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 = "2459799"
data_path = "/mnt/sn1/2459799"
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: 8-7-2022

# 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/sn1/2459799/zen.2459799.25315.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 1862 ant_metrics files matching glob /mnt/sn1/2459799/zen.2459799.?????.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 187 ant_metrics files matching glob /mnt/sn1/2459799/zen.2459799.?????.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	2459799
Date	8-7-2022
LST Range	16.586 -- 2.607 hours
X-Engine Status	✅ ✅ ✅ ✅ ✅ ✅ ✅ ✅
Number of Files	1862
Total Number of Antennas	147
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 31 RF_ok: 11 digital_maintenance: 3 digital_ok: 96 not_connected: 3
Commanded Signal Source	antenna
Antennas in Commanded State	107 / 147 (72.8%)
Cross-Polarized Antennas	93
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N08, N09
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	72 / 147 (49.0%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	111 / 147 (75.5%)
Redcal Done?	✅
Redcal Flagged Antennas	23 / 147 (15.6%)
Never Flagged Antennas	6 / 147 (4.1%)
A Priori Good Antennas Flagged	91 / 96 total a priori good antennas: 5, 7, 10, 15, 17, 19, 20, 21, 29, 30, 31, 37, 40, 41, 42, 45, 46, 50, 51, 53, 54, 55, 56, 57, 65, 66, 68, 69, 71, 72, 73, 81, 83, 84, 85, 86, 88, 91, 93, 94, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 111, 112, 116, 117, 118, 121, 122, 123, 127, 128, 129, 130, 140, 141, 142, 143, 144, 145, 156, 157, 158, 160, 161, 162, 163, 164, 165, 167, 169, 170, 176, 177, 178, 179, 181, 183, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	1 / 51 total a priori bad antennas: 67

# 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_2459799.csv

Build DataFrame

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

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

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

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

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

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

Table 2: RTP Per-Antenna Metrics Summary Table

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

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

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

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

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

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

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
3	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	3.168996	-0.474828	-0.534224	-0.831177	2.490439	-0.596556	0.964243	0.811796	0.722007	0.625193	0.417763	1.683073	1.466373
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.169699	4.582294	-0.648479	1.664303	0.830351	1.076113	5.330989	0.825888	0.740015	0.623730	0.424576	5.172924	4.631326
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.205464	1.285169	-0.274476	4.799260	-0.398795	4.618603	1.686051	-1.134799	0.745290	0.638055	0.421676	4.380948	3.978111
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.224399	-1.145971	0.543090	0.606268	-0.061937	2.910416	0.401844	5.849635	0.736388	0.632489	0.421853	4.396672	4.944107
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.918683	12.247828	21.597855	22.223141	42.878561	43.525151	-0.091802	-2.504503	0.726256	0.604521	0.428377	5.319505	5.276921
9	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	2.14%	0.00%	-0.219026	-0.807235	0.223541	-0.123632	1.857612	0.354799	-0.669722	-0.866807	0.723321	0.617769	0.426601	1.701633	1.492255
10	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.14%	-0.109834	-1.100947	-0.673177	0.756074	0.014316	1.685575	0.097536	-0.583200	0.711974	0.604422	0.439544	1.731202	1.562720
15	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.498140	1.733260	1.472520	0.497614	0.919434	1.072909	1.373159	10.779112	0.743052	0.630662	0.417859	5.253443	4.788023
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	1.07%	0.00%	-0.878225	-0.678193	-0.946547	-0.052361	-1.261661	-0.892823	2.485895	2.460260	0.751016	0.649750	0.414186	1.589142	1.456781
17	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.286493	0.949650	0.282016	0.489491	0.946191	0.938040	7.599994	2.071759	0.744328	0.647407	0.407505	4.260908	4.625356
18	N01	RF_maintenance	100.00%	0.00%	73.68%	0.00%	100.00%	0.00%	13.956285	12.697665	0.328952	0.091582	19.426868	10.707703	84.127646	44.223036	0.671448	0.400817	0.438393	2.863191	2.113734
19	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.837914	9.329608	3.883758	17.811658	3.517826	31.366062	0.065934	-2.029932	0.742335	0.641339	0.418748	4.669788	5.824216
20	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	1.07%	2.14%	-1.221252	1.570890	-0.087775	-0.336940	0.988861	0.637533	1.873995	-0.951499	0.732706	0.618116	0.424008	1.848396	1.703811
21	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.397549	1.216173	0.510216	-0.325822	-0.729865	13.516039	-0.199023	0.096825	0.717533	0.611909	0.429660	4.796054	4.398664
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.095812	23.514118	38.631404	39.684267	60.366549	58.903424	3.507420	2.379972	0.041152	0.046890	0.004006	1.264997	1.260587
28	N01	RF_maintenance	100.00%	44.68%	100.00%	0.00%	100.00%	0.00%	15.245327	33.848121	1.918358	3.703983	34.447403	42.016717	3.918758	27.903919	0.403569	0.184680	0.240796	7.272235	2.077144
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	1.07%	5.35%	-0.399474	-0.704020	0.675805	-0.386907	-1.424990	-1.092314	-0.653506	0.183718	0.754597	0.656301	0.406479	1.601888	1.484197
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.550948	-0.224481	0.446509	-0.488219	0.158014	-0.425550	9.969291	-0.593621	0.747896	0.655841	0.404193	4.731687	5.048394
31	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.520555	-0.423895	-0.758267	0.902197	3.490149	4.549763	1.903351	0.409111	0.754077	0.648321	0.417561	4.311283	4.300929
32	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	40.049298	12.821821	2.658495	1.707330	17.669465	24.024939	42.286333	167.120394	0.637749	0.617013	0.319339	4.803280	4.395635
33	N02	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.962507	16.839770	inf	inf	4424.599005	4405.521826	11823.940699	11823.337339	nan	nan	nan	0.000000	0.000000
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.985883	9.379634	0.141279	0.411743	1.980281	1.687616	0.144219	-0.314331	0.737599	0.635653	0.414371	4.767689	3.693331
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.865541	1.354998	-0.629440	0.232905	0.552521	2.040013	-0.061235	10.566946	0.743180	0.651980	0.405914	4.343644	3.937914
38	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.379627	0.052160	-0.464909	-0.894765	4.133253	3.001237	5.160231	0.307578	0.750215	0.663649	0.406650	4.481498	4.599573
40	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	1.07%	20.86%	0.936627	-0.274936	-0.034774	-0.336627	0.678074	-0.021540	-0.407668	-0.725441	0.746023	0.661951	0.401124	1.770600	1.598649
41	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.056097	-0.556901	2.962731	1.292031	4.505598	-1.154899	-0.714756	0.660117	0.755932	0.660984	0.409176	5.027333	5.027339
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	19.25%	-0.075860	1.707591	2.215423	0.874258	0.104175	-0.377670	-0.017618	-0.577819	0.758617	0.658118	0.416189	1.713229	1.540785
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.303551	0.841819	-0.638449	0.329672	-1.378600	0.314494	-0.330098	7.241087	0.743812	0.631681	0.423750	3.930568	3.529652
46	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.671124	-0.132501	0.592839	-0.937101	-1.156748	-0.752967	-0.130352	4.710681	0.736298	0.629908	0.429078	4.013263	3.421680
50	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	37.456386	1.708449	3.122494	2.984579	18.718423	2.932651	33.540198	3.203061	0.654167	0.640467	0.352334	7.143155	5.124013
51	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	2.14%	13.90%	1.067902	2.974800	-0.793920	-0.694454	0.275502	-0.867217	-0.846288	1.786809	0.743165	0.659198	0.401786	1.665611	1.559969
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.740458	8.880516	-0.192187	0.084355	5.014256	1.283457	0.487312	0.291372	0.750738	0.668912	0.398264	4.251038	4.522501
53	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.970186	2.390269	-0.530795	-0.090906	0.995962	0.369020	2.309468	6.147201	0.756481	0.677692	0.401027	4.038170	4.076770
54	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	67.91%	0.791664	-0.124855	0.915457	1.200727	-0.126731	2.069628	-0.164151	1.835990	0.751798	0.671522	0.398033	3.994250	3.891707
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.140944	1.482203	-0.718663	1.066113	9.101355	2.167096	5.696499	-0.600373	0.752477	0.668422	0.400044	4.501145	4.049683
56	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	58.29%	-0.976595	2.056072	1.236861	1.063293	1.785176	-0.513719	-0.336854	2.248327	0.754165	0.672026	0.401499	1.960467	1.985379
57	N04	digital_ok	100.00%	100.00%	59.72%	0.00%	100.00%	0.00%	19.068712	17.946099	36.344038	1.677815	60.681618	37.682779	1.295872	3.256583	0.048894	0.384068	0.234956	1.232278	3.845121
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	5.35%	9.63%	1.125101	0.685574	0.664375	0.776619	2.710269	2.184251	0.642104	0.854909	0.735204	0.641752	0.412999	1.721248	1.533480
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	16.04%	1.421015	0.977227	-0.797942	1.100424	0.106052	3.303190	-0.265353	1.805231	0.744948	0.659482	0.402310	1.673142	1.460899
67	N03	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.110093	-0.233756	0.170343	-0.357854	0.428366	-0.109131	0.059099	0.850614	0.748461	0.671077	0.394506	4.823390	5.048111
68	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.317187	1.042693	0.031838	0.379346	1.161844	3.439450	0.960080	7.259729	0.750026	0.673552	0.394313	4.684812	4.783113
69	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	51.87%	0.027778	-0.587571	-0.647420	-0.243403	-0.257450	-0.049936	-0.181514	-0.057074	0.750913	0.675879	0.399841	3.939621	3.170182
70	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.148046	-1.218809	0.103814	-0.759676	4.691566	-0.901640	-0.327733	-0.369404	0.757244	0.667257	0.411804	15.868382	10.410755
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	100.00%	0.409360	-0.618808	-0.913994	-0.248316	0.670908	-0.725146	-0.713827	-0.714426	0.750317	0.674807	0.405123	14.557667	12.954291
72	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.897365	-0.054517	-0.623355	1.455231	2.941527	0.614195	8.310630	4.292199	0.748658	0.666495	0.411265	5.009392	3.829691
73	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.865225	2.167148	-0.582250	-0.397038	0.310430	1.403256	6.546626	5.309534	0.746170	0.646167	0.426251	4.679265	3.590754
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.516160	2.915005	-0.640819	3.842545	2.385516	6.042818	8.097377	0.083979	0.717484	0.630690	0.406273	4.614290	4.631962
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.810184	-0.148079	-0.098615	1.464070	6.348366	1.480844	7.760758	2.532869	0.723047	0.640997	0.404708	5.331288	4.481164
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.229695	2.889424	2.155539	4.934476	1.206923	4.417711	-0.193700	-1.330962	0.741458	0.663862	0.396513	4.524703	4.412089
84	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.105581	11.913869	0.141617	1.732071	-1.039106	0.830229	2.251834	-1.021031	0.051716	0.063586	0.005120	1.244424	1.238261
85	N08	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.134317	1.754999	1.042752	2.026819	-0.831215	0.289439	-0.916222	-1.019340	0.069623	0.067643	0.007212	1.224003	1.220997
86	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.932723	5.450581	-0.778807	-0.217438	0.822768	5.608038	0.453275	0.826946	0.080119	0.078476	0.011330	1.234206	1.235572
87	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.703037	12.825147	4.048288	1.184595	30.456187	1.572002	191.703118	-0.169430	0.082955	0.084993	0.018301	1.236740	1.232624
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.298903	21.907054	32.835668	34.248455	60.855226	59.463103	5.753571	2.361075	0.032011	0.030515	0.001725	1.204912	1.204524
90	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.787335	21.014273	32.728014	33.873582	60.735718	59.338892	1.101928	0.139126	0.031755	0.028739	0.001050	1.124093	1.122409
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.949690	21.991612	32.733224	34.859146	60.732271	59.212778	3.565483	2.916692	0.030999	0.031204	0.000181	0.876965	0.888745
92	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	49.870470	63.424510	4.047264	5.567960	45.133966	50.323118	2.584566	13.335164	0.238632	0.187698	0.076138	2.846731	2.520834
93	N10	digital_ok	100.00%	8.59%	8.59%	91.41%	100.00%	0.00%	0.047165	1.750175	3.480959	-0.554017	8.344040	6.260451	-0.145635	0.603544	0.246068	0.245145	-0.252711	3.677737	3.836537
94	N10	digital_ok	100.00%	9.13%	26.32%	0.00%	100.00%	0.00%	-0.589138	-1.668669	-0.305788	-0.466373	0.881563	1.796324	4.961724	3.844644	0.576316	0.458061	0.357078	4.248754	3.625544
98	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.672866	7.534634	0.516956	-0.393221	3.229712	4.067515	2.643980	1.607490	0.717193	0.612485	0.412228	5.047941	5.129608
99	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.151690	0.950971	3.327558	-0.331764	8.750431	-0.213393	5.307592	2.900057	0.721017	0.632911	0.400566	5.328934	5.369001
100	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	3.21%	14.97%	0.621522	0.031951	0.777119	0.156579	1.352511	-1.291489	-0.442257	-1.001076	0.737537	0.644628	0.410969	1.585807	1.418484
101	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.313930	11.508071	3.285981	0.035426	0.924011	-1.048183	3.973579	2.595074	0.071901	0.065928	0.009777	1.309686	1.278821
102	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.749199	2.553912	13.914333	2.924648	123.148519	29.392186	255.614495	72.327165	0.061830	0.053477	0.004401	1.222884	1.225917
103	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
104	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
105	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.000107	27.510732	32.711211	34.778221	60.786535	59.302147	2.147767	1.584608	0.033330	0.032035	0.000975	1.200646	1.199723
106	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.565640	24.516545	32.058967	34.218836	60.599218	59.116234	2.929946	1.466591	0.030884	0.030765	0.001479	1.213605	1.204834
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.710638	19.085558	33.248886	34.498457	60.801282	59.440404	2.489587	2.579116	0.029426	0.031904	0.001360	1.168902	1.165025
108	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.279872	20.458220	32.225251	34.448059	60.784653	59.424669	0.666013	0.363802	0.030957	0.031617	0.001381	0.981188	0.975683
109	N10	digital_ok	0.00%	2.69%	25.24%	0.00%	25.13%	4.81%	-0.749508	0.404833	0.204617	0.403759	-1.291932	-0.221074	0.428955	-0.049020	0.600942	0.480072	0.361654	1.926000	2.018968
110	N10	RF_maintenance	100.00%	23.63%	42.96%	0.00%	100.00%	0.00%	43.063784	20.370205	2.885295	0.674915	13.637344	20.593722	18.097619	141.902229	0.498039	0.423390	0.226825	6.777503	7.159039
111	N10	digital_ok	100.00%	8.06%	25.78%	0.00%	100.00%	0.00%	1.079149	0.912525	-0.019925	1.456501	1.628509	1.224963	7.792458	2.109799	0.581337	0.466182	0.349506	4.545782	4.875201
112	N10	digital_ok	0.00%	9.13%	25.78%	0.00%	29.41%	0.00%	-0.145571	-0.395272	-0.476129	1.063028	-0.827884	-0.498899	0.437031	-1.308650	0.571470	0.460726	0.354374	1.837730	1.952825
116	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	10.16%	2.67%	0.820969	2.056938	-0.641595	-0.780820	1.832887	3.563066	3.307123	0.398800	0.709542	0.617436	0.418865	1.510300	1.358980
117	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
118	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
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.184312	4.455198	10.453020	0.029930	15.245432	1.432011	-1.287478	-0.859568	0.743126	0.635669	0.421817	5.011840	4.227522
120	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
121	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.822997	6.926365	-0.572256	0.913933	0.861205	3.565118	23.829800	17.815501	0.065371	0.068120	0.008566	1.221815	1.218351
122	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.339943	8.981072	1.195507	1.432830	0.124420	-0.092258	0.006503	0.304688	0.077459	0.076315	0.013418	1.351638	1.341166
123	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.885034	11.157113	0.536979	0.144019	-0.430950	0.954611	0.137150	-0.904132	0.083626	0.082772	0.019194	1.299015	1.294626
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.549246	21.679258	33.325485	35.067018	60.777651	59.384292	1.406715	3.284240	0.029192	0.029623	0.000335	1.275589	1.270328
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.216449	22.127022	32.813997	35.395466	60.808999	59.403891	2.292230	2.895720	0.029469	0.029599	0.000501	1.225090	1.219140
127	N10	digital_ok	0.00%	2.69%	24.70%	0.00%	25.13%	0.53%	0.045864	-0.194096	0.037520	-0.217633	-0.292184	0.759398	0.024733	0.005776	0.601308	0.486896	0.358096	2.178901	2.012454
128	N10	digital_ok	0.00%	2.69%	25.24%	0.00%	25.13%	0.00%	-1.226601	3.441911	-0.098701	2.087326	-0.611595	3.167313	-0.210734	-0.901454	0.599410	0.478078	0.351529	1.859638	1.684445
129	N10	digital_ok	0.00%	6.98%	25.24%	0.00%	27.81%	0.00%	-0.027778	-1.112514	-0.481796	-0.827289	-0.862127	-0.302468	-0.794720	-0.666929	0.591522	0.476125	0.349614	2.009588	1.701227
130	N10	digital_ok	0.00%	9.67%	25.78%	0.00%	29.95%	0.00%	0.733698	0.586768	0.290225	1.138498	0.864707	0.597364	-0.358776	2.987429	0.573459	0.462076	0.347223	2.094023	1.859999
135	N12	digital_maintenance	0.00%	0.00%	0.54%	0.00%	100.00%	0.00%	-0.620803	-0.194419	-0.982250	-0.948558	-0.477693	-0.371415	2.273220	0.989071	0.639841	0.533063	0.395763	3.735021	3.281001
136	N12	digital_maintenance	100.00%	0.00%	0.54%	0.00%	100.00%	0.00%	3.031594	11.951747	-0.708009	0.528601	3.572794	4.027819	0.659315	5.056625	0.640687	0.519540	0.389818	7.239952	5.915806
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
138	N07	RF_maintenance	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	25.932376	3.104390	29.814308	-0.699565	60.602585	0.008105	2.875846	-0.332846	0.049769	0.625996	0.465741	1.334774	4.949261
140	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.124030	21.309908	37.709706	39.917393	60.412772	59.086627	1.030658	0.936486	0.041643	0.044295	0.002117	1.174311	1.164750
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.560517	3.590847	0.199302	9.520008	2.115083	4.075759	2.221431	35.422066	0.736532	0.621178	0.423459	7.443328	6.872907
142	N13	digital_ok	100.00%	50.59%	100.00%	0.00%	100.00%	0.00%	29.128214	26.217480	3.061017	40.109595	40.565395	58.993585	2.410911	2.164421	0.396885	0.046154	0.251706	6.766582	1.332318
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	5.35%	11.76%	0.249458	-0.196418	1.032772	1.052019	-0.101158	0.048945	-0.394589	-0.417356	0.744017	0.661732	0.412221	1.299111	1.399792
144	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.406671	0.893082	-0.557553	-0.514056	-0.630870	1.154476	3.742375	4.741897	0.749093	0.654402	0.420516	9.153991	8.816139
145	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.628780	21.863622	38.919172	40.452290	60.500540	59.169154	2.558830	3.572171	0.036923	0.038113	0.000143	1.370106	1.335453
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.175748	25.161449	38.726890	41.279670	60.392026	58.950068	3.234213	3.189860	0.053844	0.055547	0.001727	1.252429	1.251896
155	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.346740	21.553396	37.826722	39.545617	60.307193	58.920342	3.277257	2.886270	0.040078	0.037840	0.000715	1.403252	1.390454
156	N12	digital_ok	100.00%	0.00%	2.15%	0.00%	100.00%	0.00%	1.478554	1.026148	1.503619	0.994447	0.019881	15.583133	0.551990	17.944706	0.646117	0.524126	0.404462	5.024791	5.094591
157	N12	digital_ok	0.00%	0.00%	0.54%	0.00%	3.21%	0.00%	-0.167763	-0.046170	0.087853	3.244487	0.245186	1.738251	1.348641	-0.005776	0.647149	0.543200	0.401407	1.793113	1.563478
158	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	1.60%	1.07%	-0.504241	-1.525499	0.034774	0.160540	-0.517124	-0.515573	1.397390	0.560882	0.657635	0.553080	0.404418	1.755479	1.601318
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.297922	20.165330	38.424976	39.970566	60.491769	59.150973	2.835538	3.476002	0.043158	0.046889	0.003999	1.267855	1.262774
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.789628	46.184457	-0.370653	3.203586	-0.748884	15.549786	0.863087	0.507414	0.739250	0.537979	0.407640	5.422006	6.634696
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.53%	2.14%	1.305311	0.670789	0.098107	-0.810791	0.954793	1.071123	-0.125339	1.528902	0.742892	0.653921	0.412884	2.113568	1.939435
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.53%	0.53%	0.844425	0.260995	0.143939	-0.824707	-0.831878	-1.472208	0.373298	0.337502	0.748603	0.661241	0.407644	1.916445	1.671017
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.53%	0.53%	-1.107762	-1.061635	-0.798607	-0.718232	-1.424158	0.524103	0.912553	0.808705	0.749380	0.659407	0.409496	2.018716	1.832647
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.466262	0.967748	6.215519	-0.837780	5.797389	-1.351263	4.528980	0.933652	0.754739	0.659522	0.409754	4.911922	5.173341
166	N14	RF_maintenance	100.00%	0.00%	4.30%	0.00%	100.00%	0.00%	33.255186	29.364531	2.088954	1.745735	22.603470	19.010086	14.908819	14.538188	0.638526	0.532311	0.234596	3.589372	2.882163
167	N15	digital_ok	100.00%	6.44%	3.76%	0.00%	100.00%	0.00%	32.730086	19.464904	18.759661	21.542776	56.185112	46.350268	158.926758	39.769518	0.555994	0.510327	0.206984	2.652975	2.349294
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.473310	15.212319	21.957032	24.725077	42.916068	49.067553	-1.501048	-2.907404	0.735552	0.616589	0.417172	5.604736	4.164631
169	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.255918	13.880188	24.378397	23.256363	48.790369	45.952699	-2.419226	-1.578547	0.722156	0.602132	0.423531	5.803949	3.787109
170	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.259887	12.381508	24.676725	22.111066	49.785039	42.472055	-1.588197	-2.285789	0.707029	0.607666	0.428526	4.954382	3.875528
176	N12	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
177	N12	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
178	N12	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
179	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	1.60%	3.74%	-0.290225	1.072842	-0.867554	-0.048430	3.862761	-0.559388	0.775944	-1.392994	0.657760	0.546999	0.408054	1.740386	1.751251
180	N13	RF_maintenance	100.00%	0.00%	87.65%	0.00%	100.00%	0.00%	0.583081	17.165665	0.631374	37.673500	-0.377672	48.191057	-0.047810	2.309490	0.734841	0.311181	0.540441	13.021014	2.769575
181	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.891121	65.080284	39.047210	5.023968	60.493468	46.717650	2.595253	12.162117	0.046032	0.257053	0.143841	1.223466	3.511241
182	N13	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	12.205082	12.928997	21.709355	22.603221	42.193035	43.560699	-1.595077	-1.995902	0.733047	0.629482	0.431675	4.742851	5.561607
183	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.540946	-0.699735	-0.195828	-0.667354	-1.011743	-0.661645	1.389893	10.215906	0.742400	0.647448	0.420054	4.561974	4.477325
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	1.60%	0.00%	0.377886	-0.051895	-0.332027	0.617638	-0.341904	-0.208213	0.098384	-0.622220	0.750070	0.656362	0.410740	2.016886	1.719077
185	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	1.60%	0.00%	1.950789	0.479613	2.681390	1.262611	0.978253	-0.014316	-0.323213	2.228745	0.755268	0.654723	0.411745	1.790670	1.579303
186	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.858064	0.214517	2.963016	1.767070	0.183619	0.150789	4.661138	3.155093	0.742503	0.648072	0.405776	5.249682	5.440181
187	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.263923	1.376318	-0.012249	0.599508	-0.409158	-1.352280	12.562992	2.426049	0.741858	0.653471	0.408979	4.535445	3.549152
189	N15	digital_ok	0.00%	0.00%	0.00%	0.00%	24.06%	68.45%	3.576824	3.870086	0.550864	-0.657755	1.091760	0.518042	0.679259	-0.465336	0.724262	0.625437	0.423336	4.281470	3.227617
190	N15	digital_ok	100.00%	11.28%	100.00%	0.00%	100.00%	0.00%	55.179041	25.422852	3.684478	40.438149	34.495265	59.100220	135.991667	2.987028	0.548903	0.050405	0.393869	2.845659	1.280324
191	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.440989	0.417750	-0.753649	-0.905295	0.389375	-1.348591	17.468177	0.725988	0.715451	0.609376	0.440676	3.241647	2.626830
203	N18	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
205	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.532172	5.157242	15.030206	4.161246	27.199922	11.094107	20.641241	32.280055	0.730325	0.608811	0.417471	6.682814	4.826532
206	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.836750	6.662880	-0.359690	10.257493	11.597969	18.014902	24.005566	24.179109	0.685006	0.614505	0.409867	4.399614	4.415781
207	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.641999	8.791069	18.104087	14.855483	35.422843	27.794681	7.859908	5.111628	0.708183	0.613344	0.401243	4.213687	3.410638
220	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
221	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
222	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.967034	8.362035	68.756482	70.794268	3179.627724	3558.545472	7207.702488	9162.672748	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.923101	9.698676	8.753453	16.494537	14.684107	28.028217	0.283811	-1.594136	0.711685	0.607509	0.420997	4.512731	3.154021
224	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.712260	16.510563	28.225027	27.245524	57.664885	54.623521	-3.342844	-3.703089	0.680516	0.571882	0.406090	4.828074	3.689345
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%	8.454554	10.837189	87.110453	79.431882	4220.208404	4262.945656	11182.422533	11313.701533	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.607159	25.191512	26.488499	27.104736	60.180523	58.828443	5.474481	3.243176	0.058928	0.055255	0.002033	0.000000	0.000000
321	N02	not_connected	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.334770	10.960120	86.762112	79.196769	4228.770040	4272.575329	11792.813426	11791.337205	nan	nan	nan	0.000000	0.000000
323	N02	not_connected	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
324	N04	not_connected	100.00%	0.00%	19.33%	0.00%	100.00%	0.00%	11.538341	12.027650	19.863920	20.075114	39.437243	38.996761	4.133216	2.992946	0.639112	0.480562	0.414960	0.000000	0.000000
329	N12	dish_maintenance	100.00%	16.65%	36.52%	0.00%	100.00%	0.00%	4.818456	4.909575	10.545259	12.411485	20.658068	23.015566	7.287846	0.310250	0.566049	0.423973	0.374579	0.000000	0.000000
333	N12	dish_maintenance	100.00%	33.94%	49.41%	0.00%	100.00%	0.00%	4.097267	5.006241	-0.477389	11.325635	16.099684	20.409475	0.794997	0.248444	0.507903	0.402450	0.348810	0.000000	0.000000

# print ex_ants for easy copy-pasting to YAML file
proposed_ex_ants = [ant for i, ant in enumerate(ants) if np.any([col[i] > 0 for col in bar_cols.values()])]
print('ex_ants: [' + ", ".join(str(ant) for ant in proposed_ex_ants) + ']')
print(f'\nunflagged_ants: [{", ".join([str(ant) for ant in ants if ant not in proposed_ex_ants])}]')
# "golden" means no flags and good a priori status
golden_ants = ", ".join([str(ant) for ant in ants if ((ant not in proposed_ex_ants) and (a_priori_statuses[ant] in good_statuses.split(',')))])
print(f'\ngolden_ants: [{golden_ants}]')

ex_ants: [4, 5, 7, 8, 9, 10, 15, 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, 73, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 130, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 150, 155, 156, 157, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 176, 177, 178, 179, 180, 181, 182, 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: [3]

golden_ants: [3]

# 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_2459799.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