from pandas import *
import matplotlib.pyplot as plt
import datetime
from IPython.core.display import Image, HTML
from pandas.core.datetools import *
from numpy import arange,array,ones#,random,linalg
from scipy import stats
figsize(12,10)
pandas.__version__
#matplotlib.__version__

pandas.set_option('display.height', 500)
pandas.set_option('display.max_rows', 500)
pandas.set_option('display.max_columns', 500)
pandas.set_option('display.width', 500)
pandas.set_option('display.mpl_style', False)

xkcd()
matplotlib.matplotlib_fname()
rcParams['lines.linewidth'] = 3.0
rcParams['legend.shadow'] = True
rcParams['legend.fancybox'] = True
rcParams['legend.borderpad'] = 0.25

eq_data = read_excel('eq_disasters_by_year&region_dollarss.xlsx', 0, index_col=None, parse_dates=[1])
eq_data.drop(['id','Affected','Impacted'],axis=1,inplace=True)
eq_data.head()

storm_data = read_excel('storm_disasters_by_year&region_dollarss.xlsx', 0, index_col=None, parse_dates=[1])
storm_data.drop(['id','Affected','Impacted'],axis=1,inplace=True)
#storm_data.drop(['id','Affected','Impacted', 'Homeless','Killed','Injured'],axis=1,inplace=True)
storm_data.head()

eq_dmg = eq_data.ix[eq_data['Damage'] > 1000000] # Number over $1billion
print "Number of earthquake disasters over $1billion since 1900 = ", len(eq_dmg)
eq_kill = eq_data.ix[eq_data['Killed'] > 10000] # Number over 10,000
print "Number of earthquake disasters with over 10,000 dead since 1900 = ", len(eq_kill)
eq_inj = eq_data.ix[eq_data['Injured'] > 10000] # Number over 10,000
print "Number of earthquake disasters with over 10,000 injured since 1900 = ", len(eq_inj)
eq_home = eq_data.ix[eq_data['Homeless'] > 10000] # Number over 10,000
print "Number of earthquake disasters with over 10,000 homeless since 1900 = ", len(eq_home)

storm_dmg = storm_data.ix[storm_data['Damage'] > 1000000] # Number over $1billion
print "Number of storm disasters over $1billion since 1900 = ", len(storm_dmg)
storm_kill = storm_data.ix[storm_data['Killed'] > 10000] # Number over 10,000
print "Number of storm disasters with over 10,000 dead since 1900 = ", len(storm_kill)
storm_inj = storm_data.ix[storm_data['Injured'] > 10000] # Number over 10,000
print "Number of storm disasters with over 10,000 injured since 1900 = ", len(storm_inj)
storm_home = storm_data.ix[storm_data['Homeless'] > 10000] # Number over 10,000
print "Number of storm disasters with over 10,000 homeless since 1900 = ", len(storm_home)

eq_region = eq_data.groupby('Region')
eq_region_max = eq_region.max()

eq_max_occur_region = eq_region_max['Occurrence'].idxmax()
eq_max_occur_count = eq_region['Occurrence'].max().max()
print 'Region with maximum earthquake disaster occurrences:', eq_max_occur_region, '=', eq_max_occur_count

eq_max_kill_region = eq_region_max['Killed'].idxmax()
eq_max_kill_count = eq_region['Killed'].max().max()
print 'Region with maximum earthquake disaster deaths:', eq_max_kill_region, '=', eq_max_kill_count

eq_max_inj_region = eq_region_max['Injured'].idxmax()
eq_max_inj_count = eq_region['Injured'].max().max()
print 'Region with maximum earthquake disaster injuries:', eq_max_inj_region, '=', eq_max_inj_count

eq_max_home_region = eq_region_max['Homeless'].idxmax()
eq_max_home_count = eq_region['Homeless'].max().max()
print 'Region with maximum earthquake disaster homeless population:', eq_max_home_region, '=', eq_max_home_count

eq_max_dmg_region = eq_region_max['Damage'].idxmax()
eq_max_dmg_count = eq_region['Damage'].max().max()
print 'Region with maximum earthquake disaster damage ($):', eq_max_dmg_region, '=', eq_max_dmg_count

storm_region = storm_data.groupby('Region')
storm_region_max = storm_region.max()

storm_max_occur_region = storm_region_max['Occurrence'].idxmax()
storm_max_occur_count = storm_region['Occurrence'].max().max()
print 'Region with maximum storm disaster occurrences:', storm_max_occur_region, '=', storm_max_occur_count

storm_max_kill_region = storm_region_max['Killed'].idxmax()
storm_max_kill_count = storm_region['Killed'].max().max()
print 'Region with maximum storm disaster deaths:', storm_max_kill_region, '=', storm_max_kill_count

storm_max_inj_region = storm_region_max['Injured'].idxmax()
storm_max_inj_count = storm_region['Injured'].max().max()
print 'Region with maximum storm disaster injuries:', storm_max_inj_region, '=', storm_max_inj_count

storm_max_home_region = storm_region_max['Homeless'].idxmax()
storm_max_home_count = storm_region['Homeless'].max().max()
print 'Region with maximum storm disaster homeless population:', storm_max_home_region, '=', storm_max_home_count

storm_max_dmg_region = storm_region_max['Damage'].idxmax()
storm_max_dmg_count = storm_region['Damage'].max().max()
print 'Region with maximum storm disaster damage ($):', storm_max_dmg_region, '=', storm_max_dmg_count

eq_yr = eq_data.groupby('Year')
eq_yr_sum = eq_yr.sum()

storm_yr = storm_data.groupby('Year')
storm_yr_sum = storm_yr.sum()

#storm_eq_yr_sum = concat([eq_yr_sum, storm_yr_sum])
storm_eq_yr_sum = concat([eq_yr_sum.rename(columns={'Occurrence': 'Occurrence (Earthquake)', 
                        'Killed': 'Killed (Earthquake)', 
                        'Injured': 'Injured (Earthquake)',
                        'Damage': 'Damage (Earthquake)',
                        'Homeless': 'Homeless (Earthquake)'}),
                         storm_yr_sum.rename(columns={'Occurrence': 'Occurrence (Storm)', 
                        'Killed': 'Killed (Storm)', 
                        'Injured': 'Injured (Storm)',
                        'Damage': 'Damage (Storm)',
                        'Homeless': 'Homeless (Storm)'})], axis=1)

print 'Earthquake Spearman correlation = ', eq_yr_sum['Occurrence'].corr(eq_yr_sum['Damage'], method='spearman')
print 'Storm Spearman correlation = ', storm_yr_sum['Occurrence'].corr(storm_yr_sum['Damage'], method='spearman')

print 'Earthquake Spearman correlation from 1980 = ', eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'].corr(eq_yr_sum['1980-01-01':'2013-01-01']['Damage'], method='spearman')
print 'Storm Spearman correlation from 1980 = ', storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'].corr(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], method='spearman')

print 'Earthquake Spearman correlation (rolling mean) = ', rolling_mean(eq_yr_sum['Occurrence'], 3, center=True).corr(rolling_mean(eq_yr_sum['Damage'], 3, center=True), method='spearman')
print 'Storm Spearman correlation (rolling mean) = ', rolling_mean(storm_yr_sum['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['Damage'], 3, center=True), method='spearman')

print 'Earthquake Spearman correlation from 1980 (rolling mean) = ', rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True).corr(rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True), method='spearman')
print 'Storm Spearman correlation from 1980 (rolling mean) = ', rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True), method='spearman')

print 'Earthquake Pearson correlation = ', eq_yr_sum['Occurrence'].corr(eq_yr_sum['Damage'], method='pearson')
print 'Storm Pearson correlation = ', storm_yr_sum['Occurrence'].corr(storm_yr_sum['Damage'], method='pearson')

print 'Earthquake Pearson correlation from 1980 = ', eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'].corr(eq_yr_sum['1980-01-01':'2013-01-01']['Damage'], method='pearson')
print 'Storm Pearson correlation from 1980 = ', storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'].corr(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], method='pearson')

print 'Earthquake Pearson correlation (rolling mean) = ', rolling_mean(eq_yr_sum['Occurrence'], 3, center=True).corr(rolling_mean(eq_yr_sum['Damage'], 3, center=True), method='pearson')
print 'Storm Pearson correlation (rolling mean) = ', rolling_mean(storm_yr_sum['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['Damage'], 3, center=True), method='pearson')

print 'Earthquake Pearson correlation from 1980 (rolling mean) = ', rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True).corr(rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True), method='pearson')
print 'Storm Pearson correlation from 1980 (rolling mean) = ', rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True), method='pearson')

print 'Occurrence Spearman correlation = ', eq_yr_sum['Occurrence'].corr(storm_yr_sum['Occurrence'], method='spearman')
print 'Damage Spearman correlation = ', eq_yr_sum['Damage'].corr(storm_yr_sum['Damage'], method='spearman')

print 'Occurrence Spearman correlation from 1980 = ', eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'].corr(storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], method='spearman')
print 'Damage Spearman correlation from 1980 = ', eq_yr_sum['1980-01-01':'2013-01-01']['Damage'].corr(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], method='spearman')


print 'Occurrence Spearman correlation (rolling mean) = ', rolling_mean(eq_yr_sum['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['Occurrence'], 3, center=True), method='spearman')
print 'Damage Spearman correlation (rolling mean) = ', rolling_mean(eq_yr_sum['Damage'], 3, center=True).corr(rolling_mean(storm_yr_sum['Damage'], 3, center=True), method='spearman')

print 'Occurrence Spearman correlation from 1980 (rolling mean) = ', rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True), method='spearman')
print 'Damage Spearman correlation from 1980 (rolling mean) = ', rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True).corr(rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True), method='spearman')

print 'Occurrence Pearson correlation = ', eq_yr_sum['Occurrence'].corr(storm_yr_sum['Occurrence'], method='pearson')
print 'Damage Pearson correlation = ', eq_yr_sum['Damage'].corr(storm_yr_sum['Damage'], method='pearson')

print 'Occurrence Pearson correlation from 1980 = ', eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'].corr(storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], method='pearson')
print 'Damage Pearson correlation from 1980 = ', eq_yr_sum['1980-01-01':'2013-01-01']['Damage'].corr(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], method='pearson')

print 'Occurrence Pearson correlation (rolling mean) = ', rolling_mean(eq_yr_sum['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['Occurrence'], 3, center=True), method='pearson')
print 'Damage Pearson correlation (rolling mean) = ', rolling_mean(eq_yr_sum['Damage'], 3, center=True).corr(rolling_mean(storm_yr_sum['Damage'], 3, center=True), method='pearson')

print 'Occurrence Pearson correlation from 1980 (rolling mean) = ', rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True).corr(rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Occurrence'], 3, center=True), method='pearson')
print 'Damage Pearson correlation from 1980 (rolling mean) = ', rolling_mean(eq_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True).corr(rolling_mean(storm_yr_sum['1980-01-01':'2013-01-01']['Damage'], 3, center=True), method='pearson')

fig, ax1 = subplots()


ax1.plot(eq_yr_sum.index, eq_yr_sum['Occurrence'], label='Earthquake Occurrences', color='g', linewidth=3.0)
ax1.plot(storm_yr_sum.index, storm_yr_sum['Occurrence'], label='Storm Occurrences', color='m', linewidth=3.0)

ax1.tick_params(labelsize=20)
ax1.set_xlabel('Year', size='24')
ax1.set_ylabel('Occurrences (EMDAT criteria)', size='24')

frame = legend(loc='upper left', fontsize='20').get_frame()
frame.set_facecolor('1.0')

ax2 = ax1.twinx()
ax2.plot(eq_yr_sum.index, eq_yr_sum['Damage'] / 1000000000.0, label='Earthquake Losses', color='g', linestyle='--', linewidth=3.0)
ax2.plot(storm_yr_sum.index, storm_yr_sum['Damage']  / 1000000000.0, label='Storm Losses', color='m', linestyle='--', linewidth=3.0)
ax2.ticklabel_format(style='plain', axis='y')
ax2.tick_params(labelsize=20)
ax2.set_ylabel('Losses ($billion)', size='24')
#xlim(['1980-01-01', '2015-01-01'])
xlim(['1900-01-01', '2015-01-01'])

frame = legend(loc='upper right', fontsize='20').get_frame()
frame.set_facecolor('1.0')

fig, ax1 = subplots()


ax1.plot(eq_yr_sum.index, rolling_mean(eq_yr_sum['Occurrence'], 3), label='Earthquake Occurrences', color='g')
ax1.plot(storm_yr_sum.index, rolling_mean(storm_yr_sum['Occurrence'], 3), label='Storm Occurrences', color='m')

ax1.tick_params(labelsize=20)
ax1.set_xlabel('Year', size='24')
ax1.set_ylabel('Occurrences (EMDAT criteria)', size='24')

frame = legend(loc='upper left', fontsize='20').get_frame()
frame.set_facecolor('1.0')

ax2 = ax1.twinx()
ax2.plot(eq_yr_sum.index, rolling_mean(eq_yr_sum['Damage'] / 1000000000.0, 3), label='Earthquake Losses', color='g', linestyle='--')
ax2.plot(storm_yr_sum.index, rolling_mean(storm_yr_sum['Damage'] / 1000000000.0, 3), label='Storm Losses', color='m', linestyle='--')
ax2.ticklabel_format(style='plain', axis='y')
ax2.tick_params(labelsize=20)
ax2.set_ylabel('Losses ($billion)', size='24')
xlim(['1980-01-01', '2015-01-01'])

frame = legend(loc='upper right', fontsize='20').get_frame()
frame.set_facecolor('1.0')

storm_eq_occur_diff = (storm_yr_sum['Occurrence'] - eq_yr_sum['Occurrence'] )
storm_eq_dmg_diff = (storm_yr_sum['Damage'] - eq_yr_sum['Damage'] ) 

storm_eq_occur_diff = rolling_mean(storm_yr_sum['Occurrence'] - eq_yr_sum['Occurrence'], 3, center=True) 
storm_eq_dmg_diff = rolling_mean(storm_yr_sum['Damage'] - eq_yr_sum['Damage'], 3, center=True ) 

fig, ax1 = subplots()

ax1.plot(storm_eq_occur_diff.index, storm_eq_occur_diff, label='Occurrences', color='g')

ax1.tick_params(labelsize=20)
ax1.set_xlabel('Year', size='24')
ax1.set_ylabel('Storm - Earthquake Occurrences (EMDAT criteria)', size='24')

frame = legend(loc='upper left', fontsize='20').get_frame()
frame.set_facecolor('1.0')

ax2 = ax1.twinx()
ax2.plot(storm_eq_occur_diff.index, storm_eq_dmg_diff / 1000000000.0, label='Damage', color='m')

ax2.ticklabel_format(style='plain', axis='y')
ax2.tick_params(labelsize=20)
ax2.set_ylabel('Storm - Earthquake Losses ($billion)', size='24')

xlim(['1980-01-01', '2015-01-01'])
#xlim(['1900-01-01', '2015-01-01'])

frame = legend(loc='upper right', fontsize='20').get_frame()
frame.set_facecolor('1.0')

fig, ax1 = subplots()

ax1.plot(storm_eq_dmg_diff.index, storm_eq_occur_diff, label='Occurrences', color='g')

ax1.tick_params(labelsize=20)
ax1.set_xlabel('Year', size='24')
ax1.set_ylabel('Storm - Earthquake Occurrences (EMDAT criteria)', size='16')

frame = legend(loc='upper left', fontsize='20').get_frame()
frame.set_facecolor('1.0')

ax2 = ax1.twinx()
ax2.plot(storm_eq_dmg_diff.index, storm_eq_dmg_diff / 1000000000.0, label='Damage ($)', color='m')
ax2.ticklabel_format(style='plain', axis='y')
ax2.tick_params(labelsize=20)
ax2.set_ylabel('Storm - Earthquake Losses ($billion)', size='24')
xlim(['1980-01-01', '2015-01-01'])

frame = legend(loc='upper right', fontsize='20').get_frame()
frame.set_facecolor('1.0')

storm_eq_kill_diff = rolling_mean(storm_yr_sum['Killed'] - eq_yr_sum['Killed'], 3, center=True ) 
storm_eq_inj_diff = rolling_mean(storm_yr_sum['Injured'] - eq_yr_sum['Injured'], 3, center=True )
storm_eq_occur_diff = rolling_mean(storm_yr_sum['Occurrence'] - eq_yr_sum['Occurrence'], 3, center=True) 
storm_eq_home_diff = rolling_mean(storm_yr_sum['Homeless'] - eq_yr_sum['Homeless'], 3, center=True )

occur_diff_roll = rolling_mean(storm_eq_occur_diff, 3, center=True).dropna()
dmg_diff_roll = rolling_mean(storm_eq_dmg_diff, 3, center=True).dropna()
kill_diff_roll = rolling_mean(storm_eq_kill_diff, 3, center=True).dropna()
inj_diff_roll = rolling_mean(storm_eq_inj_diff, 3, center=True).dropna()
home_diff_roll = rolling_mean(storm_eq_home_diff, 3, center=True).dropna()
years = occur_diff_roll.index.year

occur_slope, occur_int, occur_r, occur_p, occur_std = stats.linregress(years,occur_diff_roll)
occur_trend = occur_slope*years+occur_int

dmg_slope, dmg_int, dmg_r, dmg_p, dmg_std = stats.linregress(years,dmg_diff_roll)
dmg_trend = dmg_slope*years+dmg_int

kill_slope, kill_int, kill_r, kill_p, kill_std = stats.linregress(years,kill_diff_roll)
kill_trend = kill_slope*years+kill_int

inj_slope, inj_int, inj_r, inj_p, inj_std = stats.linregress(years,inj_diff_roll)
inj_trend = inj_slope*years+inj_int

home_slope, home_int, home_r, home_p, home_std = stats.linregress(years,home_diff_roll)
home_trend = home_slope*years+home_int

occur_diff_roll1980 = rolling_mean(storm_eq_occur_diff['1980-01-01':'2015-01-01'], 3, center=True).dropna()
dmg_diff_roll1980 = rolling_mean(storm_eq_dmg_diff['1980-01-01':'2015-01-01'], 3, center=True).dropna()
kill_diff_roll1980 = rolling_mean(storm_eq_kill_diff['1980-01-01':'2015-01-01'], 3, center=True).dropna()
inj_diff_roll1980 = rolling_mean(storm_eq_inj_diff['1980-01-01':'2015-01-01'], 3, center=True).dropna()
home_diff_roll1980 = rolling_mean(storm_eq_home_diff['1980-01-01':'2015-01-01'], 3, center=True).dropna()
years1980 = occur_diff_roll1980.index.year

occur_slope1980, occur_int1980, occur_r1980, occur_p1980, occur_std1980 = stats.linregress(years1980,occur_diff_roll1980)
occur_trend1980 = occur_slope1980*years1980+occur_int1980

dmg_slope1980, dmg_int1980, dmg_r1980, dmg_p1980, dmg_std1980 = stats.linregress(years1980,dmg_diff_roll1980)
dmg_trend1980 = dmg_slope1980*years1980+dmg_int1980

kill_slope1980, kill_int1980, kill_r1980, kill_p1980, kill_std1980 = stats.linregress(years1980,kill_diff_roll1980)
kill_trend1980 = kill_slope1980*years1980+kill_int1980

inj_slope1980, inj_int1980, inj_r1980, inj_p1980, inj_std1980 = stats.linregress(years1980,inj_diff_roll1980)
inj_trend1980 = inj_slope1980*years1980+inj_int1980

home_slope1980, home_int1980, home_r1980, home_p1980, home_std1980 = stats.linregress(years1980,home_diff_roll1980)
home_trend1980 = home_slope1980*years1980+home_int1980

fig, ax = subplots(5, sharex=True)

ax[0].plot(storm_eq_occur_diff.index, storm_eq_occur_diff, label='Occurrence', color='k')
ax[0].plot(occur_diff_roll.index,occur_trend, linestyle='--', linewidth=1, color='k')
ax[0].set_title('Storm - Earthquake', size='24')
ax[0].set_ylabel('Occurrences')
#ax[0].locator_params(axis='y',nbins = 5)
ax[1].plot(storm_eq_dmg_diff.index, storm_eq_dmg_diff / 1000000000.0, label='Damage', color='k')
ax[1].plot(dmg_diff_roll.index,dmg_trend / 1000000000.0, linestyle='--', linewidth=1, color='k')
ax[1].set_ylabel('Losses \n  ($billion)')
ax[1].locator_params(axis='y',nbins = 5)
ax[2].plot(storm_eq_kill_diff.index, storm_eq_kill_diff / 1000.0, label='Killed', color='k')
ax[2].plot(kill_diff_roll.index,kill_trend / 1000.0, linestyle='--', linewidth=1, color='k')
ax[2].set_ylabel('Deaths \n (1,000s)')
ax[2].locator_params(axis='y',nbins = 5)
ax[3].plot(storm_eq_inj_diff.index, storm_eq_inj_diff / 1000.0, label='Injured', color='k')
ax[3].plot(inj_diff_roll.index,inj_trend / 1000.0, linestyle='--', linewidth=1, color='k')
ax[3].set_ylabel('Injuries \n (1,000s)')
ax[3].locator_params(axis='y',nbins = 5)
ax[4].plot(storm_eq_home_diff.index, storm_eq_home_diff / 1000.0, label='Homeless', color='k')
ax[4].plot(occur_diff_roll.index,home_trend / 1000.0, linestyle='--', linewidth=1, color='k')
ax[4].set_ylabel('Homeless \n (1,000s)')
ax[4].locator_params(axis='y',nbins = 5)
setp(ax[4].get_xticklabels(), fontsize=20)

#xlim(['1980-01-01', '2015-01-01'])
xlim(['1900-01-01', '2015-01-01'])

fig, ax = subplots(5, sharex=True)

ax[0].plot(storm_eq_occur_diff.index, storm_eq_occur_diff, label='Occurrence', color='k')
ax[0].plot(occur_diff_roll1980.index,occur_trend1980, linestyle='--', linewidth=1, color='k')
ax[0].set_title('Storm - Earthquake', size='24')
ax[0].set_ylabel('Occurrences')
#ax[0].locator_params(axis='y',nbins = 5)
ax[1].plot(storm_eq_dmg_diff.index, storm_eq_dmg_diff / 1000000000.0, label='Damage', color='k')
ax[1].plot(dmg_diff_roll1980.index,dmg_trend1980 / 1000000000.0, linestyle='--', linewidth=1, color='k')
ax[1].set_ylabel('Losses \n  ($billion)')
ax[1].locator_params(axis='y',nbins = 5)
ax[2].plot(storm_eq_kill_diff.index, storm_eq_kill_diff / 1000.0, label='Killed', color='k')
ax[2].plot(kill_diff_roll1980.index,kill_trend1980 / 1000.0, linestyle='--', linewidth=1, color='k')
ax[2].set_ylabel('Deaths \n (1,000s)')
ax[2].locator_params(axis='y',nbins = 5)
ax[3].plot(storm_eq_inj_diff.index, storm_eq_inj_diff / 1000.0, label='Injured', color='k')
ax[3].plot(inj_diff_roll1980.index,inj_trend1980 / 1000.0, linestyle='--', linewidth=1, color='k')
ax[3].set_ylabel('Injuries \n (1,000s)')
ax[3].locator_params(axis='y',nbins = 5)
ax[4].plot(storm_eq_home_diff.index, storm_eq_home_diff / 1000.0, label='Homeless', color='k')
ax[4].plot(occur_diff_roll1980.index,home_trend1980 / 1000.0, linestyle='--', linewidth=1, color='k')
ax[4].set_ylabel('Homeless \n (1,000s)')
ax[4].locator_params(axis='y',nbins = 5)
setp(ax[4].get_xticklabels(), fontsize=20)

xlim(['1980-01-01', '2015-01-01'])

fig, ax = subplots(5, sharex=True)

ax[0].plot(storm_eq_occur_diff.index, storm_eq_occur_diff, color='k')
ax[0].plot(occur_diff_roll.index,occur_trend, linestyle=':', linewidth=3, color='k', label='Trend from 1900 to 2013')
ax[0].plot(occur_diff_roll1980.index,occur_trend1980, linestyle='--', linewidth=2, color='k', label='Trend from 1980 to 2013')
ax[0].set_title('Storm - Earthquake (rolling average)', size='24')
ax[0].set_ylabel('Occurrence \n Difference ')
ax[0].legend(loc='lower right', fontsize='14')
#ax[0].locator_params(axis='y',nbins = 5)
ax[1].plot(storm_eq_dmg_diff.index, storm_eq_dmg_diff / 1000000000.0, label='Damage', color='k')
ax[1].plot(dmg_diff_roll.index,dmg_trend / 1000000000.0, linestyle=':', linewidth=3, color='k')
ax[1].plot(dmg_diff_roll1980.index,dmg_trend1980 / 1000000000.0, linestyle='--', linewidth=2, color='k')
ax[1].set_ylabel('Loss \n Difference \n  ($billion)')
ax[1].locator_params(axis='y',nbins = 5)
ax[2].plot(storm_eq_kill_diff.index, storm_eq_kill_diff / 1000.0, label='Killed', color='k')
ax[2].plot(kill_diff_roll.index,kill_trend / 1000.0, linestyle=':', linewidth=3, color='k')
ax[2].plot(kill_diff_roll1980.index,kill_trend1980 / 1000.0, linestyle='--', linewidth=2, color='k')
ax[2].set_ylabel('Death \n Difference \n (1,000s)')
ax[2].locator_params(axis='y',nbins = 5)
ax[3].plot(storm_eq_inj_diff.index, storm_eq_inj_diff / 1000.0, label='Injured', color='k')
ax[3].plot(inj_diff_roll.index,inj_trend / 1000.0, linestyle=':', linewidth=3, color='k')
ax[3].plot(inj_diff_roll1980.index,inj_trend1980 / 1000.0, linestyle='--', linewidth=2, color='k')
ax[3].set_ylabel('Injury \n Difference \n (1,000s)')
ax[3].locator_params(axis='y',nbins = 5)
ax[4].plot(storm_eq_home_diff.index, storm_eq_home_diff / 1000.0, label='Homeless', color='k')
ax[4].plot(occur_diff_roll.index,home_trend / 1000.0, linestyle=':', linewidth=3, color='k')
ax[4].plot(occur_diff_roll1980.index,home_trend1980 / 1000.0, linestyle='--', linewidth=2, color='k')
ax[4].set_ylabel('Homeless \n Difference  \n (1,000s)')
ax[4].locator_params(axis='y',nbins = 5)
setp(ax[4].get_xticklabels(), fontsize=20)
xlim(['1980-01-01', '2015-01-01'])

occur_diff_roll1980.index.year