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.

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 os
import h5py
from copy import deepcopy
from IPython.display import display, HTML
from hera_notebook_templates.utils import status_colors

%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_'
# 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

# 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']
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}"')

JD = "2459763"
data_path = "/mnt/sn1/2459763"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H5C_Notebooks/_rtp_summary_"

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

Date: 7-2-2022

# 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/sn1/2459763/zen.2459763.25307.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 12 ant_metrics files matching glob /mnt/sn1/2459763/zen.2459763.?????.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 2 ant_metrics files matching glob /mnt/sn1/2459763/zen.2459763.?????.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
hd = io.HERAData(sorted(glob.glob(os.path.join(data_path, 'zen.*.sum.uvh5')))[0])
unused_ants = [ant for ant in hd.antpos if ant not in ants]    

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}

Build DataFrame

# build dataframe
to_show = {'Ant': [f'<a href="{ant_to_report_url(ant)}" target="_blank">{ant}</a>' for ant in ants],
           'Node': [nodes[ant] 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 1: 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)
0	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.398879	102.585524	74.638016	117.192818	51.375299	127.190875	132.051687	456.172092	0.018192	0.016914	0.001932	1.092473	1.082730
1	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.731898	68.626305	86.822950	89.462788	58.503687	58.967866	396.959581	314.786003	0.016306	0.016106	0.000105	1.062595	1.058662
2	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.289927	57.581225	72.628899	79.414020	45.817514	37.251923	167.592561	192.912052	0.017583	0.016342	0.000859	1.093156	1.085127
3	1	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.077730	-0.189577	0.899755	0.927046	0.036166	-0.287291	-0.047420	0.033840	0.709516	0.638688	0.456878	3.754149	3.653071
4	1	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.400911	0.943309	-0.842968	-0.644757	-1.719207	-0.414309	-1.393941	-0.610430	0.718071	0.634047	0.448815	3.951771	4.321756
5	1	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.649601	-0.591265	0.325866	0.435093	-0.642189	0.081373	0.174981	0.328411	0.726473	0.639050	0.448832	1.984662	1.849359
7	2	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.076232	-0.883671	0.051146	-0.149425	-0.725606	1.370690	0.622482	3.027021	0.734085	0.661912	0.458812	3.254074	3.442339
8	2	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.707420	2.134690	3.533957	4.367409	3.436660	2.979067	3.920940	2.820952	0.721899	0.638220	0.465117	2.946470	2.813472
9	2	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.074426	-0.251984	0.004132	-0.176200	1.299439	1.714915	5.108437	5.354688	0.723085	0.645270	0.464029	2.902242	2.737042
10	2	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.536736	-0.461712	0.536343	-0.524419	0.128314	1.553386	0.049418	0.667853	0.711070	0.631933	0.461425	2.704954	2.597813
11	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	136.686105	56.459037	95.273696	85.478465	31.888191	40.355090	189.065473	176.213216	0.017523	0.016903	0.000896	1.164327	1.172737
12	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	68.729486	37.682204	96.243713	80.944102	143.673879	44.400167	506.895692	263.158408	0.018187	0.017103	0.000601	1.092639	1.100414
13	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	45.773336	42.213763	81.005083	79.372602	47.891019	31.428917	278.936543	197.733737	0.018375	0.017868	0.000322	1.098240	1.088502
14	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	66.947915	61.227069	86.192837	78.776845	50.574641	24.398380	269.542865	151.080936	0.017821	0.016944	0.000541	1.087260	1.082776
15	1	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.022925	1.275321	0.753436	-0.230941	1.142821	1.203120	3.823507	3.983223	0.736026	0.645162	0.450135	2.097099	1.977780
16	1	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.634912	1.069962	0.080956	-0.539436	9.759171	8.444480	14.419254	12.688810	0.742403	0.662183	0.439533	5.010313	5.818039
17	1	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.343635	-0.275826	0.394681	0.199715	0.751266	-0.368474	0.121837	-0.617183	0.731914	0.663128	0.431394	2.124528	2.043207
18	1	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.819043	10.786353	1.403774	5.317988	3.460229	12.771280	8.179092	17.696348	0.727226	0.439357	0.488825	3.630981	2.053504
19	2	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.564228	0.163946	0.144764	0.084137	1.657039	85.610625	7.053334	51.971660	0.748560	0.675467	0.452459	3.617891	3.660399
20	2	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.285595	1.078396	1.324787	-0.430398	0.138252	-0.148677	-0.168168	-0.322819	0.738286	0.654044	0.457125	3.143749	2.864262
21	2	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.560730	1.884753	0.518647	14.696374	-1.327241	153.400096	-0.843234	90.541140	0.727329	0.641958	0.464559	3.037213	2.767037
23	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.106342	24.653405	76.054950	75.451715	45.365531	29.893932	188.782800	170.705279	0.017338	0.016468	0.000383	1.073624	1.073011
24	0	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.301103	65.529575	97.915308	99.928533	89.956982	130.636179	367.943810	388.073890	0.016904	0.016455	0.000641	1.072368	1.063497
25	0	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
26	0	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
27	1	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.269835	20.759068	39.807312	40.460613	9.665100	7.009284	7.377241	5.659195	0.051851	0.052483	0.004128	1.197245	1.221848
28	1	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	5.909998	27.677495	5.386643	23.078688	5.877757	3.439443	3.868541	5.974310	0.494224	0.234073	0.281897	11.310474	3.325899
29	1	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.447634	-0.504094	0.093023	-0.712144	0.059401	-0.149630	1.849201	2.090993	0.747560	0.668676	0.430391	1.933435	2.006891
30	1	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.767562	0.818638	1.085453	-0.545352	30.813217	2.435475	28.867181	6.268069	0.742733	0.681354	0.420646	4.173548	4.908061
31	2	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.674912	-0.302595	0.349401	-0.448570	-0.348672	2.035932	0.141260	2.490706	0.748256	0.677619	0.435261	1.691992	1.767058
32	2	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	27.865502	24.334403	3.875645	2.667170	2.017324	2.960136	1.398559	0.767415	0.645898	0.591680	0.227113	5.894643	4.993089
33	2	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.112159	4.683050	-0.255943	-0.484106	-1.296040	-0.037995	-0.868198	1.166711	0.734258	0.481640	0.539778	3.767285	2.236446
36	3	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.494450	6.443215	0.575854	3.073899	0.496300	0.895368	0.016579	0.536561	0.726734	0.637065	0.474796	3.298593	3.130504
37	3	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
38	3	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	491.471755	724.451085	inf	inf	2776.516747	4927.844467	10450.947105	19352.855679	nan	nan	nan	0.000000	0.000000
39	0	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
40	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.521135	0.211967	1.238175	0.797734	-1.512412	-0.608586	-1.079663	-0.650679	0.729921	0.657990	0.433805	2.296713	2.284907
41	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.736514	-0.849889	-0.834304	-0.705915	1.329555	-0.747607	0.292500	-0.740561	0.740338	0.668610	0.428897	2.271908	2.134362
42	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.672306	0.674070	-0.707273	-0.140140	1.627395	-0.168916	2.931203	1.672329	0.754511	0.685808	0.443659	2.169408	1.981905
45	5	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.122490	19.945566	38.959120	40.097767	9.641647	5.501698	6.797075	4.440408	0.028995	0.039100	0.008517	1.220931	1.476291
46	5	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.988199	-1.010980	-0.889984	-0.769801	-0.005433	-0.967224	-0.235398	-0.858251	0.720122	0.643850	0.459101	1.451517	1.506853
50	3	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.376958	0.894123	2.548228	-0.504677	19.788844	-0.057330	14.697560	1.738425	0.693230	0.626977	0.436821	4.267910	3.442403
51	3	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.598526	36.595974	-0.127220	48.831767	-0.557627	5.801282	-0.473952	5.869821	0.730945	0.043128	0.433516	3.471789	1.175111
52	3	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	6.291021	35.716880	0.844562	49.209772	5.916690	7.095348	5.709667	8.714054	0.712990	0.038629	0.400270	4.223307	1.154135
53	3	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.597139	1.159713	0.257466	0.029588	-1.473824	-0.614254	0.057071	1.488043	0.733798	0.663206	0.451985	3.678938	4.370059
54	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.592761	-0.810866	-0.808125	0.020068	-0.900169	0.278326	1.951592	2.089287	0.743519	0.663987	0.444205	2.041508	2.004020
55	4	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.292409	3.688112	17.314041	5.936239	1.439419	3.067348	3.663777	3.129756	0.731675	0.637897	0.446929	4.205730	4.798315
56	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.755211	0.997815	-0.534893	0.269186	-1.304995	0.243537	-0.944082	-0.088504	0.741031	0.672467	0.437045	2.121376	2.009042
57	4	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.008281	0.699305	3.381337	2.755729	14.828729	2.063228	10.794072	3.232638	0.749206	0.663993	0.453348	4.568617	5.175604
65	3	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.788150	1.365625	1.809682	1.966516	1.919273	0.666624	0.623025	0.119145	0.722530	0.626181	0.474606	1.814500	1.690193
66	3	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.123980	1.337984	2.989700	0.663355	0.611605	1.277523	1.067905	2.025946	0.721414	0.636103	0.462405	1.788261	1.654792
67	3	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.341913	2.067461	1.689611	7.450052	0.058515	2.226265	0.176515	1.589454	0.720752	0.648277	0.447896	3.108980	3.770100
68	3	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	917.812110	283.590478	inf	inf	4127.991908	1020.569498	12841.948801	3862.520689	nan	nan	nan	0.000000	0.000000
69	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.644259	-0.279450	-0.772910	-0.262809	0.005433	-0.548832	-0.162877	-0.682454	0.732603	0.668685	0.451576	2.086304	1.937971
70	4	RF_maintenance	100.00%	0.00%	0.00%	100.00%	100.00%	0.00%	4.384883	6.671589	2.043625	9.834270	5.511254	1.900382	2.785437	1.179957	0.246414	0.274699	-0.325935	3.644228	3.644296
71	4	digital_ok	0.00%	0.00%	0.00%	100.00%	100.00%	0.00%	0.581560	0.756157	1.035106	1.667624	3.596115	0.874407	2.323661	-0.154294	0.251082	0.269155	-0.331801	3.138502	3.095664
72	4	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	2.315578	-0.240733	0.977978	-0.712260	3.462885	0.295247	2.468575	-0.083641	0.733544	0.661505	0.435410	1.911667	1.672098
73	5	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.600987	1.375743	1.347512	1.932809	4.072675	1.978964	4.974471	3.739020	0.737879	0.649535	0.456464	3.667037	3.637233
81	7	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
82	7	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
83	7	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
84	8	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.473431	7.976042	0.332480	0.522307	0.514705	-0.765313	0.049037	-0.693492	0.036251	0.078731	0.001958	1.167574	1.175752
85	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.733189	21.161128	34.576276	35.283138	8.273629	4.478406	5.645966	3.686678	0.032985	0.031362	0.001459	1.130338	1.127618
86	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.211877	22.560031	35.564682	36.224281	8.643462	4.963058	5.623594	3.953099	0.030117	0.031071	0.002297	1.126363	1.114810
87	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.795658	3.806641	0.144786	4.419663	35.111086	3.384338	24.791486	3.381395	0.086472	0.091692	0.019709	1.172637	1.167254
88	9	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.918815	24.188424	35.636021	36.576724	8.684651	4.887403	6.945485	4.095553	0.041340	0.043163	-0.001037	1.164328	1.162212
89	9	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1121.476096	554.783701	inf	inf	-7229.572231	-3281.296514	-27939.170920	-12611.759756	nan	nan	nan	0.000000	0.000000
90	9	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.420080	-0.139365	3.469979	-0.777091	5.458853	-0.699397	5.460106	-0.269321	0.734876	0.654090	0.466506	3.981091	4.410823
91	9	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.000363	20.614017	35.654224	37.089918	8.389657	4.457094	6.276235	3.716226	0.040074	0.043832	0.000563	1.247827	1.784215
92	10	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
93	10	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
94	10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	682.060384	312.288229	inf	inf	4906.524660	1587.466314	17785.987909	5998.488013	nan	nan	nan	0.000000	0.000000
98	7	digital_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.195763	2.996332	5.154571	1.707381	5.359177	2.182673	2.731918	2.394453	0.702566	0.634692	0.450455	3.542807	4.009912
99	7	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.499849	0.311227	0.776663	0.081727	0.430312	2.971039	1.968039	3.377192	0.721885	0.643794	0.446984	1.942094	1.766001
100	7	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.857370	2.721477	5.967191	5.076129	4.300164	2.295777	1.887289	0.702985	0.710754	0.633499	0.458581	3.346061	3.488350
101	8	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.226079	8.786398	-0.223062	1.837646	-0.417357	2.607393	2.576515	3.473919	0.047150	0.067695	0.003659	1.189307	1.188835
102	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.225975	20.928769	35.170294	37.323258	8.318758	4.565628	5.491628	3.501257	0.034454	0.032890	0.000610	1.140352	1.131145
103	8	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.776072	8.748274	1.168254	1.252214	-0.481223	0.049114	1.739923	1.963707	0.065191	0.059044	0.009584	1.160023	1.164346
104	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.388851	64.832994	2.833328	37.166117	2.572799	12.055091	4.711110	14.050133	0.089319	0.080531	-0.013899	1.162439	1.142741
105	9	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.134442	25.243031	34.114889	35.536439	7.739140	3.859886	5.745398	3.399017	0.037693	0.042871	0.004757	1.260111	1.499842
106	9	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	274.976749	281.619815	inf	inf	1236.236140	1196.230168	4886.598546	4628.653901	nan	nan	nan	0.000000	0.000000
107	9	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.398663	19.159362	34.286884	35.167539	8.126911	4.256883	5.543647	3.448526	0.044601	0.046478	0.003885	1.217006	1.223106
108	9	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.141369	2.373804	0.742363	-0.250141	-1.718390	-1.030600	-1.172662	-0.976568	0.724696	0.645062	0.464034	1.426009	1.407408
109	10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.561883	-0.342832	0.165464	-0.828876	-0.457022	-1.013787	-0.485038	-0.912663	0.726508	0.644783	0.467122	1.690990	1.499195
110	10	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	39.805504	4.272012	5.091909	6.408693	2.383891	4.008354	3.597530	3.032042	0.633055	0.618880	0.365580	6.510862	6.352845
111	10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.118838	0.223276	-0.068734	-0.768835	-1.226327	-0.369729	-0.922803	-0.022510	0.720416	0.630972	0.484139	1.670751	1.481633
112	10	RF_maintenance	100.00%	0.00%	0.00%	100.00%	100.00%	0.00%	-0.494754	0.571577	0.373856	-0.396834	0.392594	4.830233	0.551956	2.676860	0.209224	0.217387	-0.325030	2.601677	2.233583
116	7	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.845987	3.491760	2.250059	5.876867	2.833082	3.329456	3.527385	3.427024	0.710728	0.617038	0.450907	4.461945	5.384105
117	7	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.552727	0.441943	1.728116	-0.368824	-0.261666	-0.569817	-0.669686	-0.700742	0.733026	0.653672	0.457646	1.913288	1.597389
118	7	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.958415	1.299378	0.547062	-0.020068	0.648598	0.778877	0.037809	0.062045	0.741097	0.655877	0.459927	2.107433	1.758125
119	7	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.906398	1.238167	2.799455	1.882721	1.751699	0.052385	0.596180	-0.478564	0.731307	0.651376	0.474337	3.761239	4.193003
120	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.753497	32.553931	4.520460	44.206024	2.804372	4.515101	1.504546	4.345770	0.090556	0.036027	0.049617	1.204311	1.167839
121	8	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.576325	7.275471	0.918479	7.273353	0.309044	0.934329	10.921503	9.738704	0.055899	0.035532	0.006149	1.198464	1.204089
122	8	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.131018	6.617964	2.380045	0.562639	-1.481960	-0.305671	-1.003727	-0.370081	0.074361	0.048203	0.011363	1.179279	1.175495
123	8	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.878247	5.376993	0.033334	1.515054	-1.141159	0.493128	-1.065500	-0.130079	0.080709	0.087119	0.015203	1.212860	1.217491
124	9	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	506.331127	368.492127	inf	inf	2491.370765	-1483.162202	8946.860435	-4995.184171	nan	nan	nan	0.000000	0.000000
125	9	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	9	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	274.793782	272.549371	inf	inf	1082.236171	1082.207565	4107.157852	4067.081317	nan	nan	nan	0.000000	0.000000
127	10	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.671719	1.128776	1.978553	1.148316	5.169349	2.000063	5.745585	3.674417	0.720152	0.639840	0.467945	4.295824	4.945859
128	10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.199729	2.401200	-0.082761	-0.637608	-1.106919	0.464018	-0.811724	0.022510	0.716992	0.624766	0.469988	1.853715	1.613085
129	10	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.266278	-0.297070	5.764254	0.673663	2.648771	0.240870	3.093998	1.314123	0.707424	0.625987	0.467536	4.874062	4.694506
130	10	digital_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.199115	5.024347	39.698170	4.380252	8.749121	0.582383	6.039234	-0.032781	0.047696	0.361881	0.188207	1.303387	5.245086
135	12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.846725	-0.307690	-0.617083	-0.843005	-0.498299	0.284268	2.444576	2.747215	0.717805	0.630627	0.455852	1.895112	1.897121
136	12	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.122054	7.390722	-0.485952	0.644691	0.143830	4.572390	-0.050951	2.554454	0.729492	0.619446	0.429929	5.226004	6.115980
137	7	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	9.618597	21.889488	4.915890	35.905878	26.874632	4.816486	17.813942	4.274940	0.481483	0.053818	0.307401	7.530495	1.813459
138	7	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	19.866293	2.662759	33.419444	1.031144	8.100971	-0.635232	5.665470	-0.656905	0.046052	0.649925	0.448896	1.242943	3.683939
140	13	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.695251	0.970670	0.194553	0.076274	-0.682358	-0.508445	-0.379803	-0.504508	0.093488	0.067566	0.015045	1.200347	1.202011
141	13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.990111	3.962682	-0.688829	25.827843	2.479405	3.769944	3.837214	9.893278	0.085111	0.076124	0.015971	1.248140	1.238357
142	13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.682112	21.392780	23.533636	40.777328	2.153949	5.502089	2.966278	5.419771	0.098564	0.037522	0.038973	1.256536	1.234301
143	14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.449640	-0.828979	-0.606524	-0.435149	-1.092940	1.428551	-0.833741	0.353185	0.714996	0.643806	0.477554	1.472063	1.358626
144	14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.510648	3.391756	5.046880	10.486436	1.116268	28.426773	0.942055	16.951392	0.722816	0.637111	0.483717	8.426824	7.112143
145	14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.157220	20.653082	40.034860	40.977720	9.781826	5.625535	9.895470	7.189077	0.035408	0.036975	-0.000787	1.420154	1.376780
150	15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.893706	22.984379	39.951475	41.726137	9.117758	5.546235	7.205946	5.215657	0.044100	0.045059	0.001856	1.255551	1.215372
155	12	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.848884	17.347594	39.197999	40.309582	9.514082	5.680970	6.620899	6.032103	0.050611	0.044805	0.003568	1.585410	1.585789
156	12	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.623597	1.611728	-0.346240	0.281949	-0.775794	-0.258522	-0.328138	2.125520	0.724317	0.619183	0.458658	6.132328	4.907184
157	12	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.432834	-0.436039	1.760980	-0.253548	0.446386	-0.495904	-0.204672	-0.679296	0.721564	0.637911	0.466183	5.280895	4.997834
158	12	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.358664	-1.340895	-0.685627	-0.638513	2.615601	0.494372	3.921937	2.794143	0.716838	0.633438	0.468714	6.865274	6.078206
160	13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.095300	17.899589	39.473803	40.438784	8.962199	4.659232	6.127920	3.463781	0.040803	0.040149	0.003104	1.314035	1.294256
161	13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.143001	43.934950	2.628559	11.904933	0.570193	3.441647	0.326950	2.097539	0.046536	0.034259	0.000672	1.298558	1.302536
162	13	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.339935	-0.614182	0.534391	-0.660441	1.287491	0.121098	0.464514	-0.205432	0.057234	0.044943	0.001910	1.286064	1.277448
163	14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.024650	-0.110881	0.088444	-0.743893	-0.708339	0.595798	-0.616616	0.478605	0.696960	0.615033	0.474897	0.829390	0.753193
164	14	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.289627	-0.127523	-0.550660	-0.072343	1.021717	18.162924	2.190023	14.623268	0.699129	0.613238	0.470317	5.272867	5.613164
165	14	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
166	14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	379.998179	885.905701	inf	inf	2118.850845	6327.763122	8579.735866	25331.367619	nan	nan	nan	0.000000	0.000000
167	15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.338129	14.966346	3.225452	3.781184	2.197548	4.020813	0.812722	3.643127	0.571034	0.446839	0.244551	3.597764	2.784045
168	15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	707.737884	430.793868	inf	inf	5669.522737	2630.815612	23421.046247	9825.946052	nan	nan	nan	0.000000	0.000000
169	15	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	678.105792	1381.976130	inf	inf	5259.301711	5849.628642	12617.390090	15791.956782	nan	nan	nan	0.000000	0.000000
170	15	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
176	12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.584541	-0.909133	0.251297	-0.491467	-1.227884	-0.454303	-1.078115	-0.616954	0.703532	0.610789	0.470378	1.285760	1.139792
177	12	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.532022	0.911324	-0.340059	1.969166	-0.777974	9.567708	1.818682	7.950092	0.714616	0.604425	0.472883	7.377069	8.223071
178	12	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.022925	-1.111494	2.079546	-0.842858	-0.822065	-0.875558	-0.529636	-0.909748	0.711491	0.619890	0.471610	1.252488	1.124675
179	12	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
180	13	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.491420	21.843830	1.632746	41.023186	4.715885	6.815050	15.402357	7.381676	0.094135	0.038578	0.038813	1.341512	1.306221
181	13	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.674140	1.371870	7.456582	1.777904	5.457585	0.438525	4.107813	1.559539	0.034784	0.054566	0.005577	1.323116	1.297439
182	13	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.603520	8.923714	3.862314	8.145834	4.627796	383.849254	3.271791	236.948983	0.077968	0.070312	0.028829	1.317257	1.291472
183	13	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.777921	1.247901	1.686123	2.220488	1.096726	-0.616450	0.438641	0.072032	0.067208	0.063611	0.008865	1.298404	1.311406
184	14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
185	14	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
186	14	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	487.129325	1418.354422	inf	inf	2053.013459	9116.354728	3494.837199	20294.454835	nan	nan	nan	0.000000	0.000000
187	14	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
189	15	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.932595	1.650551	-0.736030	-0.057705	-1.439667	1.192740	-1.119820	0.705418	0.691505	0.591532	0.478226	1.892719	1.361320
190	15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	47.554616	31.547414	6.523752	6.238113	11.712400	2.863847	34.398199	9.554307	0.537680	0.478836	0.239544	4.122017	3.700531
191	15	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.167386	-0.746457	1.096847	-0.870617	0.851769	0.707383	0.239649	1.739375	0.680593	0.590862	0.489264	4.039124	3.913260
220	18	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	18	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	18	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
320	3	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.717822	21.619271	30.994200	31.303591	10.657743	6.708437	9.035419	6.199318	0.060654	0.054420	-0.000182	0.000000	0.000000
321	2	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.485283	1.048142	2.293712	2.722416	1.130969	1.905509	4.553401	4.418261	0.613568	0.503528	0.473257	0.000000	0.000000
323	2	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.557468	3.554740	3.213286	5.903742	2.447038	1.761006	4.412357	1.140239	0.503052	0.490855	0.359495	0.000000	0.000000
324	4	not_connected	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.408805	4.814138	6.691758	7.017920	6.073098	3.590874	3.744647	2.241717	0.583364	0.466605	0.457019	0.000000	0.000000
329	12	dish_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	278.280352	271.647419	inf	inf	1217.131904	-1120.627583	4488.600163	-4011.958695	nan	nan	nan	0.000000	0.000000
333	12	dish_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

# 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] > .1 for col in bar_cols.values()])]
print('ex_ants: [' + ", ".join(str(ant) for ant in proposed_ex_ants) + ']')

ex_ants: [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 30, 32, 33, 36, 37, 38, 39, 45, 50, 51, 52, 53, 55, 57, 67, 68, 70, 71, 73, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 98, 100, 101, 102, 103, 104, 105, 106, 107, 110, 112, 116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 129, 130, 136, 137, 138, 140, 141, 142, 144, 145, 150, 155, 156, 157, 158, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 177, 179, 180, 181, 182, 183, 184, 185, 186, 187, 190, 191, 220, 221, 222, 320, 321, 323, 324, 329, 333]

# write to csv
outpath = os.path.join(nb_outdir, f'rtp_summary_table_{JD}.csv')
print(f'Now saving Table 1 to a csv at {outpath}')
df.to_csv(outpath)

Now saving Table 1 to a csv at /home/obs/src/H5C_Notebooks/_rtp_summary_/rtp_summary_table_2459763.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)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_11179/3583557015.py in <module>
      1 # Load antenna positions
      2 data_list = sorted(glob.glob(os.path.join(data_path, f'zen.{JD}.?????.sum.uvh5')))
----> 3 hd = io.HERAData(data_list[len(data_list) // 2])
      4 
      5 # Figure out where to draw the nodes

~/anaconda/envs/RTP/lib/python3.7/site-packages/hera_cal/io.py in __init__(self, input_data, upsample, downsample, filetype, **read_kwargs)
    495             temp_paths = copy.deepcopy(self.filepaths)
    496             self.filepaths = self.filepaths[0]
--> 497             self.read(read_data=False, **read_kwargs)
    498             self.filepaths = temp_paths
    499 

~/anaconda/envs/RTP/lib/python3.7/site-packages/hera_cal/io.py in read(self, bls, polarizations, times, time_range, lsts, lst_range, frequencies, freq_chans, axis, read_data, return_data, run_check, check_extra, run_check_acceptability, **kwargs)
    762         # process data into DataContainers
    763         if read_data or self.filetype in ['uvh5', 'uvfits']:
--> 764             self._determine_blt_slicing()
    765             self._determine_pol_indexing()
    766         if read_data and return_data:

~/anaconda/envs/RTP/lib/python3.7/site-packages/hera_cal/io.py in _determine_blt_slicing(self)
    569     def _determine_blt_slicing(self):
    570         '''Determine the mapping between antenna pairs and slices of the blt axis of the data_array.'''
--> 571         self._blt_slices = get_blt_slices(self)
    572 
    573     def _determine_pol_indexing(self):

~/anaconda/envs/RTP/lib/python3.7/site-packages/hera_cal/io.py in get_blt_slices(uvo, tried_to_reorder)
    414         elif not (len(set(np.ediff1d(indices))) == 1):  # checks if the consecutive differences are all the same
    415             if not tried_to_reorder:
--> 416                 uvo.reorder_blts(order='time')
    417                 return get_blt_slices(uvo, tried_to_reorder=True)
    418             else:

~/anaconda/envs/RTP/lib/python3.7/site-packages/pyuvdata/uvdata/uvdata.py in reorder_blts(self, order, minor_order, conj_convention, uvw_tol, conj_convention_use_enu, run_check, check_extra, run_check_acceptability, strict_uvw_antpos_check)
   4117                 check_extra=check_extra,
   4118                 run_check_acceptability=run_check_acceptability,
-> 4119                 strict_uvw_antpos_check=strict_uvw_antpos_check,
   4120             )
   4121 

~/anaconda/envs/RTP/lib/python3.7/site-packages/pyuvdata/uvdata/uvdata.py in check(self, check_extra, run_check_acceptability, check_freq_spacing, strict_uvw_antpos_check)
   2476         if self.Ntimes != len(np.unique(self.time_array)):
   2477             raise ValueError(
-> 2478                 "Ntimes must be equal to the number of unique "
   2479                 "times in the time_array"
   2480             )

ValueError: Ntimes must be equal to the number of unique times in the time_array

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)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipykernel_11179/3620142779.py in <module>
      1 core_ants = [ant for ant in ants if ant < 320]
      2 outrigger_ants = [ant for ant in ants if ant >= 320]
----> 3 Plot_Array(ants=core_ants, unused_ants=unused_ants, outriggers=False)
      4 if len(outrigger_ants) > 0:
      5     Plot_Array(ants=outrigger_ants, unused_ants=sorted(set(unused_ants + core_ants)), outriggers=True)

/tmp/ipykernel_11179/1497970787.py in Plot_Array(ants, unused_ants, outriggers)
      7     # connect every antenna to their node
      8     for ant in ants:
----> 9         if nodes[ant] in node_centers:
     10             plt.plot([hd.antpos[ant][0], node_centers[nodes[ant]][0]], 
     11                      [hd.antpos[ant][1], node_centers[nodes[ant]][1]], 'k', zorder=0)

NameError: name 'node_centers' is not defined

Metadata

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

2.0.2
3.1.1.dev2+g1b5039f