Add application file

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto

.streamlit/config.toml

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+[theme]
+base="light"

app.py

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+import streamlit as st
+from streamlit_echarts import st_pyecharts
+from plot import *
+from cpc_iri_web import header
+from st_on_hover_tabs import on_hover_tabs
+from datetime import datetime, timedelta
+import requests
+from PIL import Image
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+# Configurando a página
+st.set_page_config(layout='wide')
+
+# Estilos
+streamlit_style = """
+        <style>
+        footer {visibility: hidden;}
+        iframe[title="streamlit_echarts.st_echarts"]{ height: 500px;} 
+        ul.streamlit-expander {border-top: 0;}
+        ul.streamlit-expander {border-left: 0;}
+        ul.streamlit-expander {border-right: 0;}
+        ul.streamlit-expander {border-bottom: solid 2px;}
+        ul.streamlit-expander {border-radius: 0px;}
+        .css-11qx4gg {display: none;}
+       </style>
+        """
+st.markdown(streamlit_style, unsafe_allow_html=True) 
+st.markdown('<style>' + open('estilos/style.css').read() + '</style>', unsafe_allow_html=True)
+
+# Sidebar
+with st.sidebar:
+
+    logo = Image.open('imgs/logo.png')
+    st.image(logo)
+    st.divider()
+
+    component = on_hover_tabs(
+        tabName=['Índices oceânicos', 'Índice atmosférico', 'Mapas e índices diários', 'Previsão POAMA', 'Previsão CPC/IRI'], 
+        iconName=['storm', 'cloud', 'maps', 'public', 'public'], 
+        styles = {'navtab': {'background-color':'#111','color': '#818181','font-size': '15px',
+                            'transition': '0.5s',
+                            'white-space': 'nowrap',
+                            'text-transform': 'uppercase'},
+                            'tabOptionsStyle': {':hover :hover': {'color': 'white','cursor': 'pointer'}},
+                            'iconStyle':{'position':'fixed','left':'7.5px', 'text-align': 'left'},
+                            'tabStyle' : {'list-style-type': 'none', 'margin-bottom': '30px', 'padding-left': '30px'}},
+         default_choice=0)
+
+    # #st.header('Menu')
+    # component = sac.menu(
+
+    #     items=[
+    #         sac.MenuItem('Monitoramento', type='group', children=['Índices oceânicos', 'Índice atmosféricos', 'Mapas']),
+    #         sac.MenuItem('Previsão', type='group', children=['CPC/IRI']),
+    #         #sac.MenuItem('Estudo de casos', type='group', children=['ENA', 'Precipitação'])
+    #     ],
+    #     index=1
+    # )
+
+if component == 'Índices oceânicos':
+
+    st.title('Evolução dos Índices ENSO por região do Oceano Pacíficio', anchor=False)
+
+    descricao = '''
+    O índice ONI é calculado a partir da região 3.4 do Oceano Pacífico e pode ser utilizado para a caracterização dos episódios de <span style="color: red">aquecimento da TSM equatorial do oceano Pacífico</span> e <span style="color: blue">resfriamento da TSM equatorial do oceano Pacífico</span>. A duração de pelo menos cinco médias móveis consecutivas de três meses <span style="color: red">acima de 0.5°C</span> classifica um episódio de <span style="color: red">El Niño</span>, enquanto que <span style="color: blue">abaixo de -0.5°C</span> classifica um episódio de <span style="color: blue">La Ninã</span>. Além da região 3.4, as demais regiões do pacíficio equatorial (Nino3, Nino4, Nino1+2) também possuem influência nos padrões de teleconexão e devem ser monitorados. O Índice ONI e as anomalias de TSM são calculados a partir do conjunto de dados <i><a href='https://www.ncei.noaa.gov/products/extended-reconstructed-sst'>Extended Reconstructed Sea Surface Temperature (ERSSTv5).</i></a>
+    '''
+
+    st.write(descricao, unsafe_allow_html=True)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.header('Sazonal')
+        tab1, tab2, tab3, tab4, tab5 = st.tabs(['Índice ONI','Niño 1+2', 'Niño 3', 'Niño 3.4', 'Niño 4'])
+        with tab1:
+            plot = plot_oni_season()
+            st_pyecharts(plot_oni_season(), height="500px", width="100%")
+        with tab2:
+            plot = plot_sst_indexes_season('nino12')
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab3:
+            plot = plot_sst_indexes_season('nino3')
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab4:
+            plot = plot_sst_indexes_season('nino34')
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab5:
+            plot = plot_sst_indexes_season('nino4')
+            st_pyecharts(plot, height="500px", width="100%")    
+        
+    with col2:
+        st.header('Mensal')
+        tab1, tab2, tab3, tab4, tab5 = st.tabs(['Índice ONI','Niño 1+2', 'Niño 3', 'Niño 3.4', 'Niño 4'])
+        with tab1:
+            plot = plot_oni()
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab2:
+            plot = plot_sst_indexes('nino12')
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab3:
+            plot = plot_sst_indexes('nino3')
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab4:
+            plot = plot_sst_indexes('nino34')
+            st_pyecharts(plot, height="500px", width="100%")
+        with tab5:
+            plot = plot_sst_indexes('nino4')
+            st_pyecharts(plot, height="500px", width="100%")    
+
+    with st.expander('**Clique para ver a distribuição geográfica das regiões**'):
+        ex1, ex2, ex3 = st.columns(3)
+        with ex2:
+            st.image('https://www.ncei.noaa.gov/monitoring-content/teleconnections/nino-regions.gif', use_column_width=True)
+            st.markdown('<strong>Fonte: NOAA/NCEI</strong>', unsafe_allow_html=True)
+
+if component == 'Índice atmosférico':
+
+    st.title('Evolução do Índice SOI', anchor=False)
+    descricao = '''
+
+    O Índice de Oscilação Sul (Southern Oscillation Index - SOI) é um índice baseado nas diferenças observadas de pressão ao nível do mar (PNM) entre Tahti e Darwin, na Austrália, e pode ser um indicador das flutuações de larga escala durante as fases do fenômeno ENSO. Durante episódios de <span style="color: red">El Niño</span>, espera-se um um <span style="color: blue">Índice SOI negativo</span>. Para episódios de <span style="color: blue">La Niña</span>, espera-se um um <span style="color: red">Índice SOI positivo</span>. <a href="https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensocycle/soi.shtml">Mais informações sobre o Índice SOI.</a><br> 
+    '''
+    st.write(descricao, unsafe_allow_html=True)
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header('Sazonal')
+        plot = plot_soi_season()
+        st_pyecharts(plot, height="500px", width="100%") 
+    with col2:
+        st.header('Mensal')
+        plot = plot_soi()
+        st_pyecharts(plot, height="500px", width="100%") 
+
+# Mapas
+
+if component == 'Mapas e índices diários':
+
+    st.title('Mapas de diários de temperatura da superífice do mar (TSM), anomalia da temperatura da superífice do mar e tendência da TSM nos últimos 7 dias')
+    descricao = '''
+    <a href="https://coralreefwatch.noaa.gov/product/5km/">Fonte: Coral Reef Watch.</a><br> 
+    '''
+    st.write(descricao, unsafe_allow_html=True)
+    st.info('Clique ao lado da figura para ampliar seu tamanho')
+    fmt = '%Y%m%d'
+    now = datetime.now()
+
+    now_1day = now - timedelta(days=1)
+    now_1day_fmt = now_1day.strftime(fmt)
+    now_2day = now - timedelta(days=2)
+    now_2day_fmt = now_2day.strftime(fmt)
+
+    data_escolhida_tsm = st.date_input('Escolha ou digite a data desejada', now_1day, max_value=now_1day,  key='tsm')
+    data_escolhida_tsm_fmt = data_escolhida_tsm.strftime(fmt)
+    data_escolhida_tsm_year_fmt = data_escolhida_tsm.year
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.header('Temperatura')
+        url = f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/sst/png/{data_escolhida_tsm_year_fmt}/coraltemp_v3.1_global_{data_escolhida_tsm_fmt}.png'
+        response = requests.get(url)
+        if response.status_code == 200:
+            st.image(f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/sst/png/{data_escolhida_tsm_year_fmt}/coraltemp_v3.1_global_{data_escolhida_tsm_fmt}.png')
+        else:
+            st.image(f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/sst/png/{data_escolhida_tsm_year_fmt}/coraltemp_v3.1_global_{now_2day_fmt}.png')
+    with col2:
+        st.header('Anomalia')
+        url = f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/ssta/png/{data_escolhida_tsm_year_fmt}/ct5km_ssta_v3.1_global_{data_escolhida_tsm_fmt}.png'
+        response = requests.get(url)
+        if response.status_code == 200:
+            st.image(f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/ssta/png/{data_escolhida_tsm_year_fmt}/ct5km_ssta_v3.1_global_{data_escolhida_tsm_fmt}.png')
+        else:
+            st.image(f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/ssta/png/{data_escolhida_tsm_year_fmt}/ct5km_ssta_v3.1_global_{now_2day_fmt}.png')
+    with col3:
+        st.header('Tendência')
+        url = f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/sst-trend-7d/png/{data_escolhida_tsm_year_fmt}/ct5km_sst-trend-7d_v3.1_global_{data_escolhida_tsm_fmt}.png'
+        response = requests.get(url)
+        if response.status_code == 200:
+            st.image(f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/sst-trend-7d/png/{data_escolhida_tsm_year_fmt}/ct5km_sst-trend-7d_v3.1_global_{data_escolhida_tsm_fmt}.png')
+        else:
+            st.image(f'https://www.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1_op/image_browse/daily/sst-trend-7d/png/{data_escolhida_tsm_year_fmt}/ct5km_sst-trend-7d_v3.1_global_{now_2day_fmt}.png')
+
+    st.title('Índices diários da anomalia de temperatura da superífice do mar para regiões do ENSO.')
+    days = 1800
+    tab1, tab2, tab3, tab4 = st.tabs(['Niño 1+2', 'Niño 3', 'Niño 3.4', 'Niño 4'])
+    with tab1:
+        plot = plot_daily_ssta('nino12', days)
+        st_pyecharts(plot, height="500px", width="100%")
+    with tab2:
+        plot = plot_daily_ssta('nino3', days)
+        st_pyecharts(plot, height="500px", width="100%")
+    with tab3:
+        plot = plot_daily_ssta('nino34', days)
+        st_pyecharts(plot, height="500px", width="100%")
+    with tab4:
+        plot = plot_daily_ssta('nino4', days)
+        st_pyecharts(plot, height="500px", width="100%")  
+
+# if component == 'NOAA/CPC/NCEP':
+#     st.title('Apresentação da NOAA/CPC/NCEP a respeito das condições de ENSO observadas semanalmente')
+#     components.iframe('https://docs.google.com/presentation/d/e/2PACX-1vRTlPQuAwEDdVyjkwCwYabSuqL30Xfir6RVdYURyhlxd6-5fVhfEccAc-dXWmG2JA/embed?start=false&loop=false&delayms=3000', height=500)
+
+if component == 'Previsão POAMA':
+
+    # Figuras de previsão 
+
+    st.title('Previsão dos modelos para de anomalia a TSM na região 3.4')
+    descricao = '''
+    <a href="http://www.bom.gov.au/climate/enso/">Fonte: Poama</a><br> 
+    '''
+    st.write(descricao, unsafe_allow_html=True)
+    st.write('Última inicialização: <strong>01 Agosto 2023</strong>', unsafe_allow_html=True)
+    
+    tab1, tab2, tab3, tab4, tab5 = st.tabs(['Mês 1', 'Mês 2', 'Mês 3', 'Mês 4', 'Mês 5'])
+
+    with tab1:
+        st.image('https://github.com/josepaulo1233/enso-assets/blob/main/imgs/poama_model_lead1.png?raw=true')
+    with tab2:
+        st.image('https://github.com/josepaulo1233/enso-assets/blob/main/imgs/poama_model_lead2.png?raw=true')
+    with tab3:
+        st.image('https://github.com/josepaulo1233/enso-assets/blob/main/imgs/poama_model_lead3.png?raw=true')
+    with tab4:
+        st.image('https://github.com/josepaulo1233/enso-assets/blob/main/imgs/poama_model_lead4.png?raw=true')
+    with tab5:
+        st.image('https://github.com/josepaulo1233/enso-assets/blob/main/imgs/poama_model_lead5.png?raw=true')
+
+    # Gráfico
+    
+    import pandas as pd
+
+    st.title('Anomalia de TSM e probabilidade do ENSO')
+    descricao = '''
+    Previsão de anomalia de TSM e probabilidades da temperatura estar acima de +0.8°C (vermelho), em condições de neutralidade (verde), e abaixo de -0.8°C (azul) na região do Niño 3.4. <a href="http://www.bom.gov.au/climate/ocean/outlooks/#region=NINO34">Mais informações sobre a previsão do centro Australiano (POAMA).</a><br> 
+    '''
+    st.write(descricao, unsafe_allow_html=True)
+
+    url = 'http://www.bom.gov.au/climate/ocean/outlooks/archive/20230729//plumes/sstOutlooks.nino34.hr.png'
+    last_update = pd.to_datetime(url.split('/')[7], format='%Y%m%d')
+    last_update = last_update.strftime('%B %d, %Y')
+    st.write(f'Última atualização: <strong>{last_update}</strong>', unsafe_allow_html=True)
+    plot = plot_poama()
+    st_pyecharts(plot, height="300px", width="100%")
+
+if component == 'Previsão CPC/IRI':
+
+    st.title('Previsão do CPC/IRI para a anomalia de TSM na região do Niño 3.4', anchor=False)
+    descricao = '''
+    Dados fornecidos por <a href='https://iri.columbia.edu/ENSO'> The International Research Institute for Climate and Society, Columbia University Climate School </a><br>
+    '''
+    st.write(descricao, unsafe_allow_html=True)
+    st.write(f'Última atualização: <strong>{header}</strong>', unsafe_allow_html=True)
+    dicas = '''
+    Dicas: Passe o mouse sobre a legenda para destacar a previsão de determinado modelo ou clique para remover.
+    '''
+    tab1, tab2, tab3, tab4 = st.tabs(['Modelos dinâmicos', 'Modelos estatisticos', 'Todos modelos', 'Previsão de concenso'])
+    with tab1:
+        plot = plot_cpc_iri_dinamico()
+        st_pyecharts(plot, height="500px", width="100%", key='dinamico')
+        st.info(dicas, icon="ℹ️")
+    with tab2:
+        plot = plot_cpc_iri_estatistico()
+        st_pyecharts(plot, height="500px", width="100%", key='estatistico')
+        st.info(dicas, icon="ℹ️")
+    with tab3:
+        plot = plot_cpc_iri_todos()
+        st_pyecharts(plot, height="500px", width="100%", key='todos')
+        st.info(dicas, icon="ℹ️")
+    with tab4:
+        plot = plot_iri_concenso()
+        st_pyecharts(plot, height="500px", width="100%", key='concenso')
+        st.info('Dicas: Clique na legenda para remover alguma barra', icon="ℹ️")
+
+
+    
+
+

cpc_iri_web.py

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+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.chrome.service import Service
+from webdriver_manager.chrome import ChromeDriverManager
+from bs4 import BeautifulSoup
+import pandas as pd
+import streamlit as st
+
+#st.set_page_config(layout='wide')
+
+options = Options()
+options.add_argument('--headless')
+#@st.cache_resource
+def get_driver():
+    return webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
+
+driver = get_driver()
+driver.get("https://iri.columbia.edu/our-expertise/climate/forecasts/enso/current/?enso_tab=enso-sst_table")
+page = driver.page_source
+soup = BeautifulSoup(page, 'lxml')
+
+#cabeçalho
+header = soup.find('h4')
+header = header.text.split(':')[1]
+
+# table
+table = soup.find('table')
+df_table = pd.read_html(str(table))[0]
+df_table = pd.DataFrame(df_table.to_records())
+df_table = df_table[df_table.columns[1:]]
+df_table.set_index('Season', inplace=True)
+
+# Divs
+div = soup.find('div', {"id": "ENSO-Plume-Models-Table"})
+df = pd.read_html(str(div))[0]
+df = pd.DataFrame(df.to_records())
+df = df[df.columns[1:]]
+colunas = [x.split(',')[1].split(')')[0] for x in df.columns]
+colunas = [x.replace(' ', '').replace("'", "") for x in colunas]
+df.columns = colunas
+
+# modelos dinamicos
+modelos_dinamicos = df[1:18]
+modelos_dinamicos.set_index('Model', inplace=True)
+modelos_dinamicos = modelos_dinamicos.astype(float)
+modelos_dinamicos = modelos_dinamicos.T
+modelos_dinamicos['Média'] = modelos_dinamicos.mean(axis=1)
+
+# modelos estatisticos
+modelos_estatisticos = df[20:36]
+modelos_estatisticos.set_index('Model', inplace=True)
+modelos_estatisticos = modelos_estatisticos.astype(float)
+modelos_estatisticos = modelos_estatisticos.T
+modelos_estatisticos['Média'] = modelos_estatisticos.mean(axis=1)
+
+
+
+

estilos/style.css

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+section[data-testid='stSidebar'] {
+    background-color: #111;
+    flex-shrink: unset !important;
+    
+}
+
+@media(hover:hover) and (min-width: 600px) and (max-width: 769px){
+
+    header[data-testid="stHeader"] {
+        display:none;
+    }
+
+    section[data-testid='stSidebar'] {
+        height: 100%;
+        min-width:95px !important;
+        width: 95px !important;
+        margin-left: 305px;
+        position: relative;
+        z-index: 1;
+        top: 0;
+        left: 0;
+        background-color: #111;
+        overflow-x: hidden;
+        transition: 0.5s ease;
+        padding-top: 60px;
+        white-space: nowrap;
+    }
+
+    section[data-testid='stSidebar']:hover{
+        min-width: 330px !important;
+        }
+
+    button[kind="header"] {
+        display: none;
+    }
+
+    div[data-testid="collapsedControl"]{
+        display: none;
+    }
+
+}
+
+@media(hover: hover) and (min-width: 769px){
+
+    header[data-testid="stHeader"] {
+        display:none;
+    }
+
+    section[data-testid='stSidebar'] {
+        height: 100%;
+        min-width:95px !important;
+        width: 95px !important;
+        transform:translateX(0px);
+        position: relative;
+        z-index: 1;
+        top: 0;
+        left: 0;
+        background-color: #111;
+        overflow-x: hidden;
+        transition: 0.5s ease;
+        padding-top: 60px;
+        white-space: nowrap;
+    }
+
+    section[data-testid='stSidebar']:hover{
+        min-width: 330px !important;
+        }
+
+    button[kind="header"] {
+        display: none;
+    }
+
+    div[data-testid="collapsedControl"]{
+        display: none;
+    }
+}

packages.txt

@@ -0,0 +1 @@
+chromium

plot.py

@@ -0,0 +1,706 @@
+import streamlit as st
+import pandas as pd
+from pyecharts import options as opts
+from pyecharts.charts import Bar, Line
+from pyecharts.commons.utils import JsCode
+import numpy as np
+from cpc_iri_web import modelos_dinamicos, modelos_estatisticos, df_table
+
+#@st.cache_data
+def plot_oni():
+
+    df = pd.read_table('https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt', delim_whitespace=True)
+
+    data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0])
+    data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1])
+    daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M')
+    index = daterange.strftime('%b/%Y').tolist()
+    data = df['ANOM'].values.tolist()
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    oni_plot = (
+        Bar()
+        .add_xaxis(index)
+        .add_yaxis("Indice ONI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title="Índice ONI", subtitle="Fonte: NOAA/CPC", subtitle_link='https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return oni_plot
+
+###############################################################################################################
+
+#@st.cache_data
+def plot_oni_season():
+
+    df = pd.read_table('https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt', delim_whitespace=True)
+
+    data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0])
+    data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1])
+    daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M')
+    index = daterange.strftime('%b/%Y').tolist()
+    data = df['ANOM'].values.tolist()
+
+    dff = df.copy()
+    dff.index = index
+    dff_resample = dff['ANOM'].rolling(3).mean().dropna()
+
+    new_index = []
+    for i in range(len(index)):
+        if i+2 < len(index) and i+1 < len(index):
+            new_index.append(index[i][0] + index[i+1][0] + index[i+2][0] + index[i+2][3:])
+    dff_resample.index = new_index
+
+    data = dff_resample.round(2).values.tolist()
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    oni_plot = (
+        Bar()
+        .add_xaxis(new_index)
+        .add_yaxis("Indice ONI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title="Índice ONI", subtitle="Fonte: NOAA/CPC", subtitle_link='https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return oni_plot
+
+###############################################################################################################
+
+#@st.cache_data
+def plot_sst_indexes(nino):
+
+    df = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices', delim_whitespace=True)
+    data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0])
+    data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1])
+    daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M')
+    index = daterange.strftime('%b/%Y').tolist() 
+
+    if nino == 'nino12':
+        data = df['ANOM'].values.tolist()  
+        title = 'Anomalia TSM Niño 1+2 Index'
+    elif nino == 'nino3':
+        data = df['ANOM.1'].values.tolist()  
+        title = 'Anomalia TSM Niño 3 Index'
+    elif nino == 'nino34':
+        data = df['ANOM.2'].values.tolist()
+        title = 'Anomalia TSM Niño 3+4 Index'
+    elif nino == 'nino4':
+        data = df['ANOM.3'].values.tolist()
+        title = 'Anomalia TSM Niño 4 Index'
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    oni_plot = (
+
+        Bar()
+        .add_xaxis(index)
+        .add_yaxis("Indice SST", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/sst'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return oni_plot
+
+
+###############################################################################################################
+
+#@st.cache_data
+def plot_sst_indexes_season(nino):
+
+    df = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices', delim_whitespace=True)
+    data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0])
+    data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1])
+    daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M')
+    index = daterange.strftime('%b/%Y').tolist() 
+    dff = df.copy()
+    dff.index = index
+
+    if nino == 'nino12':
+        data = dff['ANOM']
+        title = 'Anomalia TSM Niño 1+2 Index'
+    elif nino == 'nino3':
+        data = dff['ANOM.1']
+        title = 'Anomalia TSM Niño 3 Index'
+    elif nino == 'nino34':
+        data = dff['ANOM.2']
+        title = 'Anomalia TSM Niño 3+4 Index'
+    elif nino == 'nino4':
+        data = dff['ANOM.3']
+        title = 'Anomalia TSM Niño 4 Index'
+
+    dff_resample = data.rolling(3).mean().dropna()
+
+    new_index = []
+    for i in range(len(index)):
+        if i+2 < len(index) and i+1 < len(index):
+            new_index.append(index[i][0] + index[i+1][0] + index[i+2][0] + index[i+2][3:])
+    dff_resample.index = new_index
+
+    data = dff_resample.round(2).values.tolist()
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    oni_plot = (
+
+        Bar()
+        .add_xaxis(new_index)
+        .add_yaxis("Indice SST", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/sst'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return oni_plot
+
+###############################################################################################################
+
+#@st.cache_data
+def plot_soi():
+
+    soi = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/soi', delim_whitespace=True, skiprows=87, index_col=0)
+    soi = soi[0:72]
+    soi.loc['2023'] = [1.4, 1.4, 0.2, 0.2, 1.0, 0.3, -0.3, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN] # INSERINDO MANUALMENTE OS VALORES 
+    t = soi.loc[soi.index[0]]
+    for h in soi.index[1:]:
+        if h == soi.index[1]:
+            r = pd.concat([t, soi.loc[h]])
+        else: 
+            r = pd.concat([r, soi.loc[h]])
+    df_soi = pd.DataFrame(r).astype(float)
+    date_range2 = pd.date_range(start='1/1/1951', end='1/1/2024', freq='M')
+    df_soi.index = date_range2
+    df_soi.columns = ['Índice SOI']
+    index = df_soi.index.strftime('%b/%Y').tolist()
+    data = df_soi['Índice SOI'].tolist()
+    title = 'Índice SOI'
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    soi_plot = (
+
+        Bar()
+        .add_xaxis(index)
+        .add_yaxis("Indice SOI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/soi'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        #yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return soi_plot
+
+###############################################################################################################
+
+#@st.cache_data
+def plot_soi_season():
+
+    soi = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/soi', delim_whitespace=True, skiprows=87, index_col=0)
+    soi = soi[0:72]
+    soi.loc['2023'] = [1.4, 1.4, 0.2, 0.2, 1.0, 0.3, -0.3, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN] # INSERINDO MANUALMENTE OS VALORES 
+    t = soi.loc[soi.index[0]]
+    for h in soi.index[1:]:
+        if h == soi.index[1]:
+            r = pd.concat([t, soi.loc[h]])
+        else: 
+            r = pd.concat([r, soi.loc[h]])
+    df_soi = pd.DataFrame(r).astype(float)
+    date_range2 = pd.date_range(start='1/1/1951', end='1/1/2024', freq='M')
+    df_soi.index = date_range2
+    df_soi.columns = ['Índice SOI']
+    index = df_soi.index.strftime('%b/%Y').tolist()
+    data = df_soi['Índice SOI'].tolist()
+    title = 'Índice SOI'
+
+    dff = df_soi.copy()
+    dff.index = index
+    dff_resample = dff.rolling(3).mean()
+    dff_resample = dff[2:]
+
+    new_index = []
+    for i in range(len(dff.index)):
+        if i+2 < len(dff.index) and i+1 < len(dff.index):
+            new_index.append(dff.index[i][0] + dff.index[i+1][0] + dff.index[i+2][0] + dff.index[i+2][3:])
+
+    dff_resample.index = new_index
+    data = dff_resample['Índice SOI'].dropna().values.tolist()
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    soi_plot = (
+
+        Bar()
+        .add_xaxis(new_index)
+        .add_yaxis("Indice SOI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/soi'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        #yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return soi_plot
+
+###############################################################################################################
+
+def plot_poama():
+
+    csv = 'https://raw.githubusercontent.com/josepaulo1233/enso-assets/main/xlsx/poama.csv'
+    df = pd.read_csv(csv, sep=';')
+    df.columns = ['Mês', 'Anomalia região 3.4', 'Abaixo', 'Neutro', 'Acima']
+
+    index = df['Mês'].values.tolist()
+    title = 'Previsão POAMA' 
+    data_anomalia = df['Anomalia região 3.4'].values.tolist()
+    data_abaixo = (df['Abaixo']*100).values.tolist()
+    data_neutro = (df['Neutro']*100).values.tolist()
+    data_acima = (df['Acima']*100).values.tolist()
+
+    poama_plot_bar = (
+
+        Bar()
+        .add_xaxis(index)
+        .add_yaxis("La Niña", data_abaixo, color='blue')
+        .add_yaxis("Neutro", data_neutro, color='green')
+        .add_yaxis("El Niño", data_acima, color='red')
+        .set_global_opts(title_opts=opts.TitleOpts(
+                                    #title=title, 
+                                    # subtitle="Fonte: POAMA", subtitle_link='http://www.bom.gov.au/climate/ocean/outlooks/'
+                                    ), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), 
+                        xaxis_opts=opts.AxisOpts(axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} %"), 
+                                                 name='Probabilidade', 
+                                                 name_location='middle', 
+                                                 name_gap=100,
+                                                 name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold')
+                                                 ),
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+        .extend_axis(yaxis=opts.AxisOpts(
+            type_="value",
+            min_=-1.5,
+            max_=3.5,
+            interval=1,
+            axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
+        )
+    )
+
+    )
+
+    poama_plot_line = (
+
+        Line()
+        .add_xaxis(index)
+        .add_yaxis("Anomalia de TSM", 
+                   data_anomalia,
+                   color='black',
+                   linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=10,
+                   #color=JsCode(color_function),
+                   #itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
+                   yaxis_index=1, 
+                   )
+
+    )
+
+    poama_plot_bar.overlap(poama_plot_line)
+
+    return poama_plot_bar
+
+###############################################################################################################
+
+def plot_cpc_iri_dinamico():
+
+    data1 = modelos_dinamicos['GFDL SPEAR'].values.tolist()
+    data2 = modelos_dinamicos['AUS-ACCESS'].values.tolist()
+    data3 = modelos_dinamicos['BCC_CSM11m'].values.tolist()
+    data4 = modelos_dinamicos['COLA CCSM4'].values.tolist()
+    data5 = modelos_dinamicos['CS-IRI-MM'].values.tolist()
+    data6 = modelos_dinamicos['DWD'].values.tolist()
+    data7 = modelos_dinamicos['ECMWF'].values.tolist()
+    data8 = modelos_dinamicos['IOCAS ICM'].values.tolist()
+    data9 = modelos_dinamicos['JMA'].values.tolist()
+    data10 = modelos_dinamicos['KMA'].values.tolist()
+    data11 = modelos_dinamicos['LDEO'].values.tolist()
+    data12 = modelos_dinamicos['MetFRANCE'].values.tolist()
+    data13 = modelos_dinamicos['NASA GMAO'].values.tolist()
+    data14 = modelos_dinamicos['NCEP CFSv2'].values.tolist()
+    data15 = modelos_dinamicos['SINTEX-F'].values.tolist()
+    data16 = modelos_dinamicos['UKMO'].values.tolist()
+    data17 = modelos_dinamicos['Média'].round(2).values.tolist()
+
+    index = modelos_dinamicos.index.values.tolist()
+
+    cpc_plot = (
+
+        Line()
+        .add_xaxis(index)
+        .add_yaxis("GFDL SPEAR", data1, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("AUS-ACCESS", data2, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("BCC_CSM11m", data3, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("COLA CCSM4", data4, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("CS-IRI-MM", data5, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("DWD", data6, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("ECMWF", data7, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("IOCAS ICM", data8, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("JMA", data9, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("KMA", data10, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("LDEO", data11, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("MetFRANCE", data12, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("NASA GMAO", data13, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("NCEP CFSv2", data14, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("SINTEX-F", data15, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("UKMO", data16, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("Média Dinâmicos", data17, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=10, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .set_global_opts(title_opts=opts.TitleOpts(
+                                                   title='Modelos dinâmicos', 
+                                                   #subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/soi'
+                                                   ), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, 
+                                                      trigger="item", 
+                                                      #axis_pointer_type="cross",
+                                                      #trigger="item", 
+                                                      #trigger_on="mousemove"
+                                                      ),
+                        legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C"), 
+                                                 name='Anomalia de TSM (°C) na região do Niño 3.4', 
+                                                 name_location='middle', 
+                                                 name_gap=100,
+                                                 name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold')
+                                                 ),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        #datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
+    )
+
+    return cpc_plot
+
+###############################################################################################################
+
+def plot_cpc_iri_estatistico():
+
+    data1 = modelos_estatisticos['BCC_RZDM'].values.tolist()
+    data2 = modelos_estatisticos['CDC LIM'].values.tolist()
+    data3 = modelos_estatisticos['CPC CA'].values.tolist()
+    data4 = modelos_estatisticos['CPC CCA'].values.tolist()
+    data5 = modelos_estatisticos['CPC MRKOV'].values.tolist()
+    data6 = modelos_estatisticos['CSU CLIPR'].values.tolist()
+    data7 = modelos_estatisticos['FSU REGR'].values.tolist()
+    data8 = modelos_estatisticos['IAP-NN'].values.tolist()
+    data9 = modelos_estatisticos['NTU CODA'].values.tolist()
+    data10 = modelos_estatisticos['PSD-CU LIM'].values.tolist()
+    data11 = modelos_estatisticos['UBC NNET'].values.tolist()
+    data12 = modelos_estatisticos['UNB/CWC'].values.tolist()
+    data13 = modelos_estatisticos['UCLA'].values.tolist()
+    data14 = modelos_estatisticos['UCLA-TCD'].values.tolist()
+    data15 = modelos_estatisticos['UW PSL-CSLIM'].values.tolist()
+    data16 = modelos_estatisticos['UW PSL-LIM'].values.tolist()
+    data17 = modelos_estatisticos['Média'].round(2).values.tolist()
+
+    index = modelos_estatisticos.index.values.tolist()
+
+    cpc_plot = (
+
+        Line()
+        .add_xaxis(index)
+        .add_yaxis("BCC_RZDM", data1, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("CDC LIM", data2)
+        .add_yaxis("CPC CA", data3, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("CPC CCA", data4)
+        .add_yaxis("CPC MRKOV", data5, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("CSU CLIPR", data6, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("FSU REGR", data7)
+        .add_yaxis("IAP-NN", data8, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("NTU CODA", data9, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("PSD-CU LIM", data10)
+        #.add_yaxis("UBC NNET", data11)
+        #.add_yaxis("UNB/CWC", data12)
+        #.add_yaxis("UCLA", data13)
+        .add_yaxis("UCLA-TCD", data14, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("UW PSL-CSLIM", data15, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("UW PSL-LIM", data16, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("Média Estatisticos", data17, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=10, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .set_global_opts(title_opts=opts.TitleOpts(title='Modelos estatísticos', 
+                                                   ), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, 
+                                                      trigger="item",
+                                                      ),
+                        legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C"), 
+                                                 name='Anomalia de TSM (°C) na região do Niño 3.4', 
+                                                 name_location='middle', 
+                                                 name_gap=100,
+                                                 name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold')
+                                                 ),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
+    )
+
+    return cpc_plot
+
+###############################################################################################################
+
+def plot_cpc_iri_todos():
+
+    data1 = modelos_estatisticos['BCC_RZDM'].values.tolist()
+    data2 = modelos_estatisticos['CDC LIM'].values.tolist()
+    data3 = modelos_estatisticos['CPC CA'].values.tolist()
+    data4 = modelos_estatisticos['CPC CCA'].values.tolist()
+    data5 = modelos_estatisticos['CPC MRKOV'].values.tolist()
+    data6 = modelos_estatisticos['CSU CLIPR'].values.tolist()
+    data7 = modelos_estatisticos['FSU REGR'].values.tolist()
+    data8 = modelos_estatisticos['IAP-NN'].values.tolist()
+    data9 = modelos_estatisticos['NTU CODA'].values.tolist()
+    data10 = modelos_estatisticos['PSD-CU LIM'].values.tolist()
+    data11 = modelos_estatisticos['UBC NNET'].values.tolist()
+    data12 = modelos_estatisticos['UNB/CWC'].values.tolist()
+    data13 = modelos_estatisticos['UCLA'].values.tolist()
+    data14 = modelos_estatisticos['UCLA-TCD'].values.tolist()
+    data15 = modelos_estatisticos['UW PSL-CSLIM'].values.tolist()
+    data16 = modelos_estatisticos['UW PSL-LIM'].values.tolist()
+    data17 = modelos_estatisticos['Média'].round(2).values.tolist()
+
+    data18 = modelos_dinamicos['GFDL SPEAR'].values.tolist()
+    data19 = modelos_dinamicos['AUS-ACCESS'].values.tolist()
+    data20 = modelos_dinamicos['BCC_CSM11m'].values.tolist()
+    data21 = modelos_dinamicos['COLA CCSM4'].values.tolist()
+    data22 = modelos_dinamicos['CS-IRI-MM'].values.tolist()
+    data23 = modelos_dinamicos['DWD'].values.tolist()
+    data24 = modelos_dinamicos['ECMWF'].values.tolist()
+    data25 = modelos_dinamicos['IOCAS ICM'].values.tolist()
+    data26 = modelos_dinamicos['JMA'].values.tolist()
+    data27 = modelos_dinamicos['KMA'].values.tolist()
+    data28 = modelos_dinamicos['LDEO'].values.tolist()
+    data29 = modelos_dinamicos['MetFRANCE'].values.tolist()
+    data30 = modelos_dinamicos['NASA GMAO'].values.tolist()
+    data31 = modelos_dinamicos['NCEP CFSv2'].values.tolist()
+    data32 = modelos_dinamicos['SINTEX-F'].values.tolist()
+    data33 = modelos_dinamicos['UKMO'].values.tolist()
+    data34 = modelos_dinamicos['Média'].round(2).values.tolist()
+
+
+    index = modelos_estatisticos.index.values.tolist()
+
+    cpc_plot = (
+
+        Line()
+        .add_xaxis(index)
+        .add_yaxis("BCC_RZDM", data1, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("CDC LIM", data2)
+        .add_yaxis("CPC CA", data3, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("CPC CCA", data4)
+        .add_yaxis("CPC MRKOV", data5, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("CSU CLIPR", data6, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("FSU REGR", data7)
+        .add_yaxis("IAP-NN", data8, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("NTU CODA", data9, itemstyle_opts={"emphasis": {"focus": "series"}})
+        #.add_yaxis("PSD-CU LIM", data10)
+        #.add_yaxis("UBC NNET", data11)
+        #.add_yaxis("UNB/CWC", data12)
+        #.add_yaxis("UCLA", data13)
+        .add_yaxis("UCLA-TCD", data14, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("UW PSL-CSLIM", data15, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("UW PSL-LIM", data16, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("M.Estatistica", data17, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=13, symbol='triangle', itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("GFDL SPEAR", data18, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("AUS-ACCESS", data19, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("BCC_CSM11m", data20, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("COLA CCSM4", data21, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("CS-IRI-MM", data22, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("DWD", data23, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("ECMWF", data24, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("IOCAS ICM", data25, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("JMA", data26, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("KMA", data27, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("LDEO", data28, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("MetFRANCE", data29, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("NASA GMAO", data30, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("NCEP CFSv2", data31, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("SINTEX-F", data32, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("UKMO", data33, itemstyle_opts={"emphasis": {"focus": "series"}})
+        .add_yaxis("M.Dinamica", data34, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=13, symbol='rect', itemstyle_opts={"emphasis": {"focus": "series"}})
+        .set_global_opts(title_opts=opts.TitleOpts(title='Todos modelos', 
+                                                   ),
+                        tooltip_opts=opts.TooltipOpts(is_show=True, 
+                                                      trigger="item", 
+                                                      position='bottom'
+                                                      ),
+                        legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C"), 
+                                                 name='Anomalia de TSM (°C) na região do Niño 3.4', 
+                                                 name_location='middle', 
+                                                 name_gap=100,
+                                                 name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold')
+                                                 ),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
+    )
+
+    return cpc_plot
+
+###############################################################################################################
+
+def plot_iri_concenso():
+    
+    data_nina = df_table['La Niña'].values.tolist()
+    data_neutro = df_table['Neutral'].values.tolist()
+    data_nino = df_table['El Niño'].values.tolist()
+    index = df_table.index.to_list()
+    title = 'Previsão por consenso'
+
+    cpc_bar = (
+
+        Bar()
+        .add_xaxis(index)
+        .add_yaxis("La Niña", data_nina, color='blue')
+        .add_yaxis("Neutro", data_neutro, color='green')
+        .add_yaxis("El Niño", data_nino, color='red')
+        .set_global_opts(title_opts=opts.TitleOpts(title=title), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True),
+                        legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), 
+                        xaxis_opts=opts.AxisOpts(axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} %"), 
+                                                 name='Probabilidade', 
+                                                 name_location='middle', 
+                                                 name_gap=100,
+                                                 name_textstyle_opts=opts.TextStyleOpts(font_size=14, font_weight='bold')
+                                                 ),        
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+
+    )
+
+    return cpc_bar
+
+###############################################################################################################
+#@st.cache_data
+def plot_daily_ssta(nino, days):
+
+    df = pd.read_table('https://conmet.com.br/public/txt_enso/sst_nino_daily.txt', delim_whitespace=True, index_col=0, parse_dates=True)
+
+    df = df[-int(days):]
+    index = df.index.strftime('%d/%b/%Y').tolist() 
+
+    if nino == 'nino12':
+        data = df['anom12'].round(2).values.tolist()  
+        title = 'Anomalia TSM Niño 1+2'
+    elif nino == 'nino3':
+        data = df['anom3'].round(2).values.tolist()  
+        title = 'Anomalia TSM Niño 3'
+    elif nino == 'nino34':
+        data = df['anom34'].round(2).values.tolist()
+        title = 'Anomalia TSM Niño 3+4'
+    elif nino == 'nino4':
+        data = df['anom4'].round(2).values.tolist()
+        title = 'Anomalia TSM Niño 4'
+
+    color_function = """
+            function (params) {
+                if (params.value > 0) {
+                    return 'red';
+                } else {
+                    return 'blue';
+                }
+            }
+            """
+
+    oni_plot = (
+
+        Bar()
+        .add_xaxis(index)
+        .add_yaxis("Indice SST", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
+        .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/products/extended-reconstructed-sst'), 
+                        tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
+                        legend_opts=opts.LegendOpts(is_show=False), 
+                        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")),
+                        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),
+                        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")]
+                        )
+        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
+    )
+
+    return oni_plot

requirements.txt

@@ -0,0 +1,10 @@
+streamlit
+pyecharts
+pandas
+numpy 
+streamlit_echarts
+streamlit-on-Hover-tabs
+selenium
+webdriver-manager
+beautifulsoup4
+lxml