Update apps/Covid19.py

apps/Covid19.py

@@ -1,369 +1,2 @@
 import streamlit as st
-from plotly import graph_objs as go
-import pandas as pd
-from pandas.core.groupby.groupby import DataError
-from pytrends.request import TrendReq
-from datetime import datetime, timedelta, date
-import numpy as np
-from plotly.subplots import make_subplots
-from metodos import colores_corporativos
-import pybase64 as base64
-import io
-from logs_portal import log
-from Scheduler import Scheduler_Covid as sc
-import os
-
-def button_style():
-    style_button = """
-        <style>
-      button {
-        margin-top:-100px;
-        display: inline-block;
-        background-color: #e8e8e8;
-        border-radius: 15px;
-        border: 4px  #cccccc;
-        color: #4a4a4a;
-        text-align: center;
-        font-size: 15px;
-        padding: 2px;
-        width: 260px;
-        transition: all 0.5s;
-        cursor: pointer;
-        margin: 5px;
-      }
-      button span {
-        cursor: pointer;
-        display: inline-block;
-        position: relative;
-        transition: 0.5s;
-      }
-      button span:after {
-        content: '\00bb';
-        position: absolute;
-        opacity: 0;
-        top: 0;
-        right: -20px;
-        transition: 0.5s;
-      }
-      button:hover {
-        background-color: #bb1114;
-        color:#e8e8e8;
-      }
-      button:hover span {
-        padding-right: 25px;
-      }
-      button:hover span:after {
-        opacity: 1;
-        right: 0;
-      }
-    </style>
-    """
-    st.markdown(style_button, unsafe_allow_html=True)
-
-
-def get_table_download_link(df):
-    """Generates a link allowing the data in a given panda dataframe to be
-    downloaded
-    in:  dataframe
-    out: href string
-    """
-    csv = df.to_csv(index=False)
-    b64 = base64.b64encode(csv.encode()).decode()
-    name_arch = "Scoring_filtrado.csv"
-    name_mark = "Descargar .csv "
-    style = '"color:black;text-decoration: none;font-size:18px;"'
-    href = f'<center><a href="data:file/csv;base64,{b64}" style='  + style+' download="'+name_arch+'" ><button>'+name_mark+'</button></a></center>'
-    return href
-
-
-def get_table_excel_link(df, name_arch):
-    towrite = io.BytesIO()
-    downloaded_file = df.to_excel(towrite, encoding='utf-8', index=False,
-                                  header=True)
-    towrite.seek(0)  # reset pointer
-    file_name = name_arch
-    style = 'style="color:black;text-decoration: none; font-size:18px;" '
-    name_mark = "Descargar "+name_arch
-    b64 = base64.b64encode(towrite.read()).decode()  # some strings
-    linko= f'<center><a href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}" '+style+'download="'+file_name+'"><button>'+name_mark+'</button></a></center>'
-    return linko
-
-
-@st.cache(show_spinner=True)
-def charged_data():
-    regiones = {}
-    regiones['Latam'] = ['Argentina', 'Brazil', 'Chile', 'Colombia',
-                         'Mexico', 'Peru']
-    regiones['Europa'] = ['Italy', 'Spain', 'Germany', 'United Kingdom',
-                          'France']
-    regiones['Asia Emergente'] = ['South Korea', 'Taiwan', 'Hong Kong',
-                                  'India', 'Thailand', 'Indonesia']
-    regiones['USA'] = ['United States']
-    data_dict = np.load('Scheduler/dict_movilidad.npy',
-                        allow_pickle='TRUE').item()
-    return data_dict, regiones
-
-
-@st.cache(show_spinner=True)
-def charged_data2():
-    covid_data = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')
-    paises = {'CL': 'Chile', 'AR': 'Argentina', 'BR': 'Brazil',
-              'MX': 'Mexico'}
-    covid_data = covid_data.loc[covid_data['location'].isin(paises.values())]
-    covid_data['date'] = pd.to_datetime(covid_data['date'])
-    covid_data.set_index(['date', 'location'], inplace=True)
-    # Creamos diccionario con cada una de las variables para distintos países
-    data_dict = {}
-    for col in covid_data.columns:
-        try:
-            data_dict[col] = covid_data[col].unstack().fillna(0).rolling(1).mean()
-        except DataError:
-            pass
-    # Descargamos la data de google trends
-    pytrends = TrendReq(retries=5, backoff_factor=0.2,
-                        requests_args={'verify': False})
-    start = (datetime.today() - timedelta(180)).strftime("%Y-%m-%d")
-    start = datetime(2020, 2, 1).strftime("%Y-%m-%d")
-    end = datetime.today().strftime("%Y-%m-%d")
-    tf = f'{start} {end}'
-    kw_lists = {
-                'CL': ['PCR', 'sintomas covid', 'examen covid',
-                       'covid positivo'],
-                'AR': ['PCR', 'olfato', 'sintomas covid', 'perdida gusto',
-                       'covid positivo'],
-                'MX': ['PCR', 'olfato', 'sintomas covid', 'covid positivo',
-                       'perdida gusto'],
-                'BR': ['PCR', 'sintomas covid', 'exame covid', 'covid positivo']
-                }
-    gt_data = {}
-    for p, kw in kw_lists.items():
-        pytrends.build_payload(kw, timeframe=tf, geo=p)
-        df = pytrends.interest_over_time().drop(columns='isPartial')
-        gt_data[paises[p]] = df.div(df.mean(0).values)
-    data_dict['GT Index'] = pd.DataFrame({p: gt_data[p].mean(1).rolling(1).mean()
-                                          for p in gt_data.keys()})
-    return data_dict, paises
-
-
-@log
-def Movilidad():
-    largo = 400
-    ancho = 550
-    button_style()
-    placebar = st.empty()
-    percent_complete = 0
-    my_bar = placebar.progress(percent_complete)
-    data_cargada = charged_data()
-    data_dict = data_cargada[0]
-    regiones = data_cargada[1]
-    europa = data_dict['Mobility Index'][regiones.keys()]["Europa"]
-    latam = data_dict['Mobility Index'][regiones.keys()]["Latam"]
-    asia = data_dict['Mobility Index'][regiones.keys()]["Asia Emergente"]
-    USA = data_dict['Mobility Index'][regiones.keys()]["USA"]
-    mov_region = data_dict['Mobility Index'][regiones.keys()][["USA", "Europa","Asia Emergente", "Latam"]]
-    percent_complete = percent_complete+33
-    placebar.progress(percent_complete)
-    colores = list(colores_corporativos().values())
-    colores2 = []
-    for i in range(len(colores)):
-        colores2.append("rgb"+str(colores[i]))
-    def plot_raw_data():
-        fig = go.Figure()
-        europa_ = go.Scatter(x=europa.index, y=europa.values, name="Europa",
-                             line=dict(color=colores2[0]))
-        latam_ = go.Scatter(x=latam.index, y=latam.values, name="Latam",
-                            line=dict(color=colores2[1]))
-        USA_ = go.Scatter(x=USA.index, y=USA.values, name="USA",
-                          line=dict(color=colores2[2]))
-        asia_ = go.Scatter(x=asia.index, y=asia.values, name="Asia Emergente",
-                           line=dict(color=colores2[3]))
-        fig.add_trace(europa_)
-        fig.add_trace(latam_)
-        fig.add_trace(USA_)
-        fig.add_trace(asia_)
-        fig.layout.update(title_text="Evolución por region",
-                          xaxis_rangeslider_visible=True,
-                          margin_b=20,
-                          margin_r=20,
-                          margin_l=20,
-                          width=ancho,
-                          height=largo,
-                          legend=dict(orientation="h",
-                                      yanchor="bottom",
-                                      y=1.02,
-                                      xanchor="right",
-                                      x=1))
-        fig2 = go.Figure()
-        i = 0
-        for pais in regiones["Latam"]:
-            data_pais = data_dict['Mobility Index'][regiones['Latam']][pais]
-            pais_gr = go.Scatter(x=data_pais.index,
-                                 y=data_pais.values, name=pais,
-                                 line=dict(color=colores2[i]))
-            fig2.add_trace(pais_gr)
-            i = i+1
-        fig2.layout.update(title_text="Evolución LATAM",
-                           xaxis_rangeslider_visible=True, margin_b=20,
-                           margin_r=20,margin_l=20,
-                           width=ancho, height=largo,
-                           legend=dict(orientation="h",
-                                       yanchor="bottom",
-                                       y=1.0,
-                                       xanchor="right",
-                                       x=1))
-        col1, col2 = st.columns(2)
-        col1.plotly_chart(fig, use_container_width=True)
-        col2.plotly_chart(fig2, use_container_width=True)
-        link_excel_1 = get_table_excel_link(data_dict['Mobility Index'][regiones['Latam']], "Movilidad Latam.xlsx")
-        link_excel_2 = get_table_excel_link(mov_region, "Movilidad por region.xlsx")
-        col1.markdown(link_excel_1, unsafe_allow_html=True)
-        col2.markdown(link_excel_2, unsafe_allow_html=True)
-    percent_complete = percent_complete + 33
-    placebar.progress(percent_complete)
-    placebar.empty()
-    plot_raw_data()
-    percent_complete = percent_complete + 34
-    my_bar.progress(percent_complete)
-    my_bar.empty()
-    data_desag = pd.read_excel("Scheduler/Movilidad_desagrada.xlsx",
-                               engine="openpyxl")
-    st.markdown(get_table_excel_link(data_desag, "Movilidad desagregada.xlsx"),
-                unsafe_allow_html=True)
-    try:
-        user = os.getlogin()
-        if user == 'bullm':
-            act = st.button('Actualizar')
-            if act:
-                sc.run_data_covid()
-                ud = pd.read_excel('Data/update_data.xlsx')
-                ud = ud[ud['View'] != 'Covid19']
-                today = date.today().strftime('%d-%m-%Y')
-                ud = ud.append({"View": "Covid19",
-                                "Last_Update": today}, ignore_index=True)
-                ud.to_excel('Data/update_data.xlsx', index=False)
-    except Exception:
-        pass
-
-
-@log
-def Correlacion_GT():
-    largo = 400
-    ancho = 550
-    button_style()
-    # Cargamos la data relevante
-    percent_complete = 0
-    my_bar = st.progress(percent_complete)
-    percent_complete = percent_complete + 33
-    my_bar.progress(percent_complete)
-    data_cargada = charged_data2()
-    data_dict = data_cargada[0]
-    paises = data_cargada[1]
-    corr_df = pd.DataFrame(index=paises.values(), columns=np.arange(-3, 1))
-    percent_complete = percent_complete + 33
-    my_bar.progress(percent_complete)
-    i = 0
-    cols = st.columns(2)
-    col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
-    for p in corr_df.index:
-        df = pd.concat([data_dict['GT Index'][p],
-                        data_dict['new_cases_per_million'][p]],
-                       axis=1).dropna()
-        df.columns = ['GT Index', 'Nuevos Casos Confirmados']
-        fig = make_subplots(specs=[[{"secondary_y": True}]])
-        CC = go.Scatter(x=df['GT Index'].index,
-                        y=df['GT Index'].values, name='GT index',
-                        line=dict(color='dimgrey'))
-        GT = go.Scatter(x=df['Nuevos Casos Confirmados'].index,
-                        y=df['Nuevos Casos Confirmados'].values,
-                        name='Casos confirmados', line=dict(color='darkred'))
-        fig.add_trace(CC, secondary_y=False,)
-        fig.add_trace(GT, secondary_y=True,)
-        fig.layout.update(title_text="Evolución {}".format(p),
-                          xaxis_rangeslider_visible=True, margin_b=20,
-                          margin_r=20, margin_l=20,
-                          width=ancho, height=largo,
-                          legend=dict(orientation="h",
-                                      yanchor="bottom",
-                                      y=1.02,
-                                      xanchor="right",
-                                      x=1))
-        link_excel = get_table_excel_link(df, "Correlacion GT.xlsx")
-        if i % 2 == 0:
-            cols[0].plotly_chart(fig, use_container_width=True)
-            cols[0].markdown(link_excel, unsafe_allow_html=True)
-        else:
-            cols[1].plotly_chart(fig, use_container_width=True)
-            cols[1].markdown(link_excel, unsafe_allow_html=True)
-            cols = st.columns(2)
-            col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
-        i = i + 1
-    percent_complete = percent_complete + 34
-    my_bar.progress(percent_complete)
-    my_bar.empty()
-
-
-
-@log
-def vacunas():
-    largo = 400
-    ancho = 550
-    button_style()
-    vac_data = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv').set_index(['date','location'])
-    country_pop = (vac_data['population'].reset_index().set_index('location')
-                   .drop(columns='date').squeeze().drop_duplicates())
-    min_pop = 1000000
-    idx = country_pop[country_pop > min_pop].index
-    vac_data = vac_data['total_vaccinations_per_hundred'].unstack().ffill().fillna(0)
-    vac_data.index = pd.to_datetime(vac_data.index)
-    N = 15
-    top_vac = vac_data[idx].iloc[-1].nlargest(N).sort_values()
-    regiones = {}
-    regiones['Latam'] = ['Argentina', 'Brazil', 'Chile', 'Colombia',
-                         'Mexico', 'Peru']
-    regiones['Europa'] = ['Italy', 'Spain', 'Germany', 'United Kingdom',
-                          'France', 'Russia']
-    regiones['Asia Emergente'] = ['South Korea', 'Taiwan', 'Hong Kong',
-                                  'China', 'Japan']
-    regiones['Norteamérica'] = ['United States', 'Canada']
-    inicio = datetime(2020, 11, 15)
-    vac_data = vac_data.loc[vac_data.index > inicio].resample('W').last()
-    vac_data.index.name = ''
-    colores = colores_corporativos().values()
-    colores = list(colores_corporativos().values())
-    colores2 = []
-    for i in range(len(colores)):
-        colores2.append("rgb"+str(colores[i]))
-
-    def plot_raw_data():
-        i = 0
-        cols = st.columns(2)
-        col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
-        for region in list(regiones.keys()):
-            fig = go.Figure()
-            j = 0
-            for pais in regiones[region]:
-                data_pais = vac_data[regiones[region]][pais]
-                pais_gr = go.Scatter(x=data_pais.index,
-                                     y=data_pais.values, name=pais,
-                                     line=dict(color=colores2[j]))
-                fig.add_trace(pais_gr)
-                j = j+1
-            fig.layout.update(title_text="Evolución "+region,
-                              xaxis_rangeslider_visible=True, height=largo,
-                              width=ancho, margin_b=20,
-                              legend=dict(orientation="h",
-                                          yanchor="bottom",
-                                          y=1.0,
-                                          xanchor="right",
-                                          x=1))
-            link_excel = get_table_excel_link(data_pais, "Vacunacion.xlsx")
-            if i % 2 == 0:
-                cols[0].plotly_chart(fig, use_column_width=True)
-                cols[0].markdown(link_excel, unsafe_allow_html=True)
-            else:
-                cols[1].plotly_chart(fig, use_column_width=True)
-                cols[1].markdown(link_excel, unsafe_allow_html=True)
-                cols = st.columns(2)
-                col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
-            i = i+1
-    plot_raw_data()
+st.write("Covid View Turn Off")