def test_calib():
entry = sc.loadjson(calibfile)[0]
params = sc.dcp(entry['pars'])
params['rand_seed'] = int(entry['index'])
scen = generate_scenarios()['all_remote']
testing = generate_testing()['None']
#testing[0]['delay'] = 0
for stype, spec in scen.items():
if spec is not None:
spec['testing'] = testing
scen['testing'] = testing
scen['es']['verbose'] = scen['ms']['verbose'] = scen['hs'][
'verbose'] = debug
sim = cs.create_sim(params, pop_size=pop_size, folder=folder)
sm = cvsch.schools_manager(scen)
sim['interventions'] += [sm]
sim.run(keep_people=debug)
stats = evaluate_sim(sim)
print(stats)
if debug:
sim.plot(to_plot='overview')
#t = sim.make_transtree()
else:
sim.plot()
cv.savefig('sim.png')
return sim
def make():
print('Making figure')
cv.Sim().run(do_plot=True)
filename = cv.savefig(comments='good figure')
return filename
interval=90, # Number of days between tick marks
dateformat='%m/%Y', # Date format for ticks
fig_args={'figsize': (14, 8)}, # Size of the figure (x and y)
axis_args={'left': 0.15}, # Space on left side of plot
)
msim.plot_result('r_eff', **plot_customizations)
#sim.plot_result('r_eff')
pl.axhline(1.0, linestyle='--', c=[0.8, 0.4, 0.4], alpha=0.8,
lw=4) # Add a line for the R_eff = 1 cutoff
pl.title('')
pl.savefig('R.pdf')
msim.plot_result('cum_deaths', **plot_customizations)
pl.title('')
cv.savefig('Deaths.pdf')
msim.plot_result('new_infections', **plot_customizations)
pl.title('')
cv.savefig('Infections.pdf')
msim.plot_result('cum_diagnoses', **plot_customizations)
pl.title('')
cv.savefig('Diagnoses.pdf')
##for calibration figures
#msim.plot_result('cum_deaths', interval=20, fig_args={'figsize':(12,7)}, axis_args={'left':0.15})
#pl.title('')
#cv.savefig('Deaths.png')
# msim.plot_result('cum_diagnoses', interval=20, fig_args={'figsize':(12,7)}, axis_args={'left':0.15})
scatter=False,
color=c)
ax.set_xlabel('School size (students)')
ax.set_ylabel('Infection on First Day (%)')
yt = [0.0, 0.25, 0.50, 0.75]
ax.set_yticks(yt)
ax.set_yticklabels([int(100 * t) for t in yt])
ax.grid(True)
lmap = {
'None': 'No PCR testing',
'2020-10-26': 'One week prior',
'2020-10-27': 'Six days prior',
'2020-10-28': 'Five days prior',
'2020-10-29': 'Four days prior',
'2020-10-30': 'Three days prior',
'2020-10-31': 'Two days prior',
'2020-11-01': 'One day prior',
'2020-11-02': 'On the first day of school'
}
handles, labels = ax.get_legend_handles_labels()
handles = [handles[-1]] + handles[:-1]
labels = [lmap[labels[-1]]] + [lmap[l] for l in labels[:-1]]
ax.legend(handles, labels)
plt.tight_layout()
cv.savefig(os.path.join(imgdir, 'PCR_Days_Sweep.png'), dpi=300)
fig.tight_layout()
sims,
ax[2],
calib=True,
label='Cumulative infections\n(modeled)',
ylabel='',
flabel=False)
plotter('n_infectious',
sims,
ax[2],
calib=True,
label='Active infections\n(modeled)',
ylabel='Estimated infections',
flabel=False)
pl.legend(loc='upper left', frameon=False)
# d. cumulative deaths
ax[3] = pl.axes([xgapl + xgapm + dx1, ygapb, dx2, dy])
format_ax(ax[3], sim)
plotter('cum_deaths',
sims,
ax[3],
calib=True,
label='Deaths\n(modeled)',
ylabel='Cumulative deaths',
flabel=False)
pl.legend(loc='upper left', frameon=False)
#pl.ylim([0, 10e3])
cv.savefig(f'{figsfolder}/fig2_calibration.pdf')
sc.toc(T)
def plot_reff_combined(num_param_set, date_of_file):
colors = ['forestgreen', 'mediumpurple', 'tab:orange', 'maroon']
case = '50_cases_re_0.9'
rel_trans = 'under10_0.5trans'
beta_layer = '3xschool_beta_layer'
rel_sus = 'rel_sus'
sens = [rel_trans, None, rel_sus]#, beta_layer]
name = 'Infectivity/Susceptibility Assumptions'
sens_labels = {
None: 'Baseline: children under 10 as infectious as adults; '
'\nper-contact infectivity in school 20% relative to households;'
'\n0-20 year olds 33-66% less susceptible than 20+',
rel_trans: '50% reduced transmission in children under 10',
beta_layer: 'Same infectivity per contact in schools as households',
rel_sus: '0-20 year olds as susceptible as 20+'
}
df_by_prev = []
df_by_prev_std = []
for sen in sens:
df_mean = []
df_std = []
for strat in strategies:
df_mean.append(combine_results_dfs(strat, num_param_set, 'r_eff', True, date_of_file, case, sen))
df_std.append(combine_results_dfs(strat, num_param_set, 'r_eff', False, date_of_file, case, sen))
scenario_strategies = strats
df_comb = pd.DataFrame(df_mean).transpose()
df_comb.columns = scenario_strategies
df_comb_std = pd.DataFrame(df_std).transpose()
df_comb_std.columns = scenario_strategies
df_by_prev.append(df_comb)
df_by_prev_std.append(df_comb_std)
base = dt.datetime(2020, 7, 1)
date_list = [base + dt.timedelta(days=x) for x in range(len(df_comb))]
x = []
for date in date_list:
x.append(date.strftime('%b %d'))
date_to_x = {d: i for i, d in enumerate(x)}
min_x = date_to_x['Aug 30']
max_x = date_to_x['Dec 01']
for i, _ in enumerate(sens):
df_by_prev[i] = df_by_prev[i].iloc[min_x:max_x,]
df_by_prev[i] = df_by_prev[i].mean(axis=0)
df_by_prev_std[i] = df_by_prev_std[i].iloc[min_x:max_x, ]
df_by_prev_std[i] = df_by_prev_std[i].mean(axis=0)
x = np.arange(len(strategy_labels))
width = [-.2, 0, .2] #[-.3, -.1, .1, .3]
left = 0.07
right = 0.99
# right = 0.63
bottom = 0.15
top = 0.96
fs = 24
fig, ax = plt.subplots(figsize=(13,9))
fig.subplots_adjust(left=left, right=right, top=top, bottom=bottom)
for i, sen in enumerate(sens):
ax.bar(x + width[i], df_by_prev[i].values, yerr = df_by_prev_std[i].values/2, width=0.2,
label=sens_labels[sen], color=colors[i], alpha = 0.87)
# ax.bar(x, df_comb.values, yerr=df_comb_std.values / 2, width=0.4, alpha=0.87)
ax.axhline(y=1, xmin=0, xmax=1, color='black', ls='--')
ax.set_ylabel('Effective Reproductive Number', size=fs)
#ax.set_title(f'Effective Reproductive Number by School Reopening Strategy', size=18, horizontalalignment='center')
ax.set_ylim(0.7, 1.4)
leg_i = ax.legend(fontsize=fs, title=name)
leg_i.set_title(name, prop={'size': fs})
ax.set_xticks(x)
ax.set_xlim(-0.45, 6.45)
ax.tick_params(labelsize=21)
ax.set_xticklabels(strategy_labels_brief.values(), fontsize=20)
cv.savefig(f'r_eff_{date_of_file}.png')
plt.savefig(f'r_eff_{date_of_file}.pdf')
c1 = [0.3, 0.3, 0.6] # diags
c2 = [0.6, 0.7, 0.9] #diags
pl.bar(X, pos, width=w, label='Data', facecolor=c1)
pl.bar(XX, mpbest, width=w, label='Model', facecolor=c2)
for i, ix in enumerate(XX):
pl.plot([ix, ix], [mplow[i], mphigh[i]], c='k')
ax[0].set_xticks((X + XX) / 2)
ax[0].set_xticklabels(x)
pl.xlabel('Age')
pl.ylabel('Diagnoses')
sc.boxoff(ax[0])
pl.legend(frameon=False, bbox_to_anchor=(0.3, 0.7))
# Deaths
ax[1] = pl.axes([xl + dx + xm, yb, dx, dy])
c1 = [0.5, 0.0, 0.0] # deaths
c2 = [0.9, 0.4, 0.3] # deaths
pl.bar(X, deaths, width=w, label='Data', facecolor=c1)
pl.bar(XX, mdbest, width=w, label='Model', facecolor=c2)
for i, ix in enumerate(XX):
pl.plot([ix, ix], [mdlow[i], mdhigh[i]], c='k')
ax[1].set_xticks((X + XX) / 2)
ax[1].set_xticklabels(x)
pl.xlabel('Age')
pl.ylabel('Deaths')
sc.boxoff(ax[1])
pl.legend(frameon=False, bbox_to_anchor=(0.3, 0.7))
plotname = 'uk_stats_by_age_agg.png' if aggregate else 'uk_stats_by_age.png'
cv.savefig(plotname, dpi=100)
ax[pn].set_ylabel('R')
elif pn in range(ncols, ncols * 2):
plotter('cum_deaths', sims[pn % ncols], ax[pn])
ax[pn].set_ylim(0, 150_000)
if (pn % ncols) == 0:
ax[pn].set_ylabel('Total deaths')
else:
plotter('new_infections', sims[pn % ncols], ax[pn])
ax[pn].set_ylim(0, 250_000)
if (pn % ncols) == 0:
ax[pn].set_ylabel('New infections')
if pn not in range(ncols):
ax[pn].set_xticklabels([])
cv.savefig(f'{figsfolder}/fig_UK_school_scens.png', dpi=100)
################################################################################
# ## Fig 3
################################################################################
pl.figure(figsize=(24, 12))
#font_size = 24
#pl.rcParams['font.size'] = font_size
# Subplot sizes
xgapl = 0.06
xgapm = 0.1
xgapr = 0.01
ygapb = 0.11
ygapm = 0.1
ygapt = 0.02
def plot_calibration(sims, date, do_save=0):
sim = sims[0] # For having a sim to refer to
# Draw plots
fig1_path = f'calibration_{date}_fig1.png'
fig2_path = f'calibration_{date}_fig2.png'
fig_args = sc.mergedicts({'figsize': (16, 14)})
axis_args = sc.mergedicts({'left': 0.10, 'bottom': 0.05, 'right': 0.95, 'top': 0.93, 'wspace': 0.25, 'hspace': 0.40})
# Handle input arguments -- merge user input with defaults
low_q = 0.1
high_q = 0.9
# Figure 1: Calibration
pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
pl.figtext(0.42, 0.95, 'Model calibration', fontsize=30)
#%% Figure 1, panel 1
ax = pl.subplot(4,1,1)
format_ax(ax, sim)
plotter('new_tests', sims, ax, calib=True, label='Number of tests per day', ylabel='Tests')
plotter('new_diagnoses', sims, ax, calib=True, label='Number of diagnoses per day', ylabel='Tests')
#%% Figure 1, panel 2
ax = pl.subplot(4,1,2)
format_ax(ax, sim)
plotter('cum_diagnoses', sims, ax, calib=True, label='Cumulative diagnoses', ylabel='People')
#%% Figure 1, panel 3
ax = pl.subplot(4,1,3)
format_ax(ax, sim)
plotter('cum_deaths', sims, ax, calib=True, label='Cumulative deaths', ylabel='Deaths')
#%% Figure 1, panels 4A and 4B
agehists = []
for s,sim in enumerate(sims):
agehist = sim['analyzers'][0]
if s == 0:
age_data = agehist.data
agehists.append(agehist.hists[-1])
x = age_data['age'].values
pos = age_data['cum_diagnoses'].values
death = age_data['cum_deaths'].values
# From the model
mposlist = []
mdeathlist = []
for hists in agehists:
mposlist.append(hists['diagnosed'])
mdeathlist.append(hists['dead'])
mposarr = np.array(mposlist)
mdeatharr = np.array(mdeathlist)
mpbest = pl.median(mposarr, axis=0)
mplow = pl.quantile(mposarr, q=low_q, axis=0)
mphigh = pl.quantile(mposarr, q=high_q, axis=0)
mdbest = pl.median(mdeatharr, axis=0)
mdlow = pl.quantile(mdeatharr, q=low_q, axis=0)
mdhigh = pl.quantile(mdeatharr, q=high_q, axis=0)
# Plotting
w = 4
off = 2
bins = x.tolist() + [100]
ax = pl.subplot(4,2,7)
c1 = [0.3,0.3,0.6]
c2 = [0.6,0.7,0.9]
xx = x+w-off
pl.bar(x-off,pos, width=w, label='Data', facecolor=c1)
pl.bar(xx, mpbest, width=w, label='Model', facecolor=c2)
for i,ix in enumerate(xx):
pl.plot([ix,ix], [mplow[i], mphigh[i]], c='k')
ax.set_xticks(bins[:-1])
pl.title('Diagnosed cases by age')
pl.xlabel('Age')
pl.ylabel('Cases')
pl.legend()
ax = pl.subplot(4,2,8)
c1 = [0.5,0.0,0.0]
c2 = [0.9,0.4,0.3]
pl.bar(x-off,death, width=w, label='Data', facecolor=c1)
pl.bar(x+w-off, mdbest, width=w, label='Model', facecolor=c2)
for i,ix in enumerate(xx):
pl.plot([ix,ix], [mdlow[i], mdhigh[i]], c='k')
ax.set_xticks(bins[:-1])
pl.title('Deaths by age')
pl.xlabel('Age')
pl.ylabel('Deaths')
pl.legend()
# Tidy up
if do_save:
cv.savefig(fig1_path)
# Figure 2: Projections
pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
pl.figtext(0.42, 0.95, 'Model estimates', fontsize=30)
#%% Figure 2, panel 1
ax = pl.subplot(4,1,1)
format_ax(ax, sim)
plotter('cum_infections', sims, ax,calib=True, label='Cumulative infections', ylabel='People')
plotter('cum_recoveries', sims, ax,calib=True, label='Cumulative recoveries', ylabel='People')
#%% Figure 2, panel 2
ax = pl.subplot(4,1,2)
format_ax(ax, sim)
plotter('n_infectious', sims, ax,calib=True, label='Number of active infections', ylabel='People')
plot_intervs(sim, labels=True)
#%% Figure 2, panel 3
ax = pl.subplot(4,1,3)
format_ax(ax, sim)
plotter('new_infections', sims, ax,calib=True, label='Infections per day', ylabel='People')
plotter('new_recoveries', sims, ax,calib=True, label='Recoveries per day', ylabel='People')
plot_intervs(sim)
#%% Figure 2, panels 4
ax = pl.subplot(4,1,4)
format_ax(ax, sim)
plotter('r_eff', sims, ax, calib=True, label='Effective reproductive number', ylabel=r'$R_{eff}$')
ylims = [0,4]
pl.ylim(ylims)
xlims = pl.xlim()
pl.plot(xlims, [1, 1], 'k')
plot_intervs(sim)
# Tidy up
if do_save:
cv.savefig(fig2_path)
return
# Plot D: severe cases
pl.subplot(2, 2, 4)
colors = pl.cm.YlOrBr([0.9, 0.6, 0.3])
for i, l in enumerate(labels):
if i == 0:
ds = np.arange(0, len(tvec_d), 1) # Downsample
thissim = msims[l].sims[0]
datatoplot = thissim.data['new_severe'][date_inds_d[0]:date_inds_d[1]]
pl.plot(tvec_d[ds],
datatoplot[ds],
'd',
c='k',
markersize=12,
alpha=0.75,
label='Data')
toplot = plotdict['new_severe'][l][date_inds[0]:date_inds[1]]
pl.plot(tvec, toplot, c=colors[i], label=l, lw=4, alpha=1.0)
low = plotdict_l['new_severe'][l][date_inds[0]:date_inds[1]]
high = plotdict_h['new_severe'][l][date_inds[0]:date_inds[1]]
pl.fill_between(tvec, low, high, facecolor=colors[i], alpha=0.2)
pl.ylabel('Daily Hospitalisations')
ax = pl.gca()
ax.set_xticks(datemarks)
cv.date_formatter(start_day=start_day, ax=ax, dateformat=dateformat)
sc.setylim()
sc.commaticks()
pl.legend(frameon=False)
sc.boxoff()
cv.savefig('figs_Vac/uk_scens_plots.png')
# Frac in-person days lost
d = pd.melt(df, id_vars=['key1', 'key2', 'key3'], value_vars=[f'perc_inperson_days_lost_{gkey}' for gkey in grp_dict.keys()], var_name='Group', value_name='Days lost (%)')
d.replace( {'Group': {f'perc_inperson_days_lost_{gkey}':gkey for gkey in grp_dict.keys()}}, inplace=True)
d = d.loc[d['key1']==school_scenario] # K-5 only
g = sns.FacetGrid(data=d, row='Group', height=4, aspect=3, row_order=['Teachers & Staff', 'Students'], legend_out=False)
g.map_dataframe( sns.barplot, x='key2', y='Days lost (%)', hue='key3', order=test_order, hue_order=sens_order, palette='Set2')
g.set_titles(row_template="{row_name}", fontsize=24)
g.set_axis_labels(y_var="Days lost (%)")
plt.tight_layout()
for axi, ax in enumerate(g.axes.flat):
box = ax.get_position()
ax.set_position([box.x0, box.y0 + (axi+1)*box.height * 0.1, box.width, box.height * 0.9])
g.axes.flat[1].legend(loc='upper center',bbox_to_anchor=(0.48,-0.16), ncol=4, fontsize=14)
cv.savefig(os.path.join(imgdir, '3mInPersonDaysLost_countermeasures.png'), dpi=300)
# Attack rate
for aspect, fontsize in zip([2.5, 3], [12,14]):
d = pd.melt(df, id_vars=['key1', 'key2', 'key3'], value_vars=[f'attackrate_{gkey}' for gkey in grp_dict.keys()], var_name='Group', value_name='Cum Inc (%)')
d.replace( {'Group': {f'attackrate_{gkey}':gkey for gkey in grp_dict.keys()}}, inplace=True)
d = d.loc[d['key1']==school_scenario] # K-5 only
g = sns.FacetGrid(data=d, row='Group', height=4, aspect=aspect, row_order=['Teachers & Staff', 'Students'], legend_out=False) # col='key1',
g.map_dataframe( sns.barplot, x='key2', y='Cum Inc (%)', hue='key3', order=test_order, hue_order=sens_order, palette='Set2')
g.set_titles(row_template="{row_name}")
g.set_axis_labels(y_var="3-Month Attack Rate (%)")
plt.tight_layout()
for axi, ax in enumerate(g.axes.flat):
box = ax.get_position()
interval=90, # Number of days between tick marks
dateformat='%m/%Y', # Date format for ticks
fig_args={'figsize': (14, 8)}, # Size of the figure (x and y)
axis_args={'left': 0.15}, # Space on left side of plot
)
msim.plot_result('r_eff', **plot_customizations)
#sim.plot_result('r_eff')
pl.axhline(1.0, linestyle='--', c=[0.8, 0.4, 0.4], alpha=0.8,
lw=4) # Add a line for the R_eff = 1 cutoff
pl.title('')
pl.savefig('R.pdf')
msim.plot_result('cum_deaths', **plot_customizations)
pl.title('')
cv.savefig('Deaths.pdf')
msim.plot_result('new_infections', **plot_customizations)
pl.title('')
cv.savefig('Casespdf')
#msim.plot_result('cum_diagnoses', **plot_customizations)
#pl.title('')
#cv.savefig('Diagnoses18_68.pdf')
##for calibration figures
#msim.plot_result('cum_deaths', interval=20, fig_args={'figsize':(12,7)}, axis_args={'left':0.15})
#pl.title('')
#cv.savefig('Deaths.png')
# msim.plot_result('cum_diagnoses', interval=20, fig_args={'figsize':(12,7)}, axis_args={'left':0.15})
symp_ax.bar(xax[0]-2, asymp_frac, label='Asymptomatic', color=colors[0])
symp_ax.bar(xax[:presymp], sympcounts[:presymp], label='Presymptomatic', color=colors[1])
symp_ax.bar(xax[presymp:], sympcounts[presymp:], label='Symptomatic', color=colors[2])
symp_ax.set_xlabel('Days since symptom onset')
symp_ax.set_ylabel('Proportion of transmissions (%)')
symp_ax.set_xticks([minind-3, 0, 5, 10, maxind])
symp_ax.set_xticklabels(['Asymp.', '0', '5', '10', f'>{maxind}'])
sc.boxoff(ax=symp_ax)
spie_ax = pl.axes([sympx+0.05, 0.20, 0.2, 0.2])
labels = [f'Asymp-\ntomatic\n{asymp_frac:0.0f}%', f' Presymp-\n tomatic\n {pre_frac:0.0f}%', f'Symp-\ntomatic\n{symp_frac:0.0f}%']
spie_ax.pie([asymp_frac, pre_frac, symp_frac], labels=labels, colors=colors, **pieargs)
return fig
# Actually plot
fig = plot()
#%% Tidy up
if do_save:
cv.savefig(fig_path, dpi=150)
if do_show:
pl.show()
sc.toc(T)
print('Done.')
if do_plot:
# Make individual plots
plot_customizations = dict(
interval = 90, # Number of days between tick marks
dateformat = '%m/%Y', # Date format for ticks
fig_args = {'figsize':(14,8)}, # Size of the figure (x and y)
axis_args = {'left':0.15}, # Space on left side of plot
)
for mkey, msim in final_msims.items():
msim.plot_result('r_eff', do_show=do_show, **plot_customizations)
pl.axhline(1.0, linestyle='--', c=[0.8,0.4,0.4], alpha=0.8, lw=4) # Add a line for the R_eff = 1 cutoff
pl.title('')
if do_save: cv.savefig(f'R_eff_{mkey}.png')
msim.plot_result('cum_deaths', do_show=do_show, **plot_customizations)
pl.title('')
if do_save: cv.savefig(f'Deaths_{mkey}.png')
msim.plot_result('new_infections', do_show=do_show, **plot_customizations)
pl.title('')
if do_save: cv.savefig(f'Infections_{mkey}.png')
msim.plot_result('cum_diagnoses', do_show=do_show, **plot_customizations)
pl.title('')
if do_save: cv.savefig(f'Diagnoses_{mkey}.png')
ax[pn].set_xticks(datemarks)
if pn == 1:
ax[pn] = sns.swarmplot(x="variable",
y="value",
data=df2,
color="grey",
alpha=0.5)
ax[pn] = sns.violinplot(x="variable",
y="value",
data=df2,
color="lightblue",
alpha=0.5,
inner=None)
ax[pn] = sns.pointplot(x="variable",
y="value",
data=df2,
ci=None,
color="steelblue",
markers='D',
scale=1.2)
ax[pn].set_ylabel('Cumulative infections, 1 Dec 2020 - 1 Mar 2021')
ax[pn].set_xlabel('Symptomatic testing rate')
cv.savefig(f'{figsfolder}/fig4_multiscens.pdf')
print([np.median(cuminf[tn]) for tn in range(len(thresholds))])
print([np.quantile(cuminf[tn], q=0.025) for tn in range(len(thresholds))])
print([np.quantile(cuminf[tn], q=0.975) for tn in range(len(thresholds))])
sc.toc(T)
if pn==4: pl.legend(loc='upper right', frameon=False, fontsize=20)
if pn not in [0,5,10,15]:
ax[pn].set_yticklabels([])
else:
ax[pn].set_ylabel('New infections')
if pn not in range(nv):
ax[pn].set_xticklabels([])
else:
xmin, xmax = ax[pn].get_xlim()
ax[pn].set_xticks(pl.arange(xmin+5, xmax, 40))
if thisfig==resultsfolder: figname = figsfolder+'/fig2_grid.png'
elif thisfig==sensfolder: figname = figsfolder+'/figS2_grid.png'
cv.savefig(figname, dpi=100)
#d = {'testing': [0.067]*nv*nm+[0.1]*nv*nm+[0.15]*nv*nm+[0.19]*nv*nm, 'tracing': [0.0]*nm+[0.25]*nm+[0.5]*nm+[0.75]*nm+[1.0]*nm+[0.0]*nm+[0.25]*nm+[0.5]*nm+[0.75]*nm+[1.0]*nm+[0.0]*nm+[0.25]*nm+[0.5]*nm+[0.75]*nm+[1.0]*nm+[0.0]*nm+[0.25]*nm+[0.5]*nm+[0.75]*nm+[1.0]*nm, 'masks': [0.0,0.25,0.5,0.75]*nt*nv}
#d['val'] = []
#for t in tlevels:
# for v in vlevels:
# d['val'].extend(sc.sigfig(results['cum_infections'][t][v],3))
#import pandas as pd
#df = pd.DataFrame(d)
#df.to_excel('sweepresults.xlsx')
################################################################################################################
# Figure 3: bar plot of cumulative infections
################################################################################################################
import covasim as cv
fn = 'inputs/kc_synthpops_clustered_withstaff_seed0.ppl'
sim = cv.Sim(pop_size=225e3, pop_type='synthpops', popfile=fn, load_pop=True)
sim.initialize()
sim.people.plot()
cv.savefig('people_plot.png')
# Save the key figures
plot_customizations = dict(
interval=90, # Number of days between tick marks
dateformat='%m/%Y', # Date format for ticks
fig_args={'figsize': (14, 8)}, # Size of the figure (x and y)
axis_args={'left': 0.15}, # Space on left side of plot
)
msim.plot_result('r_eff', **plot_customizations)
#sim.plot_result('r_eff')
pl.axhline(1.0, linestyle='--', c=[0.8, 0.4, 0.4], alpha=0.8,
lw=4) # Add a line for the R_eff = 1 cutoff
pl.title('')
pl.savefig('%s/test%s-trace%s-R.pdf' % (scenario, args.test, args.trace))
msim.plot_result('cum_deaths', **plot_customizations)
pl.title('')
cv.savefig('%s/test%s-trace%s-Deaths.pdf' %
(scenario, args.test, args.trace))
msim.plot_result('new_infections', **plot_customizations)
pl.title('')
cv.savefig('%s/test%s-trace%s-Infections.pdf' %
(scenario, args.test, args.trace))
msim.plot_result('cum_diagnoses', **plot_customizations)
pl.title('')
cv.savefig('%s/test%s-trace%s-Diagnoses.pdf' %
(scenario, args.test, args.trace))
ci = 1
rvec = []
for sim in msim.sims:
r = sim.results[ch].values
ps = 100000 / sim.pars['pop_size'] * sim.pars['pop_scale']
if ch in ['new_diagnoses']:
r *= 14 * ps
if ch in ['new_infections', 'new_tests']:
r *= ps
rvec.append(r)
ax = axv[ri, ci]
med = np.median(rvec, axis=0)
sd = np.std(rvec, axis=0)
ax.fill_between(sim.results['date'],
med + 2 * sd,
med - 2 * sd,
color='lightgray')
ax.plot(sim.results['date'], med + 2 * sd, color='gray', lw=1)
ax.plot(sim.results['date'], med - 2 * sd, color='gray', lw=1)
ax.plot(sim.results['date'], med, color='k', lw=2)
if 'ref' in info and info['ref'] is not None:
ax.axhline(y=info['ref'], color='r', ls='--', lw=2)
ax.set_title(info['title'])
ri += 1
f.tight_layout()
cv.savefig(os.path.join(imgdir, f'calib.png'), dpi=300)
time_num_new = np.append(
time_num_new,
sim.people.date_diagnosed[idx] - sim.people.date_symptomatic[idx])
yield_num_new = yield_num_new + sim.results['test_yield'].values
idx_dia = sim.people.diagnosed
idx = ~np.isnan(sim.people.date_symptomatic)
stage_new['mild'] += sum(idx[idx_dia]) / sum(idx)
idx = ~np.isnan(sim.people.date_severe)
stage_new['sev'] += sum(idx[idx_dia]) / sum(idx)
idx = ~np.isnan(sim.people.date_critical)
stage_new['crit'] += sum(idx[idx_dia]) / sum(idx)
sc.toc(t)
sim.plot(to_plot={'test': ['new_tests']})
pl.show()
if do_save:
cv.savefig('testScalingNum.png')
pl.close()
sim = single_sim_new(rand_seed=i, dist=None)
t = sc.tic()
sim.run()
idx = sim.people.diagnosed
time_num_flat = np.append(
time_num_flat,
sim.people.date_diagnosed[idx] - sim.people.date_symptomatic[idx])
yield_num_flat = yield_num_flat + sim.results['test_yield'].values
idx_dia = sim.people.diagnosed
idx = ~np.isnan(sim.people.date_symptomatic)
stage_flat['mild'] += sum(idx[idx_dia]) / sum(idx)
idx = ~np.isnan(sim.people.date_severe)
stage_flat['sev'] += sum(idx[idx_dia]) / sum(idx)
print('Deaths: ', msim.results['cum_deaths'][-1])
print('Infections: ', msim.results['cum_infectious'][-1])
# Save the key figures
plot_customizations = dict(
interval=90, # Number of days between tick marks
dateformat='%Y/%m', # Date format for ticks
fig_args={'figsize': (14, 8)}, # Size of the figure (x and y)
axis_args={'left': 0.15}, # Space on left side of plot
)
msim.plot_result('r_eff', **plot_customizations)
pl.axhline(1.0, linestyle='--', c=[0.8, 0.4, 0.4], alpha=0.8,
lw=4) # Add a line for the R_eff = 1 cutoff
pl.title('')
cv.savefig('R_eff.png')
msim.plot_result('cum_deaths', **plot_customizations)
pl.title('')
cv.savefig('Deaths.png')
msim.plot_result('new_infections', **plot_customizations)
pl.title('')
cv.savefig('Infections.png')
msim.plot_result('cum_diagnoses', **plot_customizations)
pl.title('')
cv.savefig('Diagnoses.png')
msim.plot_result('new_tests', **plot_customizations)
cv.savefig('Test.png')
fontsize=36, fontweight='bold', bbox={'edgecolor': 'none', 'facecolor': 'white', 'alpha': 0.5, 'pad': 4})
pl.figtext(xgapl + dx * ncols + xgapm + rlpad, ygapb + (ygapm + dy) * 0 + epsy,' Masks: 30% EC ',
rotation=90, fontweight='bold',
bbox={'edgecolor': 'none', 'facecolor': 'white', 'alpha': 0.5, 'pad': 4})
pl.figtext(xgapl + dx * ncols + xgapm + rlpad, ygapb + (ygapm + dy) * 1 + epsy,' Masks: 15% EC ',
rotation=90, fontweight='bold',
bbox={'edgecolor': 'none', 'facecolor': 'white', 'alpha': 0.5, 'pad': 4})
for pn in range(nplots):
ax[pn] = pl.axes([xgapl + (dx + xgapm) * (pn % ncols), ygapb + (ygapm + dy) * (pn // ncols), dx, dy])
print([xgapl + (dx + xgapm) * (pn % ncols), ygapb + (ygapm + dy) * (pn // ncols)])
print(list(sims.keys())[pn])
format_ax(ax[pn], sim)
ax[pn].axvline(masks_begin, c=[0, 0, 0], linestyle='--', alpha=0.4, lw=3)
# pl.figtext(xgapl + (dx + xgapm) * (pn % ncols) + dx, ygapb + (ygapm + dy) * (pn // ncols) + dy, ' 24% ')
ax[pn].annotate(labels[tti_scen][pn], xy=(700, 340_000), xycoords='data', ha='right', va='top')
plotter('new_infections', sims[pn], ax[pn])
ax[pn].set_ylim(0, 150_000)
ax[pn].set_yticks(np.arange(0, 150_000, 25_000))
if (pn%ncols) != 0:
ax[pn].set_yticklabels([])
else:
ax[pn].set_ylabel('New infections')
if pn not in range(ncols):
ax[pn].set_xticklabels([])
cv.savefig(f'{figsfolder}/fig_scens_{tti_scen}TTI.png', dpi=100)
sc.toc(T)
import pandas as pd
data = pd.read_csv(swabfile)
data = data.loc[data['Test Delay'] != '#NAME?', ]
pdf = cvu.get_pdf('lognormal', 10, 170)
# Check that not using a distribution gives the same answer as before
pl.hist(time_prob_old, np.arange(-2.5, 25.5), density=True)
pl.hist(time_prob_flat, np.arange(-2.5, 25.5), density=True, alpha=.25)
pl.xlim([-2, 20])
pl.xlabel('Symptom onset to swab')
pl.ylabel('Percent of tests')
pl.show()
if do_save:
cv.savefig('testprobOld.png')
pl.close()
# See how close the default distribution is the the WA data and what the model
# produces
pl.hist(time_prob_new, np.arange(-2.5, 25.5), density=True)
pl.plot(data['Test Delay'], data['Percent'] / 100)
pl.plot(np.arange(100), pdf.pdf(np.arange(100)))
pl.xlim([-2, 20])
pl.xlabel('Symptom onset to swab')
pl.ylabel('Percent of tests')
pl.legend(['Data', 'Distribution', 'Sim Histogram'])
pl.show()
if do_save:
cv.savefig('testprobEmperical.png')
pl.close()
layer_counts[date, layer_num] += sim.rescale_vec[date]
lockdown1 = [sc.readdate('2020-03-23'),sc.readdate('2020-05-31')]
lockdown2 = [sc.readdate('2020-11-05'),sc.readdate('2020-12-03')]
lockdown3 = [sc.readdate('2021-01-04'),sc.readdate('2021-02-08')]
labels = ['Household', 'School', 'Workplace', 'Community']
for l in range(n_layers):
ax.plot(sim.datevec, layer_counts[:,l], c=colors[l], lw=3, label=labels[l])
ax.axvspan(lockdown1[0], lockdown1[1], color='steelblue', alpha=0.2, lw=0)
ax.axvspan(lockdown2[0], lockdown2[1], color='steelblue', alpha=0.2, lw=0)
ax.axvspan(lockdown3[0], lockdown3[1], color='lightblue', alpha=0.2, lw=0)
sc.setylim(ax=ax)
sc.boxoff(ax=ax)
ax.set_ylabel('Transmissions per day')
ax.set_xlim([sc.readdate('2020-01-21'), sc.readdate('2021-03-01')])
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%y'))
datemarks = pl.array([sim.day('2020-02-01'), sim.day('2020-03-01'), sim.day('2020-04-01'), sim.day('2020-05-01'), sim.day('2020-06-01'),
sim.day('2020-07-01'), sim.day('2020-08-01'), sim.day('2020-09-01'), sim.day('2020-10-01'),
sim.day('2020-11-01'), sim.day('2020-12-01'), sim.day('2021-01-01'), sim.day('2021-02-01'), sim.day('2021-03-01')])
ax.set_xticks([sim.date(d, as_date=True) for d in datemarks])
ax.legend(frameon=False)
yl = ax.get_ylim()
labely = yl[1]*1.015
cv.savefig(f'{figsfolder}/fig_trans.png', dpi=100)
sc.toc(T)
def test_misc():
sc.heading('Testing miscellaneous functions')
sim_path = 'test_misc.sim'
json_path = 'test_misc.json'
gitinfo_path = 'test_misc.gitinfo'
fig_path = 'test_misc.png'
fig_comments = 'Test comment'
# Data loading
cv.load_data(csv_file)
cv.load_data(xlsx_file)
with pytest.raises(NotImplementedError):
cv.load_data('example_data.unsupported_extension')
with pytest.raises(ValueError):
cv.load_data(xlsx_file, columns=['missing_column'])
# Dates
d1 = cv.date('2020-04-04')
d2 = cv.date(sc.readdate('2020-04-04'))
ds = cv.date('2020-04-04', d2)
assert d1 == d2
assert d2 == ds[0]
with pytest.raises(ValueError):
cv.date([(2020, 4, 4)]) # Raises a TypeError which raises a ValueError
with pytest.raises(ValueError):
cv.date('Not a date')
cv.daydiff('2020-04-04')
# Run sim for more investigations
sim = cv.Sim(pop_size=500, verbose=0)
sim.run()
sim.plot(do_show=False)
# Saving and loading
cv.savefig(fig_path, comments=fig_comments)
cv.save(filename=sim_path, obj=sim)
cv.load(filename=sim_path)
# Version checks
cv.check_version('0.0.0') # Nonsense version
print('↑ Should complain about version')
with pytest.raises(ValueError):
cv.check_version('0.0.0', die=True)
# Git checks
cv.git_info(json_path)
cv.git_info(json_path, check=True)
# Poisson tests
c1 = 5
c2 = 8
for alternative in ['two-sided', 'larger', 'smaller']:
cv.poisson_test(c1, c2, alternative=alternative)
for method in ['score', 'wald', 'sqrt', 'exact-cond']:
cv.poisson_test(c1, c2, method=method)
with pytest.raises(ValueError):
cv.poisson_test(c1, c2, method='not a method')
# Test locations
for location in [None, 'viet-nam']:
cv.data.show_locations(location)
# Test versions
with pytest.raises(ValueError):
cv.check_save_version('1.3.2', die=True)
cv.check_save_version(cv.__version__,
filename=gitinfo_path,
comments='Test')
# Test PNG
try:
metadata = cv.get_png_metadata(fig_path, output=True)
assert metadata['Covasim version'] == cv.__version__
assert metadata['Covasim comments'] == fig_comments
except ImportError as E:
print(
f'Cannot test PNG function since pillow not installed ({str(E)}), skipping'
)
# Tidy up
remove_files(sim_path, json_path, fig_path, gitinfo_path)
return
def plot_attack_rate(date_of_file, cases, sens):
colors = ['lightseagreen', 'lightsteelblue', 'lightcoral']
name = 'Cases per 100k people during the\ntwo weeks prior to school reopening'
if '1.1' in cases[0]:
prev = 'by_cases_rising'
subtitle = '(Re > 1)'
else:
prev = 'by_cases_falling'
subtitle = '(Re < 1)'
label = inc_labels
if sens is not None:
prev = prev + '_' + sens
subtitle += f'\n ({sens_label[sens]})'
staff_by_case = []
students_by_case = []
for _, case in enumerate(cases):
df = outputs_df(date_of_file, case, sens)
scenario_strategies = df.columns[1:]
scenario_strategies = scenario_strategies.tolist()
scenario_strategies.remove('all_remote')
staff = []
students = []
num_staff = df[df['Unnamed: 0'] == 'num_staff']['as_normal'].values
num_staff += df[df['Unnamed: 0'] == 'num_teachers']['as_normal'].values
num_students = df[df['Unnamed: 0'] ==
'num_students']['as_normal'].values
for n, strategy in enumerate(scenario_strategies):
#num_staff = df[df['Unnamed: 0'] == 'num_staff'][strategy].values
#num_staff += df[df['Unnamed: 0'] == 'num_teachers'][strategy].values
num_staff_cases = df[df['Unnamed: 0'] ==
'num_staff_cases'][strategy].values
num_staff_cases += df[df['Unnamed: 0'] ==
'num_teacher_cases'][strategy].values
staff.append(100 * num_staff_cases[0] / num_staff[0])
#num_students = df[df['Unnamed: 0'] == 'num_students'][strategy].values
num_student_cases = df[df['Unnamed: 0'] ==
'num_student_cases'][strategy].values
students.append(100 * num_student_cases[0] / num_students[0])
staff = pd.DataFrame(staff).transpose()
staff_by_case.append(staff)
students = pd.DataFrame(students).transpose()
students_by_case.append(students)
x = np.arange(len(scenario_strategies))
width = [-.2, 0, .2]
width_text = [-.28, -.09, .12]
fig, axs = plt.subplots(nrows=2,
sharex=True,
sharey=False,
figsize=(13, 10)) # 13 x 9
fig.subplots_adjust(hspace=0.3, right=0.9, bottom=0.15)
#fig.suptitle(f'Predicted cumulative COVID-19 infection rate from Sep. 1 to Dec. 1 for people in schools', size=24, horizontalalignment='center')
for i, ax in enumerate(axs):
for j, case in enumerate(cases):
if i == 0:
ax.bar(x + width[j],
staff_by_case[j].values[0],
width=0.2,
label=label[case],
color=colors[j])
for h in range(len(x)):
ax.text(h + width_text[j],
0.5 + staff_by_case[j][h].values,
round(staff_by_case[j][h].values[0], 1),
fontsize=13)
ax.set_title('Teachers and staff',
size=20,
horizontalalignment='center')
else:
ax.bar(x + width[j],
students_by_case[j].values[0],
width=0.2,
label=label[case],
color=colors[j])
for h in range(len(x)):
ax.text(h + width_text[j],
0.5 + students_by_case[j][h].values,
round(students_by_case[j][h].values[0], 1),
fontsize=13)
ax.set_title('Students', size=20, horizontalalignment='center')
ax.set_ylabel('COVID-19 infection rate (%)', size=20)
ax.set_ylim([0, 27])
ax.set_xticks(x)
ax.set_xticklabels(strategy_labels_brief.values(), fontsize=17) # 14
if i == 0:
leg_i = ax.legend(fontsize=20, title=name)
leg_i.set_title(name, prop={'size': 20})
#ax.legend(fontsize=16, title=name)
cv.savefig(f'attack_rate_{prev}_{date_of_file}.png')
plt.savefig(f'attack_rate_{prev}_{date_of_file}.pdf')
msim.run(n_runs=20, par_args={'ncpus': 5})
msim.reduce()
msim.save(msimfile)
else:
msim = cv.load(msimfile)
#%% Plotting
for interv in msim.base_sim['interventions']:
interv.do_plot = False
to_plot = ['cum_diagnoses', 'new_diagnoses', 'cum_deaths', 'new_deaths']
fig_args = dict(figsize=(18, 18))
scatter_args = dict(alpha=0.3, marker='o')
dateformat = '%d %b'
fig = msim.plot(to_plot=to_plot,
n_cols=1,
fig_args=fig_args,
scatter_args=scatter_args,
dateformat=dateformat)
fit = msim.base_sim.compute_fit(weights={'cum_diagnoses': 1, 'cum_deaths': 1})
print('Average daily mismatch: ',
fit.mismatch / msim.base_sim['n_days'] / 2 * 100)
for ax in fig.axes:
ax.legend(['Model', '80% modeled interval', 'Data'], loc='upper left')
if do_save:
cv.savefig(figfile)
print('Done.')
def plot_dimensions(date_of_file, cases, sens):
name = 'Cases per 100k in last 14 days'
if '1.1' in cases[0]:
prev = 'by_cases_rising'
subtitle = '(Re > 1)'
else:
prev = 'by_cases_falling'
subtitle = '(Re < 1)'
label = inc_labels
if sens is not None:
prev = prev + '_' + sens
subtitle += f'\n ({sens_label[sens]})'
attack_rate_by_case = []
perc_school_days_lost_by_case = []
efficient_y_by_case = []
efficient_x_by_case = []
for _, case in enumerate(cases):
df = outputs_df(date_of_file, case, sens)
scenario_strategies = df.columns[1:]
scenario_strategies = scenario_strategies.tolist()
scenario_strategies.remove('all_remote')
perc_school_days_lost = []
# perc_school_days_lost = [0, 0, 60.76923077, 34.72494893, 59.94845764, 84.83944482]
# total = df[df['Unnamed: 0'] == 'num_staff']['as_normal'].values
# total += df[df['Unnamed: 0'] == 'num_teachers']['as_normal'].values
# total = df[df['Unnamed: 0'] == 'num_students']['as_normal'].values
attack_rate = []
efficient_y = []
efficient_x = []
weekend_days = df[df['Unnamed: 0'] ==
'school_days_lost']['with_screening'].values
total_school_days = df[df['Unnamed: 0'] ==
'student_school_days']['with_screening'].values
for n, strategy in enumerate(scenario_strategies):
total = df[df['Unnamed: 0'] == 'num_staff'][strategy].values
total += df[df['Unnamed: 0'] == 'num_teachers'][strategy].values
total += df[df['Unnamed: 0'] == 'num_students'][strategy].values
num_cases = df[df['Unnamed: 0'] ==
'num_staff_cases'][strategy].values
num_cases += df[df['Unnamed: 0'] ==
'num_teacher_cases'][strategy].values
num_cases += df[df['Unnamed: 0'] ==
'num_student_cases'][strategy].values
attack_rate.append(100 * num_cases[0] / total[0])
school_days_lost = df[df['Unnamed: 0'] ==
'school_days_lost'][strategy].values
if strategy != 'as_normal':
school_days_lost = school_days_lost[0] - weekend_days[0]
perc_school_days_lost.append(100 * school_days_lost /
total_school_days[0])
else:
perc_school_days_lost.append(100 * school_days_lost[0] /
total_school_days[0])
if strategy != 'with_hybrid_scheduling':
efficient_y.append(attack_rate[n])
efficient_x.append(perc_school_days_lost[n])
attack_rate = pd.DataFrame(attack_rate).transpose()
attack_rate_by_case.append(attack_rate)
efficient_y = pd.DataFrame(efficient_y).transpose()
efficient_y_by_case.append(efficient_y)
efficient_x = pd.DataFrame(efficient_x).transpose()
efficient_x_by_case.append(efficient_x)
perc_school_days_lost = pd.DataFrame(perc_school_days_lost).transpose()
perc_school_days_lost_by_case.append(perc_school_days_lost)
n_strategies = len(scenario_strategies)
size_min = 8
size_max = 50
num_sizes = len(cases)
intervals = (size_max - size_min) / num_sizes
sizes = np.arange(size_min, size_max, step=intervals).tolist()
left = 0.09
right = 0.62
bottom = 0.10
top = 0.95
fig = plt.figure(figsize=(13, 9))
fig.subplots_adjust(left=left, right=right, top=top, bottom=bottom)
ax = fig.add_subplot(111)
alpha = 0.67
fs = 24
for j, rate in enumerate(cases):
ax.plot(efficient_x_by_case[j].iloc[0, :].values,
efficient_y_by_case[j].iloc[0, :].values,
linewidth=3,
alpha=0.33,
color='grey',
linestyle='-')
for i in range(n_strategies):
ax.plot(
perc_school_days_lost_by_case[j][i],
attack_rate_by_case[j][i],
marker='o',
markersize=sizes[j],
alpha=alpha,
markerfacecolor=colors[i],
markeredgewidth=0,
)
ax.set_xlabel('Days of Distance Learning (% of Total School Days)',
fontsize=fs)
ax.set_ylabel('COVID-19 infection rate (%)',
fontsize=fs) # 'Within-School Attack Rate (%)'
# ax.set_ylim(0, 15)
# ax.set_xlim(0, 10)
ax.tick_params(labelsize=fs)
# Strategies Legend
ax_left = right + 0.04
ax_bottom = bottom + 0.02
ax_right = 0.95
ax_width = ax_right - ax_left
ax_height = (top - bottom) / 2.
ax_leg = fig.add_axes([ax_left + 0.01, ax_bottom, ax_width, ax_height])
for s, strat in enumerate(scenario_strategies):
ax_leg.plot(-5,
-5,
color=colors[s],
label=strategy_labels_brief2[strat])
leg = ax_leg.legend(loc='center', fontsize=fs) # loc=10,
leg.draw_frame(False)
ax_leg.axis('off')
# Mobility size legend
ax_bottom_2 = ax_bottom + ax_height + 0.0
ax_leg_2 = fig.add_axes([ax_left, ax_bottom_2, ax_width, ax_height])
ybase = 0.8
ytop = 1
yinterval = 0.7 * (ytop - ybase) / len(sizes)
for i in range(len(sizes)):
xi = 1
yi = ybase + yinterval * i
si = sizes[i]
ax_leg_2.plot(xi * 1.5,
yi,
linestyle=None,
marker='o',
markersize=si,
markerfacecolor='white',
markeredgecolor='black')
ax_leg_2.text(xi * 4,
yi, (label[cases[i]]),
verticalalignment='center',
fontsize=fs)
ax_leg_2.text(xi * 7 + 1.5,
0.95,
name,
horizontalalignment='center',
fontsize=fs)
ax_leg_2.text(xi * 7 + 0.3,
0.75,
'School reopening scenario',
horizontalalignment='center',
fontsize=fs)
ax_leg_2.axis('off')
ax_leg_2.set_xlim(left=0, right=20)
ax_leg_2.set_ylim(bottom=ybase * 0.9, top=ytop * 1.0)
#ax.set_title(f'Trade-Offs with School Reopening', fontsize=20)
cv.savefig(f'tradeoffs_{prev}_{date_of_file}.png')
plt.savefig(f'tradeoffs_{prev}_{date_of_file}.pdf')
palette='tab10')
###g.add_legend(fontsize=14)
g.set_titles(row_template="{row_name}", fontsize=24)
#xtl = g.axes[1,0].get_xticklabels()
#xtl = [l.get_text() for l in xtl]
#g.set(xticklabels=[scen_names[k] if k in scen_names else k for k in xtl])
g.set_axis_labels(y_var="Days lost (%)")
plt.tight_layout()
for ax in g.axes.flat:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.7, box.height])
g.axes.flat[0].legend(loc='upper left', bbox_to_anchor=(1, 0.3))
cv.savefig(os.path.join(imgdir, '3mInPersonDaysLost_sensitivity.png'), dpi=300)
# Attack rate
d = pd.melt(df,
id_vars=['key1', 'key2', 'key3'],
value_vars=[f'attackrate_{gkey}' for gkey in grp_dict.keys()],
var_name='Group',
value_name='Cum Inc (%)')
d.replace({'Group': {f'attackrate_{gkey}': gkey
for gkey in grp_dict.keys()}},
inplace=True)
d = d.loc[d['key1'] == 'k5'] # K-5 only
g = sns.FacetGrid(data=d,
row='Group',
height=4,
aspect=3,
ax[pn] = pl.axes([
xgapl + (dx + xgapm) * (pn % ncols),
ygapb + (ygapm + dy) * (pn // ncols), dx, dy
])
ax[pn] = sns.heatmap(dfs[res][scen],
xticklabels=4,
yticklabels=4,
cmap=sns.cm.rocket_r,
vmin=0,
vmax=cbar_lims[res],
cbar=pn == 0,
cbar_ax=None if pn else cbar_ax,
cbar_kws={'label': label})
ax[pn].set_ylim(ax[pn].get_ylim()[::-1])
if (pn % ncols) != 0:
ax[pn].set_yticklabels([])
else:
ax[pn].set_ylabel('Symptomatic testing')
ax[pn].set_yticklabels(
[f'{int(i*100)}%' for i in np.linspace(0, 1, 6)], rotation=0)
if pn not in range(ncols):
ax[pn].set_xticklabels([])
else:
ax[pn].set_xlabel('% of contacts traced')
ax[pn].set_xticklabels(
[f'{int(i * 100)}%' for i in np.linspace(0, 1, 6)])
cv.savefig(f'{figsfolder}/fig_sweeps_{res}.png', dpi=100)
sc.toc(T)
