def map_recovery_time(myC,HAZ,RP=[50,200],RECO=['50','75','90'],drop_spots=None,_mapres=2000,special_event=None):
df = pd.read_csv('../output_country/'+myC+'/time_to_recovery_no.csv')
# hack
#if myC == 'MW':
# df.loc[df.district=='Blantyre'] = df.loc[(df.district=='Blantyre')|(df.district=='Blantyre City')].sum()
# df.loc[df.district=='Lilongwe'] = df.loc[(df.district=='Lilongwe')|(df.district=='Lilongwe City')].sum()
# df.loc[df.district=='Mzimba'] = df.loc[(df.district=='Mzimba')|(df.district=='Mzuzu City')].sum()
# df.loc[df.district=='Zomba Non-City'] = df.loc[(df.district=='Zomba Non-City')|(df.district=='Zomba City')].sum()
# df = df_prov.drop(['Blantyre City','Lilongwe City', 'Mzuzu City', 'Zomba City'],axis=0)
# Look for the map (svg) file here
svg_file = get_svg_file(myC)
_pop = pd.read_csv('../intermediate/'+myC+'/hazard_ratios.csv')
_pop = _pop.set_index([get_economic_unit(myC),'hazard','rp'])
_pop_event = _pop['pcwgt'].sum(level=_pop.index.names).to_frame(name='pop')
_pop_event['fa'] = _pop['fa'].mean(level=_pop.index.names)
_pop_event['Naff'] = _pop_event[['pop','fa']].prod(axis=1)
_pop_event = _pop_event.reset_index().set_index(get_economic_unit(myC))
if special_event and special_event.lower() == 'idai':
df = pd.merge(df.reset_index(),_pop_event.reset_index(),on=['district','hazard','rp'])
for _haz in [HAZ]:
for _rp in RP:
for _reco in RECO:
_ = df.loc[(df.hazard == _haz)&(df.rp == _rp)].set_index(get_economic_unit(myC))
if special_event and special_event.lower() == 'idai': _ = _.loc[_['fa']!=0]
else: _.loc[_['time_recovery_'+_reco]==-1,'time_recovery_'+_reco] = 10
__pop_event = _pop_event.loc[(_pop_event.hazard == _haz)&(_pop_event.rp == _rp)]
_mean = str(round((_['time_recovery_'+_reco]*__pop_event['Naff']).sum()/__pop_event['Naff'].sum(),1))
_label = (('Time to reconstruct '+_reco+'% of\nassets destroyed by '+haz_dict[_haz]+' [years]\nAverage = '+_mean+' years') if special_event and special_event.lower() == 'idai'
else ('Time to reconstruct '+_reco+'% of assets destroyed'
+'\nby '+str(_rp)+'-year '+haz_dict[_haz].lower()+' [years]'
+'\nNational avg. = '+_mean+' years'))
try:
make_map_from_svg(
_['time_recovery_'+_reco],
svg_file,
outname=myC+'_time_to_recover_'+_reco+'pct_'+_haz+str(_rp),
color_maper=plt.cm.get_cmap('Purples'),
label=_label,
new_title='',
do_qualitative=True,
drop_spots=['Jaffna','Matara','Kilinochchi'],
res=_mapres)
except: pass
purge('img/','map_of_*.png')
purge('img/','legend_of_*.png')
purge('img/','map_of_*.svg')
purge('img/','legend_of_*.svg')
def plot_relative_losses(myCountry, aProv, aDis, anRP, iah):
economy = get_economic_unit(myCountry)
iah = iah.loc[iah.affected_cat == 'a']
iah = iah.reset_index().set_index('quintile').sort_index()
_poor = 100. * iah.loc[(
(iah.ispoor == 1)
& (iah[economy] == aProv)
& (iah.hazard == aDis)
&
(iah.rp == anRP)), ['dk0', 'pcwgt_no']].prod(axis=1).sum() / iah.loc[(
(iah.ispoor == 1)
& (iah[economy] == aProv)
& (iah.hazard == aDis)
& (iah.rp == anRP)), ['k', 'pcwgt_no']].prod(axis=1).sum()
_nonpoor = 100 * iah.loc[(
(iah.ispoor == 0)
& (iah[economy] == aProv)
& (iah.hazard == aDis)
&
(iah.rp == anRP)), ['dk0', 'pcwgt_no']].prod(axis=1).sum() / iah.loc[(
(iah.ispoor == 0)
& (iah[economy] == aProv)
& (iah.hazard == aDis)
& (iah.rp == anRP)), ['k', 'pcwgt_no']].prod(axis=1).sum()
print(aProv, aDis, anRP)
print('Poor:', _poor)
print('Non-poor:', _nonpoor)
print('Ratio:', _poor / _nonpoor, '\n\n')
def run_urban_plots(myC,df):
# use isrural to sort households in Bolivia
global _upper_cut
_upper_cut = 1.5 #times poverty_line
#
yor_pal = sns.color_palette('tab20b', n_colors=20)
#
global _colUS; global _colRS
global _colUP; global _colRP
global _colUN; global _colRN
global _colUW; global _colRW
_colUS = yor_pal[12]; _colRS = yor_pal[15]
_colUP = yor_pal[8]; _colRP = yor_pal[11]
_colUN = yor_pal[4]; _colRN = yor_pal[7]
_colUW = yor_pal[0]; _colRW = yor_pal[3]
#
global _colUV; global _colRV
_colUV = yor_pal[0]; _colRV = yor_pal[3]
#
global _alphaU; global _alphaR
_alphaU = 0.50; _alphaR = 0.8
#
global _e
_e = get_economic_unit(myC)
if 'isrural' not in df.columns: return False
else:
# plot poverty gap, by urban/rural, & by department
df = df.reset_index().set_index([_e,'ispoor','isrural'])[['pcwgt','totper','c','pcsoc','has_ew','pov_line','sub_line','issub']]
#
social_scatter(myC,df,'rel')
social_scatter(myC,df,'abs')
#
poverty_gap(myC,df)
#
for plot_rural in [True, False]:
populations(myC,df,'rel',plot_rural)
populations(myC,df,'abs',plot_rural)
#
# this code shouldn't be here!
# we're analyzing model results in a function that gets called in gather_data
# but it's easiest...
#try:
model_results_plots(myC,'assets','abs',('PF','aal'),redo_intermediate=True)
model_results_plots(myC,'assets','rel',('PF','aal'))
model_results_plots(myC,'assets','tot',('PF','aal'))
model_results_plots(myC,'wellbeing','abs',('PF','aal'))
model_results_plots(myC,'wellbeing','rel',('PF','aal'))
model_results_plots(myC,'wellbeing','tot',('PF','aal'))
model_results_plots(myC,'resilience','',('PF','aal'))
model_results_plots(myC,'assets','abs',('PF',50),redo_intermediate=True)
model_results_plots(myC,'assets','rel',('PF',50))
model_results_plots(myC,'assets','tot',('PF',50))
model_results_plots(myC,'wellbeing','abs',('PF',50))
model_results_plots(myC,'wellbeing','rel',('PF',50))
model_results_plots(myC,'wellbeing','tot',('PF',50))
model_results_plots(myC,'resilience','',('PF',50))
def plot_income_and_consumption_distributions(myC,
iah,
aReg,
aDis,
anRP,
labels=(False, False, False),
currency=''):
label_subsistence, label_poverty, label_middleclass = labels
iah = iah.reset_index()
economy = get_economic_unit(myC)
iah = iah.loc[iah.pcwgt_no != 0].copy()
iah['pov_line'] = get_poverty_line(myC)
try:
plt.close('all')
except:
pass
if aReg == 'ompong':
reg_crit = "((region=='I - Ilocos')|(region=='II - Cagayan Valley')|(region=='CAR'))"
aReg = 'path of Typhoon Mangkhut'
else:
reg_crit = '(' + economy + '==@aReg)'
economy = get_economic_unit(myC)
output_plots = os.getcwd() + '/../output_plots/' + myC + '/'
# Number of bins in histograms. Ignore the None argument
c_bins = [None, 50]
# Dictionary for labeling
# GLOBAL VARIABLE in new_process_data.
haz_dict = {
'SS': 'Storm surge',
'PF': 'Precipitation flood',
'HU': 'Typhoon',
'EQ': 'Earthquake',
'DR': 'Drought',
'FF': 'Fluvial flood',
'CY': 'Cyclone Idai'
}
simple_plot = True
stack_wealthy = True
upper_clip = 1E6
if myC == 'PH':
if aReg == 'VIII - Eastern Visayas':
upper_clip = 1.25E5 # Hack hack hack
else:
upper_clip = 1.5E5
elif myC == 'FJ':
upper_clip = 2E4
elif myC == 'SL':
upper_clip = 3.25E5
if aReg == 'Rathnapura': upper_clip = 3.0E5
elif myC == 'MW':
if aReg == 'Lilongwe': upper_clip = 4.0E5
else: upper_clip = 2.5E5
elif myC == 'BO': upper_clip = 5E4
elif myC == 'AM': upper_clip = 9E3
elif myC == 'BG': upper_clip = 2E4
elif myC == 'AL': upper_clip = 5.6E3
elif myC == 'RO': upper_clip = 5E4
elif myC == 'GR': upper_clip = 3E4
elif myC == 'TR': upper_clip = 3E4
elif myC == 'HR': upper_clip = 5E4
else: upper_clip = 1E4
sf_x = 1
#if currency.lower() == 'usd': sf_x = get_currency(myC)[2]
currency = get_currency(myC)[0]
for _fom, _fom_lab in [('c', 'Consumption')]:
ax = plt.gca()
plt.cla()
plt.xlim(0, sf_x * upper_clip)
if not stack_wealthy:
upper_clip *= 1.1
if currency.lower() != 'usd' and myC == 'PH': plt.xlim([0, 100])
if aReg == 'II - Cagayan Valley' and aDis == 'HU': plt.ylim(0, 400)
elif aReg == 'VIII - Eastern Visayas' and aDis == 'HU':
plt.ylim(0, 500)
elif aReg == 'Rathnapura':
plt.ylim(0, 130)
#elif aReg == 'Bucharest-Ilfov': plt.ylim(0,220)
elif aReg == 'Beni':
plt.ylim(0, 40)
if aReg == 'Gergharkunik': plt.ylim(0, 25)
if aReg == 'Yerevan': plt.ylim(0, 70)
if myC == 'BG': plt.ylim(0, 250)
if myC == 'GE': plt.ylim(0, 100)
if myC == 'GR': plt.ylim(0, 330)
if myC == 'TR': plt.ylim(0, 6000)
if myC == 'HR': plt.ylim(0, 500)
plt.xlabel(_fom_lab + r' (' + currency + ' per person, per year)',
labelpad=8,
fontsize=8)
plt.ylabel('Population' + get_pop_scale_fac(myC)[1],
labelpad=8,
fontsize=8)
if _fom == 'i':
plt.title(
str(anRP) + '-year ' + haz_dict[aDis].lower() + ' in ' + aReg)
# Income/Cons dist immediately after disaster
cf_heights, cf_bins = np.histogram(
sf_x * iah.loc[iah.eval(reg_crit + '&(hazard==@aDis)&(rp==@anRP)'),
_fom + '_pre_reco'].clip(upper=upper_clip),
bins=c_bins[1],
weights=iah.loc[iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)'),
'pcwgt_no'] / get_pop_scale_fac(myC)[0])
print(aDis, anRP, _fom,
(iah.loc[iah.eval(reg_crit + '&(hazard==@aDis)&(rp==@anRP)'),
['c_initial', 'pcwgt_no']].prod(axis=1).sum() /
iah.loc[iah.eval(reg_crit + '&(hazard==@aDis)&(rp==@anRP)'),
'pcwgt_no'].sum()))
print(_fom, ' final -->',
(iah.loc[iah.eval(reg_crit + '&(hazard==@aDis)&(rp==@anRP)'),
[_fom + '_pre_reco', 'pcwgt_no']].prod(axis=1).sum() /
iah.loc[iah.eval(reg_crit + '&(hazard==@aDis)&(rp==@anRP)'),
'pcwgt_no'].sum()))
if c_bins[0] is None: c_bins = [cf_bins, cf_bins]
# Income dist before disaster
ci_heights, _bins = np.histogram(
sf_x * iah.loc[iah.eval(reg_crit + '&(hazard==@aDis)&(rp==@anRP)'),
'c_initial'].clip(upper=upper_clip),
bins=c_bins[1],
weights=iah.loc[iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)'),
'pcwgt_no'] / get_pop_scale_fac(myC)[0])
# Income dist after reconstruction
#cf_reco_hgt, _bins = np.histogram(sf_x*iah.loc[iah.eval(reg_crit+'&(hazard==@aDis)&(rp==@anRP)'),'c_post_reco'].clip(upper=upper_clip), bins=c_bins[1],
# weights=iah.loc[iah.eval(reg_crit+'&(hazard==@aDis)&(rp==@anRP)'),'pcwgt_no']/get_pop_scale_fac(myC)[0])
sns.despine()
plt.gca().grid(False)
pre_step = ax.step(c_bins[1][1:],
ci_heights,
label=aReg + ' - FIES income',
linewidth=1.25,
color=greys_pal[7])
#leg = ax.legend(loc='best',labelspacing=0.75,ncol=1,fontsize=9,borderpad=0.75,fancybox=True,frameon=True,framealpha=0.9)
plt.ylim(0)
if stack_wealthy:
pre_ann = plt.annotate(
'Pre-disaster ' + _fom_lab.lower() + '\n(FIES data)',
xy=(c_bins[1][-2], ci_heights[-1]),
xytext=(c_bins[1][-4], ci_heights[-1] * 1.075),
arrowprops=dict(
arrowstyle="-",
color=greys_pal[8],
connectionstyle="angle,angleA=0,angleB=90,rad=5"),
annotation_clip=False,
size=8,
weight='light',
ha='right',
va='center',
color=greys_pal[8])
else:
pre_ann = plt.annotate(
'Pre-disaster ' + _fom_lab.lower() + '\n(FIES data)',
xy=((c_bins[1][5] + c_bins[1][6]) / 2, ci_heights[5]),
xytext=(c_bins[1][8], ci_heights[5] * 1.02),
arrowprops=dict(
arrowstyle="-",
color=greys_pal[8],
connectionstyle="angle,angleA=0,angleB=90,rad=5"),
annotation_clip=False,
size=8,
weight='light',
ha='left',
va='bottom',
color=greys_pal[8])
_success = False
_counter = 0
while not _success and _counter < 15:
try:
_fout = output_plots + 'npr_poverty_' + _fom + '_' + aReg.replace(
' ', '').replace('-', '') + '_' + aDis + '_' + str(
anRP) + '_' + currency[-3:].lower() + '_1of3.pdf'
#ax.get_figure().savefig(_fout,format='pdf',bbox_inches='tight')
_success = True
except:
print(
'no good! try again in plot_income_and_consumption_distributions (1/3-'
+ str(_counter) + ')')
_counter += 1
#ax.step(c_bins[1][:-1], cf_heights, label=aReg+' - post-disaster', facecolor=q_colors[1],alpha=0.45)
#ax.bar(c_bins[1][:-1], -(ci_heights-cf_heights), width=(c_bins[1][1]-c_bins[1][0]), align='edge',
# label=aReg+' - post-disaster', facecolor=paired_pal[4],edgecolor=None,linewidth=0,alpha=0.65,bottom=ci_heights)
ax.bar(c_bins[1][:-1],
cf_heights,
width=(c_bins[1][1] - c_bins[1][0]),
align='edge',
label=aReg + ' - post-disaster',
facecolor=paired_pal[4],
edgecolor=None,
linewidth=0,
alpha=0.75)
#leg = ax.legend(loc='best',labelspacing=0.75,ncol=1,fontsize=9,borderpad=0.75,fancybox=True,frameon=True,framealpha=0.9)
if stack_wealthy:
post_ann = plt.annotate(
'Post-disaster ' + _fom_lab.lower() + '\n(modeled)',
xy=((c_bins[1][-2] + c_bins[1][-1]) / 1.99,
cf_heights[-1] * 0.90),
xytext=(c_bins[1][-4], cf_heights[-1] * 0.90),
arrowprops=dict(arrowstyle="-", facecolor=greys_pal[8]),
annotation_clip=False,
size=8,
weight='light',
ha='right',
va='center',
color=paired_pal[5])
else:
ax.lines.remove(ax.lines[0])
pre_step = ax.step(c_bins[1][1:],
ci_heights,
label=aReg + ' - FIES income',
linewidth=1.0,
color=greys_pal[7],
alpha=0.65)
pre_ann.remove()
pre_ann = plt.annotate(
'Pre-disaster ' + _fom_lab.lower() + '\n(FIES data)',
xy=((c_bins[1][5] + c_bins[1][6]) / 2, ci_heights[5]),
xytext=(c_bins[1][8], ci_heights[5] * 1.02),
arrowprops=dict(
arrowstyle="-",
color=greys_pal[8],
connectionstyle="angle,angleA=0,angleB=90,rad=5",
alpha=0.65),
annotation_clip=False,
size=8,
weight='light',
ha='left',
va='bottom',
color=greys_pal[8],
alpha=0.65)
post_ann = plt.annotate(
'Post-disaster ' + _fom_lab.lower() + '\n(modeled)',
xy=(c_bins[1][5], 1.1 * cf_heights[6]),
xytext=(c_bins[1][9], 1.1 * cf_heights[6]),
annotation_clip=False,
size=8,
weight='light',
ha='left',
va='center',
color=paired_pal[5],
arrowprops=dict(
arrowstyle="-",
color=paired_pal[5],
connectionstyle="angle,angleA=0,angleB=90,rad=5"))
_success = False
_counter = 0
while not _success and _counter < 10:
try:
_fout = output_plots + 'npr_poverty_' + _fom + '_' + aReg.replace(
' ', '').replace('-', '') + '_' + aDis + '_' + str(
anRP) + '_' + currency[-3:].lower() + '_2of3.pdf'
#ax.get_figure().savefig(_fout,format='pdf',bbox_inches='tight')
_success = True
except:
print(
'no good! try again in plot_income_and_consumption_distributions (2/3)'
)
_counter += 1
# These are done above
#plt.annotate('Pre-disaster '+_fom_lab.lower()+'\n(FIES data)',xy=(c_bins[1][-2],ci_heights[-1]),xytext=(c_bins[1][-4],ci_heights[-1]*1.075),
# arrowprops=dict(arrowstyle="-",facecolor=greys_pal[8],connectionstyle="angle,angleA=0,angleB=90,rad=5"),
# annotation_clip=False,size=7,weight='light',ha='right',va='center',color=greys_pal[8])
#plt.annotate('Post-disaster '+_fom_lab.lower()+'\n(modeled)',xy=((c_bins[1][-2]+c_bins[1][-1])/1.99,cf_heights[-1]*0.90),xytext=(c_bins[1][-4],cf_heights[-1]*0.90),
# arrowprops=dict(arrowstyle="-",facecolor=greys_pal[8]),annotation_clip=False,size=7,weight='light',ha='right',va='center',color=paired_pal[5])
if not stack_wealthy:
pre_ann.remove()
post_ann.remove()
#ax.bar(c_bins[1][:-1], cf_reco_hgt, width=(c_bins[1][1]-c_bins[1][0]), label=aReg+' - post-reconstruction', facecolor=q_colors[1],edgecolor=q_colors[1],alpha=0.65)
#ax.step(c_bins[1][1:], ci_heights, label=aReg+' - FIES income', linewidth=1.2,color=greys_pal[8])
#if myC_ylim == None: myC_ylim = ax.get_ylim()
#plt.ylim(myC_ylim[0],2.5*myC_ylim[1])
# ^ Need this for movie making, but better to let the plot limits float if not
#leg = ax.legend(loc='best',labelspacing=0.75,ncol=1,fontsize=9,borderpad=0.75,fancybox=True,frameon=True,framealpha=0.9)
#leg.get_frame().set_color('white')
#leg.get_frame().set_edgecolor(greys_pal[7])
#leg.get_frame().set_linewidth(0.2)
if not simple_plot:
ax.annotate('Total asset losses: ' + str(
round(
sf_x * iah.loc[iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)'),
['pcwgt_no', 'dk0']].prod(axis=1).sum(), 1))
+ currency,
xy=(0.03, -0.18),
xycoords=leg.get_frame(),
size=8,
va='top',
ha='left',
annotation_clip=False,
zorder=100)
ax.annotate('Reg. well-being losses: ' + str(
round(
sf_x * iah.loc[iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)'),
['pcwgt_no', 'dw']].prod(axis=1).sum() /
df.wprime.mean(), 1)) + currency,
xy=(0.03, -0.50),
xycoords=leg.get_frame(),
size=8,
va='top',
ha='left',
annotation_clip=False,
zorder=100)
ax.annotate('Natl. liability: ' + str(
round(
float(sf_x * public_costs.loc[
(public_costs.contributer != aReg) &
(public_costs[economy] == aReg) &
(public_costs.hazard == aDis) &
(public_costs.rp == anRP), ['transfer_pub']].sum() *
1.E3), 1)) + currency,
xy=(0.03, -0.92),
xycoords=leg.get_frame(),
size=8,
va='top',
ha='left',
annotation_clip=False,
zorder=100)
ax.annotate('Natl. well-being losses: ' + str(
round(
sf_x * float(public_costs.loc[
(public_costs.contributer != aReg) &
(public_costs[economy] == aReg) &
(public_costs.hazard == aDis) &
(public_costs.rp == anRP), 'dw_tot_curr'].sum()), 1)) +
',000 ' + currency,
xy=(0.03, -1.24),
xycoords=leg.get_frame(),
size=8,
va='top',
ha='left',
annotation_clip=False,
zorder=100)
try:
net_chg_pov_c = int(iah.loc[iah.eval(
reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(c_pre_reco<=pov_line)'
), 'pcwgt_no'].sum() - iah.loc[
iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(c_initial<=pov_line)'),
'pcwgt_no'].sum())
net_chg_pov_i = int(iah.loc[iah.eval(
reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(i_pre_reco<=pov_line)'
), 'pcwgt_no'].sum() - iah.loc[
iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(c_initial<=pov_line)'),
'pcwgt_no'].sum())
except:
net_chg_pov_c = int(iah.loc[
iah.
eval('district==@aReg & hazard==@aDis & c_pre_reco<=pov_line'),
'pcwgt_no'].sum() - iah.loc[iah.eval(
'district==@aReg & hazard==@aDis & c_initial<=pov_line'),
'pcwgt_no'].sum())
net_chg_pov_i = int(iah.loc[
iah.
eval('district==@aReg & hazard==@aDis & i_pre_reco<=pov_line'),
'pcwgt_no'].sum() - iah.loc[iah.eval(
'district==@aReg & hazard==@aDis & c_initial<=pov_line'),
'pcwgt_no'].sum())
try:
iah['midclass_line_lo'] = get_middleclass_range(myC)[0]
iah['midclass_line_hi'] = get_middleclass_range(myC)[1]
net_chg_midclass_c = int(iah.loc[iah.eval(reg_crit + (
'&(hazard==@aDis)&(rp==@anRP)&' +
'(c_initial>=midclass_line_lo)&(c_pre_reco<midclass_line_lo)')
), 'pcwgt_no'].sum())
net_chg_midclass_i = int(iah.loc[iah.eval(reg_crit + (
'&(hazard==@aDis)&(rp==@anRP)&' +
'(c_initial>=midclass_line_lo)&(i_pre_reco<midclass_line_lo)')
), 'pcwgt_no'].sum())
except:
net_chg_midclass_c, net_chg_midclass_i = 0, 0
net_chg_pov = int(
round(
(net_chg_pov_i if _fom == 'i' else net_chg_pov_c) / 100., 0) *
100)
net_chg_midclass = int(
round((net_chg_midclass_i if _fom == 'i' else net_chg_midclass_c) /
100., 0) * 100)
try:
net_chg_pov_pct = abs(
round(
100. * float(net_chg_pov) /
float(iah.loc[iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)'),
'pcwgt_no'].sum()), 1))
except:
net_chg_pov_pct = 0
try:
_slice = reg_crit + '&(hazard==@aDis)&(rp==@anRP)&(c_initial>=midclass_line_lo)&(c_initial<midclass_line_hi)'
net_chg_midclass_pct = abs(
round(
100. * float(net_chg_midclass) /
float(iah.loc[iah.eval(_slice), 'pcwgt_no'].sum()), 1))
except:
net_chg_midclass_pct = 0
trans = ax.get_xaxis_transform() # x in data units, y in axes fraction
mc_anno_y = 0.80
pov_anno_y = 0.99
sub_anno_y = 0.95
anno_y_offset = 0.045
_, mc_anno_y_data = axis_data_coords_sys_transform(ax,
0,
mc_anno_y,
inverse=False)
_, pov_anno_y_data = axis_data_coords_sys_transform(ax,
0,
pov_anno_y,
inverse=False)
_, sub_anno_y_data = axis_data_coords_sys_transform(ax,
0,
sub_anno_y,
inverse=False)
if label_middleclass:
middleclass = get_middleclass_range('RO')
plt.plot([sf_x * middleclass[0], sf_x * middleclass[0]],
[0, mc_anno_y_data],
'k-',
lw=1.0,
color=greys_pal[8],
zorder=100,
alpha=0.85,
ls=':')
#plt.fill_between([sf_x*middleclass[0],sf_x*middleclass[1]],[pov_anno_y_data,pov_anno_y_data],color=greys_pal[2],alpha=0.3)
#plt.fill_between([0,sf_x*middleclass[0]],[pov_anno_y_data,pov_anno_y_data],color=greys_pal[2],alpha=0.3)
ax.annotate(
'Middle class',
xy=(sf_x * (middleclass[0] + 0.1 * iah.pov_line.mean()),
mc_anno_y),
xycoords=trans,
ha='left',
va='top',
fontsize=9,
annotation_clip=False,
weight='bold',
color=greys_pal[7])
ax.annotate(int_w_commas(net_chg_midclass) +
' drop from middle class\n (' +
str(net_chg_midclass_pct) + '% decrease)',
weight='light',
color=greys_pal[7],
xy=(sf_x *
(middleclass[0] + 0.1 * iah.pov_line.mean()),
mc_anno_y - anno_y_offset),
xycoords=trans,
ha='left',
va='top',
fontsize=9,
annotation_clip=False)
if label_poverty:
plt.plot([sf_x * iah.pov_line.mean(), sf_x * iah.pov_line.mean()],
[0, pov_anno_y_data],
'k-',
lw=1.0,
color=greys_pal[8],
zorder=100,
alpha=0.85,
ls=':')
ax.annotate('Poverty line',
xy=(sf_x * 1.1 * iah.pov_line.mean(), pov_anno_y),
xycoords=trans,
ha='left',
va='top',
fontsize=9,
annotation_clip=False,
weight='bold',
color=greys_pal[7])
ax.annotate('Increase of ' + int_w_commas(net_chg_pov) + ' (' +
str(net_chg_pov_pct) + '% of regional pop.)\n in ' +
_fom_lab.lower() + ' poverty',
weight='light',
color=greys_pal[7],
xy=(sf_x * 1.1 * iah.pov_line.mean(),
pov_anno_y - anno_y_offset),
xycoords=trans,
ha='left',
va='top',
fontsize=9,
annotation_clip=False)
sub_line, net_chg_sub = (None if not label_subsistence else
get_subsistence_line(myC)), None
if sub_line is not None:
net_chg_sub = int(
round((iah.loc[iah.eval(
reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(c_pre_reco<=@sub_line)'
), 'pcwgt_no'].sum() - iah.loc[iah.eval(
reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(c_initial<=@sub_line)'),
'pcwgt_no'].sum()) / 100., 0) *
100)
if _fom == 'i':
net_chg_sub = int(
round((iah.loc[iah.eval(
reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(i_pre_reco<=@sub_line)'
), 'pcwgt_no'].sum() - iah.loc[iah.eval(
reg_crit +
'&(hazard==@aDis)&(rp==@anRP)&(c_initial<=@sub_line)'),
'pcwgt_no'].sum()) / 100.,
0) * 100)
try:
net_chg_sub_pct = round(
100. * float(net_chg_sub) /
float(iah.loc[iah.eval(reg_crit +
'&(hazard==@aDis)&(rp==@anRP)'),
'pcwgt_no'].sum()), 1)
except:
net_chg_sub_pct = 0
plt.plot([sf_x * sub_line, sf_x * sub_line], [0, sub_anno_y_data],
'k-',
lw=1.0,
color=greys_pal[8],
zorder=100,
alpha=0.85,
ls=':')
ax.annotate('Subsistence line',
xy=(sf_x * 1.1 * sub_line, sub_anno_y),
xycoords=trans,
ha='left',
va='top',
color=greys_pal[7],
fontsize=9,
annotation_clip=False,
weight='bold')
ax.annotate('Increase of ' + int_w_commas(net_chg_sub) + ' (' +
str(net_chg_sub_pct) + '% of regional pop.)\n in ' +
_fom_lab.lower() + ' subsistence',
weight='light',
color=greys_pal[7],
xy=(sf_x * 1.1 * sub_line, sub_anno_y - anno_y_offset),
xycoords=trans,
ha='left',
va='top',
fontsize=9,
annotation_clip=False)
#print(aReg,aDis,anRP,net_chg_pov,'people into poverty &',net_chg_sub,'into subsistence')
fig = ax.get_figure()
_success = False
_counter = 0
while not _success and _counter < 4:
try:
_fout = output_plots + 'npr_poverty_' + _fom + '_' + aReg.replace(
' ', '').replace('-', '') + '_' + aDis + '_' + str(
anRP) + '_' + currency[-3:].lower() + '.pdf'
fig.savefig(_fout, format='pdf', bbox_inches='tight')
plt.clf()
plt.close('all')
_success = True
print('wrote ' + aReg + '_poverty_' + _fom + '_' + aDis + '_' +
str(anRP) + '.pdf')
except:
print(
'no good! try again in plot_income_and_consumption_distributions'
)
_counter += 1
def plot_impact_by_quintile(myCountry,
aProv,
aDis,
anRP,
iah,
my_PDS='no',
currency='USD'):
iah = iah.loc[iah.affected_cat == 'a']
output_plots = '../output_plots/' + myCountry + '/'
economy = get_economic_unit(myCountry)
plt.figure(1)
ax = plt.subplot(111)
plt.figure(2)
ax2 = plt.subplot(111)
_curr = 1
_curr_sf = 1E-3
_curr_sf_str = ',000 '
if currency == 'USD':
_curr = get_currency(myCountry)[2]
_curr_sf = 1
_curr_sf_str = ''
for myQ in range(1, 6): #nQuintiles
#print(aProv,aDis,anRP,'shape:',iah.loc[(iah[economy]==aProv)&(iah.hazard==aDis)&(iah.rp==anRP)&(iah.quintile==myQ),'pcwgt_no'].shape[0])
k = _curr * _curr_sf * (0.01 * iah.loc[
(iah[economy] == aProv) & (iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ), ['k', 'pcwgt_no']].prod(axis=1).sum() /
iah.loc[
(iah[economy] == aProv) &
(iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ), 'pcwgt_no'].sum())
dk = _curr * _curr_sf * (iah.loc[
(iah[economy] == aProv) & (iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ), ['dk0', 'pcwgt_no']].prod(axis=1).sum() /
iah.loc[
(iah[economy] == aProv) &
(iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ), 'pcwgt_no'].sum())
dc = _curr * _curr_sf * (iah.loc[
(iah[economy] == aProv) & (iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ),
['dc_pre_reco', 'pcwgt_no']].prod(axis=1).sum() / iah.loc[
(iah[economy] == aProv) & (iah.hazard == aDis) &
(iah.rp == anRP) & (iah.quintile == myQ), 'pcwgt_no'].sum())
dw = _curr * _curr_sf * (iah.loc[
(iah[economy] == aProv) & (iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ), ['dw_no', 'pcwgt_no']].prod(axis=1).sum() /
iah.loc[
(iah[economy] == aProv) &
(iah.hazard == aDis) & (iah.rp == anRP) &
(iah.quintile == myQ), 'pcwgt_no'].sum())
pds_nrh = _curr * _curr_sf * (
iah.loc[(iah[economy] == aProv) & (iah.hazard == aDis) &
(iah.rp == anRP) & (iah.quintile == myQ),
['net_help_received_' + my_PDS, 'pcwgt_' +
my_PDS]].prod(axis=1).sum() /
iah.loc[(iah[economy] == aProv) & (iah.hazard == aDis) &
(iah.rp == anRP) &
(iah.quintile == myQ), 'pcwgt_' + my_PDS].sum())
pds_dw = _curr * _curr_sf * (
iah.loc[(iah[economy] == aProv) & (iah.hazard == aDis) &
(iah.rp == anRP) & (iah.quintile == myQ),
['dw_' + my_PDS, 'pcwgt_' + my_PDS]].prod(axis=1).sum() /
iah.loc[(iah[economy] == aProv) & (iah.hazard == aDis) &
(iah.rp == anRP) &
(iah.quintile == myQ), 'pcwgt_' + my_PDS].sum())
pds2_dw = _curr * 0
pds3_dw = _curr * 0
ax.bar([6 * ii + myQ for ii in range(1, 5)], [dk, dw, pds_nrh, pds_dw],
color=q_colors[myQ - 1],
alpha=0.7,
label=q_labels[myQ - 1])
#np.savetxt('/Users/brian/Desktop/BANK/hh_resilience_model/output_plots/PH/dk_dc_dw_pds_'+aProv+'_'+aDis+'_'+str(anRP)+'_Q'+str(myQ)+'.csv',[dk,dc,dw,pds_nrh,pds_dw],delimiter=',')
if myQ == 1:
ax2.bar([0], [0],
color=[q_colors[0]],
alpha=0.7,
label='No post-disaster support')
ax2.bar([0], [0],
color=[q_colors[2]],
alpha=0.7,
label='80% of avg Q1 losses covered for Q1')
ax2.bar([0], [0],
color=[q_colors[3]],
alpha=0.7,
label='80% of avg Q1 losses covered for Q1-Q2')
ax2.bar([0], [0],
color=[q_colors[1]],
alpha=0.7,
label='80% of avg Q1 losses covered for Q1-Q5')
try:
ax2.bar([5 * myQ + ii for ii in range(0, 4)],
[dw, pds3_dw, pds3_dw, pds_dw],
color=[q_colors[0], q_colors[1], q_colors[2], q_colors[3]],
alpha=0.7)
np.savetxt(
'/Users/brian/Desktop/BANK/hh_resilience_model/output_plots/PH/pds_comparison_'
+ aProv + '_' + aDis + '_' + str(anRP) + '_Q' + str(myQ) +
'.csv', [dw, pds_dw, pds2_dw],
delimiter=',')
except:
pass
out_str = None
if myCountry == 'FJ':
out_str = [
'Asset loss', 'Consumption\nloss (NPV)', 'Wellbeing loss',
'Net cost of\nWinston-like\nsupport',
'Wellbeing loss\npost support'
]
else:
out_str = [
'Asset loss', 'Wellbeing loss',
'Net cash benefit of\n' + pds_dict[my_PDS],
'Wellbeing loss\nwith ' + pds_dict[my_PDS]
]
trans = ax.get_xaxis_transform() # x in data untis, y in axes fraction
for ni, ii in enumerate(range(1, 5)):
ann = ax.annotate(out_str[ni],
xy=(6 * ii + 0.5, +0.005),
xycoords=trans,
ha='left',
va='top',
fontsize=8,
annotation_clip=False)
#ax.annotate(out_str[ni],xy=(6*ii+0.5,ax.get_ylim()[0]),xycoords='data',ha='left',va='top',weight='bold',fontsize=9,annotation_clip=False)
fig = ax.get_figure()
leg = ax.legend(loc='best',
labelspacing=0.75,
ncol=1,
fontsize=8,
borderpad=0.75,
fancybox=True,
frameon=True,
framealpha=0.9)
leg.get_frame().set_color('white')
leg.get_frame().set_edgecolor(greys_pal[7])
leg.get_frame().set_linewidth(0.2)
plt.figure(1)
myxlim = ax.get_xlim()
plt.plot([xlim for xlim in myxlim], [0, 0],
'k-',
lw=0.75,
color=greys_pal[3],
zorder=100,
alpha=0.85)
plt.xlim(myxlim)
ax.xaxis.set_ticks([])
ax.tick_params(axis='y', labelsize=8)
#plt.plot(ax.get_xlim(),[0,0],color=greys_pal[2],linewidth=0.5,zorder=10)
if currency != 'USD': currency = get_currency(myCountry)[0][-3:]
plt.ylabel('Disaster losses (' + _curr_sf_str +
currency.replace('USD', 'US$') + ' per affected person)',
labelpad=10,
fontsize=10)
sns.despine(bottom=True)
plt.grid(False)
_success = False
while not _success:
try:
fig.savefig(output_plots + 'npr_' + aProv.replace(' ', '') + '_' +
aDis + '_' + str(anRP) + '_' + my_PDS + '_' +
currency.lower() + '.pdf',
format='pdf',
bbox_inches='tight')
#fig.savefig(output_plots+'png/npr_'+aProv.replace(' ','')+'_'+aDis+'_'+str(anRP)+'_'+my_PDS+'_'+currency.lower()+'.png',format='png',bbox_inches='tight')
_success = True
except:
print('having trouble...')
plt.figure(2)
fig2 = ax2.get_figure()
leg = ax2.legend(loc='best',
labelspacing=0.75,
ncol=1,
fontsize=9,
borderpad=0.75,
fancybox=True,
frameon=True,
framealpha=0.9)
leg.get_frame().set_color('white')
leg.get_frame().set_edgecolor(greys_pal[7])
leg.get_frame().set_linewidth(0.2)
ann_y = -ax2.get_ylim()[1] / 30
n_pds_options = 4
out_str = ['Q1', 'Q2', 'Q3', 'Q4', 'Q5']
for ni, ii in enumerate(range(1, 6)): # quintiles
ax2.annotate(out_str[ni],
xy=(5 * ii + 0.05, ann_y),
zorder=100,
xycoords='data',
ha='left',
va='center',
weight='bold',
fontsize=9,
annotation_clip=False)
plt.plot([5 * ii + 1.20, 5 * ii + 3.78], [ann_y, ann_y],
'k-',
lw=1.50,
color=greys_pal[7],
zorder=100,
alpha=0.85)
plt.plot([5 * ii + 3.78, 5 * ii + 3.78], [ann_y * 0.9, ann_y * 1.1],
'k-',
lw=1.50,
color=greys_pal[7],
zorder=100,
alpha=0.85)
ax2.xaxis.set_ticks([])
plt.xlim(3, 32)
plt.plot([i for i in ax2.get_xlim()], [0, 0],
'k-',
lw=1.5,
color=greys_pal[7],
zorder=100,
alpha=0.85)
if currency != 'USD': currency = get_currency(myCountry)[0][-3:]
plt.ylabel('Wellbeing losses (' + currency.replace('USD', 'US$') +
' per capita)',
labelpad=10,
fontsize=10)
_success = False
try:
fig2.savefig(output_plots + 'npr_pds_schemes_' + aProv + '_' + aDis +
'_' + str(anRP) + '_' + currency.lower() + '.pdf',
format='pdf',
bbox_inches='tight')
#fig2.savefig(output_plots+'png/npr_pds_schemes_'+aProv+'_'+aDis+'_'+str(anRP)+'.png',format='png',bbox_inches='tight')
_success = True
except:
print('not working..NOT trying again on npr_pds_schemes_***')
plt.clf()
plt.close('all')