notebook.community

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In [1]:

    
import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import numpy as np
import fileinput
from itertools import product
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import seaborn as sns
from os import listdir

import matplotlib.pyplot as plt
import seaborn as sns
from scipy.interpolate import griddata
import matplotlib as mpl
sys.path.insert(0,'..')
from notebookFunctions import *
# from .. import notebookFunctions

%matplotlib inline
plt.rcParams['figure.figsize'] = (10,6.180)    #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2



In [4]:

    
year = np.arange(2018,2051)



In [5]:

    
def GDP_change(year, rate=0.06):
    return (1+rate)**(year-year[0])



In [7]:

    
china = 11.2*GDP_change(year,rate=0.06)



In [8]:

    
us = 18.57*GDP_change(year,rate=0.02)



In [11]:

    
pd.DataFrame(np.concatenate([year, china,us], axis=1))









    



---------------------------------------------------------------------------
AxisError                                 Traceback (most recent call last)
<ipython-input-11-ea9be3debe19> in <module>()
----> 1 pd.DataFrame(np.concatenate([year, china,us], axis=1))

AxisError: axis 1 is out of bounds for array of dimension 1



In [20]:

    
c = pd.Series(china,name="china")
u = pd.Series(us,name="us")
y = pd.Series(year,name="year")



In [23]:

    
data = pd.concat([y,c,u],axis=1)
data.plot("year", ["china","us"],figsize=(20,12.36))



In [28]:

    
y = pd.DataFrame(year,name="year")









    Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a211a1048>



In [44]:

    
pd.DataFrame(data=year, columns=["year"]).assign(china=11.2*GDP_change(year,rate=0.06)).\
    assign(us=18.57*GDP_change(year,rate=0.02))



In [55]:

    
rate_list = np.linspace(0.03, 0.1)



In [56]:

    
N = np.log(18.57/11.2)/(np.log((1+rate_list)/(1.0+0.02)))



In [57]:

    
plt.plot(rate_list, N)









    Out[57]:





[<matplotlib.lines.Line2D at 0x1a22090278>]



In [ ]:

	year	china	us
0	2018	11.200000	18.570000
1	2019	11.872000	18.941400
2	2020	12.584320	19.320228
3	2021	13.339379	19.706633
4	2022	14.139742	20.100765
5	2023	14.988126	20.502781
6	2024	15.887414	20.912836
7	2025	16.840659	21.331093
8	2026	17.851098	21.757715
9	2027	18.922164	22.192869
10	2028	20.057494	22.636726
11	2029	21.260944	23.089461
12	2030	22.536600	23.551250
13	2031	23.888797	24.022275
14	2032	25.322124	24.502721
15	2033	26.841452	24.992775
16	2034	28.451939	25.492631
17	2035	30.159055	26.002483
18	2036	31.968599	26.522533
19	2037	33.886714	27.052983
20	2038	35.919917	27.594043
21	2039	38.075112	28.145924
22	2040	40.359619	28.708842
23	2041	42.781196	29.283019
24	2042	45.348068	29.868680
25	2043	48.068952	30.466053
26	2044	50.953089	31.075374
27	2045	54.010275	31.696882
28	2046	57.250891	32.330820
29	2047	60.685944	32.977436
30	2048	64.327101	33.636985
31	2049	68.186727	34.309724
32	2050	72.277931	34.995919