Analysis en modelo SI (sin R) de los datos Covid oficiales de España

prediccion.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
dataframe=pd.read_csv("https://covid19.isciii.es/resources/serie_historica_acumulados.csv", skipfooter=3, engine="python", encoding='iso8859')


# In[2]:


dataframe['date']=pd.to_datetime(dataframe['FECHA'],format='%d/%m/%Y')


# In[3]:


data=dataframe.drop(columns='FECHA').set_index(['CCAA','date']).sort_index().unstack(level=0).fillna(0)
data


# In[4]:


data.plot(x=['CASOS'],y=['Fallecidos'],kind='scatter')


# In[5]:


data.plot(x=[('CASOS','MD')],y=[('Fallecidos','MD')],kind='scatter')


# In[6]:


#y a jugar
fallRoll=pd.concat([data[('Fallecidos','MD')].rolling(4).mean(),data[('Fallecidos','MD')].rolling(4).mean().diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[7]:


fallRoll=pd.concat([data[('Fallecidos','MD')].rolling(4).mean(),data[('Fallecidos','MD')].diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[8]:


fallRoll=pd.concat([data[('Fallecidos','MD')].rolling(4).median(),data[('Fallecidos','MD')].diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[9]:


fallRoll=pd.concat([data[('Fallecidos','MD')].rolling(4).median(),data[('Fallecidos','MD')].rolling(4).mean().diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[10]:


fallRoll=pd.concat([data[('Fallecidos','MD')].rolling(4).median(),data[('Fallecidos','MD')].rolling(4).median().diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[11]:


fallRoll=pd.concat([data[('Fallecidos','MD')],data[('Fallecidos','MD')].rolling(4).median().diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[12]:


fallRoll=pd.concat([data[('Fallecidos','MD')],data[('Fallecidos','MD')].rolling(4).mean().diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[13]:


fallRoll=pd.concat([data[('Fallecidos','MD')],data[('Fallecidos','MD')].diff()],axis=1,keys=['f','diff'])
fallRoll.plot(x=['f'],y=['diff'],kind='scatter')


# In[14]:


import numpy as np
for n in range(50,2,-1):
    f=data['CASOS'].sum(axis=1).tail(n)
    r=np.polyfit(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],2,  full=True)
    x=r[0]
    print(n,x[0],(-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))


# In[17]:


import matplotlib.pyplot as plt
import numpy as np


# In[24]:


f=data['CASOS'].sum(axis=1)
a=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.')
d=f
x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
print((-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
p=np.poly1d(x)
plt.xlim(0,250000)
plt.ylim(0,10000)
xp=np.linspace(0, 300000, 300)
b=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-')
d=f.tail(15)
x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
print((-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
p2=np.poly1d(x)
b=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-',xp,p2(xp),'-')


# In[15]:


import numpy as np
for n in range(50,2,-1):
    f=data['Fallecidos'].sum(axis=1).tail(n)
    r=np.polyfit(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],2,  full=True)
    x=r[0]
    print(n,x[0],(-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))


# In[16]:


for n in range(50,2,-1):
    try:
      fit=data['Fallecidos'].tail(n).apply(lambda x: np.polyfit(x[~np.isnan(x.diff())],x.diff()[~np.isnan(x.diff())],2))
      r=fit.apply(lambda x: (-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0])).sum()
      print(n,r)
    except np.linalg.LinAlgError:
      pass


# In[26]:


f=data['Fallecidos'].sum(axis=1)
a=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.')
d=f
x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],4)
p=np.poly1d(x)
print(p.roots)
x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
print((-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
p=np.poly1d(x)
print(p.roots)
plt.xlim(0,30000)
plt.ylim(0,1000)
xp=np.linspace(0, 300000, 300)
b=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-')
d=f.tail(12)
x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
print((-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
p2=np.poly1d(x)
b=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-',xp,p2(xp),'-')


# In[20]:


f0=data['CASOS']
for name in f0:
  f=f0[name]
  a=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.')
  d=f
  x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
  print(name,(-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
  p=np.poly1d(x) 
  plt.xlim(0,60000)
  plt.ylim(0,3000)
  xp=np.linspace(0, 300000, 300)
  b=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-')


# In[21]:


f0=data['Fallecidos']
for name in f0:
  f=f0[name]
  a=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.')
  d=f
  x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
  print(name,(-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
  p=np.poly1d(x) 
  plt.xlim(0,7000)
  plt.ylim(0,400)
  xp=np.linspace(0, 7000, 70)
  b=plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-')


# In[22]:


###%matplotlib notebook
import matplotlib.pyplot as plt
plt.ioff() 
f0=data['Fallecidos']

for name in f0:
  d=f0[name]
  f=d 
  x=np.polyfit(d[~np.isnan(d.diff())],d.diff()[~np.isnan(d.diff())],2)
  print(name,(-x[1]- np.sqrt(x[1]*x[1]-4*x[0]*x[2]))/(2*x[0]))
  p=np.poly1d(x) 
  xp=np.linspace(0, int(d.max())*1.5, int(d.max()))
  plt.plot(f[~np.isnan(f.diff())],f.diff()[~np.isnan(f.diff())],'.',xp,p(xp),'-')
  plt.axis("auto")
  plt.ylim(bottom=0)
  plt.show()