000000	0.000000
98	N07	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.804678	1.687261	1.211108	0.219122	-0.753963	-1.347906	0.580313	2.398733	0.600619	0.597468	0.342033	6.725279	5.476392
99	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.155337	-0.319056	3.606059	1.676354	5.380524	-0.471232	6.941652	1.221463	0.626300	0.628305	0.334145	0.000000	0.000000
100	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.409337	-0.403200	0.538223	0.280664	1.359607	-0.553672	0.524196	0.462953	0.655467	0.655201	0.349895	0.000000	0.000000
101	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
102	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.392459	0.810769	5.252610	-0.748069	21.922458	14.921653	168.246635	121.877519	0.675476	0.684225	0.360846	3.529960	3.909504
103	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.158461	10.088014	1.016029	0.371449	-0.327893	-0.078779	1.620232	3.561723	0.691226	0.691472	0.362791	3.182326	3.229794
104	N08	RF_maintenance	100.00%	0.00%	0.00%	100.00%	100.00%	0.00%	6.470529	92.812382	1.766895	16.801546	2.924535	6.920162	1.343475	1.264784	0.293395	0.285539	-0.318051	3.167077	3.180366
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	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.133345	1.108483	0.850950	-0.338176	-0.549479	-0.787961	3.687965	-0.026663	0.654984	0.654951	0.379390	4.711034	4.879655
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.222793	26.611656	56.780308	57.823633	52.330080	49.852596	13.396145	14.434865	0.046660	0.048848	0.002669	1.371864	1.381240
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%	-0.980981	-0.098947	-0.227923	0.879775	-1.463642	-0.240881	0.980352	1.386895	0.062229	0.067223	0.008514	0.000000	0.000000
110	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	47.140110	2.589422	5.029559	2.198000	16.549624	5.858326	1.465416	10.080065	0.057907	0.060071	0.004512	1.463879	1.455497
111	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.355277	1.590963	0.142829	2.715817	0.748878	2.849063	0.398062	15.755900	0.061940	0.066067	0.007522	1.048762	1.067766
112	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.815531	-0.687544	0.244175	2.083203	0.409354	0.665221	0.890854	-1.315716	0.080497	0.081359	0.017975	1.743739	1.737287
116	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.470116	2.651723	-0.316107	-0.638825	-0.064690	1.447876	-0.063318	-0.347673	0.585893	0.581307	0.342211	4.460398	4.324216
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.194415	4.473049	10.740342	8.237814	12.278169	6.714166	-1.737366	-1.847885	0.622959	0.621519	0.364478	2.850923	3.190933
118	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.058978	1.908054	2.876671	0.665554	3.723308	0.130584	6.011977	7.181871	0.629511	0.632195	0.358391	5.321787	4.982620
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.706646	0.916783	11.353619	0.043604	13.963089	-1.100008	-1.769958	-0.290055	0.652108	0.652679	0.360781	0.000000	0.000000
120	N08	RF_maintenance	100.00%	53.76%	100.00%	0.00%	100.00%	0.00%	21.643653	40.659884	5.118361	75.827461	48.475002	50.807187	7.785751	22.175881	0.443861	0.045755	0.297677	3.443016	1.171510
121	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.512053	6.216758	-0.443608	1.340774	-0.080833	1.327214	32.598410	37.436091	0.676889	0.677176	0.365034	3.434510	3.148235
122	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
123	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.588377	9.745803	2.235581	0.269713	0.883505	-0.274834	-0.684282	-0.060569	0.671699	0.672686	0.371188	12.954559	10.598949
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.965091	27.521529	59.982397	61.651074	52.312142	50.013284	10.948871	15.876334	0.029592	0.032331	0.001435	1.246050	1.376466
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.922332	28.270763	59.249707	62.142397	52.401390	50.117066	13.137721	15.037153	0.032541	0.032812	-0.001294	1.293506	1.544454
127	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.135916	-0.661437	0.256812	-0.075378	-0.518367	-0.129275	-0.622982	1.083586	0.070041	0.071823	0.009170	0.000000	0.000000
128	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.745048	1.076113	0.479955	2.680712	-0.937646	3.019860	-0.346189	-0.290789	0.057592	0.057702	0.004226	0.000000	0.000000
129	N10	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
130	N10	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
135	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.294857	-0.723239	-0.907770	-0.808431	-0.931648	-0.705923	0.885463	0.889021	0.065978	0.074065	0.011929	0.965052	0.945556
136	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.757288	12.545724	0.421811	0.690686	2.977805	2.985068	5.945704	13.855838	0.062345	0.067972	0.010279	0.902288	0.912669
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.643152	27.724872	58.437418	59.538503	52.392285	50.088551	12.907417	16.520611	0.036887	0.044507	0.003756	0.000000	0.000000
138	N07	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	33.299499	3.094831	57.965067	-0.543060	52.436523	-0.723559	13.822446	-0.220029	0.051627	0.624408	0.393401	1.039489	4.689081
140	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.333004	5.418773	0.090862	-0.357147	2.144367	1.374562	1.982682	2.754164	0.638900	0.634292	0.363182	26.685240	22.528323
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.291001	4.915790	-0.220982	15.239538	1.810366	4.591083	0.392788	20.946570	0.644350	0.626838	0.365226	10.379684	10.631810
142	N13	digital_ok	100.00%	61.83%	100.00%	0.00%	100.00%	0.00%	27.667528	33.327086	7.857505	69.217193	49.688228	50.229692	11.116592	12.408193	0.424806	0.048901	0.231712	24.685315	1.311462
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	0.356888	-0.019730	-0.858457	1.725236	-0.006539	1.727052	-0.327812	-1.058736	0.649285	0.644500	0.360573	3.728737	3.600189
144	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	13.16%	-0.967862	0.243754	0.728731	0.702813	1.201150	0.190432	2.741347	0.351078	0.644015	0.637426	0.378604	5.039461	5.560599
145	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.872487	27.275418	68.396559	69.761073	52.241469	50.179245	13.579275	16.580154	0.037079	0.038011	-0.000307	1.481941	1.654806
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.999162	30.051568	68.185682	71.004320	52.561875	50.439846	14.975023	16.668359	0.048371	0.051977	0.002331	0.850908	0.846118
155	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.525224	29.572702	66.830632	68.399903	52.181363	49.837343	15.233044	15.063544	0.033870	0.035192	0.001368	0.957264	0.958285
156	N12	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
157	N12	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
158	N12	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
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.639623	26.773500	67.711493	69.079112	52.259300	50.072158	13.847463	16.039035	0.043791	0.044539	0.000807	1.362817	1.361922
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.844368	51.356061	-0.503095	5.046634	-0.355278	12.870029	0.209527	0.768520	0.636579	0.544776	0.322013	10.629954	13.692698
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	2.378016	1.296056	0.685267	0.188202	1.982103	1.547543	-0.483240	-0.598979	0.635130	0.633294	0.358400	4.101602	3.478582
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	1.302265	-0.632581	0.228908	-0.959812	-1.151310	-1.242938	0.085813	-0.433354	0.638968	0.634030	0.360031	3.355208	3.080945
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.332663	-1.136638	-1.090773	-0.735517	-1.160360	-1.250706	-0.194826	0.853261	0.629745	0.623824	0.365847	2.418260	2.014802
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.605379	0.426631	8.112357	-0.875987	8.599876	-1.172675	-0.756962	0.160156	0.620576	0.614609	0.366252	10.760604	12.657324
166	N14	RF_maintenance	100.00%	8.06%	0.00%	0.00%	100.00%	0.00%	16.686452	3.855187	0.887713	0.087461	12.996951	11.273485	52.950923	12.783449	0.556705	0.578124	0.327569	1.097946	1.124826
167	N15	digital_ok	100.00%	48.39%	45.70%	0.00%	100.00%	0.00%	26.188343	26.426966	20.878333	25.550860	42.814574	39.450683	130.887177	31.447933	0.467685	0.483319	0.175607	0.877946	0.867008
168	N15	RF_maintenance	100.00%	5.38%	8.06%	0.00%	100.00%	0.00%	19.081223	22.422752	26.129801	29.658287	38.482556	41.346745	0.290582	-0.244181	0.552131	0.535322	0.350341	0.000000	0.000000
169	N15	digital_ok	100.00%	8.06%	8.06%	0.00%	100.00%	0.00%	21.387895	20.767428	28.640229	28.027099	42.253572	38.735613	0.436487	-0.446263	0.530542	0.522499	0.338098	0.000000	0.000000
170	N15	digital_ok	100.00%	8.06%	8.06%	0.00%	100.00%	0.00%	21.616470	19.334214	28.809005	26.827335	43.021611	37.069380	0.728293	-0.448810	0.512662	0.514891	0.332224	0.917103	0.913944
176	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.000498	0.000498	-0.892004	1.228907	-0.122967	1.014582	-0.518265	-1.159850	0.044987	0.053193	0.002895	3.097483	4.931696
177	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.034633	0.943045	1.181548	0.973115	-0.232683	1.226610	9.585374	3.737775	0.056937	0.051966	0.005784	7.759343	17.149603
178	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.280324	-1.487137	-0.834329	-0.545858	-1.734001	-0.742059	-0.696808	-0.661810	0.059789	0.051250	0.008480	0.928268	0.935007
179	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.265923	0.553888	0.260448	-0.065924	2.214096	0.066786	3.319799	-0.518466	0.063309	0.060211	0.012444	0.902562	0.905090
180	N13	RF_maintenance	100.00%	0.00%	64.52%	0.00%	100.00%	0.00%	0.246357	18.530623	1.372833	62.573357	0.410589	33.966128	-0.464807	10.896171	0.618321	0.381738	0.426427	14.272496	4.570975
181	N13	digital_ok	100.00%	100.00%	96.77%	0.00%	100.00%	0.00%	27.633318	66.610811	68.621922	16.028835	52.279278	51.724250	13.713843	33.777799	0.046511	0.262680	0.133535	1.086702	1.691242
182	N13	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.825397	12.077088	25.848168	19.487703	37.778860	25.668871	-0.553388	12.200350	0.601291	0.604459	0.363934	12.561511	20.213917
183	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.034791	0.540071	0.144669	-0.644948	0.087766	-1.323965	0.085196	6.034000	0.627190	0.614537	0.368241	33.143426	48.838850
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	5.26%	0.00%	-0.240224	-1.028499	0.416131	-0.535288	-0.173745	-0.580410	0.503249	-1.051818	0.622781	0.612518	0.359902	1.542528	1.378415
185	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
186	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
187	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
189	N15	digital_ok	100.00%	5.38%	5.38%	0.00%	100.00%	0.00%	4.313716	4.810605	-0.083026	-0.380512	0.041743	0.329062	0.607960	4.536450	0.560673	0.555083	0.349844	0.000000	0.000000
190	N15	digital_ok	100.00%	45.70%	100.00%	0.00%	100.00%	0.00%	45.787892	31.739477	4.615666	69.690388	25.429025	50.081818	332.034397	15.362640	0.458503	0.051188	0.292206	5.177108	2.196922
191	N15	digital_ok	0.00%	8.06%	8.06%	0.00%	21.05%	34.21%	0.242246	0.700715	-0.535031	-0.652441	0.062925	-0.683760	-0.427127	-0.687172	0.532539	0.522737	0.347876	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%	5.38%	0.00%	100.00%	0.00%	13.209357	6.165202	18.810907	0.291522	27.401646	9.069714	41.560791	55.913295	0.577085	0.564018	0.355116	6.675755	6.779139
206	N19	RF_ok	100.00%	8.06%	5.38%	0.00%	100.00%	0.00%	3.890203	9.491182	0.366426	12.836832	10.661760	19.507159	43.255454	43.162365	0.548482	0.558841	0.336797	2.131514	2.345593
207	N19	RF_ok	100.00%	5.38%	8.06%	0.00%	100.00%	0.00%	15.955783	20.977831	21.635889	28.060185	32.124613	39.471240	12.999110	13.915152	0.549835	0.535758	0.337674	1.136486	1.127508
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%	13.494971	14.337432	110.943295	109.476886	3879.975170	4654.333339	18752.358565	26759.885078	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	0.00%	2.69%	0.00%	100.00%	0.00%	7.548689	13.619314	10.432403	19.991449	15.996743	26.324033	-0.984033	-1.450823	0.565206	0.557874	0.353320	6.852306	5.700490
224	N19	RF_ok	100.00%	8.06%	8.06%	0.00%	100.00%	0.00%	24.633627	24.725187	32.272687	32.282718	48.196978	45.022679	1.016105	-0.099444	0.518031	0.517348	0.328665	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%	11.419346	15.249826	inf	inf	5391.331152	5390.940612	31512.405289	31507.511435	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	32.525636	32.403657	50.128050	50.001946	52.473399	49.901502	20.905401	15.490502	0.070350	0.059357	-0.001755	0.000000	0.000000
321	N02	not_connected	100.00%	37.63%	75.27%	0.00%	100.00%	0.00%	11.545863	9.706277	17.633690	16.438423	27.878071	23.789004	54.333773	54.280487	0.442663	0.391921	0.276798	0.000000	0.000000
323	N02	not_connected	100.00%	51.08%	83.33%	0.00%	100.00%	0.00%	24.734725	13.787838	3.478402	20.836693	21.591215	29.069344	6.873447	0.456329	0.362442	0.369452	0.237499	0.000000	0.000000
324	N04	not_connected	100.00%	43.01%	80.65%	0.00%	100.00%	0.00%	16.577175	18.098230	23.802556	24.682758	34.640794	33.713294	-0.708967	-1.604668	0.426003	0.376106	0.276817	0.000000	0.000000
329	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.056259	8.540450	1.805062	16.379903	2.986057	19.989573	8.347491	-1.656722	0.064707	0.064916	0.017052	0.000000	0.000000
333	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.816798	8.481107	11.873373	14.532565	10.680838	18.900912	3.371735	-0.553757	0.066162	0.065124	0.016651	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, 65, 66, 67, 68, 69, 70, 71, 72, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 98, 99, 100, 101, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 130, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 150, 155, 156, 157, 158, 160, 161, 162, 163, 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: [73, 106, 164]

golden_ants: [73, 106, 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_2459786.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