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 = "2459801"
data_path = "/mnt/sn1/2459801"
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: 8-9-2022

# 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/sn1/2459801/zen.2459801.27110.sum.auto_metrics.h5.

Load Ant Metrics

use_ant_metrics = False

# get a list of all ant_metrics files
glob_str = os.path.join(data_path, f'zen.{JD}.?????.sum{ant_metrics_ext}')
ant_metrics_files = sorted(glob.glob(glob_str))

# if they exist, load as many of them as possible
if len(ant_metrics_files) > 0:
    print(f'Found {len(ant_metrics_files)} ant_metrics files matching glob {glob_str}')
    ant_metrics_apriori_exants = {}
    ant_metrics_xants_dict = {}
    ant_metrics_dead_ants_dict = {}
    ant_metrics_crossed_ants_dict = {}
    ant_metrics_dead_metrics = {}
    ant_metrics_crossed_metrics = {}
    dead_cuts = {}
    crossed_cuts = {}
    for amf in ant_metrics_files:
        with h5py.File(amf, "r") as infile: # use h5py directly since it's much faster than load_metric_file
            # get out results for this file
            dead_cuts[amf] = infile['Metrics']['dead_ant_cut'][()]
            crossed_cuts[amf] = infile['Metrics']['cross_pol_cut'][()]
            xants = infile['Metrics']['xants'][:]
            dead_ants = infile['Metrics']['dead_ants'][:]
            crossed_ants = infile['Metrics']['crossed_ants'][:]        
            try:
                # look for ex_ants in history
                ex_ants_string = infile['Header']['history'][()].decode()
                ex_ants_string = ex_ants_string.split('--apriori_xants')[1]
                ex_ants_string = ex_ants_string.split('--')[0].strip()
            except:
                ex_ants_string = ''
                    
            # This only works for the new correlation-matrix-based ant_metrics
            if 'corr' in infile['Metrics']['final_metrics'] and 'corrXPol' in infile['Metrics']['final_metrics']:
                ant_metrics_dead_metrics[amf] = {eval(ant): infile['Metrics']['final_metrics']['corr'][ant][()]
                                                 for ant in infile['Metrics']['final_metrics']['corr']}
                ant_metrics_crossed_metrics[amf] = {eval(ant): infile['Metrics']['final_metrics']['corrXPol'][ant][()]
                                                    for ant in infile['Metrics']['final_metrics']['corrXPol']}                       
            else:
                raise(KeywordError)
        
        # organize results by file
        ant_metrics_xants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in xants]
        ant_metrics_dead_ants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in dead_ants]
        ant_metrics_crossed_ants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in crossed_ants]
        ant_metrics_apriori_exants[amf] = [int(ant) for ant in ex_ants_string.split()]
    
    dead_cut = np.median(list(dead_cuts.values()))
    crossed_cut = np.median(list(crossed_cuts.values()))
        
    use_ant_metrics = True
else:
    print(f'No files found matching glob {glob_str}. Skipping ant_metrics.')

Found 372 ant_metrics files matching glob /mnt/sn1/2459801/zen.2459801.?????.sum.ant_metrics.hdf5

Load chi^2 info from redcal

use_redcal = False
glob_str = os.path.join(data_path, f'zen.{JD}.?????.sum{redcal_ext}')

redcal_files = sorted(glob.glob(glob_str))
if len(redcal_files) > 0:
    print(f'Found {len(redcal_files)} ant_metrics files matching glob {glob_str}')
    post_redcal_ant_flags_dict = {}
    flagged_by_redcal_dict = {}
    cspa_med_dict = {}
    for cal in redcal_files:
        hc = io.HERACal(cal)
        _, flags, cspa, chisq = hc.read()
        cspa_med_dict[cal] = {ant: np.nanmedian(cspa[ant], axis=1) for ant in cspa}

        post_redcal_ant_flags_dict[cal] = {ant: np.all(flags[ant]) for ant in flags}
        # check history to distinguish antennas flagged going into redcal from ones flagged during redcal
        tossed_antenna_lines =  hc.history.replace('\n','').split('Throwing out antenna ')[1:]
        flagged_by_redcal_dict[cal] = sorted([int(line.split(' ')[0]) for line in tossed_antenna_lines])
        
    use_redcal = True
else:
    print(f'No files found matching glob {glob_str}. Skipping redcal chisq.')

Found 38 ant_metrics files matching glob /mnt/sn1/2459801/zen.2459801.?????.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	2459801
Date	8-9-2022
LST Range	17.149 -- 19.149 hours
X-Engine Status	✅ ✅ ✅ ✅ ✅ ✅ ✅ ✅
Number of Files	372
Total Number of Antennas	147
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 32 RF_ok: 11 digital_maintenance: 3 digital_ok: 95 not_connected: 3
Commanded Signal Source	antenna
Antennas in Commanded State	108 / 147 (73.5%)
Cross-Polarized Antennas	93
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N08, N09, N14, N19
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	84 / 147 (57.1%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	108 / 147 (73.5%)
Redcal Done?	✅
Redcal Flagged Antennas	9 / 147 (6.1%)
Never Flagged Antennas	19 / 147 (12.9%)
A Priori Good Antennas Flagged	78 / 95 total a priori good antennas: 5, 7, 9, 16, 17, 19, 20, 30, 37, 38, 42, 45, 53, 54, 55, 56, 65, 66, 67, 69, 71, 72, 73, 83, 84, 85, 86, 88, 91, 93, 94, 98, 99, 101, 103, 105, 106, 107, 108, 109, 111, 112, 117, 121, 122, 123, 127, 128, 129, 130, 140, 141, 142, 143, 144, 156, 157, 160, 161, 162, 163, 164, 165, 167, 169, 170, 177, 178, 179, 181, 183, 184, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	2 / 52 total a priori bad antennas: 4, 135

# 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_2459801.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%	0.845750	-0.443918	-0.714037	-0.931602	-1.263001	-0.631941	-0.853873	2.988454	0.747246	0.682381	0.486095	1.864220	1.510305
4	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.393535	3.874094	-0.977249	1.619694	-1.364244	0.679555	-0.177026	-0.600364	0.765389	0.694070	0.481559	8.744550	7.249452
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.059630	1.533445	-0.588939	4.268212	-1.580243	2.965895	-0.770932	-2.170819	0.777365	0.709676	0.480551	5.630559	4.947595
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.029670	-1.031322	0.320300	-0.164548	0.053988	1.642640	-0.574868	14.707753	0.781019	0.715117	0.476545	4.785112	4.743148
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.317155	16.185248	17.638603	18.119843	28.531680	31.056948	4.693474	3.529970	0.760572	0.682284	0.480016	4.026156	4.309700
9	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	47.37%	-0.339989	-1.086673	0.203652	-0.215337	3.091302	1.717874	-0.210725	-1.115672	0.764898	0.689815	0.480386	3.968327	3.790143
10	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.125321	-1.086173	-0.664094	0.343204	-0.744344	-0.243917	1.671308	2.761387	0.746179	0.665899	0.494397	2.103408	1.878182
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.724114	1.399025	1.502472	0.599509	0.642030	0.253307	0.671196	0.412881	0.781434	0.712624	0.472485	2.600982	2.244847
16	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.073832	-0.659802	-0.708921	-0.374254	-1.415001	-1.782673	8.659362	5.915650	0.788443	0.730402	0.466329	7.637705	7.946841
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	13.16%	0.663928	1.451039	0.062309	0.334526	-0.174455	-0.248577	0.949651	-0.159309	0.796024	0.737733	0.458972	2.740896	2.565881
18	N01	RF_maintenance	100.00%	0.00%	16.67%	0.00%	100.00%	0.00%	3.786214	3.537442	1.001417	0.537798	10.060039	4.200354	248.925814	103.487015	0.761901	0.577510	0.514560	4.717381	2.484198
19	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.384250	1.243745	0.226609	4.676337	-0.499158	4.091971	20.345747	32.622877	0.785988	0.726505	0.466033	4.939342	5.116114
20	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	31.58%	-1.326152	0.413243	-0.372905	-0.843226	1.898886	0.615767	0.648556	0.305219	0.773671	0.704200	0.467154	3.540033	2.396197
21	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.370288	1.764728	0.689282	1.099305	-0.316909	0.233233	3.910986	-1.160487	0.757721	0.682942	0.490966	2.195910	1.916088
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.438895	22.260131	53.740032	54.593496	41.991962	43.738922	17.747041	16.415974	0.041593	0.047074	0.004161	1.211448	1.214507
28	N01	RF_maintenance	100.00%	0.00%	97.31%	0.00%	100.00%	0.00%	13.028473	14.492942	6.835916	10.456423	38.834597	39.839961	17.381237	108.496912	0.542892	0.304096	0.325022	21.725492	4.102217
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.107047	-0.155820	0.313826	-0.370702	-1.560720	-1.981146	-0.632345	-0.572113	0.803914	0.754698	0.452474	2.393910	2.421262
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.431850	0.134972	-1.018607	-0.577616	0.215299	-0.174465	6.936415	2.767001	0.798993	0.749182	0.446967	6.846779	7.336571
31	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.373060	-0.318700	-1.017445	0.339257	-1.245937	0.141351	-0.083305	0.668384	0.796504	0.741349	0.463466	2.276302	2.183602
32	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	32.636795	29.139279	2.669828	2.398700	12.557414	12.225125	35.405381	38.536921	0.732927	0.676352	0.291760	17.127529	14.393991
33	N02	RF_maintenance	100.00%	0.00%	19.35%	0.00%	100.00%	0.00%	-0.532116	3.398354	-0.524876	0.663800	-1.100654	1.325800	5.311199	23.146607	0.766163	0.559564	0.577652	6.827533	3.204157
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.509610	6.009017	0.052675	0.449791	2.001014	0.216285	3.358861	1.596162	0.750136	0.678288	0.478297	4.382982	4.270886
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.827677	1.024740	0.593406	0.257353	2.303470	0.928991	2.021574	23.037358	0.768636	0.700746	0.470251	4.603599	4.963358
38	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.110801	-0.042186	-0.632491	-0.986365	4.376393	1.309341	10.033098	4.959226	0.781493	0.722976	0.462126	4.690900	4.298529
40	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.150766	-0.523371	0.365025	-0.060708	2.767419	1.793666	-0.262862	0.348148	0.795633	0.748664	0.445992	2.976090	2.606830
41	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.685053	-0.386775	2.014049	1.491691	1.352982	-0.686531	-0.946827	0.764194	0.803311	0.760472	0.447346	2.740949	2.138807
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	21.05%	-0.213330	0.480597	1.911107	1.200962	-0.098661	-0.838330	-1.328759	0.641599	0.806971	0.759956	0.459362	2.485552	2.439355
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.492594	0.815312	-0.690894	1.201407	-1.586603	-0.159339	-0.031239	52.656390	0.784389	0.717808	0.483200	5.231317	4.928687
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.084811	-0.802462	0.524665	-0.960487	-1.284711	-1.345365	-0.088137	2.074445	0.776237	0.698013	0.506881	2.085556	1.820224
50	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	25.979726	2.465139	5.204517	2.410124	5.057810	1.408182	6.214078	0.162838	0.706502	0.679517	0.402535	5.768753	4.139441
51	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.574893	1.458465	-0.790194	-0.764259	0.003969	-0.454073	0.334097	0.188180	0.770848	0.709362	0.462932	1.728757	1.738393
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.003000	5.160392	-0.343665	-0.127082	0.705252	0.295985	5.574047	1.320282	0.786273	0.729170	0.457014	4.976527	5.219259
53	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.905483	1.103430	-0.638720	-0.265306	-1.216183	0.379892	3.842503	13.440904	0.796878	0.752882	0.450666	5.091306	5.512264
54	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.283371	0.368615	1.412171	1.092257	2.760003	2.781324	6.375786	1.858404	0.807140	0.763370	0.453197	6.792876	6.262605
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.471024	-0.208317	-0.800417	0.651037	1.387305	2.685253	6.114337	-0.358553	0.808731	0.765666	0.460007	8.867987	8.695255
56	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	10.53%	-0.404895	0.324949	0.886438	1.102407	0.658935	0.882267	-0.821350	0.368010	0.805576	0.763774	0.459201	2.820558	2.315024
57	N04	RF_maintenance	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	20.913372	12.730125	53.501765	2.524579	42.201159	40.457275	13.778077	18.024719	0.048030	0.530529	0.276058	1.393134	9.343616
65	N03	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
66	N03	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
67	N03	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
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.045367	0.103030	0.100722	-0.234951	2.824277	2.034366	0.793501	0.469026	0.797291	0.754865	0.446343	1.983360	1.933008
69	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.243267	-0.528997	-0.721550	-0.047241	0.092809	0.560228	0.001457	5.765337	0.806198	0.763475	0.464007	4.851752	6.055554
70	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.461479	-1.209099	-0.030246	-0.936164	-0.756415	-1.035631	-0.061780	1.650774	0.804074	0.760253	0.480957	51.101986	37.843755
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	100.00%	0.330403	-1.169878	-0.585947	-0.554021	-0.339047	-0.939583	-0.283950	0.677785	0.808584	0.766337	0.469145	58.531369	57.010345
72	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.635581	-0.178820	-0.475997	1.353524	0.774086	0.672513	9.804456	-1.490854	0.798880	0.748878	0.492911	5.171649	4.121211
73	N05	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	21.736073	3.181546	52.896018	-0.542587	41.910208	-0.321114	13.354974	1.359167	0.033574	0.732276	0.409652	1.182542	3.762111
81	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.553510	2.871353	-0.421524	3.092333	0.371432	2.952522	0.729465	-1.875176	0.757916	0.684498	0.462789	1.781416	1.773074
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.323556	-0.582261	0.080345	1.089738	-0.202051	-0.984877	-1.003496	-0.497256	0.775862	0.710472	0.466599	5.969564	6.273994
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.057255	3.927713	1.940569	4.682140	0.185908	3.809135	-1.007792	-2.027343	0.785118	0.731582	0.447941	4.655625	4.836528
84	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.794571	7.557900	0.000913	1.531330	-1.001246	0.488759	-0.990890	1.077681	0.048253	0.061553	0.006848	1.215091	1.216357
85	N08	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.215112	1.010152	-0.937414	-0.234762	-1.852372	-1.618381	-0.447875	-0.446432	0.066519	0.067720	0.007326	1.211281	1.209301
86	N08	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.831842	2.776190	-0.222118	-0.703428	0.968535	0.497921	-0.006053	-0.493228	0.069435	0.072248	0.008583	1.176945	1.178971
87	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.431805	8.885271	5.383591	1.215804	7.054949	0.226105	17.849646	1.156321	0.084282	0.068681	0.012240	1.173882	1.175598
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.724734	20.027835	47.209903	48.356784	42.597323	44.423297	23.276605	17.029168	0.032905	0.032026	0.002522	1.176374	1.178152
90	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.897275	21.895176	47.106083	47.980352	42.289775	43.991288	12.850555	12.199631	0.033241	0.030994	0.001859	1.140943	1.136677
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.691951	20.813987	47.084950	49.036446	42.329297	44.120102	18.170707	17.933566	0.030169	0.030259	0.000188	1.083550	1.082415
92	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	31.054561	43.986220	9.873461	12.429914	40.968824	46.570463	13.546801	32.429715	0.271615	0.217193	0.097885	4.230938	3.231977
93	N10	digital_ok	100.00%	0.00%	0.00%	100.00%	100.00%	0.00%	0.173754	0.202632	2.879056	-0.846253	2.408423	1.244289	-0.970342	5.481782	0.206729	0.209059	-0.276206	4.524308	4.495392
94	N10	digital_ok	100.00%	0.00%	38.17%	0.00%	100.00%	0.00%	0.640232	0.228798	-0.044764	-0.789115	-0.234347	0.816431	4.432728	16.336857	0.581441	0.488790	0.386437	3.887281	3.828094
98	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.487323	3.370163	0.167880	0.132432	0.201369	-1.387546	-0.712521	8.406830	0.760412	0.679161	0.466454	10.721087	8.791706
99	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.227542	0.605764	2.625491	-0.061354	3.236042	0.824657	4.950760	-0.734075	0.770451	0.708069	0.443556	7.149379	6.473042
100	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.693988	0.109036	0.181321	0.189699	-0.669426	-0.802644	-0.827546	-0.361450	0.785192	0.727086	0.465749	1.889183	1.834198
101	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.848898	7.468087	3.466592	-0.075097	1.594801	-0.636084	4.235724	-0.584625	0.080812	0.063754	0.007432	1.207630	1.209441
102	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.168049	16.084637	7.534439	4.698869	427.849260	357.069550	14093.991715	13929.973251	0.088842	0.090144	0.017252	0.000000	0.000000
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%	22.464102	26.225017	44.673676	46.605435	42.667595	44.743587	14.848655	15.265342	0.034160	0.032383	0.001163	1.167194	1.161573
106	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.792148	22.617366	46.310945	48.409853	42.244294	44.345397	16.764049	13.767505	0.029402	0.028379	0.000886	1.192694	1.193095
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.120168	19.903121	45.358691	46.363117	42.419339	44.088685	16.099821	17.962822	0.031768	0.029996	0.001419	1.241912	1.236266
108	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.347067	20.092716	46.518552	48.579254	42.283088	44.272819	12.010886	12.814059	0.030607	0.031033	0.001246	0.000000	0.000000
109	N10	digital_ok	0.00%	0.00%	38.17%	0.00%	39.47%	0.00%	-0.685645	0.306212	0.205692	0.508109	-0.494968	-0.122892	1.624757	0.847655	0.616473	0.519114	0.419772	1.804433	1.763913
110	N10	RF_maintenance	100.00%	0.00%	38.17%	0.00%	100.00%	0.00%	29.040571	2.943170	2.458392	0.694534	25.965360	16.435449	114.534818	89.478115	0.571525	0.504726	0.343306	19.025133	11.681424
111	N10	digital_ok	100.00%	0.00%	38.17%	0.00%	100.00%	0.00%	0.460589	1.141705	-0.282091	1.203887	1.538033	2.392398	0.070245	9.577964	0.591181	0.489954	0.385704	7.063866	8.621179
112	N10	digital_ok	0.00%	0.00%	38.17%	0.00%	39.47%	0.00%	-0.454304	-0.265767	-0.508674	0.693016	-0.391446	0.273945	-0.776032	-1.839751	0.569679	0.477634	0.377847	1.565516	1.555505
116	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.785289	1.767181	-0.327597	-0.589006	-0.003969	-0.142433	-0.349766	-1.059104	0.757845	0.682746	0.463819	2.041578	2.035554
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.275175	3.490976	5.124829	5.254556	4.162591	4.867847	-2.024756	-2.347669	0.775723	0.707200	0.469291	10.080132	12.081229
118	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	2.198376	1.084610	2.500311	0.242809	3.141679	0.487983	-0.172296	1.699079	0.782334	0.720472	0.469579	1.912490	1.922175
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.704019	0.348729	7.697218	3.107327	9.356649	-0.110831	-1.781890	1.329766	0.790078	0.726034	0.487649	6.488913	4.938191
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%	2.649875	4.632505	-0.691664	0.528118	-0.689472	0.321803	52.244489	18.700806	0.067293	0.070402	0.008852	1.197181	1.194550
122	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.817238	6.216688	1.279333	1.217384	0.586556	-0.605485	-0.109185	-0.043152	0.083399	0.086471	0.014497	1.208428	1.206984
123	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.498586	7.345549	0.295574	-0.007059	-0.536864	0.180515	-0.278384	1.972627	0.087912	0.086611	0.018480	1.196518	1.192837
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.975396	20.997190	47.731980	49.270300	42.611065	44.254943	13.516120	19.490411	0.029785	0.029593	0.000550	1.153497	1.151406
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.188320	21.134793	47.172013	49.613771	42.535828	44.470344	15.435956	17.735905	0.029178	0.029029	0.000890	0.874277	0.873686
127	N10	digital_ok	0.00%	0.00%	32.80%	0.00%	34.21%	0.00%	-0.078978	-0.116950	0.209860	-0.230295	0.360641	0.465795	0.560699	2.461975	0.616672	0.517668	0.418347	1.739629	1.605827
128	N10	digital_ok	0.00%	0.00%	38.17%	0.00%	39.47%	0.00%	-0.829957	1.084070	-0.258831	1.433759	-1.130062	0.929059	0.119873	-0.794375	0.607779	0.506771	0.398022	2.286022	1.692263
129	N10	digital_ok	0.00%	0.00%	38.17%	0.00%	39.47%	5.26%	-0.100876	-1.291825	-0.599709	-1.075256	-0.684555	-0.777197	-0.839620	-0.758056	0.592694	0.498614	0.382131	2.382874	1.870771
130	N10	digital_ok	100.00%	0.00%	38.17%	0.00%	100.00%	0.00%	-0.133916	0.338002	0.911404	1.291872	0.523577	1.228271	0.741181	7.722959	0.572845	0.480272	0.373231	7.158352	6.262969
135	N12	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.750714	-0.385376	-1.003334	-1.013853	-0.889570	-1.476324	0.536670	-0.285588	0.700355	0.619452	0.454648	6.056659	5.320131
136	N12	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.241842	9.516450	-0.607371	0.478233	5.204530	4.926629	4.108566	5.609142	0.703126	0.628628	0.428084	9.454274	9.458810
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.199143	20.395992	46.494310	47.615258	42.351786	44.217515	15.617315	19.793991	0.037214	0.044482	0.003553	1.321521	1.430702
138	N07	RF_maintenance	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	25.350215	2.157774	46.195791	1.515252	42.631661	-0.153326	16.309578	0.282461	0.046082	0.715151	0.436255	1.315888	5.658664
140	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.851553	20.208434	52.675349	54.730164	41.972936	44.126661	12.499180	13.606648	0.040468	0.041525	0.001387	1.086349	1.088023
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.897696	5.915881	-0.356328	10.707895	1.646047	4.509039	1.364791	27.095843	0.764837	0.697286	0.505013	3.588497	2.896468
142	N13	digital_ok	100.00%	32.26%	100.00%	0.00%	100.00%	0.00%	19.620843	24.810818	8.252707	54.942125	36.467716	44.406858	13.120945	15.662140	0.451862	0.043593	0.239549	2.182861	1.181746
143	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.060794	-0.817868	1.353186	0.903027	0.824383	0.561438	0.073754	-1.885100	0.118984	0.119345	0.031691	1.115371	1.123856
144	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.915215	1.701989	-0.840436	1.387028	-1.357114	0.434729	1.149805	0.249490	0.107460	0.100068	0.022917	0.929315	0.913217
145	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.427922	21.299674	54.048523	55.318411	42.155625	44.098006	16.025583	19.105272	0.031442	0.034607	-0.000241	0.000000	0.000000
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.501530	23.680665	53.807992	56.220335	42.590517	44.619683	17.085061	19.170259	0.052638	0.054802	0.001634	1.272996	1.262797
155	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.011354	21.445694	52.818318	54.311232	42.002009	43.901259	17.850554	18.651592	0.043062	0.040924	0.001055	1.401534	1.387136
156	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.101326	1.186229	0.979151	-0.199469	0.324207	0.572639	6.671334	12.242218	0.696818	0.613767	0.447990	7.008262	6.929322
157	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	5.26%	0.374719	1.834198	0.119150	2.832923	0.522305	1.746573	0.626201	-0.712686	0.694887	0.624660	0.443735	2.330797	1.951400
158	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.758381	-1.312168	-0.765905	-0.432288	0.293071	0.553374	0.006053	1.194317	0.700608	0.627935	0.449235	1.943365	1.811035
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.741173	19.103926	53.500698	54.803465	42.098273	44.068175	16.320767	20.013537	0.043977	0.043809	0.002149	1.285316	1.288741
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.614619	39.848411	-0.486445	3.817645	-0.809295	13.263384	0.279275	1.064492	0.755023	0.614524	0.454389	3.831199	3.189401
162	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.351856	1.289092	-0.542594	-0.789692	2.455997	1.262098	5.375420	2.887278	0.758120	0.680739	0.506543	3.140203	2.783602
163	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.392312	-0.264302	0.060708	-0.704859	-0.596634	-0.789544	0.463917	1.322501	0.088044	0.091410	0.016162	1.227635	1.234317
164	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.197187	-0.648404	-1.096488	-0.702549	-1.355453	-0.564102	0.830948	3.772138	0.079116	0.072366	0.005968	1.230098	1.229710
165	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.865544	1.207211	5.942208	-0.871124	5.986130	-1.792174	1.899885	1.415748	0.090361	0.070354	0.005014	1.243379	1.248706
166	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.553865	2.405201	-0.069788	0.618148	15.475207	5.347032	49.035561	12.068498	0.098654	0.082969	0.009972	1.229531	1.233898
167	N15	digital_ok	100.00%	0.00%	27.42%	0.00%	100.00%	0.00%	17.991430	20.591321	17.328423	18.076867	33.137723	32.697134	8.228699	9.305230	0.676719	0.582285	0.384354	4.618798	3.466369
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.096357	18.893188	17.926112	20.054067	29.444541	35.357800	2.419703	4.728481	0.706069	0.597494	0.494868	4.496716	3.557911
169	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.088079	17.533199	19.635319	18.939698	32.598913	32.934951	4.254568	3.824838	0.689245	0.577370	0.486149	4.351645	3.478622
170	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.220671	16.034248	19.835466	18.200167	33.207690	31.419894	4.602767	3.011923	0.673889	0.566006	0.481629	3.668847	3.306751
176	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.045367	-0.394425	-0.858016	0.471897	0.275407	0.268524	-1.132693	-1.607232	0.674777	0.584389	0.453363	2.319944	2.286826
177	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.141737	2.118295	0.558818	0.702031	-1.295553	-0.675550	2.646409	10.174085	0.675413	0.590006	0.445434	14.190232	16.152373
178	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	-0.708366	-1.505785	1.138924	-0.684887	0.387304	-1.490136	-0.453396	-1.190927	0.680725	0.598451	0.441065	2.388528	2.179935
179	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.352538	0.596134	-1.003010	-0.142270	4.053279	-0.920051	12.549276	-0.936352	0.681594	0.603442	0.446302	5.421335	5.626094
180	N13	RF_maintenance	100.00%	0.00%	40.86%	0.00%	100.00%	0.00%	0.663741	13.157545	0.454745	49.346191	-1.079886	27.080527	-0.797258	15.004846	0.746996	0.464708	0.553557	12.648048	4.557259
181	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.414618	48.391119	54.198314	5.790141	42.179196	47.594267	16.107279	30.644051	0.049843	0.317371	0.149912	1.215011	2.736223
182	N13	RF_maintenance	100.00%	0.00%	27.42%	0.00%	100.00%	0.00%	15.609132	11.071183	17.862274	43.084177	28.996829	14.840664	2.148705	53.335965	0.730290	0.547436	0.508908	2.601069	1.910032
183	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.249860	-0.469672	-0.133828	-0.845193	-0.138952	-2.253775	-0.145601	6.825205	0.756669	0.670775	0.524169	3.288852	2.681448
184	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.618695	0.221694	-0.137607	0.578914	0.040332	0.237157	1.984026	-0.306787	0.066864	0.060944	0.004735	1.230188	1.233574
185	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.373720	0.560723	-0.271968	1.014993	-0.399212	0.396133	7.385999	-0.453837	0.051578	0.067054	0.005034	1.197622	1.209734
186	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.522218	0.608579	3.854039	2.105586	2.224159	0.903877	5.617586	0.071346	0.066867	0.073875	0.009983	0.000000	0.000000
187	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.977970	1.062954	0.183915	0.457982	-0.054603	-1.953230	5.251650	-0.121882	0.090739	0.102024	0.018951	1.241148	1.232815
189	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
190	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
191	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
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%	11.732694	6.184296	12.827129	-0.256647	19.866097	5.357243	41.325459	58.497832	0.054340	0.040611	0.003404	0.000000	0.000000
206	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.494773	8.388022	-0.421559	9.095100	9.119576	16.254785	40.942038	43.141294	0.041742	0.054026	0.002361	0.949665	0.974868
207	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.587382	11.399441	14.958784	12.656195	24.616591	20.993720	15.788465	13.212024	0.061046	0.061484	0.005811	0.852593	0.850340
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%	12.424364	12.061374	86.087972	88.645275	3248.944521	3770.093258	17689.446201	23837.871183	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.120865	6.178909	7.448177	6.433021	10.560181	9.597178	0.263741	5.498571	0.045123	0.047373	0.001704	0.000000	0.000000
224	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.615597	20.457771	22.112379	21.871528	37.671667	38.801892	5.201946	5.430610	0.065922	0.065850	0.005344	0.982689	0.961560
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%	14.922276	15.195961	107.525721	108.918330	4494.737933	4494.512319	29114.534210	29114.677672	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.878480	23.889455	40.016770	40.300735	42.440292	44.049103	23.196775	17.944615	0.062675	0.055762	0.001057	0.000000	0.000000
321	N02	not_connected	100.00%	0.00%	59.68%	0.00%	100.00%	0.00%	10.226781	8.277825	12.064377	11.043233	20.813006	19.060449	43.762935	41.986610	0.573759	0.402738	0.398246	0.000000	0.000000
323	N02	not_connected	100.00%	0.00%	65.05%	0.00%	100.00%	0.00%	18.913183	11.768887	2.724868	14.302588	16.936302	24.800587	12.036331	1.389322	0.484429	0.367345	0.329340	0.000000	0.000000
324	N04	not_connected	100.00%	0.00%	59.68%	0.00%	100.00%	0.00%	14.165974	14.996257	16.067266	16.478839	25.042136	27.509801	1.346706	1.694374	0.569209	0.393841	0.409308	0.000000	0.000000
329	N12	dish_maintenance	100.00%	10.75%	73.12%	0.00%	100.00%	0.00%	1.880557	7.598711	1.165600	10.349973	2.861212	14.354033	5.412892	-0.576836	0.490224	0.337579	0.335288	0.000000	0.000000
333	N12	dish_maintenance	100.00%	24.19%	97.31%	0.00%	100.00%	0.00%	0.928091	6.974555	1.624734	9.404245	2.287116	13.998491	8.184854	-0.685540	0.461797	0.300467	0.310600	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, 16, 17, 18, 19, 20, 27, 28, 30, 32, 33, 36, 37, 38, 42, 45, 50, 52, 53, 54, 55, 56, 57, 65, 66, 67, 69, 70, 71, 72, 73, 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, 117, 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, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 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, 10, 15, 21, 29, 31, 40, 41, 46, 51, 68, 81, 100, 116, 118, 158, 176]

golden_ants: [3, 10, 15, 21, 29, 31, 40, 41, 46, 51, 68, 81, 100, 116, 118, 158, 176]

# 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_2459801.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