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

# # Recovery from the Great Depression and Great Recession

# In[1]:


# JAVASCRIPT DISABLED IN JUPYTERLAB
#
# %%javascript
#
# IPython.OutputArea.prototype._should_scroll = function(lines) {
#    return false;}


# In[2]:


# the above "magic" (%%) command is there to keep output cells 
# from shifting to autoscroll, which leaves you peering at a 
# small portion of a window looking for whatever the computer
# just did in the output cell...


# In[3]:


# set up the environment by reading in libraries: 
# os... graphics... data manipulation... time... math... statistics...
# and reporting version numbers to help at debugging
#
# needs to be cleaned, as contains excess and unneeded libraries...

import sys
import os
from urllib.request import urlretrieve

import matplotlib as mpl
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
import PIL as pil

import plotly
plotly.tools.set_credentials_file(
    username='delong', 
    api_key='d6vMMwVn4sEBmR2MLN9H')

import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Scatter
from IPython.display import Image

import pandas as pd
from pandas import DataFrame, Series
import pandas_datareader
from datetime import datetime

import scipy as sp
import numpy as np
import math
import random

import seaborn as sns
import statsmodels
import statsmodels.api as sm
import statsmodels.formula.api as smf

# report library versions...

print("LIBRARY VERSIONS")
print(" ")
print("Python version:\n{}\n".format(sys.version))
print("matplotlib version: {}".format(mpl.__version__))
print("pandas version: {}".format(pd.__version__))
print("numpy version: {}".format(np.__version__))
print("statsmodels version: {}".format(statsmodels.__version__))
print("PIL version: {}".format(pil.__version__))
print("scipy version: {}".format(sp.__version__))


# In[4]:


get_ipython().run_line_magic('matplotlib', 'inline')


# In[5]:


# the above cell "magic" (%) tells the matplot lib graphics library
# to plot graphs as static figures output cells, rather than in new
# windows (or in the alternative "%matplotlib notebook" view, as a
# dynamic interactive figure)


# In[6]:


# graphics setup: seaborn-whitegrid to make the output from matplotlib
# prettier and figure_size to make the output from matplotlib
# somewhat larger...

plt.style.use('seaborn-whitegrid')

figure_size = plt.rcParams["figure.figsize"]
figure_size[0] = 10
figure_size[1] = 8
plt.rcParams["figure.figsize"] = figure_size


# In[7]:


# reading in the previously-downloaded long-run real GDP and 
# GDP per capita file from Johnston and Williamson's "Measuring 
# Worth" website
#
# constructing a dataframe to hold the data, and then wrapping
# that dataframe inside a dict object to append source notes and
# links

Source_URL = 'http://delong.typepad.com/2017-09-12_us_real_gdp_and_per_capita.csv' 

uslrnp_df = pd.read_csv(
    Source_URL, 
    converters = {'Source': str, 'Source_URL': str}, 
#    parse_dates = True,
    index_col = 0)

uslrnp_dict = {}
uslrnp_dict["Source"] = uslrnp_df.Source.values[0]
uslrnp_dict["SourceURL"] = Source_URL

sourceD1 = "See Johnston and Williamson: "
sourceD2 = "'Sources and Techniques Used in the Construction of Annual GDP, 1790 - Present' "
sourceD3 = "https://www.measuringworth.com/usgdp/#"
uslrnp_dict["SourceNotes"] = sourceD1 + sourceD2 + sourceD3

sourceD4 = "See https://www.measuringworth.com/aboutus.php; "
sourceD5 = "https://www.measuringworth.com/datasets/usgdp/export.php?"
sourceD6 = "year_source=1790&year_result=2016&use%5B%5D=REALGDP&use%5B%5D=GDPCP"
uslrnp_dict["SourceDescription"] = sourceD4 + sourceD5 + sourceD6

del uslrnp_df["Source"]
del uslrnp_df["Source_URL"]

uslrnp_dict["df"] = uslrnp_df

# commented out 
uslrnp_dict


# In[8]:


# generating four simple graphs of Real GDP — total and per capita,
# linear and log scale
# 
# this first graph is the version that belongs in the textook, IMHO...

uslrnp_df.Real_GDP_per_Capita.plot()

plt.xlabel("Year", size = 15)
plt.ylabel("Real Output per Capita", size = 15)
plt.ylim(0, )
plt.title("Real Output per Capita in the United States", size = 24)



# In[9]:


uslrnp_df.Real_GDP_per_Capita.plot(logy = True)


plt.xlabel("Year", size = 15)
plt.ylabel("Real National Product per Capita (Log Scale)", size = 15)
plt.title("Real National Product per Capita in the United States", 
    size = 30)


# In[10]:


uslrnp_df.Real_GDP.plot()

plt.xlabel("Year", size = 15)
plt.ylim(0, )
plt.ylabel("Real National Product (in Tens of Trillions of Today's Dollars)", size = 15)
plt.title("Real National Product in the United States since 1790", size = 30)


# In[11]:


uslrnp_df.Real_GDP.plot(logy = True)

plt.xlabel("Year", size = 15)
plt.ylabel("Real National Product", size = 15)
plt.title("Real National Product in the United States since 1790:\nLog Output", size = 30)


# In[35]:


depression_real_GDP = []

for t in range(1929, 1942):
    depression_real_GDP = depression_real_GDP + [uslrnp_df.Real_GDP[t]/uslrnp_df.Real_GDP[1929]]

depression_real_GDP_per_capita = []

for t in range(1929, 1942):
    depression_real_GDP_per_capita = depression_real_GDP_per_capita + [uslrnp_df.Real_GDP_per_Capita[t]/uslrnp_df.Real_GDP_per_Capita[1929]]
    
depression_real_GDP_per_capita


# In[40]:


recession_real_GDP = []

for t in range(2017, 2017):
    recession_real_GDP = recession_real_GDP + [uslrnp_df.Real_GDP[t]/uslrnp_df.Real_GDP[2007]]
    
recession_real_GDP = recession_real_GDP + [1.149] + [1.1779] + [1.2074] 


recession_real_GDP_per_capita = []

for t in range(2007, 2017):
    recession_real_GDP_per_capita = recession_real_GDP_per_capita + [uslrnp_df.Real_GDP_per_Capita[t]/uslrnp_df.Real_GDP_per_Capita[2007]]
    
recession_real_GDP_per_capita = recession_real_GDP_per_capita + [1.0635] + [1.0795] + [1.0957]

recession_real_GDP_per_capita


# In[29]:


year = range(0,11)

print(print(year))


# In[44]:


data = [('Great Recession', recession_real_GDP_per_capita),
        ('GreatDepression', depression_real_GDP_per_capita)]

comparison_per_capita_df = pd.DataFrame.from_items(data)

comparison_per_capita_df.plot()
plt.xlabel("Years After Previous Business Cycle Peak", size = 12)
plt.ylabel("Relative to Value at Previous Business Cycle Peak", size = 12)
plt.title("Downturn and Recovery Trends in the \nGreat Depression and Great Recession", size = 24)