import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import h5py
import hdf5plugin  # REQUIRED to have the compression plugins available
import numpy as np
import pandas as pd
import glob
import os
import matplotlib.pyplot as plt
from hera_cal import io, utils
from hera_qm import ant_class
from uvtools.plot import plot_antpos, plot_antclass
%matplotlib inline
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
_ = np.seterr(all='ignore')  # get rid of red warnings
%config InlineBackend.figure_format = 'retina'

# Parse settings from environment
ANT_CLASS_FOLDER = os.environ.get("ANT_CLASS_FOLDER", "./")
SUM_FILE = os.environ.get("SUM_FILE", None)
# ANT_CLASS_FOLDER = "/mnt/sn1/2460330"
# SUM_FILE = "/mnt/sn1/2460330/zen.2460330.25463.sum.uvh5"
OC_SKIP_OUTRIGGERS = os.environ.get("OC_SKIP_OUTRIGGERS", "TRUE").upper() == "TRUE"

for param in ['ANT_CLASS_FOLDER', 'SUM_FILE', 'OC_SKIP_OUTRIGGERS']:
    print(f"{param} = '{eval(param)}'")

ANT_CLASS_FOLDER = '/mnt/sn1/data2/2460428'
SUM_FILE = '/mnt/sn1/data2/2460428/zen.2460428.37690.sum.uvh5'
OC_SKIP_OUTRIGGERS = 'True'

if SUM_FILE is not None:
    from astropy.time import Time, TimeDelta
    utc = Time(float(SUM_FILE.split('zen.')[-1].split('.sum.uvh5')[0]), format='jd').datetime
    print(f'Date: {utc.month}-{utc.day}-{utc.year}')

Date: 4-27-2024

# set thresholds for fraction of the day
overall_thresh = .1
all_zero_thresh = .1
eo_zeros_thresh = .1
xengine_diff_thresh = .1
cross_pol_thresh = .5
bad_fem_thresh = .1
high_power_thresh = .1
low_power_thresh = .1
low_corr_thresh = .1
bad_shape_thresh = .5
excess_rfi_thresh = .1
chisq_thresh = .25

# Load csvs
csv_files = sorted(glob.glob(os.path.join(ANT_CLASS_FOLDER, '*.ant_class.csv')))
jds = [float(f.split('/')[-1].split('zen.')[-1].split('.sum')[0]) for f in csv_files]
tables = [pd.read_csv(f).dropna(axis=0, how='all') for f in csv_files]
table_cols = tables[0].columns[1::2]
class_cols = tables[0].columns[2::2]
print(f'Found {len(csv_files)} csv files starting with {csv_files[0]}')

Found 1851 csv files starting with /mnt/sn1/data2/2460428/zen.2460428.16886.sum.ant_class.csv

# parse ant_strings
ap_strs = np.array(tables[0]['Antenna'])
ants = sorted(set(int(a[:-1]) for a in ap_strs))
translator = ''.maketrans('e', 'n') | ''.maketrans('n', 'e')

# get node numbers
node_dict = {ant: 'Unknown' for ant in ants}
try:
    from hera_mc import cm_hookup
    hookup = cm_hookup.get_hookup('default')
    for ant_name in hookup:
        ant = int("".join(filter(str.isdigit, ant_name)))
        if ant in node_dict:
            if hookup[ant_name].get_part_from_type('node')['E<ground'] is not None:
                node_dict[ant] = int(hookup[ant_name].get_part_from_type('node')['E<ground'][1:])
except:
    pass
nodes = sorted(set(node_dict.values()))

def classification_array(col):
    class_array = np.vstack([t[col] for t in tables])
    class_array[class_array == 'good'] = 1.7
    class_array[class_array == 'suspect'] = 1
    class_array[class_array == 'bad'] = 0
    return class_array.astype(float)

if SUM_FILE is not None:
    hd = io.HERADataFastReader(SUM_FILE)
    ap_tuples = [(int(ap[:-1]), {'e': 'Jee', 'n': 'Jnn'}[ap[-1]]) for ap in ap_strs]
    bad_bools = np.mean(classification_array('Antenna Class') == 0, axis=0) > overall_thresh
    bad_aps = [ap_tuples[i] for i in np.arange(len(ap_tuples))[bad_bools]]
    suspect_bools = np.mean(classification_array('Antenna Class') == 1, axis=0) > overall_thresh
    suspect_aps = [ap_tuples[i] for i in np.arange(len(ap_tuples))[suspect_bools] if ap_tuples[i] not in bad_aps]
    good_aps = [ap for ap in ap_tuples if ap not in bad_aps and ap not in suspect_aps]
    overall_class = ant_class.AntennaClassification(bad=bad_aps, suspect=suspect_aps, good=good_aps)
    autos, _, _ = hd.read(bls=[bl for bl in hd.bls if utils.split_bl(bl)[0] == utils.split_bl(bl)[1]], read_flags=False, read_nsamples=False)
    avg_unflagged_auto = {}
    for pol in ['ee', 'nn']:
        unflagged_autos = [autos[bl] for bl in autos if bl[2] == pol and overall_class[utils.split_bl(bl)[0]] != 'bad']
        if len(unflagged_autos) > 0:
            avg_unflagged_auto[pol] = np.mean(unflagged_autos, axis=(0, 1))
        else:
            avg_unflagged_auto[pol] = np.zeros(len(hd.freqs), dtype=complex)

def print_issue_summary(bad_ant_strs, title, notes='', plot=False):
    '''Print report for list of bad antenna polarizations strings'''
    unique_bad_antnums = [int(ap[:-1]) for ap in bad_ant_strs]
    display(HTML(f'<h2>{title}: ({len(bad_ant_strs)} antpols across {len(set([ba[:-1] for ba in bad_ant_strs]))} antennas)</h2>'))
    if len(notes) > 0:
        display(HTML(f'<h4>{notes}</h4>'))
    if len(bad_ant_strs) > 0:
        print(f'All Bad Antpols: {", ".join(bad_ant_strs)}\n')
    for node in nodes:
        if np.any([node == node_dict[a] for a in unique_bad_antnums]):
            aps = [ap for ap in bad_ant_strs if node_dict[int(ap[:-1])] == node]
            whole_ants = [str(wa) for wa in set([int(ap[:-1]) for ap in aps if ap.translate(translator) in bad_ant_strs])]
            single_pols =  [ap for ap in aps if ap.translate(translator) not in bad_ant_strs]
            print(f'Node {node}:')
            print(f'\tAntpols ({len(aps)} total): {", ".join(aps)}')
            print(f'\tWhole Ants ({len(whole_ants)} total): {", ".join(whole_ants)}')
            print(f'\tSingle Pols ({len(single_pols)} total): {", ".join(single_pols)}')
            if plot and SUM_FILE is not None:
                fig, axes = plt.subplots(1, 2, figsize=(12,4), dpi=70, sharey=True, gridspec_kw={'wspace': 0})
                for ax, pol in zip(axes, ['ee', 'nn']):                    
                    ax.semilogy(autos.freqs / 1e6, avg_unflagged_auto[pol], 'k--', label='Average\nUnflagged\nAuto')
                    for ap in aps:
                        ant = int(ap[:-1]), utils.comply_pol(ap[-1])
                        auto_bl = utils.join_bl(ant, ant)
                        if auto_bl[2] == pol:
                            ax.semilogy(autos.freqs / 1e6, np.mean(autos[auto_bl], axis=0), label=ap)
                    ax.legend()
                    ax.set_xlim([40, 299])
                    ax.set_title(f'{title} on Node {node} ({pol}-antennas)')
                    ax.set_xlabel('Frequency (MHz)')
                axes[0].set_ylabel('Single File Raw Autocorrelation')
                plt.tight_layout()
                plt.show()

# precompute various helpful quantities
all_slopes = np.vstack([t['Autocorr Slope'] for t in tables])
median_slope = np.median(all_slopes)
bad_slopes = np.vstack([t['Autocorr Slope Class'] for t in tables]) == 'bad'
suspect_slopes = np.vstack([t['Autocorr Slope Class'] for t in tables]) == 'suspect'
bad_shapes = np.vstack([t['Autocorr Shape Class'] for t in tables]) == 'bad'
suspect_shapes = np.vstack([t['Autocorr Shape Class'] for t in tables]) == 'suspect'
all_powers = np.vstack([t['Autocorr Power'] for t in tables])
median_power = np.median(all_powers)
bad_powers = np.vstack([t['Autocorr Power Class'] for t in tables]) == 'bad'
suspect_powers = np.vstack([t['Autocorr Power Class'] for t in tables]) == 'suspect'
bad_rfi = np.vstack([t['RFI in Autos Class'] for t in tables]) == 'bad'
suspect_rfi = np.vstack([t['RFI in Autos Class'] for t in tables]) == 'suspect'

# find all zeros
all_zeros_strs = ap_strs[np.mean(np.vstack([t['Dead? Class'] for t in tables]) == 'bad', axis=0) > all_zero_thresh]

# find even/odd zeros
eo_zeros_strs = ap_strs[np.mean(np.vstack([t['Even/Odd Zeros Class'] for t in tables]) == 'bad', axis=0) > eo_zeros_thresh]
eo_zeros_strs = [ap for ap in eo_zeros_strs if ap not in all_zeros_strs]

# find cross-polarized antennas
cross_pol_strs = ap_strs[np.mean(np.vstack([t['Cross-Polarized Class'] for t in tables]) == 'bad', axis=0) > cross_pol_thresh]
cross_pol_strs = [ap for ap in cross_pol_strs if ap not in all_zeros_strs]

# find FEM power issues: must be low power, high slope, and bad or suspect in power, slope, rfi, and shape
fem_off_prod = (bad_powers + .5 * suspect_powers) * (bad_slopes + .5 * suspect_slopes)
fem_off_prod *= (bad_rfi + .5 * suspect_rfi) * (bad_shapes + .5 * suspect_shapes)
fem_off_strs = ap_strs[np.mean(fem_off_prod * (all_powers < median_power) * (all_slopes > median_slope), axis=0) > .1]

# find high power issues
high_power_strs = ap_strs[np.mean(bad_powers & (all_powers > median_power), axis=0) > high_power_thresh]

# find other low power issues
low_power_strs = ap_strs[np.mean(bad_powers & (all_powers < median_power), axis=0) > low_power_thresh]
low_power_strs = [ap for ap in low_power_strs if ap not in all_zeros_strs and ap not in fem_off_strs]

# find low correlation (but not low power)
low_corr_strs = ap_strs[np.mean(np.vstack([t['Low Correlation Class'] for t in tables]) == 'bad', axis=0) > low_corr_thresh]
low_corr_strs = [ap for ap in low_corr_strs if ap not in (set(low_power_strs) | set(all_zeros_strs) | set(fem_off_strs))]

# find bad bandpasses
bad_bandpass_strs = ap_strs[np.mean(bad_shapes, axis=0) > bad_shape_thresh]
bad_bandpass_strs = [ap for ap in bad_bandpass_strs if ap not in (set(low_power_strs) | set(all_zeros_strs) | set(high_power_strs) | set(fem_off_strs))]

# find antennas with excess RFI
excess_rfi_strs = ap_strs[np.mean(np.vstack([t['RFI in Autos Class'] for t in tables]) == 'bad', axis=0) > excess_rfi_thresh]
excess_rfi_strs = [ap for ap in excess_rfi_strs if ap not in (set(low_power_strs) | set(all_zeros_strs) |  set(fem_off_strs) |
                                                              set(bad_bandpass_strs) | set(high_power_strs))]

# find bad x-engine diffs
xengine_diff_strs = ap_strs[np.mean(np.vstack([t['Bad Diff X-Engines Class'] for t in tables]) == 'bad', axis=0) > xengine_diff_thresh]
xengine_diff_strs = [ap for ap in xengine_diff_strs if ap not in (set(bad_bandpass_strs) | set(low_power_strs) | set(excess_rfi_strs) | set(low_corr_strs) |
                                                                  set(all_zeros_strs) | set(high_power_strs) | set(fem_off_strs) | set(eo_zeros_strs))]

# find antennas with high redcal chi^2
chisq_strs = ap_strs[np.mean(np.vstack([t['Redcal chi^2 Class'] for t in tables]) == 'bad', axis=0) > chisq_thresh]
chisq_strs = [ap for ap in chisq_strs if ap not in (set(bad_bandpass_strs) | set(low_power_strs) | set(excess_rfi_strs) | set(low_corr_strs) |
                                                    set(all_zeros_strs) | set(high_power_strs) | set(fem_off_strs) | set(eo_zeros_strs) | set(xengine_diff_strs))]
if OC_SKIP_OUTRIGGERS:
    chisq_strs = [ap for ap in chisq_strs if int(ap[:-1]) < 320]

# collect all results
to_print = [(all_zeros_strs, 'All-Zeros', 'These antennas have visibilities that are more than half zeros.'),
            (eo_zeros_strs, 'Excess Zeros in Either Even or Odd Spectra', 
             'These antennas are showing evidence of packet loss or X-engine failure.', True),
            (xengine_diff_strs, 'Excess Power in X-Engine Diffs', 
             'These antennas are showing evidence of mis-written packets in either the evens or the odds.', True),            
            (cross_pol_strs, 'Cross-Polarized', 'These antennas have their east and north cables swapped.'),
            (fem_off_strs, 'Likely FEM Power Issue', 'These antennas have low power, anomolously high slopes, and extra channels identified as RFI.', True),
            (high_power_strs, 'High Power', 'These antennas have high median power.', True),
            (low_power_strs, 'Other Low Power Issues', 'These antennas have low power, but are not all-zeros and not FEM off.', True),
            (low_corr_strs, 'Low Correlation, But Not Low Power', 'These antennas are low correlation, but their autocorrelation power levels look OK.'),
            (bad_bandpass_strs, 'Bad Bandpass Shapes, But Not Bad Power', 
             'These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.', True),
            (excess_rfi_strs, 'Excess RFI', 'These antennas have excess strucutre (identified as possible RFI) in their bandpassed relative to the ' + \
             'median antenna, but not low or high power or a bad bandpass.', True),
            (chisq_strs, 'Redcal chi^2', 'These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.')]

def print_high_level_summary():
    for tp in sorted(to_print, key=lambda x: len(x[0]), reverse=True):
        print(f'{len(tp[0])} antpols (on {len(set([ap[:-1] for ap in tp[0]]))} antennas) frequently flagged for {tp[1]}.')
        
def print_all_issue_summaries():
    for tp in to_print:
        print_issue_summary(*tp)

print_high_level_summary()

115 antpols (on 93 antennas) frequently flagged for Redcal chi^2.
55 antpols (on 45 antennas) frequently flagged for Excess Power in X-Engine Diffs.
44 antpols (on 22 antennas) frequently flagged for All-Zeros.
25 antpols (on 22 antennas) frequently flagged for Likely FEM Power Issue.
14 antpols (on 12 antennas) frequently flagged for Low Correlation, But Not Low Power.
9 antpols (on 8 antennas) frequently flagged for Excess RFI.
8 antpols (on 6 antennas) frequently flagged for High Power.
7 antpols (on 7 antennas) frequently flagged for Bad Bandpass Shapes, But Not Bad Power.
6 antpols (on 6 antennas) frequently flagged for Other Low Power Issues.
4 antpols (on 2 antennas) frequently flagged for Cross-Polarized.
0 antpols (on 0 antennas) frequently flagged for Excess Zeros in Either Even or Odd Spectra.

print_all_issue_summaries()

All Bad Antpols: 47e, 47n, 61e, 61n, 63e, 63n, 64e, 64n, 77e, 77n, 78e, 78n, 88e, 88n, 90e, 90n, 91e, 91n, 105e, 105n, 107e, 107n, 108e, 108n, 176e, 176n, 177e, 177n, 178e, 178n, 241e, 241n, 242e, 242n, 243e, 243n, 246e, 246n, 261e, 261n, 262e, 262n, 272e, 272n

Node 6:
	Antpols (12 total): 47e, 47n, 61e, 61n, 63e, 63n, 64e, 64n, 77e, 77n, 78e, 78n
	Whole Ants (6 total): 64, 77, 78, 47, 61, 63
	Single Pols (0 total): 
Node 9:
	Antpols (12 total): 88e, 88n, 90e, 90n, 91e, 91n, 105e, 105n, 107e, 107n, 108e, 108n
	Whole Ants (6 total): 105, 107, 108, 88, 90, 91
	Single Pols (0 total): 
Node 12:
	Antpols (6 total): 176e, 176n, 177e, 177n, 178e, 178n
	Whole Ants (3 total): 176, 177, 178
	Single Pols (0 total): 
Node 19:
	Antpols (6 total): 241e, 241n, 242e, 242n, 243e, 243n
	Whole Ants (3 total): 241, 242, 243
	Single Pols (0 total): 
Node 20:
	Antpols (6 total): 246e, 246n, 261e, 261n, 262e, 262n
	Whole Ants (3 total): 261, 246, 262
	Single Pols (0 total): 
Node 23:
	Antpols (2 total): 272e, 272n
	Whole Ants (1 total): 272
	Single Pols (0 total):

All Bad Antpols: 9e, 20e, 21e, 28n, 29n, 37e, 37n, 41n, 42n, 51n, 67n, 68e, 81e, 81n, 82e, 82n, 83e, 83n, 93e, 96n, 98e, 98n, 99n, 100e, 100n, 111e, 116e, 116n, 119e, 130e, 131n, 137n, 142n, 145n, 153n, 175n, 181n, 182e, 183n, 188n, 195n, 208e, 208n, 209e, 209n, 212n, 244n, 245n, 251n, 253n, 256n, 268n, 295e, 295n, 320n

Node 1:
	Antpols (2 total): 28n, 29n
	Whole Ants (0 total): 
	Single Pols (2 total): 28n, 29n

Casting complex values to real discards the imaginary part
Casting complex values to real discards the imaginary part
Data has no positive values, and therefore cannot be log-scaled.

Node 2:
	Antpols (3 total): 9e, 20e, 21e
	Whole Ants (0 total): 
	Single Pols (3 total): 9e, 20e, 21e

Node 3:
	Antpols (6 total): 37e, 37n, 51n, 67n, 68e, 320n
	Whole Ants (1 total): 37
	Single Pols (4 total): 51n, 67n, 68e, 320n

Node 4:
	Antpols (2 total): 41n, 42n
	Whole Ants (0 total): 
	Single Pols (2 total): 41n, 42n

Node 7:
	Antpols (15 total): 81e, 81n, 82e, 82n, 83e, 83n, 98e, 98n, 99n, 100e, 100n, 116e, 116n, 119e, 137n
	Whole Ants (6 total): 98, 100, 81, 82, 83, 116
	Single Pols (3 total): 99n, 119e, 137n

Node 10:
	Antpols (3 total): 93e, 111e, 130e
	Whole Ants (0 total): 
	Single Pols (3 total): 93e, 111e, 130e

Node 11:
	Antpols (2 total): 96n, 131n
	Whole Ants (0 total): 
	Single Pols (2 total): 96n, 131n

Node 13:
	Antpols (4 total): 142n, 181n, 182e, 183n
	Whole Ants (0 total): 
	Single Pols (4 total): 142n, 181n, 182e, 183n

def classification_plot(col):
    class_array = classification_array(col)
    plt.figure(figsize=(12, len(ants) / 10), dpi=100)
    plt.imshow(class_array.T, aspect='auto', interpolation='none', cmap='RdYlGn', vmin=0, vmax=2,
               extent=[jds[0] - np.floor(jds[0]), jds[-1] - np.floor(jds[0]), len(ants), 0])
    plt.xlabel(f'JD - {int(jds[0])}')
    plt.yticks(ticks=np.arange(.5, len(ants)+.5), labels=[ant for ant in ants], fontsize=6)
    plt.ylabel('Antenna Number (East First, Then North)')
    plt.gca().tick_params(right=True, top=True, labelright=True, labeltop=True)
    plt.tight_layout()
    plt.title(f'{col}: Green is "good", Yellow is "suspect", Red is "bad"')

classification_plot('Antenna Class')

Antenna Classification Daily Summary¶

• Summary of Per Antenna Issues ¶

• Figure 1: Per File Overall Antenna Classification Summary ¶

• Figure 2: Per Classifier Antenna Flagging Summary ¶

• Figure 3: Array Visualization of Overall Daily Classification ¶

Settings¶

Load classifications and other metadata¶

Figure out and summarize per-antenna issues¶

Summary of Per-Antenna Issues¶

All-Zeros: (44 antpols across 22 antennas)

These antennas have visibilities that are more than half zeros.

Excess Zeros in Either Even or Odd Spectra: (0 antpols across 0 antennas)

These antennas are showing evidence of packet loss or X-engine failure.

Excess Power in X-Engine Diffs: (55 antpols across 45 antennas)

These antennas are showing evidence of mis-written packets in either the evens or the odds.

Cross-Polarized: (4 antpols across 2 antennas)

These antennas have their east and north cables swapped.

Likely FEM Power Issue: (25 antpols across 22 antennas)

These antennas have low power, anomolously high slopes, and extra channels identified as RFI.

High Power: (8 antpols across 6 antennas)

These antennas have high median power.

Other Low Power Issues: (6 antpols across 6 antennas)

These antennas have low power, but are not all-zeros and not FEM off.

Low Correlation, But Not Low Power: (14 antpols across 12 antennas)

These antennas are low correlation, but their autocorrelation power levels look OK.

Bad Bandpass Shapes, But Not Bad Power: (7 antpols across 7 antennas)

These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.

Excess RFI: (9 antpols across 8 antennas)

These antennas have excess strucutre (identified as possible RFI) in their bandpassed relative to the median antenna, but not low or high power or a bad bandpass.

Redcal chi^2: (115 antpols across 93 antennas)

These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.

Full-Day Visualizations¶

Figure 1: Per-File Overall Antenna Classification Summary¶

Antenna Classification Daily Summary¶

• Summary of Per Antenna Issues¶

• Figure 1: Per File Overall Antenna Classification Summary¶

• Figure 2: Per Classifier Antenna Flagging Summary¶

• Figure 3: Array Visualization of Overall Daily Classification¶

Settings¶

Load classifications and other metadata¶

Figure out and summarize per-antenna issues¶

Summary of Per-Antenna Issues¶

All-Zeros: (44 antpols across 22 antennas)

These antennas have visibilities that are more than half zeros.

Excess Zeros in Either Even or Odd Spectra: (0 antpols across 0 antennas)

These antennas are showing evidence of packet loss or X-engine failure.

Excess Power in X-Engine Diffs: (55 antpols across 45 antennas)

These antennas are showing evidence of mis-written packets in either the evens or the odds.

Cross-Polarized: (4 antpols across 2 antennas)

These antennas have their east and north cables swapped.

Likely FEM Power Issue: (25 antpols across 22 antennas)

These antennas have low power, anomolously high slopes, and extra channels identified as RFI.

High Power: (8 antpols across 6 antennas)

These antennas have high median power.

Other Low Power Issues: (6 antpols across 6 antennas)

These antennas have low power, but are not all-zeros and not FEM off.

Low Correlation, But Not Low Power: (14 antpols across 12 antennas)

These antennas are low correlation, but their autocorrelation power levels look OK.

Bad Bandpass Shapes, But Not Bad Power: (7 antpols across 7 antennas)

These antennas have unusual bandpass shapes, but are not all-zeros, high power, low power, or FEM off.

Excess RFI: (9 antpols across 8 antennas)

These antennas have excess strucutre (identified as possible RFI) in their bandpassed relative to the median antenna, but not low or high power or a bad bandpass.

Redcal chi^2: (115 antpols across 93 antennas)

These antennas have been idenfied as not redundantly calibrating well, even after passing the above checks.

Full-Day Visualizations¶

Figure 1: Per-File Overall Antenna Classification Summary¶

• Summary of Per Antenna Issues ¶

• Figure 1: Per File Overall Antenna Classification Summary ¶

• Figure 2: Per Classifier Antenna Flagging Summary ¶

• Figure 3: Array Visualization of Overall Daily Classification ¶