app.py

# -*- coding: utf-8 -*-
# author: Jorge Gomes for VOST Portugal

# ------------------------------
#      VERSIONS
# ------------------------------

# V 1.0 22 MAR 2022 - The basics

# PROCIV CHECKER

# ------------------------------
#       IMPORT LIBRARIES
# ------------------------------

# ---------- IMPORT BASIC LIBRARIES ------------

import json
import requests
import pandas as pd 
import datetime as dt 
from datetime import datetime, timedelta, date 

# ---------- IMPORT PLOTLY LIBRARIES ------------
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio

# ---------- IMPORT DASH LIBRARIES ------------
import dash
import dash_daq as daq
from dash import Input, Output, dcc, html
import dash_bootstrap_components as dbc


import brand



#

# ------------------------------
#      START DASH APP
# ------------------------------

app = dash.Dash(
    external_stylesheets=[dbc.themes.CYBORG],
    title='VOSTPT:DASHBOARD',update_title=None,
    meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}],
)

# ------------------------------
#       INITIAL VARIABLES 
# ------------------------------

# Define Start Date to Today
end_date = dt.datetime.today().strftime('%Y-%m-%d')
start_date = (datetime.today() - timedelta(days=2)).strftime('%Y-%m-%d')
# Define Initial button status
selector = 1
# Define Initial Limit 
limit = 100000

# Define Initial URLs
url_bar = f"https://api.fogos.pt/v2/incidents/search" \
f"?before={end_date}" \
f"&after={start_date}" \
f"&limit={limit}" \
f"&all={selector}" \





# ------------------------------
#      GET INITIAL DATA
# ------------------------------

# Get response from URL 
response = requests.get(url_bar)

# Get the json content from the response
json = response.json()


# ------------------------------
#        DATA TREATMENT
# ------------------------------


# UPDATE 

# Create Pandas Dataframe from the normalized json response
# that begins at "data" level. 
# Depending on your json file this may vary. 
# Use print(json) in order to check the  structure of your json fle
df_in = pd.json_normalize(json,'data')


# Create day column by extracting the day from the date column
df_in['day'] = pd.DatetimeIndex(df_in['date']).day
# Convert seconds to DateTime format 
df_in['dateTime.sec'] = pd.to_datetime(df_in['dateTime.sec'], unit='s')

# Create Dataframes for the first graphs

df_in_pie = df_in.groupby(['natureza','day','familiaName'],as_index=False)['sadoId'].nunique()
df_in_bar = df_in.groupby(['natureza','date'],as_index=False)['sadoId'].nunique()
df_in_line = df_in.groupby(['dateTime.sec'],as_index=False)['sadoId'].nunique()


# ------------------------------
#      DEFINE GRAPHS
# ------------------------------

# Define pie, bar, and line graphs 
fig_pie = px.pie(df_in_pie,names='natureza',values='sadoId',color='natureza',hole=0.5,color_discrete_sequence=px.colors.sequential.Viridis)
fig_bar = px.bar(df_in_bar,x='date',y='sadoId', color='natureza',color_discrete_sequence=px.colors.sequential.Viridis_r,template='plotly_dark')
fig_line = px.line(df_in_line,x='dateTime.sec',y='sadoId', color_discrete_sequence=px.colors.sequential.Viridis_r,template='plotly_dark')

# Styling for graphs

fig_pie.update_traces(textposition='inside', textinfo='value+percent+label')
fig_pie.update_layout(uniformtext_minsize=12, uniformtext_mode='hide',template='plotly_dark')

# ------------------------------
#      START APP LAYOUT
# ------------------------------
def serve_layout():
    return dbc.Container(
        [
        
            dbc.Row(brand.logos),
            dbc.Row(
                [
                    dbc.Col(html.Hr(style={"height":"10px","color":"black"}),xs=12, sm=12, md=12, lg=12, xl=12),
                    dbc.Col(html.H3("Escolher Data:"),xs=6, sm=6, md=2, lg=2, xl=2,style={"align":"right"}),
                    dbc.Col(
                        # DATE PICKER 
                        dcc.DatePickerRange(
                            id='date-picker',
                            min_date_allowed=date(1995, 8, 5),
                            max_date_allowed=datetime.today(),
                            initial_visible_month=date(2022, 2, 1),
                            display_format='D/M/Y',
                            start_date=date.today(),
                            end_date=date.today()
                        ),
                    xs=12, sm=12, md=6, lg=6, xl=6,
                    ),
                    html.Hr(style={"height":"10px","color":"black"}),
                    html.Hr(style={"height":"10px","color":"black"}),
                ],
            ),
            dbc.Row(
                [
                    dbc.Col(
                        
                            html.H5("Apenas Incêndios",style={"color":"white"}),
                        
                    xs=6, sm=6, md=2, lg=2, xl=2,
                    ),
                    dbc.Col(
                        daq.ToggleSwitch(
                                id='fire_switch',
                                vertical=False,
                                size=40,
                                value=False,
                                color="#F8D03D",
                                
                                
                            ), 
                        xs=6, sm=6, md=1, lg=1, xl=1,

                        ),
                    dbc.Col(
                        html.H5(id="summary",
                                style={"color":"white"}
                                ),
                    xs=12, sm=12, md=9, lg=9, xl=9,
                    ), 
                ],
            ),
            dbc.Row(
                [
                    dbc.Col(
                            html.Div(
                                [
                                html.H5("Apenas FMA",style={"color":"white"}),
                                ],
                                ),
                    xs=6, sm=6, md=2, lg=2, xl=2,
                    ),
                    
                    dbc.Col(
                        daq.ToggleSwitch(
                                id='fma_switch',
                                vertical=False,
                                size=40,
                                value=False,
                                color="#08519C",
                                
                                
                            ), 
                        xs=6, sm=6, md=1, lg=1, xl=1,

                        ),
                    ],
            ),  
            dbc.Row(
                [
                dbc.Col(html.H5("Ocorrências últimos 30m: ",style={"color":"white"}),xs=12, sm=12, md=12, lg=12, xl=2,),
                dbc.Col(html.H5(id='dispatch',style={"color":"yellow"}),xs=12, sm=12, md=12, lg=12, xl=2,),
                dbc.Col(html.H5(id='arrival',style={"color":"orange"}),xs=12, sm=12, md=12, lg=12, xl=2,),
                dbc.Col(html.H5(id='ongoing',style={"color":"red"}),xs=12, sm=12, md=12, lg=12, xl=2,),
                dbc.Col(html.H5(id='resolution',style={"color":"green"}),xs=12, sm=12, md=12, lg=12, xl=2,),
                dbc.Col(html.H5(id='conclusion',style={"color":"gray"}),xs=12, sm=12, md=12, lg=12, xl=2,),      
                ],
            ),
            dbc.Row(
                [
                    
                            dbc.Col(
                                dcc.Loading(id='loader_pie', 
                                    type='dot',
                                    color='#FFFFFF',
                                    children=[
                                        dcc.Graph(id="graph_pie", figure=fig_pie), # PIE CHART
                                    ],
                                ),
                                xs=12, sm=12, md=12, lg=12, xl=12,
                            ),
                ],
            ),
            dbc.Row(
                    [
                            dbc.Col(
                                dcc.Loading(id='loader_bar', 
                                    type='dot',
                                    color='#FFFFFF',
                                    children=[
                                        dcc.Graph(id="graph_bar", figure=fig_bar), # BAR CHART
                                        ],
                                ),
                                xs=12, sm=12, md=12, lg=12, xl=12,
                            ), 
                    ],
            ),
            dbc.Row(
                [
                    dbc.Col(dcc.Graph(id="graph_line", figure=fig_line),xs=12, sm=12, md=12, lg=12, xl=12) # LINE GRAPH
                ],
                className="g-0",
            ),
    ],
)

app.layout = serve_layout 

# ------------------------------
#      START CALLBACKS
# ------------------------------

# Graphs CallBack 
# Three inputs: two from DatePicker one from Toggle Switch

@app.callback(

    Output(component_id="summary",component_property="children"),
    Output(component_id="dispatch",component_property="children"),
    Output(component_id="arrival",component_property="children"),
    Output(component_id="ongoing",component_property="children"),
    Output(component_id="resolution",component_property="children"),
    Output(component_id="conclusion",component_property="children"),
    Output(component_id="graph_pie",component_property="figure"),
    Output(component_id="graph_bar",component_property="figure"),
    Output(component_id="graph_line",component_property="figure"),
    Input('date-picker', 'start_date'),
    Input('date-picker', 'end_date'),
    Input('fire_switch', 'value'),
    Input('fma_switch','value'),

)

# Define what happens when datepicker or toggle switch change

def new_graphs(start_date,end_date,fma_switch,fire_switch):
   
    # Toggle Switch Check 
    # If off selector stays 1 
    # If on selector changes to 0 
    # Yeah, it doesn't make sense. 
    # Should be 0 for all and 1 for specific incidents
    # Complaints? Speak with Tomahock 

    if fire_switch == False and fma_switch == False:
            selector = 1
            fma = 0
            colors = px.colors.sequential.Viridis_r 
            limit = 100000
            url_dash = f"https://api.fogos.pt/v2/incidents/search" \
            f"?before={end_date}" \
            f"&after={start_date}" \
            f"&limit={limit}" \
            f"&fma={fma}" \
            f"&all={selector}" \

    elif fire_switch == True and fma_switch == False:
            selector = 1 
            fma = 1
            colors = px.colors.sequential.Blues
            limit = 100000
            url_dash = f"https://api.fogos.pt/v2/incidents/search" \
            f"?before={end_date}" \
            f"&after={start_date}" \
            f"&limit={limit}" \
            f"&fma={fma}" \
            f"&all={selector}" \

    elif fire_switch == False and fma_switch == True:
            selector = 0
            fma = 0
            colors = px.colors.sequential.Inferno_r
            limit = 100000
            url_dash = f"https://api.fogos.pt/v2/incidents/search" \
            f"?before={end_date}" \
            f"&after={start_date}" \
            f"&limit={limit}" \
            f"&fma={fma}" \
            f"&all={selector}" \

    else:
            selector = 1
            fma = 0
            colors = px.colors.sequential.Viridis_r
            limit = 100000
            url_dash = f"https://api.fogos.pt/v2/incidents/search" \
            f"?before={end_date}" \
            f"&after={start_date}" \
            f"&limit={limit}" \
            f"&fma={fma}" \
            f"&all={selector}" \

    


    # Get response from API CALL 
    response_dash = requests.get(url_dash)
  
    # Get the json content from the response
    json_dash = response_dash.json()
  
    # Create Pandas Dataframe from the normalized json response
    # that begins at "data" level. 
    # Depending on your json file this may vary. 
    # Use print(json) in order to check the  structure of your json fle
    df_dash = pd.json_normalize(json_dash,'data')


    # Create day column by extracting the day from the date column
    df_dash['day'] = pd.DatetimeIndex(df_dash['date']).day.astype(str)
    # Convert seconds to Date Time format 
    df_dash['dateTime.sec'] = pd.to_datetime(df_dash['dateTime.sec'], unit='s')

   
    # -------------------------------------------
    # Create dataframes for the updated graphs 
    # -------------------------------------------

    df_in_pie = df_dash.groupby(['natureza','day','familiaName'],as_index=False)['sadoId'].nunique()
    df_in_bar = df_dash.groupby(['natureza','date'],as_index=False)['sadoId'].nunique()
    df_in_line = df_dash.groupby(['dateTime.sec','natureza'],as_index=False)['sadoId'].nunique()

   

    df_half = df_in_line.resample('30min', on='dateTime.sec', offset='01s').sadoId.count().to_frame().reset_index()


    # ------------------------------
    # DEFINE THE UPDATED GRAPHS
    # ------------------------------


    # Define pie, bar, and line graphs 
    fig_pie = px.pie(df_in_pie,names='natureza',values='sadoId',color='natureza',hole=0.5,color_discrete_sequence=colors,labels={"natureza":"TIPO","sadoId":"Ocorrências"})
    fig_bar = px.bar(df_in_bar,x='date',y='sadoId', color='natureza',color_discrete_sequence=colors,template='plotly_dark',labels={"date":"DATA","sadoId":"Ocorrências"})
    fig_line = px.line(df_half,x='dateTime.sec',y='sadoId',color_discrete_sequence=colors,template='plotly_dark',labels={"dateTime.sec":"DATA","sadoId":"Ocorrências"})

    # Styling for graphs

    fig_pie.update_traces(textposition='inside', textinfo='value+percent+label')
    fig_pie.update_layout(uniformtext_minsize=12, uniformtext_mode='hide',template='plotly_dark')
    fig_line.update_xaxes(nticks=5)
    fig_bar.update_xaxes(nticks=5)

    # Building Summary 

    total_records_num  = str(len(df_in_line.sadoId))
    text = "Total de Ocorrências no Período Escolhido: "
    total_records = text + total_records_num

    # OnGoing Events 

    # Define Start Date to Today
    end_date = dt.datetime.today().strftime('%Y-%m-%d')
    start_date = (datetime.today() - timedelta(days=2)).strftime('%Y-%m-%d')
    # Define Initial button status
    selector = 1
    # Define Initial Limit 
    limit = 100000

    # Define Initial URLs
    url_bar_today = f"https://api.fogos.pt/v2/incidents/search" \
    f"?before={end_date}" \
    f"&after={start_date}" \
    f"&limit={limit}" \
    f"&all={selector}" \

    # ------------------------------
    #      GET  DATA
    # ------------------------------

    # Get response from URL 
    response_today = requests.get(url_bar_today)

    # Get the json content from the response
    json_today = response.json()


    # ------------------------------
    #        DATA TREATMENT
    # ------------------------------

    # Create Pandas Dataframe from the normalized json response
    # that begins at "data" level. 
    # Depending on your json file this may vary. 
    # Use print(json) in order to check the  structure of your json fle
    df_today = pd.json_normalize(json_today,'data')


    # Create day column by extracting the day from the date column
    df_today['day'] = pd.DatetimeIndex(df_in['date']).day
    # Convert seconds to DateTime format 
    df_today['dateTime.sec'] = pd.to_datetime(df_in['dateTime.sec'], unit='s')

    last_ts = df_today['dateTime.sec'].iloc[-1]

    first_ts = last_ts - pd.Timedelta(30, 'minutes')

    df_today_30m = df_today[df_today["dateTime.sec"] >= first_ts]

    em_curso = df_today_30m['status'].value_counts()

    print(em_curso)


    dispatch_val = str(em_curso['Despacho de 1º Alerta'])
    ongoing_val = str(em_curso['Em Curso'])
    arrival_val = str(em_curso['Chegada ao TO'])
    resolution_val = str(em_curso['Em Resolução'])
    conclusion_val =str(em_curso['Conclusão'])

    # Build Strings 

    dispatch_txt = "Despacho 1ª Alerta: "
    ongoing_txt = "Em Curso: "
    arrival_txt = "Em Chegada ao TO: "
    resolution_txt = "Em Resolução: "
    conclusion_txt = "Em Conclusão: "

    dispatch_var = dispatch_txt + dispatch_val
    arrival_var = arrival_txt + arrival_val
    ongoing_var = ongoing_txt + ongoing_val
    resolution_var = resolution_txt + resolution_val
    conclusion_var = conclusion_txt + conclusion_val


  

    # ------------------------------
    #        RETURN CALLBACK
    # ------------------------------


    return total_records, dispatch_var, arrival_var, ongoing_var, resolution_var, conclusion_var, fig_pie, fig_bar, fig_line


# ------------------------------
#      RUN DASH APP
# ------------------------------

if __name__ == "__main__":
    app.run_server(host='0.0.0.0', debug=False, port=8082)



# ------------------------------
#     APP ENDS HERE 
# ------------------------------

# Made with 🤍 by Jorge Gomes MARCH 2022