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 = "2459809"
data_path = "/mnt/sn1/2459809"
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: 8-17-2022

# 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/sn1/2459809/zen.2459809.33561.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 245 ant_metrics files matching glob /mnt/sn1/2459809/zen.2459809.?????.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 25 ant_metrics files matching glob /mnt/sn1/2459809/zen.2459809.?????.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	2459809
Date	8-17-2022
LST Range	19.227 -- 22.037 hours
X-Engine Status	✅ ✅ ✅ ❌ ✅ ✅ ✅ ✅
Number of Files	258
Total Number of Antennas	147
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 32 RF_ok: 11 digital_maintenance: 3 digital_ok: 95 not_connected: 3
Commanded Signal Source	None
Antennas in Commanded State	0 / 147 (0.0%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N09, N14, N15, N19
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	116 / 147 (78.9%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	117 / 147 (79.6%)
Redcal Done?	✅
Redcal Flagged Antennas	8 / 147 (5.4%)
Never Flagged Antennas	8 / 147 (5.4%)
A Priori Good Antennas Flagged	88 / 95 total a priori good antennas: 3, 5, 7, 9, 10, 15, 16, 17, 19, 20, 21, 29, 30, 31, 37, 40, 41, 42, 45, 53, 54, 55, 56, 66, 69, 71, 72, 73, 81, 83, 84, 85, 86, 88, 91, 93, 94, 98, 99, 101, 103, 105, 106, 107, 108, 109, 111, 112, 116, 117, 118, 121, 122, 123, 127, 128, 129, 130, 140, 141, 142, 143, 144, 156, 157, 158, 160, 161, 162, 163, 164, 165, 167, 169, 170, 176, 177, 178, 179, 181, 183, 184, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	1 / 52 total a priori bad antennas: 4

# 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_2459809.csv

Build DataFrame

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

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

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

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

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

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

Table 2: RTP Per-Antenna Metrics Summary Table

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

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

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

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

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

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

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
3	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	8.00%	3.191823	-0.669178	-0.514730	-0.645229	-0.227014	-1.129125	-0.584414	0.314047	0.794744	0.528769	0.588704	1.397425	1.103090
4	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.076460	3.589360	-0.729821	1.579326	3.766723	3.215773	3.815431	0.943553	0.809267	0.528621	0.592602	4.064377	3.438299
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.328371	-0.219446	-0.670635	4.478825	24.596254	24.908774	7.712772	3.821949	0.812127	0.542328	0.591884	3.987063	3.546599
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.745547	-0.912308	0.329516	0.570123	17.211110	24.584283	4.985758	15.157919	0.803934	0.526534	0.599904	2.996219	2.446648
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.915979	8.204177	19.951813	20.589076	43.414644	42.615894	4.675455	3.037166	0.799545	0.494691	0.609149	2.823075	2.253255
9	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.270635	-1.063063	0.394639	-0.060833	21.333721	20.870586	18.465190	17.775646	0.808208	0.514175	0.621266	3.109352	2.568501
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	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.783341	0.972897	1.587203	0.834687	0.767781	0.470657	0.323325	8.852932	0.809288	0.534979	0.580243	4.297470	3.654165
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	4.00%	4.00%	-0.706909	-0.653546	-0.671126	-0.090795	1.851587	3.352170	2.557162	3.805501	0.814006	0.555229	0.571187	1.586564	1.326162
17	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.348546	0.675661	0.510478	0.436896	11.198149	10.154844	5.651375	2.418031	0.811857	0.551434	0.576813	3.816119	3.576167
18	N01	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	9.466096	12.665344	1.290584	0.312439	28.479217	16.153039	96.406096	32.593680	0.764081	0.294736	0.593425	2.765195	1.679378
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%	289.972793	290.016685	inf	inf	10229.219854	10228.621000	4053.466492	4052.637490	nan	nan	nan	0.000000	0.000000
21	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.536152	0.764090	0.699181	0.893424	0.060118	42.226930	-0.228489	1.047946	0.802660	0.507505	0.618802	3.604566	2.735963
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.803781	22.725256	32.913508	33.602357	56.489499	51.892523	8.794847	7.009109	0.038394	0.043891	0.002822	1.159004	1.157724
28	N01	RF_maintenance	100.00%	4.08%	100.00%	0.00%	100.00%	0.00%	13.784885	31.487345	0.495803	1.861012	19.353292	25.431274	2.541967	28.362879	0.431327	0.186856	0.277969	13.368417	2.442213
29	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.336352	-0.032819	0.392325	-0.170792	5.842510	5.986716	1.633733	1.559297	0.817002	0.561122	0.562880	3.518575	3.242901
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.192192	0.206368	0.104784	-0.332963	29.926851	27.930843	13.394893	3.997371	0.809419	0.558658	0.562339	3.319578	3.182146
31	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	8.00%	1.134895	-0.804950	-0.575457	0.731215	0.561183	1.335523	-0.580981	-0.762386	0.812188	0.551897	0.582528	1.499309	1.316517
32	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	35.032931	22.474157	2.457787	1.444928	24.491240	50.723437	48.246853	161.357668	0.703867	0.491536	0.404790	6.478100	3.963474
33	N02	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.226996	8.831437	-0.074092	-0.112683	1.101518	4.955393	-0.184792	8.983688	0.794611	0.321221	0.654986	3.377035	1.899677
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.734002	8.357767	0.112893	0.412939	2.570103	-0.512105	0.160754	-0.538746	0.809334	0.555019	0.575984	4.079857	3.634673
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.081353	0.848981	-0.437448	0.344350	8.637631	8.713176	2.996530	9.762594	0.814317	0.573526	0.560795	4.814395	4.229513
38	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.990921	-0.308103	-0.242359	-0.808515	0.647280	-0.293474	3.207293	-0.227977	0.812193	0.582466	0.554730	1.897226	1.574997
40	N04	digital_ok	0.00%	6.12%	6.12%	0.00%	8.00%	0.00%	0.763930	-0.244398	-0.037257	-0.392143	0.015910	-0.590318	-0.657016	-0.373419	0.784090	0.564081	0.531044	2.271786	1.897517
41	N04	digital_ok	0.00%	6.12%	6.12%	0.00%	8.00%	0.00%	0.679803	-0.536580	2.844063	1.051683	1.947172	-1.107092	-0.862182	0.691966	0.787485	0.559379	0.536580	2.701488	1.912963
42	N04	digital_ok	0.00%	6.12%	6.12%	0.00%	8.00%	0.00%	0.138109	0.781024	2.029208	0.798338	-0.606456	-0.812544	-0.648842	-1.256031	0.785675	0.557387	0.541839	2.192879	1.805088
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.376841	1.424771	-0.470970	0.074777	-1.145415	0.902572	-0.256055	4.699961	0.809604	0.529635	0.595407	3.241827	2.939321
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	8.00%	0.00%	-0.892304	-0.061456	0.045958	-0.763331	0.517235	0.588499	-0.478929	3.592903	0.806008	0.515916	0.610343	1.506051	1.353155
50	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	32.484025	17.034663	2.797346	1.160839	19.645961	19.217085	51.101217	39.265168	0.732964	0.508909	0.455643	4.261338	4.326751
51	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.365718	2.373589	-0.670878	-0.556029	-1.375496	-1.315185	-0.788919	0.634394	0.811676	0.594199	0.547118	1.708919	1.620536
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.423711	8.392777	0.476237	0.157682	3.516255	0.841823	0.751276	0.819327	0.814320	0.600320	0.538903	4.650108	4.720454
53	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	8.00%	1.886737	2.072258	-0.571638	0.060391	-0.544555	-0.734781	2.951746	2.897313	0.815769	0.612219	0.534530	2.119131	1.818778
54	N04	digital_ok	0.00%	6.12%	6.12%	0.00%	8.00%	0.00%	0.402273	-0.949613	1.225075	1.198300	0.424588	0.391406	2.010145	-0.458080	0.785771	0.577403	0.519541	2.294924	2.093559
55	N04	digital_ok	100.00%	6.12%	6.12%	0.00%	100.00%	0.00%	1.773532	1.305094	-0.492510	1.134896	18.565921	-0.352231	8.570489	0.378444	0.784147	0.568244	0.525627	4.840523	4.697634
56	N04	digital_ok	0.00%	6.12%	6.12%	0.00%	8.00%	0.00%	0.008948	0.597061	1.375914	1.241224	0.033351	-0.203809	-0.826368	3.915649	0.785201	0.570289	0.532210	2.160622	1.766834
57	N04	RF_maintenance	100.00%	6.12%	6.12%	0.00%	100.00%	0.00%	30.390356	-0.046100	14.363992	2.783909	31.502749	1.063148	2.807989	-0.029878	0.622035	0.553549	0.396822	4.235866	3.355814
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.347057	0.310171	0.947298	1.066746	-0.648078	0.565436	-0.371160	0.031304	0.810531	0.568074	0.574707	1.653635	1.582849
66	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.616279	1.070357	-0.604218	1.070452	-0.162369	3.473131	0.198504	0.832799	0.814148	0.595592	0.544838	3.807888	4.302265
67	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.490621	-0.580506	0.182063	0.418535	1.280673	3.687600	0.341674	0.222816	0.811110	0.612810	0.523219	2.039370	1.723094
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.148743	0.815179	0.035441	0.349550	0.556216	1.911199	0.165781	1.922328	0.812321	0.616884	0.518975	2.109011	1.870871
69	N04	digital_ok	100.00%	6.12%	6.12%	0.00%	100.00%	0.00%	0.503423	-0.744084	-0.310330	-0.097045	0.386842	7.329217	-0.398263	-0.861171	0.785594	0.590701	0.509709	4.462837	4.246096
70	N04	RF_maintenance	100.00%	6.12%	6.12%	0.00%	100.00%	0.00%	8.374083	-0.902167	0.168390	-0.864417	2.030311	-1.251794	-0.285892	-0.814490	0.783922	0.567123	0.528584	16.956562	13.573113
71	N04	digital_ok	0.00%	6.12%	6.12%	0.00%	8.00%	92.00%	0.536641	0.030501	-0.766810	-0.300002	0.307403	-1.717891	-0.550133	-1.052270	0.785495	0.584991	0.516890	17.740623	14.605366
72	N04	digital_ok	100.00%	6.12%	6.12%	0.00%	100.00%	0.00%	3.253971	-0.205021	-0.541128	1.574534	7.519476	-1.141506	5.215197	-1.067054	0.781917	0.555068	0.539405	4.359880	3.423064
73	N05	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	17.806838	0.175627	32.191925	0.015261	39.973970	0.771029	1.369348	0.227753	0.032696	0.544553	0.275782	1.230622	3.191255
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.280400	1.473607	-0.508920	3.670893	1.088261	0.487940	5.421461	0.228309	0.802062	0.553082	0.578718	3.207993	3.076844
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.346540	0.826168	1.451637	0.656598	-0.551944	-0.624782	3.656958	0.666178	0.808106	0.575077	0.562471	3.424263	2.760941
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.326926	1.394768	2.037768	4.493166	-0.982485	-0.586358	-0.559005	-1.321696	0.812141	0.605248	0.536769	3.482633	3.543676
84	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	10.872893	11.800042	0.071419	1.561229	6.270483	-0.278186	35.740783	-1.366485	0.750446	0.536851	0.493710	3.960215	4.450708
85	N08	digital_ok	0.00%	0.00%	4.08%	0.00%	4.00%	8.00%	0.666007	0.458280	-0.720988	-0.556465	-1.448733	-1.218910	-0.928263	-1.420782	0.746289	0.536921	0.499213	1.875856	1.687148
86	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	1.514287	4.685490	-0.663485	0.022207	2.925508	2.391792	0.128351	-0.274851	0.740974	0.498511	0.501749	4.108931	4.483211
87	N08	RF_maintenance	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	3.231391	12.266765	5.331062	1.270907	8.886113	-1.192629	24.779666	-0.642363	0.733590	0.518919	0.510220	3.154820	3.032122
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.433398	22.850876	27.722063	28.621881	40.348675	34.771791	4.289834	1.269383	0.037190	0.034075	0.002130	1.127833	1.135255
90	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.996585	1.109659	16.979170	1.024211	17.873235	0.758464	-1.421492	-0.634866	0.045853	0.037825	0.001886	1.247685	1.245845
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.026924	22.085205	5.424467	29.271853	23.433144	34.648606	5.048944	1.652675	0.064980	0.036300	0.028308	0.828881	0.812143
92	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	42.726618	55.058187	2.922592	4.499085	30.461183	45.723252	1.135684	6.542389	0.269159	0.185592	0.116616	0.000000	0.000000
93	N10	digital_ok	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	-0.196204	3.736856	3.729363	15.573598	10.839896	16.556675	2.222563	0.002214	0.665552	0.340943	0.507400	0.000000	0.000000
94	N10	digital_ok	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	-0.675612	-1.238617	0.295426	-0.329666	-0.277965	7.352763	2.505870	0.912921	0.668729	0.348890	0.509353	0.000000	0.000000
98	N07	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
99	N07	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
100	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.177687	-0.154907	0.888762	0.100317	-1.022929	-0.448359	-0.175630	-1.049011	0.810960	0.571603	0.564998	1.459865	1.314054
101	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	10.388650	11.197565	2.968206	0.137048	-0.777384	-1.197992	4.445666	0.705810	0.750389	0.522569	0.512060	3.758699	3.100939
102	N08	RF_maintenance	100.00%	0.00%	40.82%	0.00%	100.00%	0.00%	5.774495	10.993382	15.503783	22.044440	269.310362	22.825560	22.922312	0.891637	0.674532	0.405375	0.488685	3.817472	3.368624
103	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	4.489949	11.128972	0.174977	0.059997	-0.654741	-0.394245	0.390064	-0.484253	0.749095	0.516934	0.509851	3.332470	2.838672
104	N08	RF_maintenance	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	8.349740	76.449136	1.318193	11.877195	1.382333	-0.027974	1.259292	-1.191236	0.751989	0.497414	0.538044	4.048754	3.260907
105	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.333765	9.869759	20.993869	22.977600	28.335922	27.079554	-1.247354	-2.617406	0.058595	0.056850	0.012140	1.182873	1.181687
106	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.365243	9.020591	4.124676	21.547841	0.911944	23.285043	-0.427444	-2.629514	0.038074	0.045544	0.007539	1.183216	1.167467
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.475404	19.815958	20.291056	27.141184	33.376332	34.814442	-0.898967	1.505957	0.063646	0.037372	0.023935	0.820281	0.813493
108	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.814447	20.873643	27.233585	28.932601	40.338776	34.725781	0.891205	0.006785	0.029254	0.036204	0.001803	1.102061	1.106718
109	N10	digital_ok	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	-0.637347	0.520037	-0.060514	0.756382	5.863008	6.394614	2.572339	6.548182	0.675156	0.326624	0.527955	0.000000	0.000000
110	N10	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	35.472952	28.677337	2.509718	1.546265	4.855978	8.972983	-0.317452	0.380572	0.563719	0.325265	0.325774	0.000000	0.000000
111	N10	digital_ok	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	0.807954	0.245386	0.228823	1.262837	6.674163	8.982835	7.003411	5.466250	0.670183	0.335728	0.515994	0.000000	0.000000
112	N10	digital_ok	0.00%	0.00%	93.88%	0.00%	92.00%	0.00%	-0.853242	-0.008948	-0.277025	1.108593	-0.936374	-0.951834	0.242430	-1.426568	0.668324	0.345847	0.515780	0.000000	0.000000
116	N07	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
117	N07	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	0.734919	0.687022	5.460434	5.576472	2.022948	-0.100511	-0.493427	-1.505872	0.809801	0.543380	0.595062	2.768355	2.334286
118	N07	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.706925	22.535395	2.298263	28.821053	-0.789388	34.735004	0.539175	-0.199662	0.814758	0.039882	0.541603	4.220973	1.139641
119	N07	RF_maintenance	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	1.321899	0.972196	9.891321	0.304877	4.580763	-0.111993	-1.160364	-0.788600	0.821625	0.558920	0.581243	3.781358	2.862375
120	N08	RF_maintenance	100.00%	63.27%	100.00%	0.00%	100.00%	0.00%	17.317747	33.918854	0.918675	37.776790	21.853599	34.234155	2.585636	3.748196	0.387306	0.042793	0.257003	2.534049	1.246816
121	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	3.782441	7.090276	-0.476499	0.792613	1.709346	0.488113	18.770345	11.992121	0.752513	0.505769	0.516738	4.488137	2.569248
122	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	10.462723	9.245861	1.106073	1.337685	-0.763220	-1.355948	-0.566986	-1.122907	0.755254	0.498689	0.520054	3.744879	2.555805
123	N08	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	8.545227	10.225936	0.052436	0.176109	-1.571256	-1.747902	-0.663228	-1.225019	0.753516	0.483901	0.546185	3.846061	2.547567
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.164267	10.658641	21.979001	23.325496	29.224225	27.537810	-1.411597	-2.636428	0.061418	0.057858	0.008939	1.159089	1.150079
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.191527	7.794915	19.492883	19.632992	27.798334	21.524968	0.324205	-2.506187	0.070259	0.046272	0.005495	0.862974	0.870685
127	N10	digital_ok	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	0.349937	0.223500	0.437185	-0.077628	2.873853	5.411292	0.783461	0.828375	0.667702	0.331173	0.517410	0.000000	0.000000
128	N10	digital_ok	0.00%	0.00%	93.88%	0.00%	92.00%	0.00%	-1.069394	2.882227	0.139024	1.995884	-0.350446	0.669038	-0.337665	-1.020518	0.668499	0.329989	0.513555	0.000000	0.000000
129	N10	digital_ok	0.00%	0.00%	93.88%	0.00%	92.00%	0.00%	-0.576726	-0.547379	-0.368973	-0.701573	-0.079074	-0.870201	-0.210374	-0.625805	0.668214	0.333291	0.515719	0.000000	0.000000
130	N10	digital_ok	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	0.916803	0.166749	0.305738	1.152724	28.184806	29.579002	5.113385	6.389788	0.659832	0.347993	0.502246	0.000000	0.000000
135	N12	digital_maintenance	0.00%	0.00%	4.08%	0.00%	100.00%	0.00%	-0.895028	0.195381	-0.686465	-0.772294	0.348405	-0.956639	3.322159	0.811392	0.743031	0.454720	0.565272	2.316889	1.847855
136	N12	digital_maintenance	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	2.087629	9.378945	-0.640841	0.459160	3.276259	1.572494	-0.036715	4.545784	0.744414	0.439368	0.554272	2.773986	2.034190
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.468869	20.995126	27.253027	28.197765	39.965377	34.445738	1.960393	2.055739	0.033567	0.037294	0.003122	1.125138	1.128398
138	N07	RF_maintenance	100.00%	100.00%	4.08%	0.00%	100.00%	0.00%	24.554974	2.088217	25.110405	-0.633069	40.067081	-1.248460	2.427810	-0.368506	0.043397	0.540672	0.312297	1.256117	2.498521
140	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.705865	20.967468	32.004880	33.667999	40.027070	34.474884	1.099176	0.360272	0.039420	0.042130	0.001633	1.151485	1.134745
141	N13	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	1.543320	2.258315	-0.123517	8.336695	1.975000	1.425689	0.797203	5.352500	0.819473	0.526892	0.584037	4.412420	2.332179
142	N13	digital_ok	100.00%	4.08%	100.00%	0.00%	100.00%	0.00%	24.822694	25.735105	1.327251	33.832826	25.004888	34.359866	2.560623	1.511064	0.448648	0.043666	0.268192	2.325373	1.214998
143	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.485803	-0.758335	1.201016	1.119270	3.221231	2.300195	0.362536	0.965427	0.066284	0.064618	0.013805	0.000000	0.000000
144	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.675627	-0.293458	-0.833508	-0.312702	27.868956	27.608619	6.178090	5.022689	0.055419	0.063316	0.014139	0.000000	0.000000
145	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.762032	22.429482	33.056282	34.210217	40.154100	34.453662	2.194698	2.244501	0.029463	0.030712	-0.000029	0.000000	0.000000
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	207.332512	204.551431	inf	inf	6523.696314	5830.315490	3191.359819	3004.476761	nan	nan	nan	0.000000	0.000000
155	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.763870	21.018829	32.113092	33.356202	39.925598	34.312640	2.537064	2.011984	0.039783	0.037559	0.000434	1.373351	1.351212
156	N12	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	0.631177	0.556718	1.398498	1.295668	-0.055346	72.194509	2.614103	9.156185	0.758545	0.450859	0.567989	5.640500	4.863613
157	N12	digital_ok	0.00%	0.00%	4.08%	0.00%	12.00%	0.00%	-0.393741	-0.339669	0.285702	3.160241	0.982819	-0.340080	1.473709	0.267946	0.750677	0.472413	0.553720	1.860045	1.363400
158	N12	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	-0.508469	-1.022269	-0.186295	0.177786	7.226539	6.693365	3.051017	2.549152	0.755202	0.476731	0.556584	4.795882	5.231194
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.820107	20.651797	32.635088	33.733622	40.172416	34.652995	2.186624	1.925158	0.040093	0.042556	0.004325	1.269289	1.261695
161	N13	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	-0.426455	39.983565	-0.430352	2.884182	-0.267755	4.782238	0.447427	-0.241748	0.808918	0.433269	0.568771	4.518279	3.900891
162	N13	digital_ok	0.00%	0.00%	4.08%	0.00%	8.00%	4.00%	1.549013	-0.051182	-0.035441	-0.692881	0.355167	-0.015910	-0.287420	0.250588	0.815606	0.538286	0.599185	1.587902	1.372044
163	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.536791	0.515512	0.312662	-0.757579	20.802197	21.354169	5.187332	6.003464	0.058230	0.058644	0.009283	1.267351	1.243153
164	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.944764	-0.612195	-0.776956	-0.677856	-0.679944	1.656066	-0.456980	-0.374006	0.060221	0.060865	0.008362	1.321735	1.307337
165	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	304.557941	304.727095	inf	inf	7653.606507	7691.722246	5261.136120	5297.425371	nan	nan	nan	0.000000	0.000000
166	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	163.956923	163.934780	inf	inf	4900.714891	4977.700972	2136.401798	2584.090474	nan	nan	nan	0.000000	0.000000
167	N15	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
168	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.410776	10.350250	20.369095	22.899829	25.895678	27.278050	-0.680268	-1.829622	0.057391	0.064817	0.011408	0.000000	0.000000
169	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.002391	9.457012	22.420343	21.546534	35.193284	30.225440	2.513594	3.580922	0.066234	0.056363	0.007705	0.000000	0.000000
170	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.331182	8.143851	22.622714	20.622425	47.264167	40.998558	4.285294	2.763783	0.062111	0.056391	0.006097	0.000000	0.000000
176	N12	digital_ok	0.00%	0.00%	4.08%	0.00%	12.00%	0.00%	0.456492	-0.734843	-0.619149	0.611636	-1.196301	-0.410790	-0.031304	0.845975	0.752593	0.436123	0.583126	1.859065	1.331257
177	N12	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	0.770689	1.467731	0.639109	8.577691	-0.905939	36.290748	0.134953	-0.131079	0.751106	0.450247	0.569017	6.092583	6.654807
178	N12	digital_ok	0.00%	0.00%	4.08%	0.00%	8.00%	4.00%	-0.710360	-0.712957	1.175160	-0.294805	-1.348785	0.246118	0.359785	1.579745	0.744665	0.460414	0.558733	1.966782	1.335628
179	N12	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	0.158854	1.222794	-0.785487	0.053779	10.167110	-1.186644	1.030518	-0.960301	0.747687	0.462248	0.568722	3.737974	3.572393
180	N13	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.310477	17.766232	0.382870	32.044053	0.530265	28.225374	0.327467	1.072312	0.811337	0.209930	0.660138	10.735508	2.123916
181	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.114124	60.672956	33.184924	5.761502	41.577867	29.652324	3.916187	7.497271	0.044967	0.292954	0.153023	1.268771	3.041975
182	N13	RF_maintenance	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	6.843498	8.426426	20.205425	21.086240	24.681677	23.867052	-0.087295	-1.746298	0.800478	0.507984	0.610648	4.699514	4.177464
183	N13	digital_ok	100.00%	0.00%	4.08%	0.00%	100.00%	0.00%	-1.211010	-0.473654	0.186305	-0.728516	-1.385768	3.979986	0.940358	7.708906	0.806122	0.519215	0.613906	3.819738	3.130117
184	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
185	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.334828	0.447121	2.464303	1.035241	5.518605	5.429517	1.585508	0.981080	0.068903	0.057835	0.009800	0.947569	0.946056
186	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.431622	0.473973	2.995439	1.603246	2.735553	-0.284449	6.396128	5.308693	0.070054	0.063404	0.012564	1.226079	1.219480
187	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.028178	1.517425	0.295681	0.541788	0.483782	0.182790	5.301160	3.175279	0.074518	0.068331	0.015459	0.000000	0.000000
189	N15	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.125572	2.199999	0.649843	-0.604992	0.538690	0.089366	0.530655	-1.082065	0.041660	0.044018	0.003867	0.000000	0.000000
190	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	53.486866	24.761935	3.671111	34.143157	34.290291	34.581619	111.573725	2.415007	0.059518	0.034531	0.030624	0.000000	0.000000
191	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.698219	0.624140	-0.304569	-0.088896	4.694249	28.870836	12.721473	6.338341	0.044268	0.042258	0.002654	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%	100.00%	100.00%	0.00%	100.00%	0.00%	4.693413	3.754283	14.269155	-0.140793	13.762166	4.035555	-1.087070	6.834603	0.050675	0.044807	0.005277	0.000000	0.000000
206	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.442593	3.088383	-0.443180	10.110479	7.211777	4.366291	1.489175	0.704619	0.050555	0.044633	0.004461	1.201130	1.191907
207	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.344246	4.811994	17.039031	14.079808	20.709002	13.154029	-0.872782	-1.918343	0.055065	0.053743	0.007104	0.000000	0.000000
220	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
221	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
222	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.117577	5.193625	8.483050	15.875447	6.165284	13.009700	0.983895	-1.525277	0.042013	0.059086	0.002454	0.000000	0.000000
224	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.959044	11.784803	25.662611	25.062955	37.791154	31.156931	-1.659852	-2.755525	0.060824	0.065230	0.008659	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%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.407463	24.025374	22.173568	22.474810	39.939672	34.452511	4.993851	2.473339	0.052431	0.048604	0.004552	0.000000	0.000000
321	N02	not_connected	100.00%	0.00%	8.16%	0.00%	100.00%	0.00%	2.977709	1.855579	13.428060	12.126387	28.527594	23.035243	19.195835	18.243225	0.781722	0.416534	0.665489	0.000000	0.000000
323	N02	not_connected	100.00%	0.00%	73.47%	0.00%	100.00%	0.00%	23.323948	4.422720	1.288605	16.080061	18.021467	13.980649	7.085128	-1.082315	0.602156	0.395167	0.493929	0.000000	0.000000
324	N04	not_connected	100.00%	6.12%	42.86%	0.00%	100.00%	0.00%	5.572686	3.899425	17.205434	11.389106	20.211754	10.273335	5.939060	7.035720	0.764460	0.393208	0.633752	0.000000	0.000000
329	N12	dish_maintenance	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	2.656839	1.970570	5.276555	11.708974	17.466752	9.121179	6.987882	-0.236217	0.715553	0.350704	0.597748	0.000000	0.000000
333	N12	dish_maintenance	100.00%	0.00%	93.88%	0.00%	100.00%	0.00%	4.962647	2.005406	-0.308291	10.796006	7.824211	6.041189	0.188616	-1.013177	0.676458	0.338061	0.562301	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, 40, 41, 42, 45, 46, 50, 52, 53, 54, 55, 56, 57, 66, 69, 70, 71, 72, 73, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 130, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 150, 155, 156, 157, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 176, 177, 178, 179, 180, 181, 182, 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: [38, 51, 65, 67, 68, 100]

golden_ants: [38, 51, 65, 67, 68, 100]

# 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_2459809.csv

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

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

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

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

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

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

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

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

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

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

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

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

Figure 1: Array Plot of Flags and A Priori Statuses

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

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

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

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

Metadata

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

2.0.3.dev44+g7d4aa18
3.1.4.dev3+g68bd8c3