import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import csv
from scipy.interpolate import interp1d
from scipy.optimize import curve_fit
import locale
import japanize_matplotlib
from sklearn.linear_model import LinearRegression
import numpy.polynomial.polynomial as P
from numpy import arange


encoding = locale.getpreferredencoding()

plt.rcParams['figure.subplot.bottom'] = 0.25

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax2 = ax1.twinx()
ax3 = ax1.twinx()

covid = pd.read_csv('nhk_news_covid19_domestic_daily_data.csv')
df = DataFrame(covid)

start_date = "2022/1/18"  

df_s=df.set_index("日付")[start_date:]
df_r = df_s.reset_index()

df0 = pd.DataFrame(df_r)
#-----------------
df_0=df0.set_index("日付")[start_date:]
df_0r = df_0.reset_index()

#print(df_0r)

# number = df_0の行数(rows)
number = len(df_0)

#df_4 = pd.DataFrame(df_r)
ml = pd.DataFrame({'no': range(number)})

#print(ml)
df_new0 = pd.concat([df_0r, ml], axis=1)

#print('df_new0')
#print(df_new0)


#-----------x軸目盛の変更-----------------
ax1.bar(df_new0["日付"],df_new0["国内の感染者数_1日ごとの発表数"], label="国内の感染者数_1日ごとの発表数")
#ax1.bar(df_new0["no"],df_new0["国内の感染者数_1日ごとの発表数"], label="国内の感染者数_1日ごとの発表数")

ax1.xaxis.set_major_locator(mdates.DayLocator(interval=2))
ax1.tick_params(axis='x', colors ="black", rotation = 90)
ax1.tick_params(axis='y', colors ="navy")

#---------------------------------------

encoding = locale.getpreferredencoding()

df_2 = pd.read_csv('effective_reproduction_number.csv', encoding="ANSI")

n0 = len(df_2)

l_date = df_2['日付'].values[n0-1]

#-----------------------------------

df_3 = DataFrame(df_2)

df_s=df_3.set_index("日付")[start_date:]
df_r = df_s.reset_index()

#print(df_2)

# num = df_rの行数(rows)
num = len(df_r)

df_4 = pd.DataFrame(df_r)
ml = pd.DataFrame({'no': range(num)})


df_new = pd.concat([df_4, ml], axis=1)


df_new.to_csv('df_new.csv', encoding = encoding)

#特定の列の抽出
df_a=df_new.loc[:,["no", "実効再生産数"]]

df_a.to_csv('df_a.csv', encoding = encoding)

#--------線形再帰--------------------------
lr = LinearRegression()

x = df_a[['no']]
y = df_a[['実効再生産数']]
coef1 = lr.fit(x, y)
co = lr.fit(x, y)

w = lr.coef_
w0 = lr.intercept_


#------objective function----------------

def objective(x, a, b):
        y = b/(x+a)
        return y

# fit curve
popt, _ = curve_fit(objective, df_a['no'], df_a['実効再生産数'])
a, b = popt

#---------------回帰分析------------------
print('\n')
print('回帰分析 \n')
print(df_2['日付'][n0-1])
print('回帰直線')

print('lr =', w, w0)

print('objective function')
print('popt', popt)
print('a=', a)
print('b=', b)


#-----------------------------------
interp = interp1d(df_a['no'], df_a['実効再生産数'],kind="cubic")

new_t = np.arange(0, num-1, 0.2)  #range(num) num-1

#-------------------------

file = pd.DataFrame({'no': new_t, '実効再生産数':interp(new_t)})

#print(file)


ax2.plot(new_t, interp(new_t), label="実効再生産数", color='red')
ax2.tick_params(colors ="red")
ax3.tick_params(colors ="red")

#ax2.plot(df_new["no"],df_new['実効再生産数'], color='orangered', label="")

ax2.plot(df_new['no'], df_new['実効再生産数'], 'o', color='orangered')

ymin = 0.5
ymax = 6.
ax2.set_ylim(ymin, ymax)
ymin, ymax =ax2.set_ylim()
plt.ylim(ymin, ymax)


plt.xlim(0, num+20)

handler1, label1 = ax1.get_legend_handles_labels()
handler2, label2 = ax2.get_legend_handles_labels()
ax1.legend(handler1+handler2,label1+label2,borderaxespad=0)
ax1.grid(axis='x')

#---------------------------------

x1 = np.linspace(0,155,155);

plt.plot(x1, w[0]*x1+w0[0], color='skyblue', label='linear')
plt.plot(x1,  b/(x1+a), color='lightgreen', label ='b/(x+a)')

plt.plot(x1, 0*x1+1, color='purple')
#plt.legend(bbox_to_anchor=(0.5, 1.0), loc='center', borderaxespad=0)
plt.legend(bbox_to_anchor=(1.0, 1.0), loc='upper right', borderaxespad=0)
plt.grid()
plt.show()