def dynamic_poverty_count(myC='PH'):
df = pd.read_csv('../output_country/' + myC + '/poverty_duration_no.csv')
df = df.loc[(df.hazard == 'HU') & (df.rp == 10)]
df = df.loc[(df.region == 'I - Ilocos') |
(df.region == 'II - Cagayan Valley') | (df.region == 'CAR')]
df = df.reset_index().set_index(['region', 'hazard', 'rp']).drop([
_c for _c in [
'index', 'ratio_dw_lim_tot', 'dk_sub', 'dk_lim', 'dw_lim',
'dw_sub', 'res_lim', 'res_sub'
] if _c in df.columns
],
axis=1)
print(df.head())
n_pov = []
for _wk in np.linspace(0, 521, 450) / 52.:
n_pov.append(1E-3 * df.loc[(df['c'] > get_poverty_line(myC)) &
(df['t_pov_cons'] > _wk), 'pcwgt'].sum())
plt.plot(np.linspace(0, 521, 450) / 52., n_pov)
plt.xlim(0, 10.2)
plt.ylim(0)
plt.xlabel('Years after disaster', weight='bold', labelpad=8)
plt.ylabel(
'Filipinos in consumption poverty\ndue to wind damages in Ompong (,000)',
weight='bold',
labelpad=8)
sns.despine()
plt.grid(False)
plt.gcf().savefig(
'/Users/brian/Desktop/Dropbox/Bank/ompong/plots/t_pov_ompong.pdf',
format='pdf')
print(df.loc[df['c'] > get_poverty_line(myC), 'pcwgt'].sum())
print(df.loc[(df['c'] > get_poverty_line(myC)) &
(df['t_pov_cons'] > 12 / 52), 'pcwgt'].sum())
assert (False)
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 run_poverty_duration_plot(myC,myHaz='HU',drop_spots=None,special_event=None):
if special_event and special_event.lower() == 'idai': myHaz='CY'
# Load file with geographical (region/province/district) as index
df = pd.read_csv('../output_country/'+myC+'/poverty_duration_no.csv')
df = df.reset_index().set_index(df.columns[1]).sort_index()
geo = df.index.name
all_geo = np.array(df[~df.index.duplicated(keep='first')].index)
# used in groupby
df['country'] = myC
# assign deciles
listofdeciles=np.arange(0.10, 1.01, 0.10)
df = df.reset_index().groupby('country',sort=True).apply(lambda x:match_percentiles(x,perc_with_spline(reshape_data(x.c),reshape_data(x.pcwgt),listofdeciles),'decile'))
# Load additional SP runs
_sp = []
for f in glob.glob('/Users/brian/Desktop/BANK/hh_resilience_model/output_country/'+myC+'/poverty_duration_*.csv'):
_ = f.replace('/Users/brian/Desktop/BANK/hh_resilience_model/output_country/'+myC+'/poverty_duration_','').replace('.csv','')
_sp.append(_)
for iSP in _sp:
_ = pd.read_csv('../output_country/'+myC+'/poverty_duration_'+iSP+'.csv')
_['t_pov_bool'] = False
try: _.loc[_.c>_.pov_line,'t_pov_bool'] = True
except: _.loc[_.c>get_poverty_line(myC,by_district=False),'t_pov_bool'] = True
df[['t_pov_inc'+iSP,'t_pov_cons'+iSP,'t_pov_bool'+iSP]] = _[['t_pov_inc','t_pov_cons','t_pov_bool']]
############################
# Do some plotting
#plot_crit = '(t_pov_bool)&(hazard==@myHaz)&(rp==500)'
#df.loc[df.eval(plot_crit)].plot.hexbin('dk0','t_pov_cons')
#plt.gca().get_figure().savefig('../output_plots/'+myC+'/poverty_duration_hexbin_no.pdf',format='pdf')
#plt.cla()
#df.loc[df.eval(plot_crit)].plot.scatter('dk0','t_pov_cons')
#plt.gca().get_figure().savefig('../output_plots/'+myC+'/poverty_duration_scatter_no.pdf',format='pdf')
#plt.cla()
############################
df = df.reset_index().set_index(['hazard','rp','decile'])
df['t_pov_bool'] = False
try: df.loc[df.c>df.pov_line,'t_pov_bool'] = True
except: df.loc[df.c>get_poverty_line(myC,by_district=False),'t_pov_bool'] = True
df_dec = pd.DataFrame(index=df.sum(level=['hazard','rp','decile']).index).sort_index()
# Populate the df_dec dataframe now, while its index is set to ['hazard','rp','decile']
# Number of individuals who face income or consumption poverty
df_dec['n_new_pov_inc'] = df.loc[df.t_pov_bool==True,'pcwgt'].sum(level=['hazard','rp','decile'])
df_dec['n_new_pov_cons'] = df.loc[df.t_pov_bool==True,'pcwgt'].sum(level=['hazard','rp','decile'])
# Individuals who face income or consumption poverty as fraction of all individuals
df_dec['frac_new_pov_inc'] = df_dec['n_new_pov_inc']/df['pcwgt'].sum(level=['hazard','rp','decile'])
df_dec['frac_new_pov_cons'] = df_dec['n_new_pov_cons']/df['pcwgt'].sum(level=['hazard','rp','decile'])
# Among people pushed into pov: average time in poverty (months)
for iSP in _sp:
df_dec['t_pov_inc_avg'+iSP] = 12.*(df.loc[df.eval('t_pov_bool'+iSP+'==True'),['pcwgt','t_pov_inc'+iSP]].prod(axis=1).sum(level=['hazard','rp','decile'])
/df.loc[df.eval('t_pov_bool'+iSP+'==True'),'pcwgt'].sum(level=['hazard','rp','decile']))
df_dec['t_pov_cons_avg'+iSP] = 12.*(df.loc[df.eval('t_pov_bool'+iSP+'==True'),['pcwgt','t_pov_cons'+iSP]].prod(axis=1).sum(level=['hazard','rp','decile'])
/df.loc[df.eval('t_pov_bool'+iSP+'==True'),'pcwgt'].sum(level=['hazard','rp','decile']))
for iloc in all_geo:
df_dec['t_pov_inc_avg_'+iloc] = 12.*(df.loc[df.eval('(t_pov_bool==True)&('+geo+'==@iloc)'),['pcwgt','t_pov_inc']].prod(axis=1).sum(level=['hazard','rp','decile'])
/df.loc[df.eval('(t_pov_bool==True)&('+geo+'==@iloc)'),'pcwgt'].sum(level=['hazard','rp','decile']))
df_dec['t_pov_cons_avg_'+iloc] = 12.*(df.loc[df.eval('(t_pov_bool==True)&('+geo+'==@iloc)'),['pcwgt','t_pov_cons']].prod(axis=1).sum(level=['hazard','rp','decile'])
/df.loc[df.eval('(t_pov_bool==True)&('+geo+'==@iloc)'),'pcwgt'].sum(level=['hazard','rp','decile']))
df_dec.to_csv('../output_country/'+myC+'/poverty_by_decile.csv')
######################
# Scatter plot of hh that have to delay reconstruction
upper_lim = 1E15
df['t_reco'] = (np.log(1.0/0.05)/df['hh_reco_rate']).clip(upper=upper_lim)
means = []
xmax = 2.5E5
step = xmax/10.
for i in np.linspace(0,10,10):
means.append(df.loc[df.eval('(rp==1000)&(c>@i*@step)&(c<=(@i+1)*@step)&(t_reco!=@upper_lim)'),['pcwgt','t_reco']].prod(axis=1).sum()/
df.loc[df.eval('(rp==1000)&(c>@i*@step)&(c<=(@i+1)*@step)&(t_reco!=@upper_lim)'),['pcwgt']].sum())
ax = df.loc[df.eval('(c<@xmax)&(t_reco<12)')].plot.hexbin('c','t_reco',gridsize=25,mincnt=1)
plt.plot(step*np.linspace(0,10,10),means,zorder=100)
# Do the formatting
ax = title_legend_labels(ax,'Precipitation flood in '+myC,lab_x='Pre-disaster consumption [PhP per cap]',lab_y='Time to reconstruct [years]',leg_fs=9)
# Do the saving
plt.draw()
plt.gca().get_figure().savefig('../output_plots/'+myC+'/t_start_reco_scatter.pdf',format='pdf')
plt.cla()
######################
# Latex table of poorest quintile poverty time
_cons_to_tex = df_dec.drop([i for i in df_dec.columns if i not in ['t_pov_cons_avg_'+j for j in all_geo]],axis=1)
_inc_to_tex = df_dec.drop([i for i in df_dec.columns if i not in ['t_pov_inc_avg_'+j for j in all_geo]],axis=1)
_cons_to_tex = _cons_to_tex.rename(columns={'t_pov_cons_avg_'+j:j for j in all_geo}).stack()
_inc_to_tex = _inc_to_tex.rename(columns={'t_pov_inc_avg_'+j:j for j in all_geo}).stack()
_cons_to_tex.index.names = ['hazard','rp','decile',geo]
_inc_to_tex.index.names = ['hazard','rp','decile',geo]
_to_tex = pd.DataFrame(index=_cons_to_tex.index)
_to_tex['Income'] = _inc_to_tex.round(1)
_to_tex['Consumption'] = _cons_to_tex.round(1)
_to_tex = _to_tex.reset_index().set_index(geo)
_to_tex = _to_tex.loc[_to_tex.eval('(hazard==@myHaz)&(rp==10)&(decile==1)')].sort_values('Consumption',ascending=False)
_to_tex[['Income','Consumption']].to_latex('latex/'+myC+'/poverty_duration.tex')
######################
# Plot consumption and income poverty (separately)
df_dec = df_dec.reset_index()
_lab = {'t_pov_cons_avg':'Average time to exit poverty\n(income net of reconstruction & savings) [months]',
't_pov_inc_avg':'Average time to exit poverty (income only) [months]'}
for ipov in ['t_pov_cons_avg','t_pov_inc_avg']:
# Do the plotting
#ax = df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp==10)')].plot.scatter('decile',ipov+'no',color=sns_pal[1],lw=0,label='Natl. average (RP = 5 years)',zorder=99)
#df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp==1000)')].plot.scatter('decile',ipov+'no',color=sns_pal[3],lw=0,label='Natl. average (RP = 1000 years)',zorder=98,ax=ax)
try:
ax = df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp==10)')].plot('decile',ipov+'no',color=sns_pal[1],zorder=97,label='')
df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp==1000)')].plot('decile',ipov+'no',color=sns_pal[3],zorder=96,label='',ax=ax)
except: pass
icol = 4
# Which areas to plot?
_rp = 10
if myC == 'SL': focus = ['Colombo','Rathnapura','Kalutara','Mannar']
elif myC == 'PH': focus = ['NCR']
elif myC == 'RO': focus = ['Center']
elif myC == 'BO': focus = ['La Paz','Beni']
elif myC == 'MW':
focus = ['Lilongwe','Chitipa']
if special_event and special_event.lower() == 'idai':
focus = ['Chikwawa','Blantyre','Nsanje']
_rp = 1
else: assert(False)
for iloc in focus:
df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp=='+str(_rp)+')')].plot.scatter('decile',ipov+'_'+iloc,color=sns_pal[icol],lw=0,label=iloc+' (RP = '+str(_rp)+' years)',zorder=95,ax=ax)
df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp=='+str(_rp)+')')].plot('decile',ipov+'_'+iloc,color=sns_pal[icol],zorder=94,label='',ax=ax)
icol+=1
# Do the formatting
ax = title_legend_labels(ax,'Precipitation flood in '+myC,lab_x='Decile',lab_y=_lab[ipov],lim_x=[0.5,10.5],lim_y=[-0.1,42],leg_fs=9)
ax.xaxis.set_ticks([1,2,3,4,5,6,7,8,9,10])
ax.yaxis.set_ticks([0,6,12,18,24,30,36,42])
# Do the saving
ax.get_figure().savefig('../output_plots/'+myC+'/'+ipov+'_by_decile.pdf',format='pdf')
plt.cla()
######################
# Plot consumption and income poverty (separately), with alternative SPs
try:
_lab = {'t_pov_cons_avg':'Average time to exit poverty\n(income net of reconstruction & savings) [months]',
't_pov_inc_avg':'Average time to exit poverty (income only) [months]'}
icol=0
ax = plt.gca()
for ipov in ['t_pov_cons_avg','t_pov_inc_avg']:
# Do the plotting
_df_5 = df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp==5)')].copy()
_df_1000 = df_dec.loc[df_dec.eval('(hazard==@myHaz)&(rp==1000)')].copy()
for iSP in _sp:
plt.fill_between(_df_5['decile'].values,_df_5[ipov+iSP],_df_1000[ipov+iSP],alpha=0.3)
_df_5.plot.scatter('decile',ipov+iSP,lw=0,label='',zorder=99,ax=ax)
_df_1000.plot.scatter('decile',ipov+iSP,lw=0,label='',zorder=98,ax=ax)
_df_5.plot('decile',ipov+iSP,zorder=97,linestyle=':',label=iSP+' natl. average (RP = 5 years)',ax=ax)
_df_1000.plot('decile',ipov+iSP,zorder=96,label=iSP+' natl. average (RP = 1000 years)',ax=ax)
icol+=1
# Do the formatting
ax = title_legend_labels(ax,'Precipitation flood in '+myC,lab_x='Decile',lab_y=_lab[ipov],lim_x=[0.5,10.5],lim_y=[-0.1],leg_fs=9)
ax.xaxis.set_ticks([1,2,3,4,5,6,7,8,9,10])
ax.yaxis.set_ticks([0,3,6,9,12,15,18])
# Do the saving
ax.get_figure().savefig('../output_plots/'+myC+'/'+ipov+'_with_sps_by_decile.pdf',format='pdf')
plt.cla()
except: pass
plt.close('all')
return True
def load_recovery_times(myC, economy):
results_dir = '../output_country/{}/'.format(myC)
event_level = [economy, 'hazard', 'rp']
out_df = pd.DataFrame()
for file in glob.glob(results_dir + "poverty_duration_*.csv"):
pol_str = file.replace(results_dir,
'').replace('poverty_duration_',
'').replace('.csv', '')
#if 'no' not in pol_str and 'social_scaleup' not in pol_str: continue
if 'no' not in pol_str: continue
_df = pd.read_csv(file).set_index(event_level + ['hhid'])
transient_poor_inc = (_df.c >= get_poverty_line(myC)) & (_df.t_pov_inc
!= 0)
transient_poor_cons = (_df.c >=
get_poverty_line(myC)) & (_df.t_pov_cons != 0)
#
middleclass_dropout_inc = (_df.c >= get_middleclass_range(myC)[0]) & (
_df.t_mc_inc < 10)
middleclass_dropout_cons = (_df.c >= get_middleclass_range(myC)[0]) & (
_df.t_mc_cons < 10)
#
#
# Average time in poverty or out of middle class, for hh that were not poor, or were middleclass, before the disaster
out_df[pol_str + '_transpoor_inc'] = (
_df.loc[transient_poor_inc, ['pcwgt', 't_pov_inc']].prod(
axis=1).sum(level=event_level) /
_df.loc[transient_poor_inc,
'pcwgt'].sum(level=event_level)).round(2)
out_df[pol_str + '_transpoor_cons'] = (
_df.loc[transient_poor_cons, ['pcwgt', 't_pov_cons']].prod(
axis=1).sum(level=event_level) /
_df.loc[transient_poor_cons,
'pcwgt'].sum(level=event_level)).round(2)
out_df[pol_str + '_midclassdrop_inc'] = 10. - (
_df.loc[middleclass_dropout_inc, ['pcwgt', 't_mc_inc']].prod(
axis=1).sum(level=event_level) /
_df.loc[middleclass_dropout_inc,
'pcwgt'].sum(level=event_level)).round(2)
out_df[pol_str + '_midclassdrop_cons'] = 10. - (
_df.loc[middleclass_dropout_cons, ['pcwgt', 't_mc_cons']].prod(
axis=1).sum(level=event_level) /
_df.loc[middleclass_dropout_cons,
'pcwgt'].sum(level=event_level)).round(2)
#
# Number of individuals pushed into poverty or dropped out of midddle class
out_df[pol_str +
'_transpoor_inc_n'] = _df.loc[transient_poor_inc, 'pcwgt'].sum(
level=event_level).astype('int')
out_df[pol_str + '_transpoor_cons_n'] = _df.loc[
transient_poor_cons, 'pcwgt'].sum(level=event_level).astype('int')
out_df[pol_str + '_midclassdrop_inc_n'] = _df.loc[
middleclass_dropout_inc,
'pcwgt'].sum(level=event_level).astype('int')
out_df[pol_str + '_midclassdrop_cons_n'] = _df.loc[
middleclass_dropout_cons,
'pcwgt'].sum(level=event_level).astype('int')
#
# Fraction of above who recover within 1 year
out_df[pol_str + '_transpoor_inc_frac_1yr'] = (
1E2 *
(_df.loc[transient_poor_inc &
(_df.t_pov_inc <= 1), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_transpoor_inc_n'])).round(1)
out_df[pol_str + '_transpoor_cons_frac_1yr'] = (
1E2 *
(_df.loc[transient_poor_cons &
(_df.t_pov_cons <= 1), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_transpoor_cons_n'])).round(1)
out_df[pol_str + '_midclassdrop_inc_frac_1yr'] = (
1E2 *
(_df.loc[middleclass_dropout_inc &
(_df.t_mc_inc > 9), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_midclassdrop_inc_n'])).round(1)
out_df[pol_str + '_midclassdrop_cons_frac_1yr'] = (
1E2 *
(_df.loc[middleclass_dropout_cons &
(_df.t_mc_cons > 9), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_midclassdrop_cons_n'])).round(1)
# Fraction of above who recover within 2 years
out_df[pol_str + '_transpoor_inc_frac_2yr'] = (
1E2 *
(_df.loc[transient_poor_inc &
(_df.t_pov_inc <= 2), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_transpoor_inc_n'])).round(1)
out_df[pol_str + '_transpoor_cons_frac_2yr'] = (
1E2 *
(_df.loc[transient_poor_cons &
(_df.t_pov_cons <= 2), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_transpoor_cons_n'])).round(1)
out_df[pol_str + '_midclassdrop_inc_frac_2yr'] = (
1E2 *
(_df.loc[middleclass_dropout_inc &
(_df.t_mc_inc > 8), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_midclassdrop_inc_n'])).round(1)
out_df[pol_str + '_midclassdrop_cons_frac_2yr'] = (
1E2 *
(_df.loc[middleclass_dropout_cons &
(_df.t_mc_cons > 8), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_midclassdrop_cons_n'])).round(1)
# Fraction of above who recover within 5 years
out_df[pol_str + '_transpoor_inc_frac_5yr'] = (
1E2 *
(_df.loc[transient_poor_inc &
(_df.t_pov_inc <= 5), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_transpoor_inc_n'])).round(1)
out_df[pol_str + '_transpoor_cons_frac_5yr'] = (
1E2 *
(_df.loc[transient_poor_cons &
(_df.t_pov_cons <= 5), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_transpoor_cons_n'])).round(1)
out_df[pol_str + '_midclassdrop_inc_frac_5yr'] = (
1E2 *
(_df.loc[middleclass_dropout_inc &
(_df.t_mc_inc > 5), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_midclassdrop_inc_n'])).round(1)
out_df[pol_str + '_midclassdrop_cons_frac_5yr'] = (
1E2 *
(_df.loc[middleclass_dropout_cons &
(_df.t_mc_cons > 5), 'pcwgt'].sum(level=event_level) /
out_df[pol_str + '_midclassdrop_cons_n'])).round(1)
out_df.sort_index(inplace=True)
out_df.to_csv('../output_country/{}/recovery_dynamics.csv'.format(myC))
return out_df
import matplotlib.pyplot as plt
from libraries.lib_average_over_rp import *
from libraries.lib_common_plotting_functions import *
from libraries.lib_country_dir import get_all_rps, get_poverty_line, get_currency
from libraries.lib_gather_data import match_percentiles, perc_with_spline, reshape_data
pairs_pal = sns.color_palette('Paired', n_colors=12)
greys_pal = sns.color_palette('Greys', n_colors=9)
myCountry = 'PH'
try: myCountry = sys.argv[1]
except: pass
# Get poverty line
_pov = get_poverty_line(myCountry)
# Decide whether to do this at decile or quintile level
agglev = 'decile'
try: _q = pd.read_csv('../output_country/'+myCountry+'/sp_comparison_by_'+agglev+'.csv').set_index(agglev)
except:
# Load file
_f = pd.read_csv('../output_country/'+myCountry+'/poverty_duration_no.csv')
_f_up = pd.read_csv('../output_country/'+myCountry+'/poverty_duration_unif_poor.csv')
if 'quintile' not in _f.columns or 'decile' not in _f.columns:
# Assign deciles
_deciles=np.arange(0.10, 1.01, 0.10)
]
############################################
# Do quick agriculture plot
if True:
try:
_df = pd.read_csv('../inputs/PH/FIES2015_ompong.csv')
except:
_df = pd.read_csv('../inputs/PH/FIES2015.csv')
_df = _df.loc[_df.eval('(w_regn==1)|(w_regn==2)|(w_regn==14)')]
_df.to_csv('../inputs/PH/FIES2015_ompong.csv')
_df['pcwgt'] = _df.eval('hhwgt*fsize')
_df['agpcinc'] = _df.eval('aginc/fsize')
pov_line = get_poverty_line('PH')
sf_aginc = (26.8 * _df[['hhwgt', 'aginc']].prod(axis=1).sum() * 1E-9 /
202.2) / (_df[['hhwgt', 'aginc']].prod(axis=1).sum() * 1E-9)
# ^ total ag losses from Ompong ^ AG national accounts
_df['pcinc_final'] = _df.eval('pcinc-@sf_aginc*agpcinc')
# Calculate poverty headcount
pov_increase = _df.loc[(_df['pcinc'] > pov_line) &
(_df.eval('pcinc-@sf_aginc*agpcinc<=@pov_line')),
'pcwgt'].sum()
pov_gap_init = 1. - _df.loc[
_df['pcinc'] < pov_line, ['pcinc', 'pcwgt']].prod(axis=1).sum() / (
_df.loc[_df['pcinc'] < pov_line, 'pcwgt'].sum() * pov_line)
pov_gap_final = 1. - (
_df.loc[_df.eval('(pcinc_final<=@pov_line)'),