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main.py
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import dash_bootstrap_components as dbc
from dash import Dash, dcc, html, Input, Output, State
from dash import html
import plotly.express as px
import plotly.figure_factory as ff
import plotly.express as px
import dash
from dash_bootstrap_templates import ThemeChangerAIO, template_from_url
import pandas as pd
import plotly.graph_objects as go
import datamanager, readfdsn, util
import logging as Log
import time, math
import datetime
from dash.exceptions import PreventUpdate
import statistics
import numpy as np
##Imports for fdsnws retrieve data through pd.read_csv()
TIMEOUT_SEC = 10 # default timeout in seconds
import socket
socket.setdefaulttimeout(TIMEOUT_SEC)
# the class Data is for saving the configuration
# and load the data stored in csv files.
# This also has some methods to update the csv data from Firebase Firestore DB.
#
# Be sure not calling the refreshData() method many times.
#
class Data:
def __init__(self):
self.config = None
self.datahandler = None
self.dfEventsDelay = None
self.dfSilentNotif = None
self.dfAlertsIntensity = None
self.cities = None
def loadConfigData(self):
try:
#Reading the configuration values, using the datahandler to read the data from csv files.
self.config = datamanager.config("dashconfig.cfg")
self.config.readConfig()
self.datahandler = datamanager.datahandler(self.config)
self.dfEventsDelay = self.datahandler.getDfDelaysEvents()
self.dfSilentNotif = self.datahandler.getDfSilentNotif()
self.dfAlertsIntensity = self.datahandler.getDfIntensityAlerts()
self.cities = util.csvFile2dic(self.config.citiesFile)
except Exception as e:
Log.error("Problem instancing and/or getting the data in dataframes")
Log.error(repr(e))
def refreshData(self):
#This method should not be called many times to avoid to reach
#the max read quota on Firebase Firestore DB.
#
self.datahandler.getDataFirebaseForEvents()
self.datahandler.getDataFirebaseForSilentNotif()
#instancing Data class
fdc = Data()
fdc.loadConfigData()
romanNumbersArr=["I", "II", "III", "IV","V","VI","VII","VIII","IX","X", "XI","XII"]
mapbox_access_token = open(".mapbox_token").read()
#Initialise the app
dbc_css = (
"https://cdn.jsdelivr.net/gh/AnnMarieW/[email protected]/dbc.min.css"
)
app = Dash(__name__, external_stylesheets=[dbc.themes.FLATLY])
search_bar = dbc.Row(
[
dbc.Col(dbc.Input(id="eventid-input",type="search", placeholder="ID del evento...")),
dbc.Col(
dbc.Button("Buscar",id="eventid-button",
color="primary", className="ms-2", n_clicks=0
),
width="auto",
),
dbc.Col(
dbc.Button("Actualizar",id="buttonUpdate",
color="success", className="ms-2", n_clicks=0
),
width="auto",
),
],
className="g-0 ms-auto flex-nowrap mt-3 mt-md-0",
align="center",
)
navbar = dbc.Navbar(
dbc.Container(
[
html.A(
# Use row and col to control vertical alignment of logo / brand
dbc.Row(
[
dbc.Col(html.Img(src = app.get_asset_url(fdc.config.logoFile), height="100px")),
dbc.Col(dbc.NavbarBrand("Dashboard de Datos en Firebase", className="ms-2")),
],
align="center",
className="g-0",
),
href="/",
style={"textDecoration": "none"},
),
dbc.NavbarToggler(id="navbar-toggler", n_clicks=0),
dbc.Collapse(
search_bar,
id="navbar-collapse",
is_open=False,
navbar=True,
),
]
),
color="dark",
dark=True,
)
modal = dbc.Modal(
[
dbc.ModalHeader(dbc.ModalTitle("Modal Title")),
dbc.ModalBody("This is the content of the modal"),
],
id="modal",
is_open=False,
)
modal = dbc.Modal(
[
dbc.ModalHeader(dbc.ModalTitle("ID de Evento No Encontrado")),
dbc.ModalBody("Por favor, verifique que el ID sea de un evento que fue notificado a través de la App."),
],
id="modal-noevent",
is_open=False,
)
#define the app
app.layout = dbc.Container(
[
dcc.Location(id='url', refresh=True),
navbar,
html.Br(),
dcc.Interval(
id="load_interval",
n_intervals=0,
max_intervals=0, #<-- only run once
interval=1
),
dbc.Tabs(
[
dbc.Tab([
html.Br(),
dbc.Row(id="resumenTab1",justify="center"),
html.Br(),
html.Hr(),
dbc.Row(
[
dbc.Col([dcc.Graph(id="map-users")],md=5,className="shadow bg-white rounded"),
dbc.Col([dcc.Graph(id="hist-time-source")],md=5, className="shadow bg-white rounded"),
],className="d-flex justify-content-center"
),
html.Hr(),
dbc.Row(
[
dbc.Col([dcc.Graph(id="map-user-delays")],md=5,className="shadow bg-white rounded"),
dbc.Col([dcc.Graph(id="plot-users-vs-time")],md=5,className="shadow bg-white rounded"),
],className="d-flex justify-content-center"
),
html.Hr(),
dbc.Row(
[
dbc.Col([
dcc.Graph(id='distr-events-delays'),
dbc.Row([
#dbc.Col([dcc.Graph(id='pie-first-notif-delays')],md=6),
dbc.Col([dbc.Spinner(children=[dcc.Graph(id="pie-first-notif-delays")], size="lg", color="primary", type="border", fullscreen=True,)], md=6),
dbc.Col([dcc.Graph(id='pie-updates-notif-delays')],md=6)
])
],md=6, className="shadow rounded"),
dbc.Col([
dcc.Graph(id='distr-silent-notif-delays'),
dbc.Row([
dbc.Col([dcc.Graph(id='pie-silent-notif-delays')], md=8)
], className="d-flex justify-content-center")
],md=6,className="shadow rounded"),
]
)
],label="Datos De Tiempo"),
dbc.Tab(
[
modal,
html.Br(),
dbc.Row(id="resumenTab2",justify="center"),
html.Br(),
dbc.Row(
[
dbc.Col([dcc.Graph(id="intensity-map")], md=5),
dbc.Col([dcc.Graph(id="intensity-distance")],md=5)
], className="d-flex justify-content-center"),
dbc.Row(
[
dbc.Col([dbc.Spinner(children=[dcc.Graph(id="delay-distribution-event")], size="lg", color="primary", type="border", fullscreen=True,)], md=6),
dbc.Col([dcc.Graph(id="notification-histogram")],md=6)
])
],label="Datos por Evento"
),
],id="tabs",
),
html.Footer(
html.Div([
html.Small(['Alerta Temprana de Terremotos de América Central (ATTAC)'], className = "fw-bold"),
html.Br(),
html.Small('Una Colaboración con socios regionales en América Central, coordinado por El Servicio Sismológico de Suizo en ETH Zurich, Suiza.'),
html.Br(),
html.Small('Proyecto ATTAC financiado por la Agencia Suiza para el Desarrollo y Cooperación (COSUDE)'),
html.Br(),
html.Small('Dashboard Desarrollado por Billy Burgoa Rosso (Consultor Independiente) para el proyecto ATTAC.'),
]),
className="bg-gray text-inverse text-center py-4",
),
# dcc.Store stores the intermediate value
dcc.Store(id='eventid')
],
className=" dbc",
fluid=True,
)
####Callbacks for first tab plots and graphs############
#multiple output callback
@app.callback(
Output('map-users','figure'),
Output('plot-users-vs-time','figure'),
Output('hist-time-source','figure'),
Output('map-user-delays','figure'),
Output('distr-events-delays','figure'),
Output('pie-first-notif-delays','figure'),
Output('pie-updates-notif-delays','figure'),
Output('distr-silent-notif-delays','figure'),
Output('pie-silent-notif-delays','figure'),
Output('resumenTab1','children'),
Output("eventid","data"),
Input("load_interval", "n_intervals"),
)
def updatePlotsGraphsFirstTab(n_intervals):
fdc = Data()
fdc.loadConfigData()
colorMapDicPie = { "-0.5 to 0 s": "gray",
"0 to 1 s": "#00CC96",
"1 to 2 s": "#FFA15A",
"2 to 3 s": "#19D3F3",
"3 to 4 s": "#636EFA",
"4 to 5 s": "#EF553B",
"5 to 60 s": "#AB63FA"}
lastSNtimestamp = fdc.dfSilentNotif["senttime"].max()
dfMapPlot = fdc.dfSilentNotif.loc[(fdc.dfSilentNotif['delay']>-60)&(fdc.dfSilentNotif['delay']<=60)&(fdc.dfSilentNotif['userLat']>=-90)&(fdc.dfSilentNotif['userLat']<=90)&(fdc.dfSilentNotif["senttime"]==fdc.dfSilentNotif["senttime"].max())]
dfMapPlot["isotime"] = pd.to_datetime(dfMapPlot['userLocTime'], unit='ms')
now = round(time.time()*1000.0)
last24Hoursms = round(24*60*60*1000)
last7Daysms = round(7*24*60*60*1000)
last15Daysms = round(15*24*60*60*1000)
fithteenMin = round(15*60*1000)
lastHour = round(1*60*60*1000)
today = round(1*24*260*60*1000)
dfMapPlot['color']=''
for index, row in dfMapPlot.iterrows():
timediff = lastSNtimestamp - round(row['userLocTime'])
if timediff>=0 and timediff < fithteenMin:
#print("last 24 hours")
dfMapPlot['color'][index] = '< 15 minutes'
elif timediff < lastHour and timediff >= fithteenMin:
#print("last 7 days ")
dfMapPlot['color'][index] = '>=15 min but < one hour'
elif timediff < today and timediff >= lastHour:
#print("last 15 days")
dfMapPlot['color'][index] = '>= last hour but < 24 hours'
else:
#print("more than 15 days")
dfMapPlot['color'][index] = '>= 24 hours'
mapbox_access_token = open(".mapbox_token").read()
px.set_mapbox_access_token(open(".mapbox_token").read())
map_users = px.scatter_mapbox(dfMapPlot,
lat=dfMapPlot['userLat'],
lon=dfMapPlot['userLon'],
color=dfMapPlot['color'],
color_discrete_map={
"< 15 minutes": "green",
">=15 min but < one hour": "yellow",
">= last hour but < 24 hours": "orange",
">= 24 hours": "gray"},
hover_name="isotime",
#color=dfMapPlot['color'],
# color_discrete_map="identity",
zoom=1)
map_users.update_layout(
legend=dict(
x=0,
y=1,
#traceorder="reversed",
title_font_family="Times New Roman",
font=dict(
family="Courier",
size=12,
color="black"
),
bgcolor="LightSteelBlue",
bordercolor="Black",
borderwidth=2
)
)
map_users.update_layout(legend_x=0, legend_y=1)
map_users.update_layout(margin={"r":20,"t":40,"l":20,"b":10})
#map_users.update_layout(coloraxis_showscale=False)
#map_users.update_coloraxes(showscale=False)
#map_users.update_traces(showlegend=False)
tmp = fdc.config.countrylatlon.split(",")
countryLat = float(tmp[0])
countryLon = float(tmp[1])
map_users.update_layout(
mapbox=dict(
bearing=0,
center=go.layout.mapbox.Center(
lat=countryLat,
lon=countryLon
),
pitch=0,
zoom=8
)
)
map_users.update_layout(title = "Geographical Users' Distrubution and GPS Avalability",
legend_title="Users' GPS Availability")
###Number of Users Vs Time ####
tmpList = list(fdc.dfSilentNotif['senttime'].unique())
dictt = {'numusers':[],
'time': []}
for i in tmpList:
dictt['time'].append(i)
dictt['numusers'].append(fdc.dfSilentNotif.loc[fdc.dfSilentNotif['senttime'] == i ]['userid'].count())
dfTmp = pd.DataFrame(dictt)
dfTmp["dt"] = pd.to_datetime(dfTmp['time'], unit='ms')
plot_users_vs_time = px.scatter(dfTmp, x="dt", y="numusers",hover_data=['dt'],
labels={
"dt": "Date and Time",
"numusers": "Users number",
},
title = 'Total Users Vs Time')
plot_users_vs_time.update_layout({"plot_bgcolor": "rgb(240, 240, 240)"})
##USER TIME SOURCE Histogram ###
timeSource=['< 15 minutes', '>=15 min but < one hour', '>= last hour but < 24 hours', '>= 24 hours']
values = [ dfMapPlot.loc[dfMapPlot['color']=='< 15 minutes']['color'].count(),
dfMapPlot.loc[dfMapPlot['color']=='>=15 min but < one hour']['color'].count(),
dfMapPlot.loc[dfMapPlot['color']=='>= last hour but < 24 hours']['color'].count(),
dfMapPlot.loc[dfMapPlot['color']=='>= 24 hours']['color'].count()
]
colors = ["green","yellow","orange", "gray"]
hist_time_source = go.Figure([go.Bar(x=timeSource, y=values)])
hist_time_source = px.histogram( x=timeSource, y=values, color=colors,
color_discrete_map="identity",labels={
"x": "Time Range",
"y": "Users",
})
hist_time_source.update_layout(title = "Users' GPS Availability")
hist_time_source.update_layout({"plot_bgcolor": "rgb(240, 240, 240)"})
## Users' GPS availability vs Time ##
dfGPStime = pd.DataFrame(columns=['senttime','< 15 minutes','>=15 min but < one hour','>= last hour but < 24 hours','>= 24 hours'])
#getting a series of unique 'senttime' values
dfSilentSenttime = fdc.dfSilentNotif.sort_values(by='senttime', ascending=True)['senttime'].unique()
for svalue in dfSilentSenttime:
fval = sval = tval = fval = 0
fval = fdc.dfSilentNotif[(fdc.dfSilentNotif["senttime"]==svalue)&(fdc.dfSilentNotif["senttime"] - fdc.dfSilentNotif["userLocTime"] < fithteenMin)]['userLocTime'].count()#&(not math.isnan(fdc.dfSilentNotif["userLat"]))&(fdc.dfSilentNotif["userLat"]!=0.0)]["userLocTime"].count()
sval = fdc.dfSilentNotif[(fdc.dfSilentNotif["senttime"]==svalue)&(fdc.dfSilentNotif["senttime"] - fdc.dfSilentNotif["userLocTime"] >= fithteenMin)&(fdc.dfSilentNotif["senttime"] - fdc.dfSilentNotif["userLocTime"] < lastHour)]['userLocTime'].count()
tval = fdc.dfSilentNotif[(fdc.dfSilentNotif["senttime"]==svalue)&(fdc.dfSilentNotif["senttime"] - fdc.dfSilentNotif["userLocTime"] >= lastHour)&(fdc.dfSilentNotif["senttime"] - fdc.dfSilentNotif["userLocTime"] < today)]['userLocTime'].count()
foval = fdc.dfSilentNotif[(fdc.dfSilentNotif["senttime"]==svalue)&(fdc.dfSilentNotif["senttime"] - fdc.dfSilentNotif["userLocTime"] >= today)]['userLocTime'].count()
dfGPStime = pd.concat([pd.DataFrame([[svalue,fval,sval,tval,foval]], columns=dfGPStime.columns), dfGPStime], ignore_index=True)
#Some plot as a function of time but not presented on dashboard.
#############dfGPStime["dt"] = pd.to_datetime(dfGPStime['senttime'], unit='ms')
#############fig = go.Figure(data=[
#############go.Bar(name='>= 24 hours', x=dfGPStime['dt'], y=dfGPStime['>= 24 hours'],width=8*60*60*1000, marker_color='gray'),
#############go.Bar(name='>= last hour but < 24 hours', x=dfGPStime['dt'], y=dfGPStime['>= last hour but < 24 hours'],width=8*60*60*1000, marker_color='orange'),
#############go.Bar(name='>=15 min but < one hour', x=dfGPStime['dt'], y=dfGPStime['>=15 min but < one hour'],width=8*60*60*1000, marker_color='yellow'),
#############go.Bar(name='< 15 minutes', x=dfGPStime['dt'], y=dfGPStime['< 15 minutes'],width=8*60*60*1000, marker_color='green')])
############## Change the bar mode
#############fig.update_layout(barmode='stack')
##############fig.show()
###Mode Value vs Time ###
#############dfModeSilent = pd.DataFrame(columns=['senttime','mode'])
#############for svalue in dfSilentSenttime:
############# tmpDf = fdc.dfSilentNotif[(fdc.dfSilentNotif["senttime"]==svalue)]
############# modeVal = 0
############# if tmpDf.size > 0:
############# p80 = np.percentile(tmpDf['delay'], 80)
############# df80=tmpDf[tmpDf['delay']<=p80]['delay']
############# modeVal = df80.mode()
############# #if modeVal < 0:
############# # print(df80)
############# dfModeSilent = pd.concat([pd.DataFrame([[svalue,modeVal]], columns=dfModeSilent.columns), dfModeSilent], ignore_index=True)
#############
#############dfModeSilent["dt"] = pd.to_datetime(dfModeSilent['senttime'], unit='ms')
#############fig = px.line(dfModeSilent, x='dt', y='mode', markers=True)
#############fig.show()
#RESUME CARD
totalUsers = fdc.dfSilentNotif.loc[fdc.dfSilentNotif['senttime']==lastSNtimestamp]['userid'].count()
dtObj = datetime.datetime.fromtimestamp(lastSNtimestamp/1000.0)
cardTotalUsers = [
dbc.CardHeader("Usuarios Disponibles", className="card-title"),
dbc.CardBody(
[
html.H2(str(totalUsers)+" usuarios", className="card-body fw-bold"),
]
),
dbc.CardFooter("Verificado: "+ dtObj.strftime("%d-%m-%Y %H:%M:%S"),
className="card-text",)
]
diffFirstNotif = statistics.mode(fdc.dfEventsDelay.loc[fdc.dfEventsDelay['updateno']==0]['delay'])
diffAllNotif = statistics.mode(fdc.dfEventsDelay.loc[fdc.dfEventsDelay['updateno']>0]['delay'])
cardDelayFirstAndUpdates =[
dbc.CardHeader("Delay de Notificaciones - Sismos",className="card-title"),
dbc.CardBody(
[
html.P(html.H5(str(diffFirstNotif)+" s. (Primera Notificación)", className="card-body fw-bold")),
html.P(html.H5(str(diffAllNotif)+ " s. (Actualizaciones)", className="card-body fw-bold"), className=' p-0')
#html.H5(, className="card-body fw-bold"),
]
),
dbc.CardFooter("Valor de Moda en Distribución"),
]
diffSilentNotif = statistics.mode(fdc.dfSilentNotif['delay'])#.mean()
cardDelaySilentNotif = [
dbc.CardHeader("Delay de Notif. Silenciosas",className="card-title"),
dbc.CardBody(
[
html.H1(str(diffSilentNotif)+" s.", className="card-body fw-bold"),
]
),
dbc.CardFooter("Valor de Moda en Distribución")
]
resumenTab1 =dbc.CardGroup( [
dbc.Card(cardTotalUsers, color="info", inverse=True),
dbc.Card(cardDelayFirstAndUpdates, color="info", inverse=True),
dbc.Card(cardDelaySilentNotif, color="info", inverse=True),
]
)
##"heat" map with delays ##
dfMapPlot["logdelay"] = np.log( dfMapPlot['delay'])
map_user_delays = px.scatter_mapbox(dfMapPlot, lat=dfMapPlot['userLat'], lon=dfMapPlot['userLon'], color="logdelay",# size="delay",
color_continuous_scale=px.colors.sequential.Bluyl, size_max=15, zoom=5,
hover_data= {
"delay": True,
"userLat": False,
"userLon": False,
"logdelay": False
},
labels={
"logdelay": "Ln(Delay)"
})
map_user_delays.update_layout(margin={"r":20,"t":40,"l":20,"b":10})
map_user_delays.update_layout(title = "Geographical Distribution of Users' Delay",legend_title_text="Ln(Delay)")
map_user_delays.update_layout(
mapbox=dict(
bearing=0,
center=go.layout.mapbox.Center(
lat=countryLat,
lon=countryLon
),
pitch=0,
zoom=8
)
)
map_user_delays.update_layout(mapbox_style="dark")
map_user_delays.update_layout(legend_title_text="Ln(Delay)")
###Distribution - Events####
x1 = fdc.dfEventsDelay.loc[(fdc.dfEventsDelay['updateno']==0)&(fdc.dfEventsDelay['delay']>-60)&(fdc.dfEventsDelay['delay']<=60)&(fdc.dfEventsDelay['eventid'].str.contains("2023"))]
x2 = fdc.dfEventsDelay.loc[(fdc.dfEventsDelay['updateno']==1)&(fdc.dfEventsDelay['delay']>-60)&(fdc.dfEventsDelay['delay']<=60)&(fdc.dfEventsDelay['eventid'].str.contains("2023"))]
x3 = fdc.dfEventsDelay.loc[(fdc.dfEventsDelay['updateno']==2)&(fdc.dfEventsDelay['delay']>-60)&(fdc.dfEventsDelay['delay']<=60)&(fdc.dfEventsDelay['eventid'].str.contains("2023"))]
x4 = fdc.dfEventsDelay.loc[(fdc.dfEventsDelay['updateno']==3)&(fdc.dfEventsDelay['delay']>-60)&(fdc.dfEventsDelay['delay']<=60)&(fdc.dfEventsDelay['eventid'].str.contains("2023"))]
####print("Percentiles 20% - First Notification")
####p20first = np.percentile(x1['delay'], 20)
####p20second = np.percentile(x2['delay'], 20)
####p20third = np.percentile(x3['delay'], 20)
####p20fourth = np.percentile(x4['delay'], 20)
####print("First: "+str(p20first)+", second: "+ str(p20second)+", third: "+ str(p20third)+", forth: "+str(p20fourth) )
####print("Percentiles 50% - First Notification")
####p50first = np.percentile(x1['delay'], 50)
####p50second = np.percentile(x2['delay'], 50)
####p50third = np.percentile(x3['delay'], 50)
####p50fourth = np.percentile(x4['delay'], 50)
####print("First: "+str(p50first)+", second: "+ str(p50second)+", third: "+ str(p50third)+", forth: "+str(p50fourth) )
####
####print("Percentiles 80% - First Notification")
####p80first = np.percentile(x1['delay'], 80)
####p80second = np.percentile(x2['delay'], 80)
####p80third = np.percentile(x3['delay'], 80)
####p80fourth = np.percentile(x4['delay'], 80)
####print("First: "+str(p80first)+", second: "+ str(p80second)+", third: "+ str(p80third)+", forth: "+str(p80fourth) )
####
####print("Percentiles 95% - First Notification")
####p95first = np.percentile(x1['delay'], 95)
####p95second = np.percentile(x2['delay'], 95)
####p95third = np.percentile(x3['delay'], 95)
####p95fourth = np.percentile(x4['delay'], 95)
####print("First: "+str(p95first)+", second: "+ str(p95second)+", third: "+ str(p95third)+", forth: "+str(p95fourth) )
hist_data = [x1["delay"], x2["delay"], x3["delay"], x4["delay"]]
group_labels = ['First Notif.', 'Update 1', 'Update 2','Update 3']
distr_events_delays = ff.create_distplot(hist_data, group_labels, colors=px.colors.qualitative.G10,
bin_size=[0.1, 0.1, 0.1, 0.1], show_curve=True,show_rug=False)
distr_events_delays.update_layout(title_text='Delay Distribution - EQs',xaxis=dict(
title="Delay [s]"
))
distr_events_delays.update_layout(xaxis_range=[-0.5,8])
distr_events_delays.update_layout(barmode='stack')
distr_events_delays.update_layout({"plot_bgcolor": "rgb(240, 240, 240)"})
### PIE For First Notification Events ####
pieFirst = {"-0.5 to 0 s":0,"0 to 1 s":0,"1 to 2 s":0,"2 to 3 s":0,"3 to 4 s":0,"4 to 5 s":0,"5 to 60 s":0}
pieUpdates = {"-0.5 to 0 s":0,"0 to 1 s":0,"1 to 2 s":0,"2 to 3 s":0,"3 to 4 s":0,"4 to 5 s":0,"5 to 60 s":0}
pieFirst["-0.5 to 0 s"] = len( x1.loc[ (x1['delay'] > -0.5)&( x1['delay'] <= 0 ) ]['delay'] )
pieFirst["0 to 1 s"] = len( x1.loc[ (x1['delay'] > 0 )&( x1['delay'] <= 1 ) ]['delay'] )
pieFirst["1 to 2 s"] = len( x1.loc[ (x1['delay'] > 1 )&( x1['delay'] <= 2 ) ]['delay'] )
pieFirst["2 to 3 s"] = len( x1.loc[ (x1['delay'] > 2 )&( x1['delay'] <= 3 ) ]['delay'] )
pieFirst["3 to 4 s"] = len( x1.loc[ (x1['delay'] > 3 )&( x1['delay'] <= 4 ) ]['delay'] )
pieFirst["4 to 5 s"] = len( x1.loc[ (x1['delay'] > 4 )&( x1['delay'] <= 5 ) ]['delay'] )
pieFirst["5 to 60 s"] = len( x1.loc[ (x1['delay'] > 5 )&( x1['delay'] <= 60 ) ]['delay'] )
pieUpdates["-0.5 to 0 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > -0.5)&( fdc.dfEventsDelay['delay'] <= 0 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pieUpdates["0 to 1 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > 0)&( fdc.dfEventsDelay['delay'] <= 1 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pieUpdates["1 to 2 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > 1)&( fdc.dfEventsDelay['delay'] <= 2 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pieUpdates["2 to 3 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > 2)&( fdc.dfEventsDelay['delay'] <= 3 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pieUpdates["3 to 4 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > 3)&( fdc.dfEventsDelay['delay'] <= 4 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pieUpdates["4 to 5 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > 4)&( fdc.dfEventsDelay['delay'] <= 5 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pieUpdates["5 to 60 s"] = len( fdc.dfEventsDelay.loc[ (fdc.dfEventsDelay['updateno']>0)&(fdc.dfEventsDelay['delay'] > 5)&( fdc.dfEventsDelay['delay'] <= 60 )&(fdc.dfEventsDelay['eventid'].str.contains("2023")) ]['delay'] )
pie_first_notif_delays = go.Figure(data=[go.Pie(labels=list(pieFirst.keys()), values=list(pieFirst.values()),hole=.3,
marker = {'colors':list(colorMapDicPie.values())})])
pie_first_notif_delays.update_layout(title_text='% of Delays By Time Range<br>First Notification')
pie_first_notif_delays.update_layout({"plot_bgcolor": "rgb(240, 240, 240)"})
pie_updates_notif_delay = go.Figure(data=[go.Pie(labels=list(pieUpdates.keys()), values=list(pieUpdates.values()),hole=.3,
marker = {'colors':list(colorMapDicPie.values())})])
pie_updates_notif_delay.update_layout(title_text='% of Delays by Time Range<br>Updates')
pie_updates_notif_delay.update_layout({"plot_bgcolor": "rgb(240, 240, 240)"})
### Distribution - Silent Notifications ####
group_labels = ['Silent Notif.']
x1 = fdc.dfSilentNotif.loc[(fdc.dfSilentNotif['delay']>-60)&(fdc.dfSilentNotif['delay']<=60)]["delay"]
hist_data = [x1]
########print("Percentiles 20% - Silent Notifications")
########p20first = np.percentile(x1, 20)
########print("First: "+str(p20first))
########print("Percentiles 50% - Silent Notifications")
########p50first = np.percentile(x1, 50)
########print("First: "+str(p50first))
########
########print("Percentiles 80% - Silent Notifications")
########p80first = np.percentile(x1, 80)
########print("First: "+str(p80first))
########
########print("Percentiles 95% - Silent Notifications")
########p95first = np.percentile(x1, 95)
########print("First: "+str(p95first))
#print(x1.mode().mean())
distr_silent_notif_delays = ff.create_distplot(hist_data, group_labels, colors=px.colors.qualitative.G10,
bin_size=0.1, show_curve=True,show_rug=False)
distr_silent_notif_delays.update_layout(title_text='Delay Distribution - Silent Notifications',xaxis=dict(
title="Delay [s]"
))
distr_silent_notif_delays.update_layout(xaxis_range=[-0.5,8])
distr_silent_notif_delays.update_layout({"plot_bgcolor": "rgb(240, 240, 240)"})
###############################
### PIE for Events Updates ####
###############################
pieSilentNotif = {"-0.5 to 0 s":0,"0 to 1 s":0,"1 to 2 s":0,"2 to 3 s":0,"3 to 4 s":0,"4 to 5 s":0,"5 to 60 s":0}
x1 = fdc.dfSilentNotif.loc[(fdc.dfSilentNotif['delay']>-60)&(fdc.dfSilentNotif['delay']<=60)]
pieSilentNotif["-0.5 to 0 s"] = len( x1.loc[ (x1['delay'] > -0.5)&( x1['delay'] <= 0 ) ]['delay'] )
pieSilentNotif["0 to 1 s"] = len( x1.loc[ (x1['delay'] > 0 )&( x1['delay'] <= 1 ) ]['delay'] )
pieSilentNotif["1 to 2 s"] = len( x1.loc[ (x1['delay'] > 1 )&( x1['delay'] <= 2 ) ]['delay'] )
pieSilentNotif["2 to 3 s"] = len( x1.loc[ (x1['delay'] > 2 )&( x1['delay'] <= 3 ) ]['delay'] )
pieSilentNotif["3 to 4 s"] = len( x1.loc[ (x1['delay'] > 3 )&( x1['delay'] <= 4 ) ]['delay'] )
pieSilentNotif["4 to 5 s"] = len( x1.loc[ (x1['delay'] > 4 )&( x1['delay'] <= 5 ) ]['delay'] )
pieSilentNotif["5 to 60 s"] = len( x1.loc[ (x1['delay'] > 5 )&( x1['delay'] <= 60 ) ]['delay'] )
pie_silent_notif_delays = go.Figure(data=[go.Pie(labels=list(pieSilentNotif.keys()), values=list(pieSilentNotif.values()),hole=.3,
marker = {'colors':list(colorMapDicPie.values())})])
pie_silent_notif_delays.update_layout(title_text='% of Delays by Time Range <br>Silent Notifications')
##################SECOND TAB #########################
lastEvtId = fdc.dfEventsDelay.loc[fdc.dfEventsDelay['eventid'].str.contains(fdc.config.datacentercode)].sort_values(by='eventid', ascending=False)['eventid'].unique()[0]
# initialise data of lists.
data = {'eventid':[lastEvtId]}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
outfile = df.to_json(date_format='iso', orient='split')
return map_users, plot_users_vs_time, hist_time_source, map_user_delays,distr_events_delays, pie_first_notif_delays,\
pie_updates_notif_delay, distr_silent_notif_delays, pie_silent_notif_delays,resumenTab1, outfile# \
##Callback to update the local csv data with new information stored in Firestore DB
# press the "Actualizar" button when you consider is needed.
@app.callback(
Output("url", "href"),
Input("buttonUpdate", "n_clicks"),
prevent_initial_call=True,
)
def reload_data(_):
#This method will update the data collected from FIRESTORE
try:
fdc = Data()
fdc.loadConfigData()
fdc.refreshData() #This is a straight request to Firebase. Don't use too much.
except Exception as e:
Log.error("Problem updating the data from Firestore DB")
Log.error(repr(e))
return
return "/"
def plotsByEvent(eventid):
fdc = Data()
fdc.loadConfigData()
mapbox_access_token = open(".mapbox_token").read()
colorMapDicPie = { "-0.5 to 0 s": "gray",
"0 to 1 s": "#00CC96",
"1 to 2 s": "#FFA15A",
"2 to 3 s": "#19D3F3",
"3 to 4 s": "#636EFA",
"4 to 5 s": "#EF553B",
"5 to 60 s": "#AB63FA"}
if eventid == "lastevent":
lastEvtId = fdc.dfEventsDelay.loc[fdc.dfEventsDelay['eventid'].str.contains(fdc.config.datacentercode)].sort_values(by='eventid', ascending=False)['eventid'].unique()[0]
else:
lastEvtId = eventid
fdsn = readfdsn.ReadFDSNWS(fdc.config.fdsnwsurl)
dfEventInfo = fdsn.getEventInfoByID(lastEvtId)
tmpInfoText = ''
tmpDateTime = ''
if dfEventInfo.empty:
Log.error("No information from FDSN for event: "+ lastEvtId)
tmpInfoText = "Sin Información desde FDSNWS"
tmpInfoPlace=""
else:
tmpInfoText = "Magnitud: "+str(round(dfEventInfo['magnitude'][0],1))+", Prof.: "+str(round(dfEventInfo['depth'][0]))+ " km."
dtObj = datetime.datetime.strptime(dfEventInfo['originTime(UTC)'][0] , "%Y-%m-%dT%H:%M:%S.%fZ")
tmpDateTime = "Fecha y Hora del sismo: "+dtObj.strftime("%Y-%m-%d %H:%M:%S (UTC)") \
#The cities file must be loaded
nearPlace = util.findNearestPlace(fdc.cities, dfEventInfo['latitude'][0], dfEventInfo['longitude'][0])
#print("NEAR PLACE")
#print(nearPlace)
tmpDist = util.distance([float(nearPlace['lat']),dfEventInfo['latitude'][0],float(nearPlace['lon']), dfEventInfo['longitude'][0]])
azimuth = util.azimuth([float(nearPlace['lat']),dfEventInfo['latitude'][0],float(nearPlace['lon']), dfEventInfo['longitude'][0]])
direction = util.direction(azimuth,"es-US")
location = util.location( tmpDist, direction, nearPlace['city'], nearPlace['country'],"es-US")
tmpInfoPlace= location
eventResumenCard = [
dbc.CardHeader("Evento "+lastEvtId),
dbc.CardBody(
[
html.H4(tmpInfoText, className="card-title"),
html.P(tmpInfoPlace, className="card-text"),
]
),
dbc.CardFooter(tmpDateTime),
]
dfTmpEvt = fdc.dfAlertsIntensity.loc[ fdc.dfAlertsIntensity['eventid'] == lastEvtId]
dfTmpEvt['alert'] = dfTmpEvt['alert'].fillna(0)
alertsFirstNotif = len(dfTmpEvt.loc[ (dfTmpEvt['updateno'] ==0)&(dfTmpEvt['alert']==1) ])
quickNotifFirstNotif = len(dfTmpEvt.loc[ (dfTmpEvt['updateno'] ==0)&(dfTmpEvt['alert']==0) ])
alertsInUpdates = len(dfTmpEvt.loc[ (dfTmpEvt['updateno'] >0)&(dfTmpEvt['alert']==1) ])
quickNotifUpdates = len(dfTmpEvt.loc[ (dfTmpEvt['updateno'] >0)&(dfTmpEvt['alert']==0) ])
alertsFirstTxt = str(alertsFirstNotif)+ " Alertas (Primera Notif.)"
quickNotifFirstTxt = str(quickNotifFirstNotif)+ " Notificaciones Rápidas (Primera Notif.)"
alertsInUpdatesTxt = str(alertsInUpdates)+ " Alertas en Actualizaciones"
quickNotifUpdatesTxt = str(quickNotifUpdates)+ " Notificaciones Rápidas en Actualizaciones"
notifCard = [
dbc.CardHeader("Cantidad y Tipos de Notificación"),
dbc.CardBody(
[
html.H5(alertsFirstTxt, className="card-title"),
html.H5(quickNotifFirstTxt, className="card-title"),
html.H5(alertsInUpdatesTxt, className="card-title"),
html.H5(quickNotifUpdatesTxt, className="card-title"),
]
),
]
try:
maxIntensityRep = int(dfTmpEvt['intensity'].max())
minIntensityRep = int(dfTmpEvt['intensity'].min())
numUsersIntensityRep = int(dfTmpEvt.loc[dfTmpEvt['intensity']>0]['intensity'].count())
except:
maxIntensityRep = 0
numUsersIntensityRep = 0
maxIntensityTxt = ""
numRepTxt = ""
backColor = "#FFFFFF"
if maxIntensityRep == 0 or math.isnan(maxIntensityRep):
maxIntensityTxt = "Sin Reportes de Intensidad"
numRepTxt = ""
elif maxIntensityRep > 0:
roman = util.intToColorDescription(maxIntensityRep)
maxIntensityTxt = roman.split(";")[0]
backColor= roman.split(";")[1]
numRepTxt = str(numUsersIntensityRep) + " Reportes de Usuarios"
else:
maxIntensityTxt = "Sin Reportes de Intensidad"
intensityCard = [
dbc.CardHeader("Máxima Intensidad Reportada" ),
dbc.CardBody(
[
html.H1(maxIntensityTxt, className="card-title"),
],style={"background-color":backColor,"text-align": "center"}
),
dbc.CardFooter(numRepTxt),
]
dfTmp = fdc.dfEventsDelay[fdc.dfEventsDelay["eventid"]==eventid]
tmpVal = dfTmp["updateno"].unique()
if len(tmpVal) == 1:
numOfNotif = 1
else:
numOfNotif = dfTmp["updateno"].unique().max()
modeFirstNotif = statistics.mode(dfTmp.loc[(dfTmp['updateno']==0)]['delay'])
try:
modeUpdatesNotif = statistics.mode(dfTmp.loc[(dfTmp['updateno']>0)]['delay'])
except:
modeUpdatesNotif = None
if modeUpdatesNotif == None or math.isnan(modeUpdatesNotif):
tmpTxt = ""
else:
tmpTxt = str(round(modeUpdatesNotif,3))+ " s. (Actualizaciones)"
totalNotif = len(dfTmp['updateno'].unique())
delayCard = [
dbc.CardHeader("Delay de Notificaciones - Moda" ),
dbc.CardBody(
[
html.H4(str(round(modeFirstNotif,3))+ " s. (Primera Notif.)", className="card-title"),
html.H4(tmpTxt, className="card-title"),
],
),
dbc.CardFooter("Total de Notificaciones: "+ str(totalNotif)),
]
resumenTab2 =dbc.CardGroup( [
dbc.Card(eventResumenCard, color="primary", outline=True),
dbc.Card(intensityCard, color="primary", outline=True),
dbc.Card(notifCard, color="primary", outline=True),
dbc.Card(delayCard, color="primary", outline=True),
]
)
###EPICENTER MAP###
epiMap = None
if dfEventInfo.empty:
Log.error("No info for this event from FDSN. No epimap")
else:
epiLat = [dfEventInfo['latitude'][0]]
epiLon = [dfEventInfo['longitude'][0]]
epiMag = round(dfEventInfo['magnitude'][0],1)
epiMap = go.Figure(go.Scattermapbox(
lat=epiLat,
lon=epiLon,
mode='markers',
marker=go.scattermapbox.Marker(
size=14
),
text="Mag: "+str(epiMag),
))
# mapbox_access_token = open(".mapbox_token").read()
epiMap.update_layout(
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
center=go.layout.mapbox.Center(
lat=epiLat[0],
lon=epiLon[0]
),
pitch=0,
zoom=5
)
)
####INTENSITY MAP DISTRIBUTION####
dfIntensity = fdc.dfAlertsIntensity.loc[(fdc.dfAlertsIntensity['eventid']==lastEvtId)&(fdc.dfAlertsIntensity['lat']!=-999)&(fdc.dfAlertsIntensity['intensity']>0)]
dfIntensity['color']=''
dfIntensity['intDes']=''
dfIntensity['intensity'] = dfIntensity['intensity'].astype(int)
for index, row in dfIntensity.iterrows():
intVal = row['intensity']
intDesc = util.intToColorDescription(intVal).split(";")[0]
colorCode = util.intToColorDescription(intVal).split(";")[1]
dfIntensity['color'][index]=colorCode
dfIntensity['intDes'][index]=intDesc
px.set_mapbox_access_token(open(".mapbox_token").read())
intensityMap = None
color_intensities = []
intensityMap = {}
color_intensities = []
rangeValue = 0
if maxIntensityRep == 1:
rangeValue = 2
else:
rangeValue = maxIntensityRep
for i in range(1,rangeValue+1,1):
tmp = util.intToColorDescription(i)
color_intensities.append(tmp.split(";")[1])
tmp = fdc.config.countrylatlon.split(",")
countryLat = float(tmp[0])
countryLon = float(tmp[1])
if dfIntensity.empty:
Log.error("No data to present on intensity map")
else:
#intensityMap = go.Figure(go.Scattermapbox(
# lat=dfIntensity["lat"],
# lon=dfIntensity["lon"],
# mode="markers",
# marker=go.scattermapbox.Marker(
# symbol = "circle",
# size = 8,
# opacity = 0.9,
# color = dfIntensity['intensity'],
# colorscale = color_intensities,
# showscale = True,
# ),
# ))
intensityMap = px.scatter_mapbox(dfIntensity,
lat=dfIntensity['lat'],
lon=dfIntensity['lon'],
hover_name="intDes",
color=dfIntensity['intensity'],
color_continuous_scale=color_intensities,
zoom=1,
labels={
"intensity": "MMI"
},
size=np.repeat(10,dfIntensity['intensity'].size),
opacity = 1,
size_max = 10
)
#intensityMap.update_traces(cluster=dict(enabled=True,maxzoom=10))
intensityMap.update_layout(coloraxis_colorbar=dict(
title="MMI",
tickvals=[1,2,3,4,5,6,7,8,9,10,11,12],
ticktext=romanNumbersArr,
))
if not dfEventInfo.empty:
intMapCenterLat = dfEventInfo['latitude'][0]
intMapCenterLon = dfEventInfo['longitude'][0]
else:
intMapCenterLat = countryLat
intMapCenterLon = countryLon
intensityMap.update_layout(margin={"r":10,"t":40,"l":10,"b":10})
intensityMap.update_traces(showlegend=False)
intensityMap.update_layout(
mapbox=dict(
accesstoken= mapbox_access_token,
bearing=0,
center=go.layout.mapbox.Center(
lat=intMapCenterLat,
lon=intMapCenterLon
),
pitch=0,
zoom=6
)
)
intensityMap.update_layout(title = 'MMI Map',legend_title_text="MMI")
#intensityMap.update_traces(marker=dict(size=10,
# line=dict(width=1,
# color='DarkSlateGrey')),
# selector=dict(mode='markers'))
if not dfEventInfo.empty:
epicenterMap = go.Scattermapbox(
lat=epiLat,
lon=epiLon,
mode='markers',
fillcolor= 'red',
showlegend=False,
marker=go.scattermapbox.Marker(
size=14,
symbol='star',
color='#FFFFFF',
),
text="Mag: "+str(epiMag),
)
#epicenterMap.update_layout(showlegend=False)
intensityMap.add_trace(epicenterMap)
###Intensity vs Epicentral Distance
# Intensity reports with Location are used along the event information (epi lat,lon)