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 = "224" csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H5C_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 253 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_ Found 247 auto_metrics notebooks in /home/obs/src/H5C_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/H5C_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/H5C_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) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2459810 | RF_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
2459809 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 10.959044 | 11.784803 | 25.662611 | 25.062955 | 37.791154 | 31.156931 | -1.659852 | -2.755525 | 0.0608 | 0.0652 | 0.0087 | 0.000000 | 0.000000 |
2459808 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 18.857327 | 18.707355 | 26.835752 | 26.771395 | 26.292419 | 26.905523 | 1.817139 | 1.692536 | 0.0633 | 0.0662 | 0.0054 | 1.163595 | 1.162775 |
2459807 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 19.339107 | 19.074661 | 18.089455 | 17.826395 | 22.360807 | 22.862590 | 7.993794 | 8.998658 | 0.0607 | 0.0615 | 0.0048 | 1.221127 | 1.207483 |
2459806 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 18.344352 | 17.020466 | 31.006532 | 30.027462 | 43.711758 | 42.278125 | -7.577999 | -8.019557 | 0.0621 | 0.0630 | 0.0063 | 0.000000 | 0.000000 |
2459805 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 17.406021 | 17.060396 | 23.838900 | 23.856943 | 18.487755 | 19.527600 | 1.622975 | 1.395883 | 0.0613 | 0.0619 | 0.0046 | 0.000000 | 0.000000 |
2459804 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 20.795730 | 20.107682 | 22.367309 | 21.852715 | 20.252314 | 21.106122 | 1.394330 | 2.196388 | 0.0626 | 0.0634 | 0.0048 | 0.834697 | 0.827935 |
2459803 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 25.645435 | 24.951330 | 30.355268 | 30.099330 | 21.342919 | 22.597692 | 5.317783 | 5.434515 | 0.0655 | 0.0665 | 0.0049 | 0.904762 | 0.899976 |
2459802 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 21.424466 | 21.349772 | 27.126754 | 26.540112 | 38.230138 | 37.033797 | -0.549641 | -0.289166 | 0.0645 | 0.0657 | 0.0073 | 0.000000 | 0.000000 |
2459801 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 20.615597 | 20.457771 | 22.112379 | 21.871528 | 37.671667 | 38.801892 | 5.201946 | 5.430610 | 0.0659 | 0.0658 | 0.0053 | 0.982689 | 0.961560 |
2459800 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 22.000597 | 21.612284 | 20.862860 | 20.695131 | 21.370280 | 21.761544 | 8.576084 | 8.382177 | 0.0748 | 0.0760 | 0.0061 | 1.301590 | 1.308342 |
2459799 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 17.712260 | 16.510563 | 28.225027 | 27.245524 | 57.664885 | 54.623521 | -3.342844 | -3.703089 | 0.6805 | 0.5719 | 0.4061 | 4.828074 | 3.689345 |
2459798 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 21.116891 | 20.710068 | 21.806500 | 21.620730 | 18.557071 | 18.702378 | 4.972281 | 4.617152 | 0.6248 | 0.6021 | 0.4073 | 3.893654 | 3.679495 |
2459797 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 17.751703 | 17.573477 | 20.946383 | 20.827402 | 24.067063 | 23.875692 | 6.908917 | 6.335078 | 0.6118 | 0.5917 | 0.4069 | 3.244718 | 3.173013 |
2459796 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 19.586091 | 18.852220 | 28.441015 | 27.490542 | 21.415432 | 20.762488 | 6.640639 | 5.683779 | 0.6049 | 0.5878 | 0.3878 | 0.000000 | 0.000000 |
2459795 | RF_ok | 100.00% | 13.44% | 16.13% | 0.00% | 100.00% | 0.00% | 22.128006 | 21.538196 | 22.790937 | 21.849108 | 24.318476 | 23.552029 | 6.419833 | 5.990769 | 0.5794 | 0.5646 | 0.3902 | 2.066019 | 2.038814 |
2459794 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 21.585167 | 21.133065 | 23.728215 | 23.061676 | 29.504908 | 28.177359 | 7.902455 | 6.750152 | 0.6096 | 0.5969 | 0.3898 | 0.000000 | 0.000000 |
2459793 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.854471 | 16.550686 | 22.647886 | 21.914612 | 30.747117 | 29.423725 | 2.501270 | 1.866266 | 0.5936 | 0.5915 | 0.3844 | 0.000000 | 0.000000 |
2459792 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 17.612505 | 17.116407 | 28.040944 | 27.870607 | 38.589228 | 36.726546 | -2.160172 | -2.808555 | 0.6790 | 0.5711 | 0.4023 | 3.836269 | 2.838533 |
2459791 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 19.661656 | 19.271138 | 30.263125 | 29.742695 | 30.764738 | 29.457492 | 1.397356 | 0.903346 | 0.5850 | 0.5806 | 0.3745 | 1.889881 | 1.903177 |
2459790 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 30.730964 | 30.546976 | 28.020417 | 28.074435 | 42.559670 | 41.595107 | 1.241187 | 0.507024 | 0.5677 | 0.5679 | 0.3720 | 0.000000 | 0.000000 |
2459789 | RF_ok | 100.00% | 5.38% | 0.00% | 0.00% | 100.00% | 0.00% | 27.183747 | 27.386036 | 30.081105 | 30.465833 | 44.971604 | 43.637793 | 5.156678 | 3.074634 | 0.5482 | 0.5460 | 0.3630 | 3.128248 | 3.487063 |
2459788 | RF_ok | 100.00% | 2.65% | 0.00% | 0.00% | 100.00% | 0.00% | 23.520125 | 23.898668 | 31.414737 | 32.006178 | 48.155333 | 46.622124 | 1.072950 | 0.116769 | 0.5509 | 0.5506 | 0.3527 | 1.788038 | 1.845016 |
2459787 | RF_ok | 100.00% | 34.95% | 32.26% | 0.00% | 100.00% | 0.00% | 23.080920 | 23.449162 | 26.115942 | 27.001986 | 31.039658 | 30.099149 | 0.812507 | 0.465276 | 0.5161 | 0.5140 | 0.3414 | 2.522493 | 2.588558 |
2459786 | RF_ok | 100.00% | 8.06% | 8.06% | 0.00% | 100.00% | 0.00% | 24.633627 | 24.725187 | 32.272687 | 32.282718 | 48.196978 | 45.022679 | 1.016105 | -0.099444 | 0.5180 | 0.5173 | 0.3287 | 0.000000 | 0.000000 |
2459785 | RF_ok | 100.00% | 2.15% | 2.15% | 0.00% | 100.00% | 0.00% | 14.252781 | 13.902919 | 47.186961 | 46.660079 | 43.249707 | 40.020653 | -2.229168 | -2.436642 | 0.6815 | 0.5730 | 0.3980 | 3.616410 | 3.012030 |
2459784 | RF_ok | 100.00% | 16.13% | 16.13% | 0.00% | 100.00% | 0.00% | 21.984142 | 21.640855 | 35.444092 | 36.139048 | 45.755175 | 42.938416 | -0.892527 | -1.322757 | 0.4977 | 0.4974 | 0.3093 | 2.317092 | 2.309880 |
2459783 | RF_ok | 100.00% | 13.48% | 13.48% | 0.00% | 100.00% | 0.00% | 19.137319 | 18.370334 | 10.481304 | 10.027243 | 22.447921 | 20.044577 | -0.534709 | -1.285154 | 0.4889 | 0.4880 | 0.3004 | 3.103039 | 3.042687 |
2459782 | RF_ok | 100.00% | 26.88% | 16.13% | 0.00% | 100.00% | 0.00% | 17.694439 | 16.838683 | 5.341712 | 4.962413 | 12.687310 | 11.278177 | -0.796205 | -1.284838 | 0.4678 | 0.4699 | 0.2890 | 2.981516 | 2.908910 |
2459781 | RF_ok | 0.00% | 10.43% | 10.43% | 0.00% | 100.00% | 0.00% | 2.626864 | 2.299944 | 1.407315 | 1.327550 | 1.169909 | 0.518147 | -2.305114 | -2.312605 | 0.6570 | 0.5919 | 0.3468 | 3.992818 | 3.233101 |
2459778 | RF_ok | 100.00% | 8.06% | 4.30% | 0.00% | 100.00% | 0.00% | 11.270744 | 10.818618 | 8.204846 | 7.772461 | 22.515429 | 19.984843 | -4.365221 | -4.482497 | 0.6561 | 0.5468 | 0.3914 | 4.414827 | 3.277055 |
2459776 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.018695 | 10.168238 | 9.090749 | 9.278186 | 34.596892 | 34.631250 | 31.405470 | 24.314033 | 0.7050 | 0.5828 | 0.4074 | 3.462163 | 2.751149 |
2459774 | RF_ok | - | 3.12% | 2.60% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.6694 | 0.5659 | 0.3851 | nan | nan |
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 | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 37.791154 | 11.784803 | 10.959044 | 25.062955 | 25.662611 | 31.156931 | 37.791154 | -2.755525 | -1.659852 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | nn Temporal Variability | 26.905523 | 18.707355 | 18.857327 | 26.771395 | 26.835752 | 26.905523 | 26.292419 | 1.692536 | 1.817139 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | nn Temporal Variability | 22.862590 | 19.074661 | 19.339107 | 17.826395 | 18.089455 | 22.862590 | 22.360807 | 8.998658 | 7.993794 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 43.711758 | 18.344352 | 17.020466 | 31.006532 | 30.027462 | 43.711758 | 42.278125 | -7.577999 | -8.019557 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | nn Power | 23.856943 | 17.406021 | 17.060396 | 23.838900 | 23.856943 | 18.487755 | 19.527600 | 1.622975 | 1.395883 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Power | 22.367309 | 20.107682 | 20.795730 | 21.852715 | 22.367309 | 21.106122 | 20.252314 | 2.196388 | 1.394330 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Power | 30.355268 | 25.645435 | 24.951330 | 30.355268 | 30.099330 | 21.342919 | 22.597692 | 5.317783 | 5.434515 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 38.230138 | 21.349772 | 21.424466 | 26.540112 | 27.126754 | 37.033797 | 38.230138 | -0.289166 | -0.549641 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | nn Temporal Variability | 38.801892 | 20.457771 | 20.615597 | 21.871528 | 22.112379 | 38.801892 | 37.671667 | 5.430610 | 5.201946 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Shape | 22.000597 | 21.612284 | 22.000597 | 20.695131 | 20.862860 | 21.761544 | 21.370280 | 8.382177 | 8.576084 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 57.664885 | 16.510563 | 17.712260 | 27.245524 | 28.225027 | 54.623521 | 57.664885 | -3.703089 | -3.342844 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Power | 21.806500 | 21.116891 | 20.710068 | 21.806500 | 21.620730 | 18.557071 | 18.702378 | 4.972281 | 4.617152 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 24.067063 | 17.573477 | 17.751703 | 20.827402 | 20.946383 | 23.875692 | 24.067063 | 6.335078 | 6.908917 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Power | 28.441015 | 19.586091 | 18.852220 | 28.441015 | 27.490542 | 21.415432 | 20.762488 | 6.640639 | 5.683779 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 24.318476 | 21.538196 | 22.128006 | 21.849108 | 22.790937 | 23.552029 | 24.318476 | 5.990769 | 6.419833 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 29.504908 | 21.585167 | 21.133065 | 23.728215 | 23.061676 | 29.504908 | 28.177359 | 7.902455 | 6.750152 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 30.747117 | 16.854471 | 16.550686 | 22.647886 | 21.914612 | 30.747117 | 29.423725 | 2.501270 | 1.866266 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 38.589228 | 17.612505 | 17.116407 | 28.040944 | 27.870607 | 38.589228 | 36.726546 | -2.160172 | -2.808555 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 30.764738 | 19.661656 | 19.271138 | 30.263125 | 29.742695 | 30.764738 | 29.457492 | 1.397356 | 0.903346 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 42.559670 | 30.546976 | 30.730964 | 28.074435 | 28.020417 | 41.595107 | 42.559670 | 0.507024 | 1.241187 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 44.971604 | 27.386036 | 27.183747 | 30.465833 | 30.081105 | 43.637793 | 44.971604 | 3.074634 | 5.156678 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 48.155333 | 23.898668 | 23.520125 | 32.006178 | 31.414737 | 46.622124 | 48.155333 | 0.116769 | 1.072950 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 31.039658 | 23.080920 | 23.449162 | 26.115942 | 27.001986 | 31.039658 | 30.099149 | 0.812507 | 0.465276 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 48.196978 | 24.725187 | 24.633627 | 32.282718 | 32.272687 | 45.022679 | 48.196978 | -0.099444 | 1.016105 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Power | 47.186961 | 13.902919 | 14.252781 | 46.660079 | 47.186961 | 40.020653 | 43.249707 | -2.436642 | -2.229168 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 45.755175 | 21.984142 | 21.640855 | 35.444092 | 36.139048 | 45.755175 | 42.938416 | -0.892527 | -1.322757 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 22.447921 | 18.370334 | 19.137319 | 10.027243 | 10.481304 | 20.044577 | 22.447921 | -1.285154 | -0.534709 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Shape | 17.694439 | 16.838683 | 17.694439 | 4.962413 | 5.341712 | 11.278177 | 12.687310 | -1.284838 | -0.796205 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Shape | 2.626864 | 2.626864 | 2.299944 | 1.407315 | 1.327550 | 1.169909 | 0.518147 | -2.305114 | -2.312605 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | ee Temporal Variability | 22.515429 | 10.818618 | 11.270744 | 7.772461 | 8.204846 | 19.984843 | 22.515429 | -4.482497 | -4.365221 |
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
224 | N19 | RF_ok | nn Temporal Variability | 34.631250 | 11.018695 | 10.168238 | 9.090749 | 9.278186 | 34.596892 | 34.631250 | 31.405470 | 24.314033 |