Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summary
notebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics
notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
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 next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "152" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 101 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 99 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# 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=list(z_score_cols.keys())) \
.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=list(redcal_cols.keys())) \
.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=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.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('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
JDs | 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) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2459918 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.014853 | 1.284741 | -1.216581 | -1.036552 | 0.536144 | -1.396662 | 5.829130 | 0.429968 | 0.6136 | 0.6470 | 0.4127 | nan | nan |
2459917 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.496275 | 0.809670 | -1.509766 | -1.433821 | -0.779062 | -1.292767 | 3.465626 | 0.546919 | 0.6473 | 0.6813 | 0.4796 | nan | nan |
2459916 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.450812 | 0.776877 | -1.469818 | -1.265061 | -1.611318 | -1.414523 | 2.741546 | -0.092988 | 0.6136 | 0.6515 | 0.4198 | nan | nan |
2459915 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.525725 | 0.797965 | -1.517300 | -1.243532 | 1.127196 | -1.075547 | 0.671117 | -0.966573 | 0.6535 | 0.6817 | 0.3771 | nan | nan |
2459914 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.727907 | 1.075145 | -1.536430 | -1.276916 | 0.328341 | -1.057060 | -0.797689 | -2.080675 | 0.6797 | 0.7124 | 0.3265 | nan | nan |
2459913 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.237233 | 1.475534 | -1.412747 | -1.296121 | 1.165317 | -1.229846 | 6.064755 | -0.541699 | 0.6179 | 0.6429 | 0.4193 | nan | nan |
2459876 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.919208 | 2.892148 | 13.399679 | 11.027320 | 45.854244 | 8.885512 | 41.391427 | 31.078007 | 0.6473 | 0.6443 | 0.4118 | nan | nan |
2459875 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.762531 | 2.563516 | 7.847874 | 15.656843 | 0.990171 | 2.543874 | 7.023396 | 0.316825 | 0.6472 | 0.6797 | 0.4262 | nan | nan |
2459874 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.817257 | 3.778893 | 3.916357 | 7.660680 | 1.986858 | 2.523925 | 24.023958 | -0.954059 | 0.6284 | 0.6406 | 0.4104 | nan | nan |
2459873 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.246202 | 2.748907 | 5.775375 | 10.641487 | 0.311223 | 1.159047 | 6.848876 | -0.599713 | 0.6287 | 0.6373 | 0.4103 | nan | nan |
2459872 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.828666 | 2.330103 | 8.574336 | 14.546219 | 3.341590 | 3.199030 | 23.325912 | -0.897890 | 0.6300 | 0.6426 | 0.4168 | nan | nan |
2459871 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
2459870 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
2459869 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.039365 | 2.336172 | 6.495836 | 10.521888 | 14.908232 | 3.083713 | 12.506338 | 0.203172 | 0.6565 | 0.6706 | 0.4117 | nan | nan |
2459868 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.217057 | 3.461758 | 9.543604 | 17.043388 | 0.926596 | 1.711917 | 19.923912 | -1.467115 | 0.6304 | 0.6401 | 0.4279 | nan | nan |
2459867 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.028946 | 2.369316 | 8.616516 | 13.454135 | 0.221003 | 0.973469 | 12.861074 | 0.350812 | 0.6428 | 0.6423 | 0.4271 | nan | nan |
2459866 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.494376 | 2.538921 | 7.684526 | 12.191822 | -0.111101 | 0.542455 | 12.730132 | -0.796335 | 0.6472 | 0.6476 | 0.4204 | nan | nan |
2459865 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.334784 | 4.143853 | 10.897733 | 16.061746 | 1.104533 | 2.282955 | 9.789567 | 1.921835 | 0.6700 | 0.6666 | 0.3900 | nan | nan |
2459864 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.677081 | 3.681213 | 6.632979 | 8.552027 | 0.629954 | 1.776199 | 34.522296 | 2.969613 | 0.6467 | 0.6391 | 0.4384 | nan | nan |
2459863 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.547609 | 0.870772 | -1.498446 | -1.084747 | -1.187215 | -1.437619 | 13.172163 | -1.164234 | 0.6395 | 0.6298 | 0.4282 | nan | nan |
2459862 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.703794 | 1.498763 | 8.345987 | 10.370654 | 1.157580 | 2.393875 | 4.469114 | -0.608063 | 0.6213 | 0.6636 | 0.4426 | nan | nan |
2459861 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.295035 | 0.080081 | -2.512577 | -2.285977 | -1.170428 | -1.276020 | 16.578512 | -0.550184 | 0.6567 | 0.6437 | 0.4403 | nan | nan |
2459860 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.098407 | 0.462450 | 6.356476 | 8.203108 | 1.007079 | 1.935807 | 15.487045 | -1.036933 | 0.6662 | 0.6486 | 0.4397 | nan | nan |
2459859 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.146939 | -0.063677 | -2.559752 | -2.235297 | -0.341004 | -1.218276 | 7.401346 | -0.329627 | 0.6712 | 0.6525 | 0.4356 | nan | nan |
2459858 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.177396 | -0.280068 | -2.944492 | -2.703213 | -1.011706 | -1.431999 | 16.086280 | -0.431565 | 0.6809 | 0.6598 | 0.4480 | 2.431255 | 2.364868 |
2459857 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.614032 | 0.289412 | 1.792735 | 1.662215 | 0.954209 | -0.317425 | 6.461753 | -0.549683 | 0.0303 | 0.0292 | 0.0010 | nan | nan |
2459856 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.046929 | 1.228790 | 5.578687 | 7.764427 | -0.331100 | 0.878411 | 7.054303 | -1.024339 | 0.6698 | 0.6765 | 0.4350 | 2.560749 | 2.602560 |
2459855 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.730984 | 1.789143 | 6.572650 | 8.731480 | -0.149684 | 0.207591 | 10.222152 | -0.017010 | 0.6389 | 0.6885 | 0.4724 | 2.479097 | 2.489249 |
2459854 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.356670 | 1.198991 | 6.162406 | 7.823851 | -0.059544 | -0.224518 | 13.268097 | -0.243337 | 0.6678 | 0.7158 | 0.4716 | 2.412161 | 2.435934 |
2459853 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.799259 | 0.978516 | 8.182242 | 10.099620 | -0.253361 | 0.818707 | 20.816762 | -0.933598 | 0.6984 | 0.6654 | 0.4560 | 2.767134 | 2.621880 |
2459852 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.589412 | 2.371591 | 8.262696 | 9.757758 | 2.575561 | 4.821178 | 6.532314 | 7.231549 | 0.7912 | 0.8084 | 0.2824 | 3.835383 | 4.184208 |
2459851 | not_connected | 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 |
2459850 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.918878 | 1.643865 | 7.755468 | 9.339179 | 0.187475 | 2.521600 | 26.178414 | 4.225161 | 0.6982 | 0.7350 | 0.3840 | 2.509154 | 2.528587 |
2459849 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.755177 | 1.125843 | 15.570119 | 19.649885 | -0.351495 | 1.214793 | 17.491096 | -0.756825 | 0.6971 | 0.7283 | 0.3925 | 2.831048 | 2.915919 |
2459848 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.042241 | 5.200746 | 26.859032 | 28.125928 | 5.063591 | 10.601865 | 6.108585 | -2.483018 | 0.6951 | 0.7373 | 0.4159 | 3.257518 | 3.290833 |
2459847 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.480525 | 4.622079 | 25.789540 | 26.603637 | 8.074581 | 13.226368 | 8.305427 | -2.457465 | 0.7078 | 0.6701 | 0.4623 | 5.392986 | 5.045886 |
auto_metrics
notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics
notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | digital_ok | ee Temporal Discontinuties | 5.829130 | 1.284741 | 1.014853 | -1.036552 | -1.216581 | -1.396662 | 0.536144 | 0.429968 | 5.829130 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | digital_ok | ee Temporal Discontinuties | 3.465626 | 0.809670 | 0.496275 | -1.433821 | -1.509766 | -1.292767 | -0.779062 | 0.546919 | 3.465626 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | digital_ok | ee Temporal Discontinuties | 2.741546 | 0.450812 | 0.776877 | -1.469818 | -1.265061 | -1.611318 | -1.414523 | 2.741546 | -0.092988 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | digital_ok | ee Temporal Variability | 1.127196 | 0.525725 | 0.797965 | -1.517300 | -1.243532 | 1.127196 | -1.075547 | 0.671117 | -0.966573 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | digital_ok | nn Shape | 1.075145 | 0.727907 | 1.075145 | -1.536430 | -1.276916 | 0.328341 | -1.057060 | -0.797689 | -2.080675 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | digital_ok | ee Temporal Discontinuties | 6.064755 | 1.475534 | 1.237233 | -1.296121 | -1.412747 | -1.229846 | 1.165317 | -0.541699 | 6.064755 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Variability | 45.854244 | 1.919208 | 2.892148 | 13.399679 | 11.027320 | 45.854244 | 8.885512 | 41.391427 | 31.078007 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 15.656843 | 2.762531 | 2.563516 | 7.847874 | 15.656843 | 0.990171 | 2.543874 | 7.023396 | 0.316825 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 24.023958 | 4.817257 | 3.778893 | 3.916357 | 7.660680 | 1.986858 | 2.523925 | 24.023958 | -0.954059 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 10.641487 | 3.246202 | 2.748907 | 5.775375 | 10.641487 | 0.311223 | 1.159047 | 6.848876 | -0.599713 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 23.325912 | 2.330103 | 2.828666 | 14.546219 | 8.574336 | 3.199030 | 3.341590 | -0.897890 | 23.325912 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Variability | 14.908232 | 3.039365 | 2.336172 | 6.495836 | 10.521888 | 14.908232 | 3.083713 | 12.506338 | 0.203172 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 19.923912 | 3.217057 | 3.461758 | 9.543604 | 17.043388 | 0.926596 | 1.711917 | 19.923912 | -1.467115 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 13.454135 | 2.028946 | 2.369316 | 8.616516 | 13.454135 | 0.221003 | 0.973469 | 12.861074 | 0.350812 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 12.730132 | 2.538921 | 2.494376 | 12.191822 | 7.684526 | 0.542455 | -0.111101 | -0.796335 | 12.730132 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 16.061746 | 4.334784 | 4.143853 | 10.897733 | 16.061746 | 1.104533 | 2.282955 | 9.789567 | 1.921835 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 34.522296 | 3.681213 | 2.677081 | 8.552027 | 6.632979 | 1.776199 | 0.629954 | 2.969613 | 34.522296 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 13.172163 | 0.547609 | 0.870772 | -1.498446 | -1.084747 | -1.187215 | -1.437619 | 13.172163 | -1.164234 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 10.370654 | 0.703794 | 1.498763 | 8.345987 | 10.370654 | 1.157580 | 2.393875 | 4.469114 | -0.608063 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 16.578512 | 0.080081 | 0.295035 | -2.285977 | -2.512577 | -1.276020 | -1.170428 | -0.550184 | 16.578512 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 15.487045 | 0.098407 | 0.462450 | 6.356476 | 8.203108 | 1.007079 | 1.935807 | 15.487045 | -1.036933 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 7.401346 | -0.146939 | -0.063677 | -2.559752 | -2.235297 | -0.341004 | -1.218276 | 7.401346 | -0.329627 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 16.086280 | -0.280068 | -0.177396 | -2.703213 | -2.944492 | -1.431999 | -1.011706 | -0.431565 | 16.086280 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 6.461753 | 0.289412 | 1.614032 | 1.662215 | 1.792735 | -0.317425 | 0.954209 | -0.549683 | 6.461753 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 7.764427 | 1.046929 | 1.228790 | 5.578687 | 7.764427 | -0.331100 | 0.878411 | 7.054303 | -1.024339 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 10.222152 | 1.789143 | 1.730984 | 8.731480 | 6.572650 | 0.207591 | -0.149684 | -0.017010 | 10.222152 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 13.268097 | 1.198991 | 1.356670 | 7.823851 | 6.162406 | -0.224518 | -0.059544 | -0.243337 | 13.268097 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 20.816762 | 0.978516 | 0.799259 | 10.099620 | 8.182242 | 0.818707 | -0.253361 | -0.933598 | 20.816762 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 9.757758 | 2.589412 | 2.371591 | 8.262696 | 9.757758 | 2.575561 | 4.821178 | 6.532314 | 7.231549 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | ee Temporal Discontinuties | 26.178414 | 0.918878 | 1.643865 | 7.755468 | 9.339179 | 0.187475 | 2.521600 | 26.178414 | 4.225161 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 19.649885 | 0.755177 | 1.125843 | 15.570119 | 19.649885 | -0.351495 | 1.214793 | 17.491096 | -0.756825 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 28.125928 | 5.200746 | 4.042241 | 28.125928 | 26.859032 | 10.601865 | 5.063591 | -2.483018 | 6.108585 |
Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
---|---|---|---|---|---|---|---|---|---|---|---|---|
152 | N16 | not_connected | nn Power | 26.603637 | 4.622079 | 3.480525 | 26.603637 | 25.789540 | 13.226368 | 8.074581 | -2.457465 | 8.305427 |