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 = "2459808"
data_path = "/mnt/sn1/2459808"
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: 8-16-2022

# 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/sn1/2459808/zen.2459808.25312.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/sn1/2459808/zen.2459808.?????.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/sn1/2459808/zen.2459808.?????.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	2459808
Date	8-16-2022
LST Range	17.176 -- 19.176 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	None
Antennas in Commanded State	0 / 147 (0.0%)
Cross-Polarized Antennas
Total Number of Nodes	15
Nodes Registering 0s	N18
Nodes Not Correlating	N09, N14, N15, N19
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	81 / 147 (55.1%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	102 / 147 (69.4%)
Redcal Done?	✅
Redcal Flagged Antennas	10 / 147 (6.8%)
Never Flagged Antennas	22 / 147 (15.0%)
A Priori Good Antennas Flagged	75 / 95 total a priori good antennas: 5, 7, 21, 30, 31, 37, 38, 40, 42, 45, 46, 54, 55, 56, 67, 68, 69, 71, 72, 73, 81, 83, 84, 85, 86, 88, 91, 93, 94, 98, 100, 101, 103, 105, 106, 107, 108, 109, 111, 112, 117, 118, 121, 122, 123, 127, 128, 129, 130, 140, 141, 142, 143, 144, 156, 160, 161, 163, 164, 165, 167, 169, 170, 176, 177, 178, 179, 181, 184, 185, 186, 187, 189, 190, 191
A Priori Bad Antennas Not Flagged	2 / 52 total a priori bad antennas: 82, 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_2459808.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%	0.493558	-0.528883	-0.815192	-1.105284	-0.648503	-0.549366	-0.029958	-0.185549	0.741664	0.684449	0.492230	2.108473	1.869417
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.440740	3.560081	-1.063067	1.601406	-0.611086	0.263465	4.887541	0.951497	0.755977	0.688758	0.486281	10.702649	9.035532
5	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.256087	1.146091	-0.828213	5.138594	-0.829071	2.098005	2.524803	-1.213154	0.764396	0.698573	0.484463	6.846696	5.452267
7	N02	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.080431	-0.963410	0.232268	0.495329	0.086215	0.148499	1.896046	8.550045	0.767335	0.697070	0.489915	3.689021	3.704944
8	N02	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.997832	14.669807	21.383955	22.187642	19.966594	21.423114	1.613029	0.715842	0.747246	0.661431	0.497551	5.517753	5.552798
9	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.197967	-1.078296	0.058510	0.258836	1.949382	1.122412	-0.415142	-1.064799	0.753630	0.670469	0.503406	2.212007	2.401238
10	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.050475	-1.002691	-0.297966	0.397485	-0.669646	0.531339	0.188900	-0.643514	0.736447	0.647120	0.514572	2.092016	2.154458
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.535023	1.083132	1.879219	1.132884	0.511056	0.870069	0.097267	1.397208	0.774869	0.709197	0.480632	2.784987	2.549269
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.167835	-1.063514	-0.463241	-0.710098	-0.485292	-0.586302	1.659713	3.059390	0.779735	0.723080	0.474221	2.662492	2.612147
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.295338	1.331195	0.685779	0.403041	0.694986	0.399506	2.545268	-0.684286	0.785791	0.724649	0.478406	3.311072	2.901030
18	N01	RF_maintenance	100.00%	0.00%	27.42%	0.00%	100.00%	0.00%	2.616713	1.878557	3.135888	-0.743277	2.475513	4.950373	11.388072	30.474440	0.766283	0.565056	0.544008	4.731941	2.967336
19	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.796159	-0.571574	-0.204247	-0.355774	-0.753571	-0.371907	1.121853	3.757004	0.775258	0.708625	0.486254	2.287582	2.324799
20	N02	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.125524	0.314835	-0.096657	-0.200924	1.645285	0.603488	-0.001476	-0.808377	0.763378	0.683101	0.489016	2.787330	2.489974
21	N02	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.209909	19.845090	63.048945	63.862538	29.393409	30.549243	7.300937	6.480437	0.039639	0.045301	0.003408	1.205674	1.212713
28	N01	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	11.505805	11.904807	8.670059	11.067268	24.199073	29.848786	8.909466	27.914152	0.535393	0.302676	0.339218	18.058845	4.292217
29	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.862695	0.258252	0.088698	-0.428921	-0.978569	-1.296786	-0.649485	-0.112258	0.800052	0.746326	0.470927	2.725528	2.634951
30	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.478867	-0.032222	1.479937	-0.869025	-0.124868	-0.791831	12.091700	0.239877	0.791654	0.735887	0.465826	4.715026	5.226815
31	N02	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
32	N02	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.421878	9.152224	112.502221	103.958295	2307.327735	2099.744246	9425.737929	7088.571167	nan	nan	nan	0.000000	0.000000
33	N02	RF_maintenance	100.00%	0.00%	32.80%	0.00%	100.00%	0.00%	0.426365	2.622107	0.826327	0.275329	-0.478270	0.812153	2.148384	9.377065	0.755385	0.538329	0.593806	5.147952	2.568290
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.756901	4.990345	0.183349	0.446780	2.236401	-0.349610	0.954538	0.069410	0.754642	0.694810	0.468018	4.357224	4.159239
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.594990	1.083536	1.307375	0.368951	1.196727	0.655605	0.008454	7.491178	0.774292	0.716000	0.462085	5.464939	6.160808
38	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.729683	-0.228972	-0.821380	-0.923845	1.486081	0.176794	4.532388	0.408541	0.787657	0.737914	0.459207	4.120587	4.411810
40	N04	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
41	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.326016	-0.893018	2.068576	1.365637	0.689168	-0.778964	-0.808233	1.155013	0.806283	0.764176	0.460370	3.253150	2.590116
42	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	7.89%	-0.263925	0.193012	2.125657	1.334475	-0.088610	-0.887511	-0.247904	-0.410839	0.806503	0.757154	0.475451	3.092277	2.717717
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.385516	0.638965	-0.842584	1.522368	-0.973232	-0.046335	-0.084790	15.682179	0.776488	0.701818	0.501465	4.436200	3.741092
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	2.63%	-1.093038	-0.587219	0.205310	-1.053259	-0.357119	-0.877451	-0.340376	3.838287	0.763757	0.678848	0.523828	2.164971	1.765304
50	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.245008	1.312057	3.885775	2.867608	14.364410	4.579896	88.848862	18.430649	0.739898	0.698911	0.416481	8.167674	5.851174
51	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.344264	1.326877	-0.937983	-0.702179	0.060504	-0.166505	-0.483655	1.042798	0.782077	0.735238	0.444784	2.459895	2.470534
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.026086	4.825493	0.562279	0.439292	-0.382591	-1.158222	-0.204997	0.841499	0.799525	0.754986	0.446257	7.268707	6.468618
53	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.909591	0.999237	-0.775566	-0.381806	-0.546572	-0.166461	2.284784	3.341649	0.811791	0.777087	0.447448	2.072310	2.245021
54	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.135154	0.375185	1.976452	1.285479	1.731296	1.167907	6.111365	2.578816	0.819237	0.779679	0.455634	4.688783	6.576394
55	N04	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.514326	-0.292662	-0.741287	1.035100	2.422605	1.930965	6.595682	-0.552871	0.819882	0.778598	0.465906	4.381264	4.576140
56	N04	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
57	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.594240	-0.742548	11.135811	3.240584	15.428487	1.978445	0.967526	2.512031	0.734149	0.751296	0.383086	6.219789	3.251157
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.094240	0.023952	0.281010	0.552463	1.332643	1.049885	-0.123920	-0.095540	0.775171	0.715459	0.464773	2.776365	2.379494
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.279424	-0.366361	-0.668992	0.325207	0.679335	0.487195	-0.310671	0.936854	0.791590	0.748519	0.440342	2.668880	2.544065
67	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	15.79%	-0.742611	-0.605892	0.464237	-0.486308	0.967580	1.490691	-0.092571	1.503159	0.810447	0.774140	0.429322	2.859212	3.430801
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	31.58%	0.042643	-0.023918	0.094450	-0.315707	2.036868	1.434542	0.018008	-0.005551	0.820699	0.790973	0.429769	2.634739	4.016391
69	N04	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
70	N04	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.082920	-1.502975	0.388371	-1.195540	0.689985	-0.371950	-0.000855	-0.217011	0.818885	0.779561	0.473117	29.060813	28.456140
71	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	100.00%	0.341457	-1.245941	-0.413712	-0.395683	-0.499627	-1.214594	-0.407882	0.240641	0.826258	0.791175	0.458618	49.670070	47.907505
72	N04	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	97.37%	1.661862	-0.436743	-0.693852	1.988833	1.765598	1.186616	3.691086	-0.855161	0.809913	0.760421	0.488884	3.693767	3.123814
73	N05	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	19.078352	0.731432	62.085475	-0.738608	29.273289	-0.359341	5.369322	0.647965	0.031483	0.734589	0.369854	1.167078	3.326235
81	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	5.26%	0.031213	2.440868	-0.253001	3.946505	0.110540	2.588043	2.549636	-0.518837	0.772368	0.716813	0.456530	2.790629	2.660701
82	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.198611	-0.631084	2.056349	0.351216	-0.462697	-0.004410	0.001476	-0.184419	0.798406	0.748621	0.453247	8.940610	7.937435
83	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.858206	3.240647	2.584084	6.032490	-0.088536	2.089379	-0.776086	-1.464898	0.812003	0.774682	0.429469	5.149003	5.440232
84	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.542512	6.882510	0.151504	1.337710	-0.574836	-0.277242	0.894540	-0.595128	0.768469	0.732706	0.416658	3.903154	4.183182
85	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	76.32%	0.379793	-0.116672	-0.994838	-0.842426	-1.268745	-1.433406	-0.896016	-1.223991	0.759422	0.723369	0.440695	2.395546	2.254779
86	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	86.84%	1.954865	2.434191	0.858104	-0.636123	1.066846	0.191372	-0.185896	0.218391	0.756108	0.710091	0.434334	2.761773	2.531530
87	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.266599	7.913896	6.193040	1.284085	4.527437	0.942575	46.097609	-0.245218	0.739405	0.712179	0.444831	2.753670	2.613172
88	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.581043	18.855910	55.354852	56.353635	29.448102	30.563762	9.471797	6.763876	0.039657	0.035049	0.002447	1.173682	1.171041
90	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.898039	-0.008309	18.522298	1.263065	16.467437	-0.841041	-0.251503	-0.246105	0.054525	0.038707	0.003428	1.214164	1.230196
91	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.698345	18.888774	1.735852	57.267467	30.846497	30.503458	30.505383	7.458339	0.044836	0.039207	0.029918	1.433370	1.398080
92	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
93	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
94	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
98	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.544442	17.299563	0.392630	1.409252	-0.131206	0.818083	0.159724	3.413314	0.776670	0.699404	0.445999	9.373433	9.869120
99	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	3.060797	0.635757	3.417061	0.656876	2.479195	1.759876	-0.100567	1.266945	0.792547	0.741751	0.450173	2.339234	2.068960
100	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	23.68%	0.269638	-0.102205	0.329277	-0.058510	-0.956544	-1.140027	-0.947213	-0.718290	0.810062	0.764779	0.460017	2.509982	2.456816
101	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.274091	6.583445	4.136348	-0.002669	0.912884	-0.641455	3.814395	0.252943	0.762899	0.718338	0.438808	3.459448	3.256245
102	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.458144	17.406108	8.476428	8.057658	282.554595	275.930511	6229.910590	6192.332050	0.640389	0.661185	0.423108	0.000000	0.000000
103	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.839731	7.057888	-0.397399	0.351118	-0.484542	-0.509323	0.468730	-0.281737	0.749020	0.708569	0.439128	3.271352	2.995311
104	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.909461	62.881836	0.358001	16.437173	0.224332	3.587394	0.805185	-0.025072	0.744049	0.694129	0.464565	3.297793	2.875873
105	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.990305	16.562953	21.327653	24.292414	19.800879	23.973455	0.448536	1.052743	0.076166	0.059860	0.010101	1.229705	1.217834
106	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.608872	16.252560	6.133912	23.426895	4.228487	23.064181	-0.539196	0.874728	0.043213	0.050559	0.007179	1.198585	1.181926
107	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.657362	18.166517	17.794749	56.735211	27.153202	30.320278	2.506145	7.047142	0.055335	0.040098	0.034619	1.211528	1.210430
108	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.262390	18.154030	54.597347	56.752153	29.245225	30.402918	4.799372	4.985461	0.030255	0.040427	0.003976	0.865468	0.876552
109	N10	digital_ok	0.00%	0.00%	38.17%	0.00%	39.47%	0.00%	-1.288583	0.376202	0.240967	0.670812	-0.156645	0.265795	1.425499	2.776249	0.602151	0.506993	0.428026	2.222623	2.354486
110	N10	RF_maintenance	100.00%	0.00%	40.86%	0.00%	100.00%	0.00%	32.787186	5.188968	4.047359	0.296367	8.221871	14.424258	27.890012	154.272663	0.532341	0.479554	0.325671	20.110908	6.540606
111	N10	digital_ok	100.00%	0.00%	40.86%	0.00%	100.00%	0.00%	0.136019	0.674591	-0.221030	1.327195	1.235939	0.459021	5.291351	6.344401	0.561661	0.463338	0.385053	4.777169	5.801509
112	N10	digital_ok	0.00%	0.00%	40.86%	0.00%	42.11%	0.00%	-0.717350	-0.457295	-0.236170	0.972292	-0.053389	-0.836915	1.136673	-1.308237	0.536731	0.446548	0.371714	1.646359	1.808376
116	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.618721	1.762137	0.350481	-0.646042	-0.770583	0.349320	0.664695	-1.007000	0.773199	0.707478	0.475333	2.213011	1.974793
117	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.840774	2.852999	6.258874	6.627884	2.808368	3.460181	-1.263565	-1.685935	0.792487	0.734844	0.480162	5.453508	5.818526
118	N07	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.906458	19.808885	3.398230	56.648291	2.474905	30.384244	3.258005	4.567900	0.802680	0.044660	0.459803	4.223554	1.208227
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.922760	0.815714	9.302041	3.450668	5.768600	-0.318388	-1.127132	-0.311106	0.812917	0.757698	0.471081	3.526889	3.262598
120	N08	RF_maintenance	100.00%	30.11%	100.00%	0.00%	100.00%	0.00%	16.530286	25.590782	10.954861	70.251834	23.101982	31.151114	4.052987	9.764943	0.477072	0.054382	0.314352	2.465346	1.198838
121	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.268113	4.106653	-0.721825	0.627555	-0.248828	-0.404033	22.043680	11.209348	0.743961	0.698498	0.440306	3.517111	3.146054
122	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.583389	5.345126	1.756037	1.634416	0.163767	-0.626103	0.683019	1.878893	0.734621	0.681832	0.454196	3.062236	2.574544
123	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.479266	5.845048	0.066198	-0.079872	-1.076572	-1.069579	-0.444707	-0.443936	0.726422	0.671721	0.470953	3.104195	2.585485
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.077155	17.612568	23.221182	25.137564	22.128615	25.056888	1.044134	1.400095	0.073525	0.061310	0.007121	0.862784	0.858360
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.655285	14.433663	20.427096	21.138058	22.783828	20.175391	3.617860	0.192306	0.069489	0.043362	0.004554	0.847651	0.851741
127	N10	digital_ok	0.00%	0.00%	38.17%	0.00%	39.47%	0.00%	0.023918	-0.148999	0.732149	-0.362375	0.223384	0.493113	-0.228578	0.197477	0.595726	0.503888	0.422026	1.556690	1.673315
128	N10	digital_ok	0.00%	0.00%	40.86%	0.00%	42.11%	0.00%	-0.981542	0.713225	-0.188343	1.714642	-0.756977	-0.629662	-0.557262	-1.305414	0.578629	0.481772	0.402264	1.987183	2.146316
129	N10	digital_ok	0.00%	0.00%	40.86%	0.00%	42.11%	0.00%	-0.508259	-1.062723	-0.751150	-1.217744	-0.218283	-0.153568	-0.484160	2.091922	0.558804	0.465398	0.384722	1.722440	1.878646
130	N10	digital_ok	0.00%	0.00%	40.86%	0.00%	42.11%	0.00%	-0.547816	0.393227	1.350308	1.543099	-0.169290	1.866501	0.438273	2.023018	0.536288	0.448832	0.368802	1.591658	1.834626
135	N12	digital_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.146030	-0.729351	-1.156082	-1.080397	-0.802686	-0.833990	2.419553	0.809500	0.705938	0.632269	0.472972	3.345220	2.856208
136	N12	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.617529	9.193995	-0.685079	0.720436	4.004530	3.483403	2.125246	3.998810	0.713030	0.643552	0.442897	4.029719	3.640868
137	N07	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.271607	20.326927	54.435923	55.500623	29.414367	30.541195	6.495966	7.707216	0.038901	0.043789	0.003592	1.181667	1.225555
138	N07	RF_maintenance	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	23.054476	1.979298	54.177190	1.457950	29.467333	-0.098003	6.688980	-0.174346	0.045572	0.739365	0.409596	1.248086	3.399819
140	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.429418	17.056199	61.798829	63.930425	29.352808	30.681817	5.047291	5.338658	0.039340	0.042229	0.000898	1.199990	1.211115
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.554096	4.703353	-0.479385	12.464654	1.562904	2.984040	0.912955	8.150389	0.782749	0.722776	0.488788	4.066069	3.387108
142	N13	digital_ok	100.00%	21.51%	100.00%	0.00%	100.00%	0.00%	17.664002	22.660571	10.100119	64.218101	23.460617	30.784936	5.676667	6.386969	0.472479	0.042375	0.265394	2.316758	1.155374
143	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.056447	-1.157721	1.800692	1.263192	0.650136	1.201670	-0.287436	-1.366967	0.112147	0.108655	0.028372	0.854940	0.850805
144	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.091244	0.048508	-0.811941	1.575989	0.136330	0.283265	1.208949	24.742441	0.095581	0.088531	0.018847	0.000000	0.000000
145	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.333303	19.393366	63.423613	64.731003	29.411305	30.539448	6.693332	7.777446	0.030295	0.032638	-0.000427	0.000000	0.000000
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.615351	20.266157	63.243206	65.847209	29.487729	30.689436	7.126048	7.548131	0.041571	0.039546	0.002086	1.210898	1.206508
155	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.923763	19.385139	61.949886	63.466708	29.370520	30.490228	7.292673	7.505709	0.038932	0.038204	0.000280	1.432666	1.411565
156	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.739534	4.110417	1.520346	2.925094	-0.364534	16.322456	2.044081	31.258099	0.700542	0.611386	0.468934	4.820737	4.980562
157	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.440077	0.912638	0.082984	3.682884	0.580464	2.218617	0.434706	0.752443	0.703101	0.634547	0.476985	1.548588	1.254274
158	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.911135	-1.232536	-0.935401	-0.194782	0.206403	0.118552	1.203219	-0.358788	0.709233	0.638353	0.473404	1.537343	1.330497
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.867436	17.389376	62.759289	64.018846	29.394468	30.589530	6.772950	7.704759	0.043262	0.042604	0.001442	1.286420	1.282682
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.713342	35.655933	-0.205198	4.597090	-0.352694	9.192878	0.370098	0.492511	0.758676	0.628622	0.428586	4.901858	6.365991
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.755776	0.942833	-0.678170	-0.904382	1.903415	1.348479	-0.276621	-0.566158	0.755839	0.685515	0.496684	1.618496	1.409527
163	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.519377	-0.408440	0.116555	-0.831874	-0.660728	-1.174003	1.200544	0.288842	0.071643	0.078835	0.012736	1.293041	1.286884
164	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.129513	-0.875538	-1.176574	-0.807092	-0.903757	-0.346493	1.408562	1.311080	0.073373	0.068997	0.005872	1.290652	1.290596
165	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.756469	1.137073	7.486275	-0.924099	4.637947	-1.010769	2.423196	2.177514	0.085182	0.063289	0.005407	1.285264	1.292834
166	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.072150	3.755021	1.792622	-0.029854	8.567179	10.553737	75.065687	50.062881	0.088215	0.092725	0.011553	1.200358	1.200819
167	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.385798	17.168396	17.550672	21.242172	29.766307	24.811519	111.831778	34.827039	0.074899	0.071364	0.008924	1.163707	1.157013
168	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.639848	17.270161	21.630940	24.532626	20.184000	24.360605	0.568460	1.178469	0.072916	0.075442	0.011715	1.161714	1.151410
169	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.526226	15.907585	23.805610	23.075993	22.748028	22.729940	1.123208	1.463397	0.078427	0.064392	0.005557	1.173122	1.177957
170	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.558460	14.487143	24.004247	22.238563	23.312131	21.518003	1.734965	0.994329	0.080052	0.061143	0.005536	1.186078	1.197511
176	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
177	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
178	N12	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
179	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.463087	0.478740	-0.481423	-0.270418	0.655259	-0.741549	17.691110	1.482721	0.679278	0.606782	0.461387	4.221381	3.886976
180	N13	RF_maintenance	100.00%	0.00%	38.17%	0.00%	100.00%	0.00%	0.418903	12.082158	0.258144	57.735015	-0.893892	19.467213	-0.806271	4.792611	0.744100	0.466478	0.548179	165.299816	52.401593
181	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.296845	43.444940	63.609174	6.779791	29.465346	30.786158	6.573240	14.022128	0.047524	0.315843	0.148198	1.275977	2.646226
182	N13	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.213338	4.948460	21.663668	10.640829	20.283793	7.324610	0.655686	8.951344	0.725129	0.671284	0.496469	8.946828	7.733396
183	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.331061	-0.800816	0.211347	-0.905800	0.437097	-1.672459	0.367028	0.509906	0.747671	0.668407	0.516699	1.451459	1.273521
184	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.851030	-0.206658	-0.107702	0.432097	0.004410	-0.249057	-0.239258	-0.462226	0.068270	0.060056	0.006376	1.303251	1.299346
185	N14	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.277237	0.233038	1.751775	0.953441	-0.273117	0.331846	0.748761	-0.811080	0.063156	0.068226	0.005800	1.267836	1.265904
186	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.095181	0.227545	5.336984	2.506780	1.735353	0.467780	6.854463	0.863220	0.066506	0.081824	0.012264	1.288814	1.279548
187	N14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.183314	0.751252	0.431970	0.848450	-0.039304	-1.070931	5.177832	0.964215	0.093776	0.104193	0.021522	1.199779	1.202051
189	N15	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.416377	3.700395	0.642193	-0.092668	-0.633345	-0.170810	0.252751	-0.199210	0.050829	0.047054	0.003624	1.240444	1.237788
190	N15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	30.245847	21.529234	5.349308	64.693338	14.746920	30.666431	117.824464	7.263022	0.050375	0.037321	0.040562	1.140087	1.117315
191	N15	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.331661	1.098001	-0.855660	2.897613	0.955748	1.467469	3.229681	3.352373	0.049832	0.044872	0.002826	1.105230	1.090927
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%	10.787310	5.288362	15.672262	-0.294047	13.990019	3.784551	17.931199	24.122912	0.054648	0.044220	0.004621	1.208254	1.220508
206	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.265226	7.803610	-0.688526	11.384291	6.747107	11.673168	18.914516	18.722699	0.041116	0.054341	0.001969	1.271659	1.269782
207	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.392086	9.989697	18.132815	15.219042	17.228741	13.840410	6.765527	5.335436	0.057911	0.060846	0.005618	1.115665	1.104854
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%	12.063649	11.523535	102.024651	105.438376	2138.006818	2496.993893	7839.846835	10653.663619	nan	nan	nan	0.000000	0.000000
223	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.512086	2.247006	9.400847	1.076470	8.101264	1.414317	1.217091	15.876675	0.043279	0.044038	0.001983	0.951543	0.954456
224	N19	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.857327	18.707355	26.835752	26.771395	26.292419	26.905523	1.817139	1.692536	0.063329	0.066166	0.005418	1.163595	1.162775
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%	14.352558	13.695185	127.280149	142.004240	2956.952109	2956.805846	12893.387118	12894.356975	nan	nan	nan	0.000000	0.000000
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.271533	21.782337	46.754435	46.830274	29.694437	30.543689	9.775539	7.431802	0.060042	0.057182	-0.002246	0.000000	0.000000
321	N02	not_connected	100.00%	0.00%	62.37%	0.00%	100.00%	0.00%	8.819209	7.272714	14.471571	13.492644	14.268131	12.861934	18.486802	18.165547	0.564063	0.399042	0.401254	0.000000	0.000000
323	N02	not_connected	100.00%	2.69%	65.05%	0.00%	100.00%	0.00%	16.967040	10.563000	2.934323	17.582350	12.133582	17.583689	14.372550	0.542088	0.470403	0.361960	0.326698	0.000000	0.000000
324	N04	not_connected	100.00%	0.00%	62.37%	0.00%	100.00%	0.00%	12.046180	7.590555	18.548549	12.409612	17.390293	10.780187	1.870358	0.246200	0.562699	0.403914	0.404115	0.000000	0.000000
329	N12	dish_maintenance	100.00%	29.57%	70.43%	0.00%	100.00%	0.00%	3.166444	6.800810	9.228590	12.990027	3.240202	10.452619	7.007833	-0.043448	0.454753	0.333229	0.312641	0.000000	0.000000
333	N12	dish_maintenance	100.00%	29.57%	91.94%	0.00%	100.00%	0.00%	0.496021	6.385018	1.412527	12.039421	2.242549	9.943866	2.809613	0.227289	0.448753	0.294904	0.308681	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, 18, 21, 27, 28, 30, 31, 32, 33, 36, 37, 38, 40, 42, 45, 46, 50, 52, 54, 55, 56, 57, 67, 68, 69, 70, 71, 72, 73, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 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, 160, 161, 163, 164, 165, 166, 167, 168, 169, 170, 176, 177, 178, 179, 180, 181, 182, 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, 9, 10, 15, 16, 17, 19, 20, 29, 41, 51, 53, 65, 66, 99, 116, 157, 158, 162, 183]

golden_ants: [3, 9, 10, 15, 16, 17, 19, 20, 29, 41, 51, 53, 65, 66, 99, 116, 157, 158, 162, 183]

# 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_2459808.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