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 = "2459788"
data_path = "/mnt/sn1/2459788"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
good_statuses = "digital_ok,calibration_maintenance,calibration_triage,calibration_ok"

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

Date: 7-27-2022

# 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/sn1/2459788/zen.2459788.25284.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 378 ant_metrics files matching glob /mnt/sn1/2459788/zen.2459788.?????.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/sn1/2459788/zen.2459788.?????.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	2459788
Date	7-27-2022
LST Range	15.855 -- 18.269 hours
X-Engine Status	✅ ✅ ✅ ✅ ✅ ✅ ✅ ✅
Number of Files	377
Total Number of Antennas	147
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 42 RF_ok: 11 digital_maintenance: 1 digital_ok: 87 not_connected: 3
Commanded Signal Source	antenna
Antennas in Commanded State	107 / 147 (72.8%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N09, N18
Nodes Not Correlating	N10, N12
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	72 / 147 (49.0%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	118 / 147 (80.3%)
Redcal Done?	✅
Redcal Flagged Antennas	9 / 147 (6.1%)
Never Flagged Antennas	15 / 147 (10.2%)
A Priori Good Antennas Flagged	76 / 87 total a priori good antennas: 5, 7, 9, 10, 15, 16, 19, 20, 21, 30, 37, 42, 45, 46, 50, 51, 53, 54, 55, 56, 57, 65, 66, 68, 69, 71, 72, 73, 81, 83, 84, 88, 91, 92, 93, 99, 101, 103, 105, 106, 107, 108, 109, 111, 117, 121, 122, 123, 128, 129, 130, 135, 138, 140, 141, 142, 145, 160, 161, 162, 165, 167, 169, 170, 176, 177, 178, 179, 181, 183, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	4 / 60 total a priori bad antennas: 3, 4, 100, 116

# write to csv
outpath = os.path.join(nb_outdir, f'array_health_table_{JD}.csv')
print(f'Now saving Table 2 to a csv at {outpath}')
df.replace({'\u2705': 'Y'}, regex=True).replace({'\u274C': 'N'}, regex=True).replace({'<br>': ' '}, regex=True).to_csv(outpath)

Now saving Table 2 to a csv at /home/obs/src/H5C_Notebooks/_rtp_summary_/array_health_table_2459788.csv

Build DataFrame

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

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

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

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

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

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

Table 2: RTP Per-Antenna Metrics Summary Table

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

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

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

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

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

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

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
3	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.220092	-0.460255	0.690047	-0.818109	-0.376090	0.028409	-0.472174	0.678727	0.633721	0.630435	0.401957	7.924936	6.638814
4	N01	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.152015	3.983802	-0.736591	0.002588	-0.359132	-0.349808	2.787831	0.050603	0.648108	0.639150	0.398216	15.172494	11.683476
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.069626	1.558119	-0.759863	6.290653	-1.148536	5.103948	0.199699	-1.527781	0.655395	0.649152	0.397829	15.199445	15.672508
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.419881	5.775800	12.248748	13.072881	15.252936	14.857632	-1.528059	11.624775	0.644825	0.640265	0.386671	19.859125	18.384280
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.022834	18.638080	24.886648	26.780414	37.106913	38.013828	2.570357	-0.330009	0.621697	0.610829	0.379541	6.177880	6.630814
9	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.417625	6.203431	1.053564	13.730631	1.563547	16.212481	0.074075	-1.245704	0.622417	0.616054	0.383288	5.535226	5.938715
10	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.498544	16.495916	26.397984	24.891830	40.364229	35.718153	17.147431	15.154015	0.572845	0.571698	0.371614	3.404603	3.750134
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	28.95%	0.017279	1.527546	-0.549702	0.771856	-0.183984	0.168920	-0.504738	0.923285	0.668947	0.660002	0.399707	7.694375	7.354854
16	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.059436	-1.268600	0.271619	0.040580	-0.066384	0.362082	6.434504	12.884100	0.673273	0.668719	0.390760	30.975369	36.680833
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.211913	0.820588	-0.737092	0.090000	-0.030550	-0.246017	2.579394	3.701650	0.670783	0.669779	0.380747	1.568487	1.774480
18	N01	RF_maintenance	100.00%	0.00%	50.26%	0.00%	100.00%	0.00%	3.921340	4.878704	3.110275	-0.229587	9.935689	11.445146	66.188191	63.279758	0.645783	0.483625	0.423913	19.805917	7.827691
19	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.272057	15.973536	26.105263	24.145986	39.702463	36.343493	1.287541	1.050315	0.630106	0.635779	0.373136	8.188425	9.726004
20	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.993106	19.384436	15.797595	26.850124	22.351451	39.008087	-1.269653	-0.492458	0.631076	0.600041	0.382292	7.000057	5.710545
21	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.423342	-0.657076	-0.703159	1.651378	0.649538	0.099319	1.346243	19.048237	0.615838	0.604771	0.377752	5.122110	5.042281
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.347796	28.908571	69.822836	69.550646	52.853891	52.134330	10.527807	7.613470	0.043149	0.048435	0.003739	1.254410	1.269004
28	N01	RF_maintenance	100.00%	42.33%	92.06%	0.00%	100.00%	0.00%	13.514709	18.794021	8.462650	10.135592	47.775643	54.744785	12.500774	57.781391	0.475829	0.270819	0.283387	47.136540	10.832369
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.880983	-0.913621	-0.173108	0.184342	-0.785233	0.047559	-0.160209	0.348729	0.688231	0.686894	0.382538	1.433978	1.523107
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.514287	-0.061699	-0.973708	0.082876	-0.533544	-0.290400	12.980733	1.867718	0.676922	0.677203	0.378070	16.131887	24.515136
31	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.746451	-1.045494	0.042301	-0.464026	0.707772	0.070590	1.553916	1.642074	0.672207	0.664999	0.377814	1.804145	1.853415
32	N02	RF_maintenance	100.00%	7.94%	0.00%	0.00%	100.00%	0.00%	32.811139	39.559356	3.144783	4.164434	13.847343	15.114589	32.313430	37.416895	0.579425	0.588099	0.196007	10.394370	9.109716
33	N02	RF_maintenance	100.00%	0.00%	52.91%	0.00%	100.00%	0.00%	-0.001006	5.013397	1.353271	0.940772	-0.057889	4.348350	3.486642	19.806240	0.627539	0.468276	0.449284	5.793025	3.360108
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.607485	7.997569	0.405167	1.597955	0.958698	0.134226	0.184100	1.453986	0.631555	0.616023	0.378633	5.745456	5.674917
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.670201	0.575689	-0.138290	0.246995	-0.127775	0.260806	-0.161477	14.332053	0.661632	0.650048	0.388449	10.499968	8.862594
38	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.413632	0.058813	-0.085124	-0.339086	1.519869	0.793826	9.421617	4.691998	0.681119	0.673812	0.396889	10.909561	9.934950
40	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.844106	-0.010653	0.845488	-0.328713	-0.855458	-0.763631	-0.359156	-0.784424	0.697387	0.696410	0.390083	2.130506	1.958097
41	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.559195	-0.370537	2.401708	-0.721562	2.278194	-0.472172	-0.754122	-0.670254	0.697172	0.696733	0.377169	1.504906	1.453808
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	-1.283946	0.526961	1.969125	1.002842	-0.036100	0.725438	-0.641549	-0.392366	0.700593	0.700626	0.391381	2.122446	1.893785
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.130693	1.222417	0.440335	2.092343	0.131398	0.848664	0.342760	25.300660	0.659888	0.648055	0.389584	9.335058	10.869809
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	18.42%	-1.428899	-1.277426	-0.358347	-0.947373	-0.686214	-0.416865	-0.115780	3.638344	0.640538	0.632498	0.399156	2.227978	2.392486
50	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.169415	3.271884	-0.478229	0.796738	0.155155	2.628600	14.975600	16.249848	0.638245	0.626817	0.361195	11.160252	11.110631
51	N03	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.901324	39.273736	3.856458	86.884944	2.408830	52.991707	0.708849	22.771739	0.667717	0.040677	0.400364	14.654463	1.231069
52	N03	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	12.491013	46.270542	1.004182	87.843637	5.950058	53.063084	9.209835	24.470227	0.648240	0.043246	0.381581	8.767035	1.165408
53	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.961960	3.233409	5.680194	5.099305	4.335513	3.223797	2.238905	7.737730	0.698614	0.695004	0.383890	14.299307	14.734465
54	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.075438	3.287923	-0.513620	3.502531	8.249789	20.735130	4.039186	22.354567	0.700771	0.692091	0.357663	47.387636	48.570892
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.531993	-0.001084	-0.653562	0.745527	2.438571	1.891015	10.091537	0.493770	0.702071	0.702315	0.382287	68.710419	57.936434
56	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.626377	4.531387	13.933136	12.111557	17.642835	14.726825	-1.403992	0.734705	0.704081	0.705864	0.393018	23.935650	19.487293
57	N04	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	25.712822	-0.904314	66.337346	4.989209	52.570715	8.425123	7.198805	2.279250	0.050223	0.697917	0.429531	1.590031	15.605066
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	0.637420	0.401400	-0.207745	0.371209	0.243842	0.828748	-0.808142	0.447456	0.645485	0.635444	0.377322	1.326474	1.300794
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	0.512473	0.945570	0.267814	-0.323465	0.563385	-0.773690	-0.269505	1.852607	0.669977	0.660889	0.371665	1.450786	1.341772
67	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.169418	-0.755110	-0.499944	-0.778268	-1.089759	0.692920	5.249201	2.815296	0.686268	0.682131	0.367907	8.783113	13.412081
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	15.79%	0.034759	0.010653	-0.830688	1.256942	1.418285	1.616985	-0.076125	-0.363528	0.693601	0.690706	0.373023	2.690731	3.162656
69	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.349535	4.849620	12.008245	11.374945	15.166875	13.854407	-1.035024	0.090236	0.700492	0.705001	0.374527	14.413079	14.786207
70	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.855025	-1.687145	0.225069	-0.689574	-0.356294	-0.384422	1.118187	1.397297	0.706103	0.703555	0.399016	43.415464	29.782561
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	60.53%	0.302134	-1.001771	0.669097	0.267222	-0.462342	1.059865	0.129569	0.539054	0.702516	0.708396	0.389718	11.267521	12.980385
72	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.459396	-0.986794	0.763235	2.679338	0.670688	2.061032	5.546618	-0.963001	0.693385	0.700119	0.402873	8.648021	9.631431
73	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	26.32%	1.057505	0.027517	0.679885	1.290898	1.239938	0.345553	2.926170	0.824429	0.681723	0.675226	0.416917	1.727201	1.888186
81	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.186522	4.043417	0.000502	5.857195	-0.245176	6.229401	2.385558	0.016547	0.628145	0.620962	0.359002	17.319533	21.939352
82	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.103483	1.006610	2.137788	-0.315898	2.448386	-0.914360	-0.115809	-0.710979	0.657245	0.651992	0.363911	15.876524	13.347076
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.394241	2.783563	38.846825	5.784950	24.843785	4.889762	4.711633	-1.159191	0.620077	0.677677	0.384662	7.206697	11.900437
84	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.230457	10.241966	2.398630	2.763130	0.723913	1.857775	-0.663835	-0.687056	0.695933	0.694157	0.364586	12.244127	15.610568
85	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
86	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
87	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
90	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
92	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	39.405324	59.259880	10.612650	13.312654	54.024175	57.044390	9.535531	22.980692	0.076891	0.074631	0.010194	0.000000	0.000000
93	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.280742	-0.388553	5.834040	1.076966	6.915650	1.223818	-0.740814	2.969477	0.075097	0.075213	-0.012886	0.000000	0.000000
94	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.447380	-1.677080	0.680162	-0.945467	0.355566	1.793382	4.502833	6.497096	0.052986	0.060902	0.006376	0.000000	0.000000
98	N07	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.260750	29.502022	1.917332	2.686639	-0.679955	4.493988	0.468612	5.635312	0.617465	0.593230	0.339103	9.216089	7.125604
99	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.429768	-0.170732	2.906876	1.367778	4.068157	0.954423	4.409794	0.068517	0.639029	0.636657	0.349249	10.199107	8.755309
100	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.088550	-0.480644	-0.444607	0.743261	-1.069665	0.682529	-0.050603	-0.252745	0.666863	0.661629	0.367104	18.974258	13.860081
101	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
102	N08	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.684365	17.650987	108.395152	103.444962	3467.881145	3122.694343	18990.835255	16306.213376	nan	nan	nan	0.000000	0.000000
103	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.390831	9.838791	2.564937	0.548295	1.066941	-0.101583	-0.646678	-0.306039	0.699638	0.696393	0.385040	18.205448	11.969977
104	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.791187	88.048239	3.097574	19.393534	2.483236	7.900353	0.404597	0.489160	0.693964	0.691529	0.402772	9.671430	8.722649
105	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
106	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
108	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
109	N10	digital_ok	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
110	N10	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
111	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
112	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.967584	-0.399573	1.246737	1.564454	1.256995	0.169323	0.273880	-1.304354	0.048580	0.053052	0.003227	0.000000	0.000000
116	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.406112	2.227673	0.090159	-0.318771	0.666452	0.581037	-0.380446	-0.848418	0.603190	0.595842	0.359899	4.757094	5.727201
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.849648	4.308701	9.752530	7.759035	10.879023	6.438818	-1.434227	-1.710854	0.637425	0.632614	0.379761	6.330491	6.229627
118	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	23.68%	0.00%	3.853312	1.318572	2.419297	1.270661	3.169347	0.858954	1.680560	1.822831	0.643448	0.642555	0.375278	0.702304	0.658234
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.285736	1.814334	10.410020	0.333824	12.398505	0.881541	-1.435443	0.621383	0.662954	0.660156	0.384611	5.399860	5.122270
120	N08	RF_maintenance	100.00%	52.91%	100.00%	0.00%	100.00%	0.00%	20.017095	36.498069	6.265764	77.223738	48.800243	52.941938	5.693276	14.354944	0.441386	0.049326	0.315197	3.097043	1.271773
121	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.377559	5.115719	-0.078694	1.248530	-0.320022	1.701487	35.743759	19.532938	0.684578	0.685648	0.388372	5.157234	5.215240
122	N08	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
123	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.441894	8.396182	1.312710	0.300601	-0.231616	-0.647114	-0.487241	-0.309276	0.683935	0.682310	0.396425	10.639938	9.811753
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
127	N10	RF_maintenance	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.211354	-0.684160	-0.664019	0.461073	-0.599359	1.203264	-0.362068	1.017774	0.068340	0.067099	0.015599	0.000000	0.000000
128	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.436448	1.630867	2.845695	4.786393	1.456730	4.458962	-0.363787	0.056384	0.067210	0.066809	0.010441	0.000000	0.000000
129	N10	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.815638	0.014742	-0.734918	1.132080	-0.815321	-0.393414	-0.735017	-0.859787	0.055433	0.054948	0.004446	0.000000	0.000000
130	N10	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.257793	-0.410418	-0.740896	-0.597457	0.180204	1.498848	0.646197	5.915123	0.054329	0.049954	0.004411	2.667954	3.259983
135	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.742551	-0.748171	0.035522	-0.877898	-0.445517	-0.610383	5.089501	3.521214	0.087389	0.096851	0.020423	1.201814	1.203243
136	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.985879	11.953681	1.116404	0.900192	3.661522	2.730055	3.756166	5.066221	0.073555	0.087682	0.013737	1.158014	1.159858
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.823398	26.806867	60.221979	60.743489	52.895233	52.114902	8.822080	10.578630	0.037546	0.045934	0.004004	1.320421	1.416833
138	N07	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	30.808216	2.273374	59.883737	-0.442595	52.987043	-0.020720	9.540076	-0.103154	0.047685	0.637292	0.410685	1.210705	7.250101
140	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.034892	4.961017	-0.176136	-0.228687	1.948004	2.186688	0.803289	0.674916	0.649704	0.645535	0.382062	4.202708	4.288611
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.758766	4.664087	-0.330322	16.358405	1.286040	4.446973	1.244352	16.189798	0.653135	0.634776	0.382773	5.874777	6.386243
142	N13	digital_ok	100.00%	58.20%	100.00%	0.00%	100.00%	0.00%	25.548569	30.904837	10.458281	70.467753	51.079251	52.338324	8.639834	8.171269	0.419878	0.044638	0.224290	10.672191	1.325251
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	15.79%	0.00%	-0.179213	-0.415263	-0.002588	1.186115	-0.225730	0.490120	-0.421654	-1.219019	0.665856	0.660626	0.382381	2.057950	2.014917
144	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	15.79%	0.00%	-0.213345	-0.401689	2.089853	-0.809578	0.137603	-0.907954	0.360530	0.706776	0.665335	0.658253	0.396300	2.137468	2.338130
145	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.441715	26.126929	70.259122	71.094988	52.786505	52.696628	9.358189	11.147197	0.037872	0.039630	-0.000000	1.727354	1.910619
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.399791	28.347879	69.994389	72.248942	52.873129	52.228860	10.504250	10.845820	0.051353	0.053557	0.001544	0.000000	0.000000
155	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.372945	27.160244	68.593979	69.603805	52.688089	51.768801	10.228741	10.461689	0.040514	0.041567	0.000544	1.158177	1.147919
156	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.450337	19.118742	26.203698	26.876953	39.569244	38.458818	2.668470	5.114113	0.090602	0.093249	0.014647	1.225693	1.231976
157	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.891670	20.190714	25.624708	27.813239	38.708556	40.475294	0.229295	-0.464707	0.087578	0.088858	0.010868	1.306527	1.297994
158	N12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.753884	17.302017	21.666441	25.765679	31.068986	36.586038	-0.871202	-0.723615	0.085331	0.083406	0.012421	1.281643	1.279902
160	N13	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
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.791254	46.320617	-0.910845	5.036741	-0.216940	13.219311	0.132867	0.176967	0.648555	0.560589	0.348031	7.031701	7.486788
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	2.867898	1.076493	-0.325115	-0.197647	2.807739	2.577749	0.590277	-0.485939	0.648923	0.648183	0.377949	2.427456	2.365888
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	2.63%	0.00%	1.033209	-0.664822	-0.352730	-0.911232	-1.132532	-0.909547	2.020012	0.347141	0.657564	0.654339	0.373535	1.859642	1.918975
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	2.63%	0.00%	-1.268647	-1.047775	-0.254522	-0.340372	-0.028409	-0.909640	0.849565	0.761621	0.653717	0.649841	0.378229	1.864631	1.707224
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.757350	0.376996	7.792204	-0.812148	8.697241	-1.036156	-0.796854	-0.011411	0.651738	0.646215	0.377403	9.674950	10.434113
166	N14	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.680906	1.830177	0.541298	1.397053	16.375081	5.246154	95.431780	9.040918	0.599331	0.624659	0.351326	0.000000	0.000000
167	N15	digital_ok	100.00%	29.10%	37.04%	0.00%	100.00%	0.00%	18.822244	25.187510	21.264081	25.417080	55.690161	39.544403	41.387864	16.141049	0.526120	0.522200	0.187085	0.000000	0.000000
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.179466	21.844344	25.186154	29.389859	37.646485	42.616393	-0.110027	0.001633	0.588556	0.572759	0.371879	0.000000	0.000000
169	N15	digital_ok	100.00%	2.65%	2.65%	0.00%	100.00%	0.00%	20.400594	20.116879	27.804451	27.746507	42.127811	39.859623	0.825706	-0.426962	0.565586	0.557811	0.366982	0.000000	0.000000
170	N15	digital_ok	100.00%	13.23%	2.65%	0.00%	100.00%	0.00%	20.657115	18.575045	28.009363	26.529292	42.725638	37.771607	1.357434	0.130572	0.545323	0.548346	0.363464	0.852784	0.857634
176	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.230691	-0.218646	-0.391435	0.983738	-0.361161	1.024399	-0.558774	-1.028020	0.053034	0.062113	0.005226	0.901752	0.907481
177	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.873558	1.027242	0.642539	1.004586	-1.010079	0.865819	1.490657	6.155772	0.058772	0.055610	0.005301	0.888440	0.891095
178	N12	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.009123	-1.675821	-0.865146	-0.466842	-1.837784	-0.498231	-0.654347	-0.904429	0.059568	0.050838	0.007073	1.091296	1.097296
179	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.251302	0.433915	0.126685	0.444224	5.781194	0.802899	7.675548	-0.158018	0.068579	0.059390	0.013551	1.193567	1.192445
180	N13	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
181	N13	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
182	N13	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.125244	10.422023	25.072140	17.942069	37.580047	40.521319	-0.480332	81.400707	0.619165	0.625891	0.388307	15.840659	19.828322
183	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.956482	0.708662	1.318639	-0.633404	0.941262	-0.972108	0.046299	6.047701	0.647441	0.637345	0.384974	23.972408	30.274418
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	2.63%	0.00%	0.376848	-1.166745	1.842031	-0.610844	0.951536	-0.242449	2.398107	0.635583	0.647472	0.640453	0.370607	1.625125	1.911087
185	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.450711	0.415180	0.353731	5.960716	2.630173	3.086182	4.764463	0.701219	0.649619	0.644029	0.374136	8.446095	9.787337
186	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.563362	1.379539	27.619236	28.261193	3.814922	3.233630	4.528682	0.802780	0.617260	0.614314	0.356939	3.608202	4.181901
187	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.850219	19.246786	22.320761	27.133237	32.434995	38.725321	0.364331	-0.608278	0.623799	0.613604	0.366663	0.000000	0.000000
189	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.960315	4.750409	-0.267345	1.254753	-0.365134	1.754085	0.610708	3.201858	0.593017	0.589464	0.373449	0.000000	0.000000
190	N15	digital_ok	100.00%	42.33%	100.00%	0.00%	100.00%	0.00%	42.601244	29.392307	5.257262	70.982586	26.214180	52.163051	147.056718	9.880063	0.494340	0.049010	0.312247	0.000000	0.000000
191	N15	digital_ok	100.00%	5.29%	2.65%	0.00%	100.00%	0.00%	0.072047	1.177054	0.311509	-0.656049	1.662967	0.555695	2.456614	6.471108	0.561857	0.552159	0.376760	0.000000	0.000000
203	N18	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
205	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	12.776400	5.879535	18.028342	0.109140	27.034331	8.312422	26.456901	41.090935	0.611389	0.600478	0.364073	4.179811	4.435130
206	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.582475	9.428949	-0.425824	13.062041	8.539912	19.918092	28.370899	28.583715	0.588976	0.595100	0.354345	2.367255	2.495980
207	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.293754	17.428804	20.858462	24.470195	31.921647	35.059770	7.591773	8.310237	0.585579	0.581935	0.355210	0.870131	0.857420
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%	12.306399	12.025020	112.120653	112.193818	3723.318692	4384.456815	12806.302130	17705.824183	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.286802	14.100639	10.057551	20.747616	16.113036	28.423850	-0.996385	-1.336583	0.598700	0.590554	0.373362	3.519877	3.642585
224	N19	RF_ok	100.00%	2.65%	0.00%	0.00%	100.00%	0.00%	23.520125	23.898668	31.414737	32.006178	48.155333	46.622124	1.072950	0.116769	0.550907	0.550581	0.352668	1.788038	1.845016
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%	9.509730	13.680563	138.290493	128.436900	5162.217641	5162.029463	21278.327748	21273.075516	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.901748	29.670758	51.738925	51.067075	53.249243	52.092235	14.465860	10.192791	0.066692	0.056691	-0.003368	0.000000	0.000000
321	N02	not_connected	100.00%	29.10%	71.43%	0.00%	100.00%	0.00%	10.692224	9.445737	16.647798	16.144639	26.484826	23.420115	34.926466	34.791569	0.462067	0.407950	0.293892	0.000000	0.000000
323	N02	not_connected	100.00%	44.97%	74.07%	0.00%	100.00%	0.00%	23.226669	13.374410	4.486453	20.378540	21.133534	27.497558	4.187193	0.303018	0.382254	0.383952	0.252865	0.000000	0.000000
324	N04	not_connected	100.00%	39.68%	74.07%	0.00%	100.00%	0.00%	15.672314	17.437580	22.875285	24.303249	33.660798	34.210243	-0.742714	-1.270860	0.444640	0.391146	0.293761	0.000000	0.000000
329	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.074578	8.023176	3.534365	15.752456	2.135848	19.287634	13.127103	-1.177034	0.080144	0.080121	0.027257	0.000000	0.000000
333	N12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.102664	8.029908	13.288105	13.916882	9.594558	17.630559	4.560089	-0.853018	0.079344	0.079089	0.024397	0.000000	0.000000

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

ex_ants: [3, 4, 5, 7, 8, 9, 10, 15, 16, 18, 19, 20, 21, 27, 28, 30, 32, 33, 36, 37, 38, 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: [17, 29, 31, 40, 41]

golden_ants: [17, 29, 31, 40, 41]

# 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_2459788.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