Correct the name of the tables used in the dashboard (#16)

* 'Minor changes in the layout of the plots and in the texts'

* Minor change to correct the end of the data lines in the forecast plots

* Removed the line of the code that dropoed the last 3 days of data in the plots.

* Correct the name of the tables used in the dashboard

* Removed the SEIR predictions and made a minor updated in the plots of validation figures

* Solved conflicts

Author: eduardocorrearaujo

for_other_cantons.py

@@ -8,6 +8,7 @@
 import pandas as pd
 import numpy as np
 import streamlit as st
+from datetime import timedelta 
 import plotly.graph_objects as go
 from forecast import download_button
 from get_data import get_curve
@@ -64,7 +65,8 @@ def plot_cases_canton(full_name_canton, canton):
 
     last_date = df.index[-1]
     df = df['2021-11-01':]
-    df = df.iloc[:-3]
+    
+    #df = df.iloc[:-3]
 
     # computing the rolling average
     m_movel = df.rolling(7).mean().dropna()
@@ -107,9 +109,9 @@ def get_hospCapacity(canton):
     df = get_curve('hospcapacity', canton)
     df = df.resample('D').mean()
     df = df.sort_index() 
-    df = df.iloc[:-3]
+    #df = df.iloc[:-3]
     df = df.fillna(0)
-    return df.Total_Covid19Patients[-2:].astype('int'), df.TotalPercent_Covid19Patients[-2:]
+    return df.total_covid19patients[-2:].astype('int'), df.totalpercent_covid19patients[-2:]
 
 
 def plot_hosp_canton(full_name_canton, canton):
@@ -126,7 +128,7 @@ def plot_hosp_canton(full_name_canton, canton):
     df.sort_index(inplace=True)
     df = df['2021-11-01':]
 
-    df = df.iloc[:-3]
+    #df = df.iloc[:-3]
 
     # computing the rolling average
     m_movel = df.rolling(7).mean().dropna()
@@ -188,15 +190,15 @@ def plot_predictions_canton(table_name, curve, canton, full_name_canton, title=N
     y95 = df_val['upper']
 
     point = target.index[train_size]
-    min_val = min([min(target), np.nanmin(y50)])
-    max_val = max([max(target), np.nanmax(y50)])
+    min_val = min([min(target), np.nanmin(y95)])
+    max_val = max([max(target), np.nanmax(y95)])
 
     point_date = np.where(target.index == '2021-01-01')
 
     fig = go.Figure()
 
     # Dict with names for the curves
-    names = {'hosp': 'New Hospitalizations', 'ICU_patients': 'Total ICU patients',
+    names = {'hosp': 'New Hospitalizations', 'icu_patients': 'Total ICU patients',
              'total_hosp': 'Total hospitalizations'}
 
     if title == None:
@@ -267,11 +269,11 @@ def plot_forecast_canton(table_name, canton, curve, full_name_canton, title=None
 
     df_for.index = pd.to_datetime(df_for.date)
 
-    curves = {'hosp': 'hosp', 'ICU_patients': 'hospcapacity', 
+    curves = {'hosp': 'hosp', 'icu_patients': 'hospcapacity', 
               'total_hosp': 'hospcapacity'}
     ydata = get_curve(curves[curve], canton)
     ydata = ydata.resample('D').mean()
-    ydata = ydata.iloc[:-3]
+    #ydata = ydata.iloc[:-3]
     ydata = ydata.rolling(7).mean().dropna()
 
     dates_forecast = df_for.index
@@ -283,7 +285,7 @@ def plot_forecast_canton(table_name, canton, curve, full_name_canton, title=None
 
     # Dict with names for the curves
     names = {'hosp': 'Forecast New Hospitalizations',
-             'ICU_patients': 'Forecast Total ICU patients', 
+             'icu_patients': 'Forecast Total ICU patients', 
              'total_hosp': 'Total hospitalizations'}
 
     if title == None:
@@ -303,11 +305,14 @@ def plot_forecast_canton(table_name, canton, curve, full_name_canton, title=None
     # adding the traces
     # Data
 
-    column_curves = {'hosp': 'entries', 'ICU_patients': 'ICU_Covid19Patients',
-                     'total_hosp' : 'Total_Covid19Patients'}
+    column_curves = {'hosp': 'entries', 'icu_patients': 'icu_covid19patients',
+                     'total_hosp' : 'total_covid19patients'}
+
+    min_data = min(ydata.index[-1], df_for.index[0] - timedelta(days=1))
+
 
     fig.add_trace(go.Scatter(
-        x=ydata.index[-150:], y=ydata[column_curves[curve]][-150:], name='Data', line=dict(color='black')))
+        x=ydata.loc[:min_data].index[-150:], y=ydata.loc[:min_data][column_curves[curve]][-150:], name='Data', line=dict(color='black')))
 
     # Separation between data and forecast
     fig.add_trace(go.Scatter(x=[df_for.index[0], df_for.index[0]], y=[min(min(ydata[column_curves[curve]][-150:]), min(forecast95)), max(
@@ -335,7 +340,7 @@ def plot_forecast_canton(table_name, canton, curve, full_name_canton, title=None
 
     # fig.write_image(path)
 
-    del df_for['index']
+    #del df_for['index']
     df_for.index = pd.to_datetime(df_for.index)
     df_for.index = df_for.index.date
     df_for.reset_index(inplace=True)
@@ -427,7 +432,7 @@ def app():
              ''')
 
     fig_for, df_hosp = plot_forecast_canton(
-        'ml_for_hosp_all_cantons', canton=canton, curve='hosp', full_name_canton=full_name_canton)
+        'ngboost_forecast_hosp_d_results', canton=canton, curve='hosp', full_name_canton=full_name_canton)
     st.plotly_chart(fig_for, use_container_width=True)
     filename = 'forecast_hosp.csv'
     download_button_str = download_button(
@@ -436,7 +441,7 @@ def app():
     st.markdown(download_button_str, unsafe_allow_html=True)
 
     fig_total, df_total = plot_forecast_canton(
-        'ml_for_total_all_cantons', canton=canton, curve='total_hosp', full_name_canton=full_name_canton)
+        'ngboost_forecast_total_hosp_d_results', canton=canton, curve='total_hosp', full_name_canton=full_name_canton)
     st.plotly_chart(fig_total, use_container_width=True)
     filename = 'forecast_total.csv'
     download_button_str = download_button(
@@ -445,7 +450,7 @@ def app():
     st.markdown(download_button_str, unsafe_allow_html=True)
 
     fig_for_icu, df_icu = plot_forecast_canton(
-        'ml_for_icu_all_cantons', canton=canton, curve='ICU_patients', full_name_canton=full_name_canton)
+        'ngboost_forecast_icu_patients_d_results', canton=canton, curve='icu_patients', full_name_canton=full_name_canton)
     st.plotly_chart(fig_for_icu, use_container_width=True)
     filename = 'forecast_icu.csv'
     download_button_str = download_button(
@@ -460,7 +465,7 @@ def app():
 
              ''')
     fig_val = plot_predictions_canton(
-        'ml_val_hosp_all_cantons', curve='hosp', canton=canton, full_name_canton=full_name_canton)
+        'ngboost_validation_hosp_d_results', curve='hosp', canton=canton, full_name_canton=full_name_canton)
 
     st.plotly_chart(fig_val, use_container_width=True)
 
@@ -470,7 +475,7 @@ def app():
              ''')
 
     fig_val_total = plot_predictions_canton(
-        'ml_val_total_all_cantons', curve='total_hosp', canton=canton, full_name_canton=full_name_canton)
+        'ngboost_validation_total_hosp_d_results', curve='total_hosp', canton=canton, full_name_canton=full_name_canton)
 
     st.plotly_chart(fig_val_total, use_container_width=True)
 
@@ -480,6 +485,6 @@ def app():
              ''')
 
     fig_val_icu = plot_predictions_canton(
-        'ml_val_icu_all_cantons', curve='ICU_patients', canton=canton, full_name_canton=full_name_canton)
+        'ngboost_validation_icu_patients_d_results', curve='icu_patients', canton=canton, full_name_canton=full_name_canton)
 
     st.plotly_chart(fig_val_icu, use_container_width=True)