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