| # 6330425521 244 (2021-04-26 02:40) #dups = 1
def max_new_cases_date(data) :
return ( data['dates'][np.argmax(np.sum(data['new_cases'],axis=0),axis=0)] , np.max(np.sum(data['new_cases'],axis=0),axis=0) )
def max_new_cases_province(data,beg_date,end_date) :
return (data['province_names'][np.argmax(np.sum(data['new_cases'][::,int(np.where(data['dates']==beg_date)[0]):int(np.where(data['dates']==end_date)[0])+1],axis=1))], np.max(np.sum(data['new_cases'][::,int(np.where(data['dates']==beg_date)[0]):int(np.where(data['dates']==end_date)[0])+1],axis=1)))
def max_new_cases_province_by_dates(data) :
a = np.ndarray((3,int(data['dates'].shape[0])),dtype=object)
a[0]=data['dates']
a[1]=data['province_names'][np.argmax(data['new_cases'],axis=0)]
a[2]=np.max(data['new_cases'],axis=0)
return a.T
def most_similar(data,province_name) :
similarity = np.sum( (data['norm_data'] - data['norm_data'][int(np.where(data['province_names']==province_name)[0])])**2 ,axis=1)
sec_min = np.argmin(similarity[data['province_names']!=province_name])
return data['province_names'][data['province_names']!=province_name][sec_min]
def most_similar_province_pair(data):
pass
def most_similar_in_period(data, province, beg_date, end_date):
pass
### main function | # 6330510121 314 (2021-04-26 22:58) %diff = 22.21
def max_new_cases_date(data):return (data['dates'][np.argmax(sum(data['new_cases']))],max(sum(data['new_cases'])))
def max_new_cases_province(data, beg_date, end_date):return ((data['province_names'][np.argmax(np.sum(data['new_cases'].T[(np.where(data['dates']==beg_date)[0][0]):(np.where(data['dates']==end_date)[0][0])+1],axis=0))],max(np.sum(data['new_cases'].T[(np.where(data['dates']==beg_date)[0][0]):(np.where(data['dates']==end_date)[0][0])+1],axis=0))))
def max_new_cases_province_by_dates(data):
a = np.ndarray((3,len(data['dates'])), dtype=object)
a[0]=data['dates']
a[1]=data['province_names'][np.argmax(data['new_cases'],axis=0)]
a[2]=np.max(data['new_cases'],axis=0)
return print(a.T)
def most_similar(data, province):
a=np.sum((data['norm_data'][:]-data['norm_data'][np.where(data['province_names']==province)[0][0]])**2,axis=1)
a[np.where(data['province_names']==province)[0][0]]+=99
return data['province_names'][np.argmin(a)]
def most_similar_province_pair(data):
return
def most_similar_in_period(data, province, beg_date, end_date):
return |