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excess_dead_nac_weekly_csv.py
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excess_dead_nac_weekly_csv.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 4 10:30:34 2020
@author: pmaldonadol
"""
import pandas as pd
import numpy as np
# Se obtienen las defunciones desde url y se transforman a dataframe
reg_info=pd.read_json('http://192.168.2.223:5006/getStates', orient='columns')
defun_data=pd.read_json('http://192.168.2.223:5006/getAllDeathsAllStates', orient='slit')
for i in range(len(defun_data)):
if i==0:
defun=pd.DataFrame(defun_data.iloc[i].values[0])
defun['Codigo region']=i+1
reg=reg_info.description[reg_info.id==i+1].values[0]
defun['Region']=reg
else:
defun_tmp=pd.DataFrame(defun_data.iloc[i].values[0])
defun_tmp['Codigo region']=i+1
reg=reg_info.description[reg_info.id==i+1].values[0]
defun_tmp['Region']=reg
defun=defun.append(defun_tmp)
defun['Fecha']=defun['dates']
defun['Fecha']=pd.to_datetime(defun['Fecha']).dt.tz_localize(None)
defun['Defunciones']=defun['deaths']
defun=defun.groupby(['Fecha']).sum()
defun=defun.drop(columns=['deaths','Codigo region'])
defun.reset_index(inplace=True)
for i in range(1,17):
if i==1:
defun_covid_N=pd.read_json('http://192.168.2.223:5006/getDeathsByState?state='+str(i), orient='columns')
defun_covid_N['Defunciones']=defun_covid_N.confirmed+defun_covid_N.suspected
defun_covid_N['Codigo region']=i
reg=reg_info.description[reg_info.id==i].values[0]
defun_covid_N['Region']=reg
else:
defun_tmp=pd.read_json('http://192.168.2.223:5006/getDeathsByState?state='+str(i), orient='columns')
defun_tmp['Defunciones']=defun_tmp.confirmed+defun_tmp.suspected
defun_tmp['Codigo region']=i
reg=reg_info.description[reg_info.id==i].values[0]
defun_tmp['Region']=reg
defun_covid_N=defun_covid_N.append(defun_tmp)
defun_covid_N['Fecha']=defun_covid_N['dates']
defun_covid_N['Fecha']=pd.to_datetime(defun_covid_N['Fecha']).dt.tz_localize(None)
defun_covid_N=defun_covid_N.groupby(['Fecha',]).sum()
defun_covid_N=defun_covid_N.drop(columns=['confirmed', 'suspected','Codigo region'])
defun_covid_N.reset_index(inplace=True)
# Defunciones por periodo anual y mensual
defun['año']=pd.DatetimeIndex(defun['Fecha']).year
defun['mes-año']=pd.to_datetime(defun['Fecha']).dt.to_period('M')
defun['semana-mes-año']=pd.to_datetime(defun['Fecha']).dt.to_period('W')
defun_covid_N['año']=pd.DatetimeIndex(defun_covid_N['Fecha']).year
defun_covid_N['mes-año']=pd.to_datetime(defun_covid_N['Fecha']).dt.to_period('M')
defun_covid_N['semana-mes-año']=pd.to_datetime(defun_covid_N['Fecha']).dt.to_period('W')
# Defunciones por periodo anual y mensual
defun['año']=pd.DatetimeIndex(defun['Fecha']).year
defun['mes-año']=pd.to_datetime(defun['Fecha']).dt.to_period('M')
defun['semana-mes-año']=pd.to_datetime(defun['Fecha']).dt.to_period('W')
defun_covid_N['año']=pd.DatetimeIndex(defun_covid_N['Fecha']).year
defun_covid_N['mes-año']=pd.to_datetime(defun_covid_N['Fecha']).dt.to_period('M')
defun_covid_N['semana-mes-año']=pd.to_datetime(defun_covid_N['Fecha']).dt.to_period('W')
#Calce de fechas
max_date=min([max(defun['Fecha']),max(defun_covid_N['Fecha'])])
defun=defun[defun['Fecha']<=max_date]
defun_covid_N=defun_covid_N[defun_covid_N['Fecha']<=max_date]
# Modelos nacional
defun_year_all=defun.groupby(['año']).sum()
y=defun_year_all['Defunciones'].iloc[range(9,-1,-1)].values
x=range(2019,2009,-1)
anual_model_l = np.poly1d(np.polyfit(x, y, 1))
## Proyeccion de aumetno de población semanales totales
defun['semana']=pd.to_datetime(defun['Fecha']).dt.week
defun_covid_N['semana']=pd.to_datetime(defun_covid_N['Fecha']).dt.week
defun_week=defun.groupby(['año','semana']).sum()
defun_covid_week=defun_covid_N.groupby(['año','semana']).sum()
# defun_covid_week=defun_covid_week[defun_covid_week.index.get_level_values('semana')!=23]
# Defunciones proyectadas por modelo lineal pais semana
semanas_2020=defun_week.iloc[defun_week.index.get_level_values('año') == 2020].index.get_level_values('semana')
defun_pro=np.zeros(semanas_2020.shape[0])
defun_pro_min=np.zeros(semanas_2020.shape[0])
defun_pro_max=np.zeros(semanas_2020.shape[0])
acum=np.zeros((10,semanas_2020.shape[0]))
for j in range(10):
y=np.array(defun_week.iloc[defun_week.index.get_level_values('año') == 2010+j]['Defunciones'])
# print(y[0:semanas_2020.shape[0]]*models[i][0](2010+j)/models[i][0](2020))
acum[j,:]=y[0:semanas_2020.shape[0]]*anual_model_l(2020)/anual_model_l(2010+j)
# print(acum)
defun_pro=np.mean(acum,axis=0)
defun_pro_min=np.mean(acum,axis=0)-2*np.std(acum,axis=0)
defun_pro_max=np.mean(acum,axis=0)+2*np.std(acum,axis=0)
defun_pro_max_1=np.mean(acum,axis=0)+1*np.std(acum,axis=0)
defun_pro_min_1=np.mean(acum,axis=0)-1*np.std(acum,axis=0)
x=np.array(semanas_2020)
x_cov=np.array(defun_covid_week.iloc[defun_covid_week.index.get_level_values('año') == 2020].index.get_level_values('semana'))
y=defun_pro
y_1s=defun_pro_max_1
y_2s=defun_pro_max
y_1sm=defun_pro_min_1
y_2sm=defun_pro_min
y_2020=np.array(defun_week.iloc[defun_week.index.get_level_values('año') == 2020]['Defunciones'])
y_covid=np.array(defun_covid_week.iloc[defun_covid_week.index.get_level_values('año') == 2020]['Defunciones'])
y_covid=np.insert(y_covid, 0, np.zeros(len(np.setdiff1d(x, x_cov))))
excess_dead=pd.DataFrame()
tmp_excess=np.array([x,y_2020,y_covid,y,y_2020-y_covid,y_2020-y_covid-y,y_2s,y_1s,y_1sm,y_2sm]).transpose()
tmp_excess=pd.DataFrame(data=tmp_excess,columns=['semana','defunciones_totales',
'defunciones_covid','media',
'defunciones_no_covid','exceso_media','exceso_2S','exceso_1S','exceso_minus1S','exceso_minus2S'])
excess_dead=excess_dead.append(tmp_excess)
excess_dead.to_csv("excess_dead_nac_weekly.csv",index=False)