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Commit bdb1c5eb authored by Alexander Pace's avatar Alexander Pace
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Add `time-to-alert` to beta reports page 

the reports page is accessible on production, but the link to it is exposed on playground and test.
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Pipeline #661806 passed
Stage: test
Stage: branch
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config/urls.py
+ 1
− 1
View file @ bdb1c5eb
......@@ -43,7 +43,7 @@ urlpatterns = [
re_path(r'^other/$', TemplateView.as_view(template_name='other.html'),
name='other'),
re_path(r'^performance/$', events.views.performance, name="performance"),
re_path(r'^reports/$', events.reports.histo, name="reports"),
re_path(r'^reports/$', events.reports.reports_page_context, name="reports"),
re_path(r'^latest/$', search.views.latest, name="latest"),
#(r'^reports/(?P<path>.+)$', 'django.views.static.serve',
# {'document_root': settings.LATENCY_REPORT_DEST_DIR}),
......
gracedb/events/reports.py
+ 167
− 22
View file @ bdb1c5eb
......@@ -3,8 +3,10 @@ from django.http import HttpResponseForbidden
from django.template import RequestContext
from django.shortcuts import render
from django.conf import settings
from django import forms
from .models import Event, Group, Search, Pipeline
from .models import Event, Group, Search, Pipeline, Label
from superevents.models import Superevent, Labelling
from ligoauth.decorators import internal_user_required
from django.db.models import Q
......@@ -16,43 +18,191 @@ from search.query.events import parseQuery
from django.db.models import Max, Min, Avg
from django.db.models.aggregates import StdDev
from datetime import timedelta, datetime
from datetime import datetime, timedelta
from gpstime import gpstime
from django.utils import timezone
from plotly.io import to_html
from plotly.offline import plot
from tailslide import Median, Percentile
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
plot_title = "Events Uploaded Since {0} UTC"
plot_sub_title = "<br><sup> Online, Production Events in the Last Seven Days </sup>"
YEAR_RANGE = list(range(datetime.now().year, 2013, -1))
INITIAL_DAYS_BACK = 31
EVENT_SEARCH_CHOICES = (
('AllSky', 'AllSky'),
('EarlyWarning', 'EarlyWarning'),
('SSM', 'SSM'),
('BBH', 'BBH'),
('VTInjection', 'VTInjection'),
)
EVENT_SEARCH_CHOICES_INITAL = ['AllSky']
plt_title = plot_title + plot_sub_title
SUPEREVENT_CATEGORIES = (
(Superevent.SUPEREVENT_CATEGORY_PRODUCTION, 'Production'),
(Superevent.SUPEREVENT_CATEGORY_MDC, 'MDC'),
)
days_back = 7
class lookback_days_form(forms.Form):
start_date = forms.DateField(label='Start Date',
widget=forms.SelectDateWidget(years=YEAR_RANGE),
)
end_date = forms.DateField(label='End Date',
widget=forms.SelectDateWidget(years=YEAR_RANGE),
initial = datetime.now()
)
searches_choice = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple,
choices=EVENT_SEARCH_CHOICES,
required=False,
label='Event Searches',
initial=EVENT_SEARCH_CHOICES_INITAL)
superevent_type = forms.ChoiceField(widget=forms.RadioSelect,
choices=SUPEREVENT_CATEGORIES,
label='Superevent Category',
initial=Superevent.SUPEREVENT_CATEGORY_PRODUCTION)
@internal_user_required
#@method_decorator(internal_user_required(raise_exception=True),
# name='dispatch')
def histo(request):
def reports_page_context(request):
# start with a blank context dict:
context = {}
if request.method == 'POST':
form = lookback_days_form(request.POST)
if form.is_valid():
start_date = form.cleaned_data['start_date']
start_date = datetime.combine(start_date, datetime.min.time())
end_date = form.cleaned_data['end_date']
end_date = datetime.combine(end_date, datetime.min.time())
days_back = (end_date - start_date).days
event_searches = form.cleaned_data['searches_choice']
superevent_category=form.cleaned_data['superevent_type']
else:
days_back = INITIAL_DAYS_BACK
end_date = datetime.now()
start_date = end_date - timedelta(days=days_back)
event_searches = EVENT_SEARCH_CHOICES_INITAL
superevent_category = Superevent.SUPEREVENT_CATEGORY_PRODUCTION
form = lookback_days_form(initial={'start_date': start_date})
# update context with form:
context.update({'form': form})
# update context with histogram div:
context.update({'hist': latency_histo(days_back, start_date,
end_date, event_searches)})
# update with the time-to-alert div:
context.update({'tta': time_to_alert(start_date, end_date, superevent_category)})
return render(request, 'gracedb/reports.html', context)
def time_to_alert(start_date, end_date, superevent_category):
# Set up the query to get all production superevents within the date range
# that have the GCN_PRELIM_SENT label applied:
# Get the GCN_PRELIM_SENT label first so we only hit the db once:
gcn = Label.objects.get(name='GCN_PRELIM_SENT')
sevents = Superevent.objects.filter(category=superevent_category,
t_0__range=(gpstime.fromdatetime(start_date).gps(),
gpstime.fromdatetime(end_date).gps()),
labels=gcn,
).order_by('superevent_id')
# Now get the GCN_PRELIM_SENT label objects:
labs = Labelling.objects.filter(label=gcn, superevent__in=sevents).order_by('superevent__superevent_id')
# Make lists of the relevant values. One has superevent_id and t_0, the
# other has the created time of the gcn label (which is a proxy for
# when the alert goes out). Everything should be ordered correctly, but
# we're going to add a verification step just in case.
sevents_values = list(sevents.values_list('superevent_id', 't_0', 'created'))
label_values = list(labs.values_list('superevent__superevent_id', 'created'))
# Construct a list where each item looks like:
# [superevent_id (string), created (datetime), time-to-alert (float)]
#results =
if sevents_values:
results = [[i[0][0], i[0][2], gpstime.fromdatetime(i[1][1]).gps() - float(i[0][1])] for i in zip(sevents_values, label_values) if i[0][0] == i[1][0]]
np_results = np.array(results)
else:
np_results = np.zeros((1,3))
# Try making a pandas dataframe:
pd_results = pd.DataFrame(np_results,
columns=['superevent_id', 'created', 'time_to_alert'],
)
# Make a scatter plot:
scatter_fig = px.scatter(pd_results,
x='created',
y='time_to_alert',
hover_data='superevent_id',
)
scatter_fig.update_traces(marker_size=10)
scatter_fig.update_layout(title={
'text': 'Superevent Time-to-Alert',
'xanchor': 'center',
'x':0.5,},
xaxis_title="Date",
yaxis_title="Time-to-Alert (s)",
autosize=True,
paper_bgcolor='rgba(0, 0, 0, 0)',
margin={'l': 0, 'r': 0},
)
return {'tta_plot_div': to_html(scatter_fig,
include_plotlyjs=False,
full_html=False,
default_width='100%',),
'tta_stats': {'med': pd_results['time_to_alert'].median(),
'nfp': pd_results['time_to_alert'].quantile(0.95)},
}
def latency_histo(days_back, start_date, end_date, event_searches):
plot_title = "Events Uploaded Between {start} and {end}".format(
start=start_date.strftime("%m/%d/%Y"),
end=end_date.strftime("%m/%d/%Y"),
)
plot_sub_title = f"<br><sup> Online, Production Events in {days_back} Days </sup>"
plt_title = plot_title + plot_sub_title
fig = go.Figure()
# Get the timedelta and get it in a queryable form:
t_now = timezone.now()
date_cutoff = t_now - timedelta(days=days_back)
#all_events_latency = list(Event.objects.filter(created__gt=date_cutoff, reporting_latency__isnull=False).values_list('reporting_latency', flat=True))
# Zero out the list of pipeline statistics:
aggregated_stats =[]
# Loop over pipelines that are determined to be "Production" search pipelines and
# retrieve data, and populate histograms:
for pipeline in Pipeline.objects.filter(pipeline_type=Pipeline.PIPELINE_TYPE_SEARCH_PRODUCTION):
# Generate the queryset for production (G) events, uploaded online, that have a
# valid value of reporting_latency. There has to be a way to combine these so you
# hit the db once, but for now its just once for a list of values, and once for
# aggregated values.
pipeline_query = Event.objects.filter(graceid__contains='G',
pipeline_query = Event.objects.filter(search__name__in=event_searches,
offline=False,
created__gt=date_cutoff, reporting_latency__isnull=False,
pipeline=pipeline).order_by('reporting_latency')
......@@ -69,8 +219,8 @@ def histo(request):
))
# Update the statistics list with aggregated values.
aggregated_stats.append(pipeline_query.aggregate(avg=Avg('reporting_latency'),
std=StdDev('reporting_latency')))
aggregated_stats.append(pipeline_query.aggregate(med=Median('reporting_latency'),
nfp=Percentile('reporting_latency', 0.95)))
aggregated_stats[-1].update({'name': pipeline.name})
if pipeline_query:
aggregated_stats[-1].update({'min': pipeline_query.first().reporting_latency,
......@@ -92,7 +242,7 @@ def histo(request):
#fig.update_layout(barmode='stack',
fig.update_layout(barmode='overlay',
title={
'text': plt_title.format(date_cutoff.strftime("%m/%d/%Y, %H:%M:%S")),
'text': plt_title,
'xanchor': 'center',
'x':0.5,},
xaxis_title="Reporting Latency (s)",
......@@ -102,12 +252,7 @@ def histo(request):
fig.update_traces(opacity=0.75)
latency_plot_div = plot(fig, output_type='div')
return render(request, 'gracedb/reports.html',
context=
{'latency_plot_div': latency_plot_div,
return {'latency_plot_div': latency_plot_div,
'date_cutoff': date_cutoff,
'pipeline_stats': aggregated_stats,}
)
gracedb/templates/gracedb/reports.html
+ 92
− 35
View file @ bdb1c5eb
......@@ -6,44 +6,101 @@
{% block content %}
<center>
This page is currently in <span style="color:red;font-weight: bold;">beta</span>.
Use for cross-checking and validation purposes only.
This page is currently in <span style="color:red;font-weight: bold;">beta</span>.
Use for cross-checking and validation purposes only.
</center>
{% autoescape off %}
{{ latency_plot_div }}
{% endautoescape %}
<div class="row my-3 justify-content-md-center">
<div class="col-md-8">
<table class="table-hover table-condensed table-resp-gracedb shadow p-3 mb-5 rounded" style="text-align:center">
<thead>
<td> Pipeline </td>
<td> Uploads </td>
<td> Min. Latency (s) </td>
<td> Max Latency (s) </td>
<td> Avg. Latency (s) </td>
<td> Std. Dev. (s) </td>
</thead>
{% for data in pipeline_stats %}
<tr>
<!--<td style="font-family: monospace;"> {{data.name}} </td> -->
<td style="font-family: 'Courier New', Courier, monospace; font-size: 12px; white-space: pre;"> {{data.name}} </td>
<td> {{data.count}} </td>
<td> {% if data.min %} {{data.min|floatformat:3}}
(<a href="{% url "view" data.min_gid %}">{{ data.min_gid }}</a>)
{% endif %}
</td>
<td> {% if data.max %} {{data.max|floatformat:3}}
(<a href="{% url "view" data.max_gid %}">{{ data.max_gid }}</a>)
{% endif %}
</td>
<td> {{data.avg|floatformat:3}} </td>
<td> {{data.std|floatformat:3}} </td>
</tr>
{% endfor %}
</table>
<center>
<form action="" method="post">
{% csrf_token %}
<table>
{{ form.as_table }}
</table>
<input type="submit" value="Submit" class="btn btn-primary">
</form>
</center>
<ul class="nav nav-tabs" id="perfTab" role="tablist">
<li class="nav-item" role="presentation">
<button class="nav-link active" id="latency-tab" data-toggle="tab" data-target="#latency" type="button" role="tab" aria-controls="latency" aria-selected="true">Event Latency</button>
</li>
<li class="nav-item" role="presentation">
<button class="nav-link" id="tta-tab" data-toggle="tab" data-target="#tta" type="button" role="tab" aria-controls="tta" aria-selected="false">Time-to-Alert</button>
</li>
</ul>
<div class="tab-content" id="myTabContent">
<div class="tab-pane fade show active" id="latency" role="tabpanel" aria-labelledby="latency-tab">
{% autoescape off %}
{{ hist.latency_plot_div }}
{% endautoescape %}
<div class="row my-3 justify-content-md-center">
<div class="col-md-8">
<table class="table-hover table-condensed table-resp-gracedb shadow p-3 mb-5 rounded" style="text-align:center">
<thead>
<td> Pipeline </td>
<td> # Uploads </td>
<td> Median Latency (s) </td>
<td> 95th Percentile (s) </td>
<td> Min. Latency (s) </td>
<td> Max Latency (s) </td>
</thead>
{% for data in hist.pipeline_stats %}
<tr>
<!--<td style="font-family: monospace;"> {{data.name}} </td> -->
<td style="font-family: 'Courier New', Courier, monospace; font-size: 12px; white-space: pre;"> {{data.name}} </td>
<td> {{data.count}} </td>
<td> {{data.med|floatformat:3}} </td>
<td> {{data.nfp|floatformat:3}} </td>
<td> {% if data.min %} {{data.min|floatformat:3}}
(<a href="{% url "view" data.min_gid %}">{{ data.min_gid }}</a>)
{% endif %}
</td>
<td> {% if data.max %} {{data.max|floatformat:3}}
(<a href="{% url "view" data.max_gid %}">{{ data.max_gid }}</a>)
{% endif %}
</td>
</tr>
{% endfor %}
</table>
</div>
</div>
</div>
<div class="tab-pane fade" id="tta" role="tabpanel" aria-labelledby="tta-tab">
<br>
<p width='90%'>
The below chart shows the time-to-alert superevents currently in GraceDB within
the specified time range. Time-to-alert (in seconds) is calculated by subtracting
the <b>time of the astrophysical phenomenon</b>
(<span class="text-monospace">t<sub>0</sub></span> of the superevent) from the
<b>time when the <span class="text-monospace">GCN_PRELIM_SENT</span> label was applied.</b>
The actual receipt time of the alert by observing partners likely differs from this
estimate. Identify the Superevent ID by hovering over the data point.
</p>
<p width='90%'>
A single data-point at zero indicates that no superevents were labelled
<span class="text-monospace">GCN_PRELIM_SENT</span> within the specified time window.
</p>
{% autoescape off %}
{{ tta.tta_plot_div }}
{% endautoescape %}
<table class="table-hover table-condensed table-resp-gracedb shadow p-3 mb-5 rounded" style="text-align:center; width:auto; margin-left: auto; margin-right: auto;">
<thead>
<td> Median Time-to-Alert (s) </td><td> 95th Percentile (s)</td>
</thead>
<tr>
<td>{{tta.tta_stats.med|floatformat:1}}</td><td>{{tta.tta_stats.nfp|floatformat:1}}</td>
</tr>
</table>
</div>
</div>
{% endblock %}
requirements.txt
+ 3
− 3
View file @ bdb1c5eb
......@@ -38,8 +38,9 @@ matplotlib==3.6.2
mock==4.0.3
numpy==1.25.2
packaging==23.1
pandas==2.2.2
phonenumbers==8.13.18
plotly==5.15.0
plotly==5.23.0
psycopg==3.1.12
PyJWT==2.8.0
python-ldap==3.4.4
......@@ -61,5 +62,4 @@ pytest-django==4.5.2
pytz==2023.3
pyasn1==0.4.8
pyasn1-modules==0.2.8
# pinning setuptools because of 2to3 errors from ConcurrentLogHandler:
#setuptools==54.2.0
tailslide==0.2.0
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