def test_plotting():
torun = [
'hex2rgb',
'arraycolors',
'gridcolors',
'surf3d',
'bar3d',
]
if 'hex2rgb' in torun:
c1 = sc.hex2rgb('#fff')
c2 = sc.hex2rgb('fabcd8')
print(c1)
print(c2)
if 'arraycolors' in torun:
n = 1000
ncols = 5
arr = pl.rand(n, ncols)
for c in range(ncols):
arr[:, c] += c
x = pl.rand(n)
y = pl.rand(n)
colors = sc.arraycolors(arr)
if doplot:
pl.figure(figsize=(20, 16))
for c in range(ncols):
pl.scatter(x + c, y, s=50, c=colors[:, c])
if 'gridcolors' in torun:
colors_a = sc.gridcolors(ncolors=8, demo=doplot)
colors_b = sc.gridcolors(ncolors=18, demo=doplot)
colors_c = sc.gridcolors(ncolors=28, demo=doplot)
print('\n8 colors:', colors_a)
print('\n18 colors:', colors_b)
print('\n28 colors:', colors_c)
if 'surf3d' in torun:
data = pl.randn(50, 50)
smoothdata = sc.smooth(data, 20)
if doplot:
sc.surf3d(smoothdata)
if 'bar3d' in torun:
data = pl.rand(20, 20)
smoothdata = sc.smooth(data)
if doplot:
sc.bar3d(smoothdata)
return pl.gcf()
def plot_scens(to_plot=None, scens=None, do_save=None, fig_path=None, fig_args=None, plot_args=None,
scatter_args=None, axis_args=None, fill_args=None, legend_args=None, date_args=None,
show_args=None, mpl_args=None, n_cols=None, grid=False, commaticks=True, setylim=True,
log_scale=False, colors=None, labels=None, do_show=None, sep_figs=False, fig=None, ax=None, **kwargs):
''' Plot the results of a scenario -- see Scenarios.plot() for documentation '''
# Handle inputs
args = handle_args(fig_args=fig_args, plot_args=plot_args, scatter_args=scatter_args, axis_args=axis_args, fill_args=fill_args,
legend_args=legend_args, show_args=show_args, date_args=date_args, mpl_args=mpl_args, **kwargs)
to_plot, n_cols, n_rows = handle_to_plot('scens', to_plot, n_cols, sim=scens.base_sim, check_ready=False) # Since this sim isn't run
fig, figs = create_figs(args, sep_figs, fig, ax)
# Do the plotting
default_colors = sc.gridcolors(ncolors=len(scens.sims))
for pnum,title,reskeys in to_plot.enumitems():
ax = create_subplots(figs, fig, ax, n_rows, n_cols, pnum, args.fig, sep_figs, log_scale, title)
reskeys = sc.promotetolist(reskeys) # In case it's a string
for reskey in reskeys:
resdata = scens.results[reskey]
for snum,scenkey,scendata in resdata.enumitems():
sim = scens.sims[scenkey][0] # Pull out the first sim in the list for this scenario
strain_keys = sim.result_keys('strain')
if reskey in strain_keys:
ns = sim['n_strains']
strain_colors = sc.gridcolors(ns)
for strain in range(ns):
res_y = scendata.best[strain,:]
color = strain_colors[strain] # Choose the color
label = 'wild type' if strain == 0 else sim['strains'][strain - 1].label
ax.fill_between(scens.tvec, scendata.low[strain,:], scendata.high[strain,:], color=color, **args.fill) # Create the uncertainty bound
ax.plot(scens.tvec, res_y, label=label, c=color, **args.plot) # Plot the actual line
if args.show['data']:
plot_data(sim, ax, reskey, args.scatter, color=color) # Plot the data
else:
res_y = scendata.best
color = set_line_options(colors, scenkey, snum, default_colors[snum]) # Choose the color
label = set_line_options(labels, scenkey, snum, scendata.name) # Choose the label
ax.fill_between(scens.tvec, scendata.low, scendata.high, color=color, **args.fill) # Create the uncertainty bound
ax.plot(scens.tvec, res_y, label=label, c=color, **args.plot) # Plot the actual line
if args.show['data']:
plot_data(sim, ax, reskey, args.scatter, color=color) # Plot the data
if args.show['interventions']:
plot_interventions(sim, ax) # Plot the interventions
if args.show['ticks']:
reset_ticks(ax, sim, args.date) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
if args.show['legend']:
title_grid_legend(ax, title, grid, commaticks, setylim, args.legend, pnum==0) # Configure the title, grid, and legend -- only show legend for first
return tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show, args)
def plot_cascade(self, vertical=True):
if vertical:
fig_size = (12, 12)
ax_size = [0.45, 0.05, 0.5, 0.9]
else:
fig_size = (16, 8)
ax_size = [0.05, 0.45, 0.9, 0.5]
df = sc.dcp(self.data)
cutoff = 200e3
fig = pl.figure(figsize=fig_size)
df.sort(col='icer', reverse=False)
DA_data = hp.arr(df['opt_spend'])
inds = sc.findinds(DA_data > cutoff)
DA_data = DA_data[inds]
DA_data /= 1e6
DA_labels = df['shortname'][inds]
npts = len(DA_data)
colors = sc.gridcolors(npts, limits=(0.25, 0.75))
x = np.arange(len(DA_data))
pl.axes(ax_size)
for pt in range(npts):
loc = x[pt:]
this = DA_data[pt]
start = sum(DA_data[:pt])
prop = 0.9
color = colors[pt]
amount = sum(DA_data[:pt + 1])
amountstr = '%0.1f' % amount
if vertical:
pl.barh(loc, width=this, left=start, height=prop, color=color)
pl.text(amount,
x[pt],
amountstr,
verticalalignment='center',
color=colors[pt])
else:
pl.bar(loc, height=this, bottom=start, width=prop, color=color)
pl.text(x[pt],
amount + 1,
amountstr,
horizontalalignment='center',
color=colors[pt])
if vertical:
pl.xlabel('Spending for optimized investment cascade')
pl.gca().set_yticks(x)
ticklabels = pl.gca().set_yticklabels(DA_labels)
else:
pl.ylabel('Optimized investment cascade')
pl.gca().set_xticks(x)
ticklabels = pl.gca().set_xticklabels(DA_labels, rotation=90)
for t, tl in enumerate(ticklabels):
tl.set_color(colors[t])
pl.gca().set_facecolor('none')
pl.title('Investment cascade')
return fig
def plot_sim(to_plot=None, sim=None, do_save=None, fig_path=None, fig_args=None, plot_args=None,
scatter_args=None, axis_args=None, fill_args=None, legend_args=None, date_args=None,
show_args=None, mpl_args=None, n_cols=None, grid=False, commaticks=True,
setylim=True, log_scale=False, colors=None, labels=None, do_show=None, sep_figs=False,
fig=None, ax=None, **kwargs):
''' Plot the results of a single simulation -- see Sim.plot() for documentation '''
# Handle inputs
args = handle_args(fig_args=fig_args, plot_args=plot_args, scatter_args=scatter_args, axis_args=axis_args, fill_args=fill_args,
legend_args=legend_args, show_args=show_args, date_args=date_args, mpl_args=mpl_args, **kwargs)
to_plot, n_cols, n_rows = handle_to_plot('sim', to_plot, n_cols, sim=sim)
fig, figs = create_figs(args, sep_figs, fig, ax)
# Do the plotting
strain_keys = sim.result_keys('strain')
for pnum,title,keylabels in to_plot.enumitems():
ax = create_subplots(figs, fig, ax, n_rows, n_cols, pnum, args.fig, sep_figs, log_scale, title)
for resnum,reskey in enumerate(keylabels):
res_t = sim.results['t']
if reskey in strain_keys:
res = sim.results['strain'][reskey]
ns = sim['n_strains']
strain_colors = sc.gridcolors(ns)
for strain in range(ns):
color = strain_colors[strain] # Choose the color
label = 'wild type' if strain == 0 else sim['strains'][strain-1].label
if res.low is not None and res.high is not None:
ax.fill_between(res_t, res.low[strain,:], res.high[strain,:], color=color, **args.fill) # Create the uncertainty bound
ax.plot(res_t, res.values[strain,:], label=label, **args.plot, c=color) # Actually plot the sim!
else:
res = sim.results[reskey]
color = set_line_options(colors, reskey, resnum, res.color) # Choose the color
label = set_line_options(labels, reskey, resnum, res.name) # Choose the label
if res.low is not None and res.high is not None:
ax.fill_between(res_t, res.low, res.high, color=color, **args.fill) # Create the uncertainty bound
ax.plot(res_t, res.values, label=label, **args.plot, c=color) # Actually plot the sim!
if args.show['data']:
plot_data(sim, ax, reskey, args.scatter, color=color) # Plot the data
if args.show['ticks']:
reset_ticks(ax, sim, args.date) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
if args.show['interventions']:
plot_interventions(sim, ax) # Plot the interventions
if args.show['legend']:
title_grid_legend(ax, title, grid, commaticks, setylim, args.legend) # Configure the title, grid, and legend
return tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show, args)
def plot_result(self, key, colors=None, labels=None, *args, **kwargs):
''' Convenience method for plotting -- arguments passed to Sim.plot_result() '''
if self.which in ['combined', 'reduced']:
fig = self.base_sim.plot_result(key, *args, **kwargs)
else:
fig = None
if colors is None:
colors = sc.gridcolors(len(self))
if labels is None:
labels = [sim.label for sim in self.sims]
orig_setylim = kwargs.get('setylim', True)
for s,sim in enumerate(self.sims):
if s == len(self.sims)-1:
kwargs['setylim'] = orig_setylim
else:
kwargs['setylim'] = False
fig = sim.plot_result(key=key, fig=fig, color=colors[s], label=labels[s], *args, **kwargs)
return fig
def plot_scens(scens, data_check=None, dday=None, test_data=None, to_plot=None, do_save=None, fig_path=None, fig_args=None, plot_args=None,
scatter_args=None, axis_args=None, fill_args=None, legend_args=None, show_args=None,
as_dates=True, dateformat=None, interval=None, n_cols=None, font_size=18, font_family=None,
grid=False, commaticks=True, setylim=True, log_scale=False, colors=None, labels=None,
do_show=True, sep_figs=False, fig=None):
''' Plot the results of a scenario -- see Scenarios.plot() for documentation '''
# Handle inputs
args = handle_args(fig_args, plot_args, scatter_args, axis_args, fill_args, legend_args)
to_plot, n_cols, n_rows = handle_to_plot('scens', to_plot, n_cols, sim=scens.base_sim)
fig, figs, ax = create_figs(args, font_size, font_family, sep_figs, fig)
was_plot=False
# Do the plotting
default_colors = sc.gridcolors(ncolors=len(scens.sims))
for pnum,title,reskeys in to_plot.enumitems():
ax = create_subplots(figs, fig, ax, n_rows, n_cols, pnum, args.fig, sep_figs, log_scale, title)
reskeys = sc.promotetolist(reskeys) # In case it's a string
for reskey in reskeys:
resdata = scens.results[reskey]
for snum,scenkey,scendata in resdata.enumitems():
sim = scens.sims[scenkey][0] # Pull out the first sim in the list for this scenario
res_y = scendata.best
color = set_line_options(colors, scenkey, snum, default_colors[snum]) # Choose the color
label = set_line_options(labels, scenkey, snum, scendata.name) # Choose the label
if dday is not None:
ax.fill_between(scens.tvec[-dday:], smooth(scendata.low)[-dday:], smooth(scendata.high)[-dday:], color=color, **args.fill) # Create the uncertainty bound
ax.plot(scens.tvec[-dday:], smooth(res_y)[-dday:], label=label, c=color, **args.plot) # Plot the actual line
else:
ax.fill_between(scens.tvec, smooth(scendata.low), smooth(scendata.high), color=color, **args.fill) # Create the uncertainty bound
ax.plot(scens.tvec, smooth(res_y), label=label, c=color, **args.plot)
if data_check is not None and not was_plot:
ax.scatter(range(data_check.index.size), data_check[reskey], label='Data')
was_plot=True
elif data_check is None:
if args.show['data']:
plot_data(sim, ax, reskey, args.scatter, dday=dday, test_data=test_data, color=color) # Plot the data
if args.show['interventions']:
plot_interventions(sim, ax) # Plot the interventions
if args.show['ticks']:
reset_ticks(ax, sim, interval, as_dates, dateformat) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
if args.show['legend']:
title_grid_legend(ax, title, grid, commaticks, setylim, args.legend, pnum==0) # Configure the title, grid, and legend -- only show legend for first
return tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show)
def plot_scens(scens, to_plot=None, do_save=None, fig_path=None, fig_args=None, plot_args=None,
scatter_args=None, axis_args=None, fill_args=None, legend_args=None, as_dates=True, dateformat=None,
interval=None, n_cols=1, font_size=18, font_family=None, grid=False, commaticks=True, setylim=True,
log_scale=False, colors=None, labels=None, do_show=True, sep_figs=False, fig=None):
''' Plot the results of a scenario -- see Scenarios.plot() for documentation '''
# Handle inputs
args = handle_args(fig_args, plot_args, scatter_args, axis_args, fill_args, legend_args)
to_plot, n_rows = handle_to_plot('scens', to_plot, n_cols, sim=scens.base_sim)
fig, figs, ax = create_figs(args, font_size, font_family, sep_figs, fig)
# Do the plotting
default_colors = sc.gridcolors(ncolors=len(scens.sims))
for pnum,title,reskeys in to_plot.enumitems():
ax = create_subplots(figs, fig, ax, n_rows, n_cols, pnum, args.fig, sep_figs, log_scale, title)
reskeys = sc.promotetolist(reskeys) # In case it's a string
for reskey in reskeys:
resdata = scens.results[reskey]
for snum,scenkey,scendata in resdata.enumitems():
sim = scens.sims[scenkey][0] # Pull out the first sim in the list for this scenario
res_y = scendata.best
if colors is not None:
color = colors[scenkey]
else:
color = default_colors[snum]
if labels is not None:
label = labels[scenkey]
else:
label = scendata.name
ax.fill_between(scens.tvec, scendata.low, scendata.high, color=color, **args.fill) # Create the uncertainty bound
ax.plot(scens.tvec, res_y, label=label, c=color, **args.plot) # Plot the actual line
plot_data(sim, reskey, args.scatter) # Plot the data
plot_interventions(sim, ax) # Plot the interventions
reset_ticks(ax, sim, interval, as_dates) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
title_grid_legend(ax, title, grid, commaticks, setylim, args.legend, pnum==0) # Configure the title, grid, and legend -- only show legend for first
return tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show, default_name='covasim_scenarios.png')
def makepackage(self,
burdenset=None,
intervset=None,
frpwt=None,
equitywt=None,
verbose=True,
die=False):
''' Make results '''
# Handle inputs
if burdenset is not None:
self.burdenset = burdenset # Warning, name is used both as key and actual set!
if intervset is not None: self.intervset = intervset
if frpwt is None: frpwt = 0.25
if equitywt is None: equitywt = 0.25
self.frpwt = frpwt
self.equitywt = equitywt
burdenset = self.projectref().burden(key=self.burdenset)
intervset = self.projectref().interv(key=self.intervset)
intervset.parse() # Ensure it's parsed
colnames = intervset.colnames
# Create new dataframe
origdata = sc.dcp(intervset.data)
critical_cols = [
'active', 'shortname', 'unitcost', 'spend', 'icer', 'frp', 'equity'
]
df = sc.dataframe()
for col in critical_cols: # Copy columns over
df[col] = sc.dcp(origdata[colnames[col]])
df['parsedbc'] = sc.dcp(origdata['parsedbc']) # Since not named
df.filter_out(key=0, col='active', verbose=True)
# Calculate people covered (spending/unitcost)
df['coverage'] = hp.arr(
df['spend']) / (self.eps + hp.arr(df['unitcost']))
# Pull out DALYS and prevalence
df.addcol('total_dalys',
value=0) # Value=0 by default, but just to be explicit
df.addcol('max_dalys', value=0)
df.addcol('total_prevalence', value=0)
df.addcol('dalys_averted', value=0)
notfound = []
lasterror = None
for r in range(df.nrows):
theseburdencovs = df['parsedbc', r]
for burdencov in theseburdencovs:
key = burdencov[0]
val = burdencov[1] # WARNING, add validation here
try:
thisburden = burdenset.data.findrow(
key=key,
col=burdenset.colnames['cause'],
asdict=True,
die=True)
df['total_dalys',
r] += thisburden[burdenset.colnames['dalys']]
df['max_dalys',
r] += thisburden[burdenset.colnames['dalys']] * val
df['total_prevalence',
r] += thisburden[burdenset.colnames['prevalence']]
except Exception as E:
lasterror = E # Stupid Python 3
print('HIIII %s' % str(E))
print(type(df['total_dalys', r]))
print(type(df['max_dalys', r]))
print(type(df['total_prevalence', r]))
print(type(thisburden[burdenset.colnames['dalys']]))
print(type(thisburden[burdenset.colnames['prevalence']]))
notfound.append(key)
# Validation
if len(notfound):
errormsg = 'The following burden(s) were not found: "%s"\nError:\n%s' % (
notfound, str(lasterror))
raise hp.HPException(errormsg)
invalid = []
for r in range(df.nrows):
df['dalys_averted',
r] = df['spend', r] / (self.eps + df['icer', r])
if df['dalys_averted', r] > df['max_dalys', r]:
errormsg = 'Data input error: DALYs averted for "%s" greater than total DALYs (%0.0f vs. %0.0f); please reduce total spending, increase ICER, increase DALYs, or increase max coverage' % (
df['shortname', r], df['dalys_averted', r], df['max_dalys',
r])
df['dalys_averted', r] = df[
'max_dalys',
r] # WARNING, reset to maximum rather than give error if die=False
invalid.append(errormsg)
if len(invalid):
errors = '\n\n'.join(invalid)
if die: raise Exception(errors)
else: print(errors)
# To populate with optimization results and fixed spending
self.budget = hp.arr(df['spend']).sum()
df.addcol('opt_spend')
df.addcol('opt_dalys_averted')
df.addcol('fixed')
# Store colors
nintervs = df.nrows
colors = sc.gridcolors(nintervs + 2,
asarray=True)[2:] # Skip black and white
colordict = sc.odict()
for c, name in enumerate(df['shortname']):
colordict[name] = colors[c]
self.colordict = colordict
self.data = df # Store it
if verbose:
print(
'Health package %s recalculated from burdenset=%s and intervset=%s'
% (self.name, self.burdenset, self.intervset))
return None
def plot(self,
to_plot=None,
dday=None,
inds=None,
plot_sims=False,
color_by_sim=None,
max_sims=5,
colors=None,
labels=None,
alpha_range=None,
plot_args=None,
show_args=None,
**kwargs):
'''
Plot all the sims -- arguments passed to Sim.plot(). The
behavior depends on whether or not combine() or reduce() has been called.
If so, this function by default plots only the combined/reduced sim (which
you can override with plot_sims=True). Otherwise, it plots a separate line
for each sim.
Note that this function is complex because it aims to capture the flexibility
of both sim.plot() and scens.plot(). By default, if combine() or reduce()
has been used, it will resemble sim.plot(); otherwise, it will resemble
scens.plot(). This can be changed via color_by_sim, together with the
other options.
Args:
to_plot (list) : list or dict of which results to plot; see cv.get_sim_plots() for structure
inds (list) : if not combined or reduced, the indices of the simulations to plot (if None, plot all)
plot_sims (bool) : whether to plot individual sims, even if combine() or reduce() has been used
color_by_sim (bool) : if True, set colors based on the simulation type; otherwise, color by result type; True implies a scenario-style plotting, False implies sim-style plotting
max_sims (int) : maximum number of sims to use with color-by-sim; can be overriden by other options
colors (list) : if supplied, override default colors for color_by_sim
labels (list) : if supplied, override default labels for color_by_sim
alpha_range (list) : a 2-element list/tuple/array providing the range of alpha values to use to distinguish the lines
plot_args (dict) : passed to sim.plot()
show_args (dict) : passed to sim.plot()
kwargs (dict) : passed to sim.plot()
**Examples**::
sim = cv.Sim()
msim = cv.MultiSim(sim)
msim.run()
msim.plot() # Plots individual sims
msim.reduce()
msim.plot() # Plots the combined sim
'''
# Plot a single curve, possibly with a range
if not plot_sims and self.which in ['combined', 'reduced']:
fig = self.base_sim.plot(to_plot=to_plot,
dday=dday,
colors=colors,
**kwargs)
# PLot individual sims on top of each other
else:
# Initialize
fig = kwargs.pop('fig', None)
orig_show = kwargs.get('do_show', True)
orig_setylim = kwargs.get('setylim', True)
kwargs['legend_args'] = sc.mergedicts(
{'show_legend': True}, kwargs.get(
'legend_args')) # Only plot the legend the first time
# Handle indices
if inds is None:
inds = np.arange(len(self.sims))
n_sims = len(inds)
# Handle what style of plotting to use:
if color_by_sim is None:
if n_sims <= max_sims:
color_by_sim = True
else:
color_by_sim = False
# Handle what to plot
if to_plot is None:
if color_by_sim:
to_plot = cvd.get_scen_plots()
else:
to_plot = cvd.get_sim_plots()
# Handle colors
if colors is None:
if color_by_sim:
colors = sc.gridcolors(ncolors=n_sims)
else:
colors = [None] * n_sims # So we can iterate over it
else:
colors = [colors] * n_sims # Again, for iteration
# Handle alpha if not using colors
if alpha_range is None:
if color_by_sim:
alpha_range = [
0.8, 0.8
] # We're using color to distinguish sims, so don't need alpha
else:
alpha_range = [0.8, 0.3
] # We're using alpha to distinguish sims
alphas = np.linspace(alpha_range[0], alpha_range[1], n_sims)
# Plot
for s, ind in enumerate(inds):
sim = self.sims[ind]
final_plot = (s == n_sims - 1
) # Check if this is the final plot
# Handle the legend and labels
if final_plot:
merged_show_args = show_args
kwargs['do_show'] = orig_show
kwargs['setylim'] = orig_setylim
else:
merged_show_args = False # Only show things like data the last time it's plotting
kwargs[
'do_show'] = False # On top of that, don't show the plot at all unless it's the last time
kwargs['setylim'] = False
# Optionally set the label for the first max_sims sims
if color_by_sim is True and s < max_sims:
if labels is None:
merged_labels = sim.label
else:
merged_labels = labels[s]
elif final_plot and not color_by_sim:
merged_labels = labels
else:
merged_labels = ''
# Actually plot
merged_plot_args = sc.mergedicts(
{'alpha': alphas[s]},
plot_args) # Need a new variable to avoid overwriting
fig = sim.plot(fig=fig,
to_plot=to_plot,
colors=colors[s],
labels=merged_labels,
plot_args=merged_plot_args,
show_args=merged_show_args,
**kwargs)
return fig
def test_gof(doplot=False):
n = 100
noise1 = 0.1
noise2 = 0.5
noise3 = 2
normalize_data = True
normalize = True
ylim = True
minval = 0.1
subtract_min = True
true_unif = pl.rand(n)
true_normal = pl.randn(n) + 5
pairs = sc.objdict({
'uniform_low': [true_unif,
true_unif + noise1*pl.rand(n)],
'uniform_med': [true_unif,
true_unif + noise2*pl.rand(n)],
'uniform_high': [true_unif,
true_unif + noise3*pl.rand(n)],
'uniform_mul': [true_unif,
true_unif * 2*pl.rand(n)],
'normal': [true_normal,
true_normal + noise2*pl.randn(n)],
})
# Remove any DC offset, while ensuring it doesn't go negative
for pairkey,pair in pairs.items():
actual = sc.dcp(pair[0])
predicted = sc.dcp(pair[1])
if subtract_min:
minmin = min(actual.min(), predicted.min())
actual += minval - minmin
predicted += minval - minmin
if normalize_data:
a_mean = actual.mean()
p_mean = predicted.mean()
if a_mean > p_mean:
predicted += a_mean - p_mean
elif a_mean < p_mean:
actual += p_mean - a_mean
pairs[pairkey][0] = actual
pairs[pairkey][1] = predicted
for pair in pairs.values():
print(pair[0].mean())
print(pair[1].mean())
methods = [
'mean fractional',
'mean absolute',
'median fractional',
'median absolute',
'max_error',
'mean_absolute_error',
'mean_squared_error',
'mean_squared_error',
'mean_squared_log_error',
'median_absolute_error',
# 'r2_score',
'mean_poisson_deviance',
'mean_gamma_deviance',
]
npairs = len(pairs)
nmethods = len(methods)
results = pl.zeros((npairs, nmethods))
for m,method in enumerate(methods):
print(f'\nWorking on method {method}:')
for p,pairkey,pair in pairs.enumitems():
print(f' Working on data {pairkey}')
actual = pair[0]
predicted = pair[1]
try:
results[p,m] = pe.gof(actual, predicted, estimator=method)
except Exception as E:
print(' '*10 + f'Failed: {str(E)}')
if pl.isnan(results[p,m]):
print(' '*10 + f'Returned NaN: {method}')
if normalize:
results = results/results.max(axis=0)
if doplot:
pl.figure(figsize=hifigsize)
pl.subplots_adjust(**axis_args)
colors = sc.gridcolors(nmethods)
for p,pairkey,pair in pairs.enumitems():
lastrow = (p == npairs-1)
pl.subplot(npairs, 2, p*2+1)
actual = pair[0]
predicted = pair[1]
pl.scatter(actual, predicted)
pl.title(f"Data for {pairkey}")
pl.ylabel('Predicted')
if lastrow:
pl.xlabel('Actual')
pl.subplot(npairs, 2, p*2+2)
for m,method in enumerate(methods):
pl.bar(m, results[p,m], facecolor=colors[m], label=f'{m}={method}')
pl.gca().set_xticks(pl.arange(m+1))
pl.ylabel('Goodness of fit')
pl.title(f"Normalized GOF for {pairkey}")
if lastrow:
pl.xlabel('Estimator')
pl.legend()
if ylim:
pl.ylim([0,1])
return results
def plot():
# Create the figure
fig = pl.figure(num='Fig. 1: Calibration', figsize=(24, 20))
tx1, ty1 = 0.005, 0.97
tx2, ty2 = 0.545, 0.66
ty3 = 0.34
fsize = 40
pl.figtext(tx1, ty1, 'a', fontsize=fsize)
pl.figtext(tx1, ty2, 'b', fontsize=fsize)
pl.figtext(tx2, ty1, 'c', fontsize=fsize)
pl.figtext(tx2, ty2, 'd', fontsize=fsize)
pl.figtext(tx1, ty3, 'e', fontsize=fsize)
pl.figtext(tx2, ty3, 'f', fontsize=fsize)
#%% Fig. 1A: diagnoses
x0, y0, dx, dy = 0.055, 0.73, 0.47, 0.24
ax1 = pl.axes([x0, y0, dx, dy])
format_ax(ax1, base_sim)
plotter('cum_diagnoses',
sims,
ax1,
calib=True,
label='Model',
ylabel='Cumulative diagnoses')
pl.legend(loc='lower right', frameon=False)
#%% Fig. 1B: deaths
y0b = 0.42
ax2 = pl.axes([x0, y0b, dx, dy])
format_ax(ax2, base_sim)
plotter('cum_deaths',
sims,
ax2,
calib=True,
label='Model',
ylabel='Cumulative deaths')
pl.legend(loc='lower right', frameon=False)
#%% Fig. 1A-B inserts (histograms)
agehists = []
for s, sim in enumerate(sims):
agehist = sim['analyzers'][0]
if s == 0:
age_data = agehist.data
agehists.append(agehist.hists[-1])
# Observed data
x = age_data['age'].values
pos = age_data['cum_diagnoses'].values
death = age_data['cum_deaths'].values
# Model outputs
mposlist = []
mdeathlist = []
for hists in agehists:
mposlist.append(hists['diagnosed'])
mdeathlist.append(hists['dead'])
mposarr = np.array(mposlist)
mdeatharr = np.array(mdeathlist)
low_q = 0.025
high_q = 0.975
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)
w = 4
off = 2
# Insets
x0s, y0s, dxs, dys = 0.11, 0.84, 0.17, 0.13
ax1s = pl.axes([x0s, y0s, dxs, dys])
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')
ax1s.set_xticks(np.arange(0, 81, 20))
pl.xlabel('Age')
pl.ylabel('Cases')
sc.boxoff(ax1s)
pl.legend(frameon=False, bbox_to_anchor=(0.7, 1.1))
y0sb = 0.53
ax2s = pl.axes([x0s, y0sb, dxs, dys])
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')
ax2s.set_xticks(np.arange(0, 81, 20))
pl.xlabel('Age')
pl.ylabel('Deaths')
sc.boxoff(ax2s)
pl.legend(frameon=False)
sc.boxoff(ax1s)
#%% Fig. 1C: infections
x0, dx = 0.60, 0.38
ax3 = pl.axes([x0, y0, dx, dy])
format_ax(ax3, sim)
# Plot SCAN data
pop_size = 2.25e6
scan = pd.read_csv(scan_file)
for i, r in scan.iterrows():
label = "Data" if i == 0 else None
ts = np.mean(sim.day(r['since'], r['to']))
low = r['lower'] * pop_size
high = r['upper'] * pop_size
mean = r['mean'] * pop_size
ax3.plot([ts] * 2, [low, high], alpha=1.0, color='k', zorder=1000)
ax3.plot(ts,
mean,
'o',
markersize=7,
color='k',
alpha=0.5,
label=label,
zorder=1000)
# Plot simulation
plotter('cum_infections',
sims,
ax3,
calib=True,
label='Cumulative\ninfections\n(modeled)',
ylabel='Infections')
plotter('n_infectious',
sims,
ax3,
calib=True,
label='Active\ninfections\n(modeled)',
ylabel='Infections',
flabel=False)
pl.legend(loc='upper left', frameon=False)
pl.ylim([0, 130e3])
plot_intervs(sim)
#%% Fig. 1C: R_eff
ax4 = pl.axes([x0, y0b, dx, dy])
format_ax(ax4, sim, key='r_eff')
plotter('r_eff',
sims,
ax4,
calib=True,
label='$R_{eff}$ (modeled)',
ylabel=r'Effective reproduction number')
pl.axhline(1, linestyle='--', lw=3, c='k', alpha=0.5)
pl.legend(loc='upper right', frameon=False)
plot_intervs(sim)
#%% Fig. 1E
# Do the plotting
pl.subplots_adjust(left=0.04,
right=0.52,
bottom=0.03,
top=0.35,
wspace=0.12,
hspace=0.50)
for i, k in enumerate(keys):
eax = pl.subplot(2, 2, i + 1)
c1 = [0.2, 0.5, 0.8]
c2 = [1.0, 0.5, 0.0]
c3 = [0.1, 0.6, 0.1]
sns.kdeplot(df1[k],
shade=1,
linewidth=3,
label='',
color=c1,
alpha=0.5)
sns.kdeplot(df2[k],
shade=0,
linewidth=3,
label='',
color=c2,
alpha=0.5)
pl.title(mapping[k])
pl.xlabel('')
pl.yticks([])
if not i % 4:
pl.ylabel('Density')
yfactor = 1.3
yl = pl.ylim()
pl.ylim([yl[0], yl[1] * yfactor])
m1 = np.median(df1[k])
m2 = np.median(df2[k])
m1std = df1[k].std()
m2std = df2[k].std()
pl.axvline(m1, c=c1, ymax=0.9, lw=3, linestyle='--')
pl.axvline(m2, c=c2, ymax=0.9, lw=3, linestyle='--')
def fmt(lab, val, std=-1):
if val < 0.1:
valstr = f'{lab} = {val:0.4f}'
elif val < 1.0:
valstr = f'{lab} = {val:0.2f}±{std:0.2f}'
else:
valstr = f'{lab} = {val:0.1f}±{std:0.1f}'
if std < 0:
valstr = valstr.split('±')[0] # Discard STD if not used
return valstr
if k.startswith('bc'):
pl.xlim([0, 100])
elif k == 'beta':
pl.xlim([3, 5])
elif k.startswith('tn'):
pl.xlim([0, 50])
else:
raise Exception(f'Please assign key {k}')
xl = pl.xlim()
xfmap = dict(
beta=0.15,
bc_wc1=0.30,
bc_lf=0.35,
tn=0.55,
)
xf = xfmap[k]
x0 = xl[0] + xf * (xl[1] - xl[0])
ypos1 = yl[1] * 0.97
ypos2 = yl[1] * 0.77
ypos3 = yl[1] * 0.57
if k == 'beta': # Use 2 s.f. instead of 1
pl.text(x0, ypos1, f'M: {m1:0.2f} ± {m1std:0.2f}', c=c1)
pl.text(x0, ypos2, f'N: {m2:0.2f} ± {m2std:0.2f}', c=c2)
pl.text(x0,
ypos3,
rf'$\Delta$: {(m2-m1):0.2f} ± {(m1std+m2std):0.2f}',
c=c3)
else:
pl.text(x0, ypos1, f'M: {m1:0.1f} ± {m1std:0.1f}', c=c1)
pl.text(x0, ypos2, f'N: {m2:0.1f} ± {m2std:0.1f}', c=c2)
pl.text(x0,
ypos3,
rf'$\Delta$: {(m2-m1):0.1f} ± {(m1std+m2std):0.1f}',
c=c3)
sc.boxoff(ax=eax)
#%% Fig. 1F: SafeGraph
x0, y0c, dyc = 0.60, 0.03, 0.30
ax5 = pl.axes([x0, y0c, dx, dyc])
format_ax(ax5, sim, key='r_eff')
fn = safegraph_file
df = pd.read_csv(fn)
week = df['week']
days = sim.day(week.values.tolist())
s = df['p.tot.schools'].values * 100
w = df['p.tot.no.schools'].values * 100
# From Fig. 2
colors = sc.gridcolors(5)
wcolor = colors[3] # Work color/community
scolor = colors[1] # School color
pl.plot(days,
w,
'd-',
c=wcolor,
markersize=15,
lw=3,
alpha=0.9,
label='Workplace and\ncommunity mobility data')
pl.plot(days,
s,
'd-',
c=scolor,
markersize=15,
lw=3,
alpha=0.9,
label='School mobility data')
sc.setylim()
xmin, xmax = ax5.get_xlim()
ax5.set_xticks(np.arange(xmin, xmax, day_stride))
pl.ylabel('Relative mobility (%)')
pl.legend(loc='upper right', frameon=False)
plot_intervs(sim)
return fig
def plot(self,
to_plot=None,
do_save=None,
fig_path=None,
fig_args=None,
plot_args=None,
scatter_args=None,
axis_args=None,
as_dates=True,
interval=None,
dateformat=None,
font_size=18,
font_family=None,
use_grid=True,
use_commaticks=True,
do_show=True,
verbose=None):
'''
Plot the results -- can supply arguments for both the figure and the plots.
Args:
to_plot (dict): Nested dict of results to plot; see default_sim_plots for structure
do_save (bool or str): Whether or not to save the figure. If a string, save to that filename.
fig_path (str): Path to save the figure
fig_args (dict): Dictionary of kwargs to be passed to pl.figure()
plot_args (dict): Dictionary of kwargs to be passed to pl.plot()
scatter_args (dict): Dictionary of kwargs to be passed to pl.scatter()
axis_args (dict): Dictionary of kwargs to be passed to pl.subplots_adjust()
as_dates (bool): Whether to plot the x-axis as dates or time points
interval (int): Interval between tick marks
dateformat (str): Date string format, e.g. '%B %d'
font_size (int): Size of the font
font_family (str): Font face
use_grid (bool): Whether or not to plot gridlines
use_commaticks (bool): Plot y-axis with commas rather than scientific notation
do_show (bool): Whether or not to show the figure
verbose (bool): Display a bit of extra information
Returns:
fig: Figure handle
'''
if verbose is None:
verbose = self['verbose']
sc.printv('Plotting...', 1, verbose)
if to_plot is None:
to_plot = default_sim_plots
to_plot = sc.odict(to_plot) # In case it's supplied as a dict
# Handle input arguments -- merge user input with defaults
fig_args = sc.mergedicts({'figsize': (16, 12)}, fig_args)
plot_args = sc.mergedicts({'lw': 3, 'alpha': 0.7}, plot_args)
scatter_args = sc.mergedicts({'s': 150, 'marker': 's'}, scatter_args)
axis_args = sc.mergedicts(
{
'left': 0.1,
'bottom': 0.05,
'right': 0.9,
'top': 0.97,
'wspace': 0.2,
'hspace': 0.25
}, axis_args)
fig = pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
pl.rcParams['font.size'] = font_size
if font_family:
pl.rcParams['font.family'] = font_family
res = self.results # Shorten since heavily used
# Plot everything
colors = sc.gridcolors(max([len(tp) for tp in to_plot.values()]))
# Define the data mapping. Must be here since uses functions
if self.data is not None and len(self.data):
data_mapping = {
'cum_exposed': pl.cumsum(self.data['new_infections']),
'cum_diagnosed': pl.cumsum(self.data['new_positives']),
'cum_tested': pl.cumsum(self.data['new_tests']),
'infections': self.data['new_infections'],
'tests': self.data['new_tests'],
'diagnoses': self.data['new_positives'],
}
else:
data_mapping = {}
for p, title, keylabels in to_plot.enumitems():
ax = pl.subplot(2, 1, p + 1)
for i, key, label in keylabels.enumitems():
this_color = colors[i]
y = res[key].values
pl.plot(res['t'], y, label=label, **plot_args, c=this_color)
if key in data_mapping:
pl.scatter(self.data['day'],
data_mapping[key],
c=[this_color],
**scatter_args)
if self.data is not None and len(self.data):
pl.scatter(pl.nan,
pl.nan,
c=[(0, 0, 0)],
label='Data',
**scatter_args)
pl.grid(use_grid)
cvu.fixaxis(self)
if use_commaticks:
sc.commaticks()
pl.title(title)
# Optionally reset tick marks (useful for e.g. plotting weeks/months)
if interval:
xmin, xmax = ax.get_xlim()
ax.set_xticks(pl.arange(xmin, xmax + 1, interval))
# Set xticks as dates
if as_dates:
xticks = ax.get_xticks()
xticklabels = self.inds2dates(xticks, dateformat=dateformat)
ax.set_xticklabels(xticklabels)
# Plot interventions
for intervention in self['interventions']:
intervention.plot(self, ax)
# Ensure the figure actually renders or saves
if do_save:
if fig_path is None: # No figpath provided - see whether do_save is a figpath
if isinstance(do_save, str):
fig_path = do_save # It's a string, assume it's a filename
else:
fig_path = 'covasim.png' # Just give it a default name
fig_path = sc.makefilepath(
fig_path) # Ensure it's valid, including creating the folder
pl.savefig(fig_path)
if do_show:
pl.show()
else:
pl.close(fig)
return fig
testdelay=base_cum_diag,
trtime=
base_cum_diag, # Denominator is people you start contact tracing from, not those traced
)
pointcolor = [0.1, 0.4, 0.0] # High mobility, high intervention
pointcolor2 = 'k' # Status quo, darker
c1 = [0.4, 0.4, 0.4]
c2 = [0.2, 0.2, 0.2]
# Plot each seed (eind) separately
sepinds = 0
if sepinds:
einds = df1['eind'].values
eis = np.unique(einds)
cols = sc.gridcolors(len(eis))
if not sepinds:
eis = [0]
cols = [c1]
#%% Bottom row: surface plots
print('Calculating surfaces...')
df2 = cv.load(dffile2)
bottom = pl.cm.get_cmap('Oranges', 128)
top = pl.cm.get_cmap('Blues_r', 128)
newcolors = np.vstack((top(np.linspace(0, 1,
128)), bottom(np.linspace(0, 1, 128))))
newcmp = mpl.colors.ListedColormap(newcolors, name='OrangeBlue')
def run_sim(sim_pars=None, epi_pars=None, show_animation=False, verbose=True):
''' Create, run, and plot everything '''
err = ''
try:
# Fix up things that JavaScript mangles
orig_pars = cv.make_pars()
defaults = get_defaults(merge=True)
web_pars = {}
web_pars['verbose'] = verbose # Control verbosity here
for key, entry in {**sim_pars, **epi_pars}.items():
print(key, entry)
best = defaults[key]['best']
minval = defaults[key]['min']
maxval = defaults[key]['max']
try:
web_pars[key] = np.clip(float(entry['best']), minval, maxval)
except Exception:
user_key = entry['name']
user_val = entry['best']
err1 = f'Could not convert parameter "{user_key}", value "{user_val}"; using default value instead\n'
print(err1)
err += err1
web_pars[key] = best
if key in sim_pars: sim_pars[key]['best'] = web_pars[key]
else: epi_pars[key]['best'] = web_pars[key]
# Convert durations
web_pars['dur'] = sc.dcp(
orig_pars['dur']) # This is complicated, so just copy it
web_pars['dur']['exp2inf']['par1'] = web_pars.pop('web_exp2inf')
web_pars['dur']['inf2sym']['par1'] = web_pars.pop('web_inf2sym')
web_pars['dur']['crit2die']['par1'] = web_pars.pop('web_timetodie')
web_dur = web_pars.pop('web_dur')
for key in ['asym2rec', 'mild2rec', 'sev2rec', 'crit2rec']:
web_pars['dur'][key]['par1'] = web_dur
# Add the intervention
web_pars['interventions'] = []
if web_pars['web_int_day'] is not None:
web_pars['interventions'] = cv.change_beta(
days=web_pars.pop('web_int_day'),
changes=(1 - web_pars.pop('web_int_eff')))
# Handle CFR -- ignore symptoms and set to 1
prog_pars = cv.get_default_prognoses(by_age=False)
web_pars['rel_symp_prob'] = 1.0 / prog_pars.symp_prob
web_pars['rel_severe_prob'] = 1.0 / prog_pars.severe_prob
web_pars['rel_crit_prob'] = 1.0 / prog_pars.crit_prob
web_pars['rel_death_prob'] = web_pars.pop(
'web_cfr') / prog_pars.death_prob
except Exception as E:
err2 = f'Parameter conversion failed! {str(E)}\n'
print(err2)
err += err2
# Create the sim and update the parameters
try:
sim = cv.Sim()
sim['prog_by_age'] = False # So the user can override this value
sim['timelimit'] = max_time # Set the time limit
if web_pars['seed'] == 0:
web_pars['seed'] = None # Reset
sim.update_pars(web_pars)
except Exception as E:
err3 = f'Sim creation failed! {str(E)}\n'
print(err3)
err += err3
if verbose:
print('Input parameters:')
print(web_pars)
# Core algorithm
try:
sim.run(do_plot=False)
except Exception as E:
err4 = f'Sim run failed! {str(E)}\n'
print(err4)
err += err4
if sim.stopped:
try: # Assume it stopped because of the time, but if not, don't worry
day = sim.stopped['t']
time_exceeded = f"The simulation stopped on day {day} because run time limit ({sim['timelimit']} seconds) was exceeded. Please reduce the population size and/or number of days simulated."
err += time_exceeded
except:
pass
# Core plotting
graphs = []
try:
to_plot = sc.dcp(cv.default_sim_plots)
for p, title, keylabels in to_plot.enumitems():
fig = go.Figure()
colors = sc.gridcolors(len(keylabels))
for i, key, label in keylabels.enumitems():
this_color = 'rgb(%d,%d,%d)' % (
255 * colors[i][0], 255 * colors[i][1], 255 * colors[i][2])
y = sim.results[key][:]
fig.add_trace(
go.Scatter(x=sim.results['t'][:],
y=y,
mode='lines',
name=label,
line_color=this_color))
if sim['interventions']:
interv_day = sim['interventions'][0].days[0]
if interv_day > 0 and interv_day < sim['n_days']:
fig.add_shape(
dict(type="line",
xref="x",
yref="paper",
x0=interv_day,
x1=interv_day,
y0=0,
y1=1,
name='Intervention',
line=dict(width=0.5, dash='dash')))
fig.update_layout(annotations=[
dict(x=interv_day,
y=1.07,
xref="x",
yref="paper",
text="Intervention start",
showarrow=False)
])
fig.update_layout(title={'text': title},
xaxis_title='Day',
yaxis_title='Count',
autosize=True)
output = {'json': fig.to_json(), 'id': str(sc.uuid())}
d = json.loads(output['json'])
d['config'] = {'responsive': True}
output['json'] = json.dumps(d)
graphs.append(output)
graphs.append(plot_people(sim))
if show_animation:
graphs.append(animate_people(sim))
except Exception as E:
err5 = f'Plotting failed! {str(E)}\n'
print(err5)
err += err5
# Create and send output files (base64 encoded content)
files = {}
summary = {}
try:
datestamp = sc.getdate(dateformat='%Y-%b-%d_%H.%M.%S')
ss = sim.to_xlsx()
files['xlsx'] = {
'filename':
f'COVASim_results_{datestamp}.xlsx',
'content':
'data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,'
+ base64.b64encode(ss.blob).decode("utf-8"),
}
json_string = sim.to_json()
files['json'] = {
'filename':
f'COVASim_results_{datestamp}.txt',
'content':
'data:application/text;base64,' +
base64.b64encode(json_string.encode()).decode("utf-8"),
}
# Summary output
summary = {
'days': sim.npts - 1,
'cases': round(sim.results['cum_exposed'][-1]),
'deaths': round(sim.results['cum_deaths'][-1]),
}
except Exception as E:
err6 = f'File saving failed! {str(E)}\n'
print(err6)
err += err6
output = {}
output['err'] = err
output['sim_pars'] = sim_pars
output['epi_pars'] = epi_pars
output['graphs'] = graphs
output['files'] = files
output['summary'] = summary
return output
def plot(self, keys=None, width=0.8, font_size=18, fig_args=None, axis_args=None, plot_args=None):
'''
Plot the fit of the model to the data. For each result, plot the data
and the model; the difference; and the loss (weighted difference). Also
plots the loss as a function of time.
Args:
keys (list): which keys to plot (default, all)
width (float): bar width
font_size (float): size of font
fig_args (dict): passed to pl.figure()
axis_args (dict): passed to pl.subplots_adjust()
plot_args (dict): passed to pl.plot()
'''
fig_args = sc.mergedicts(dict(figsize=(36,22)), fig_args)
axis_args = sc.mergedicts(dict(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.3, hspace=0.3), axis_args)
plot_args = sc.mergedicts(dict(lw=4, alpha=0.5, marker='o'), plot_args)
pl.rcParams['font.size'] = font_size
if keys is None:
keys = self.keys + self.custom_keys
n_keys = len(keys)
loss_ax = None
colors = sc.gridcolors(n_keys)
n_rows = 4
figs = [pl.figure(**fig_args)]
pl.subplots_adjust(**axis_args)
main_ax1 = pl.subplot(n_rows, 2, 1)
main_ax2 = pl.subplot(n_rows, 2, 2)
bottom = sc.objdict() # Keep track of the bottoms for plotting cumulative
bottom.daily = np.zeros(self.sim_npts)
bottom.cumul = np.zeros(self.sim_npts)
for k,key in enumerate(keys):
if key in self.keys: # It's a time series, plot with days and dates
days = self.inds.sim[key] # The "days" axis (or not, for custom keys)
daylabel = 'Day'
else: #It's custom, we don't know what it is
days = np.arange(len(self.losses[key])) # Just use indices
daylabel = 'Index'
# Cumulative totals can't mix daily and non-daily inputs, so skip custom keys
if key in self.keys:
for i,ax in enumerate([main_ax1, main_ax2]):
if i == 0:
data = self.losses[key]
ylabel = 'Daily mismatch'
title = f'Daily total mismatch'
else:
data = np.cumsum(self.losses[key])
ylabel = 'Cumulative mismatch'
title = f'Cumulative mismatch: {self.mismatch:0.3f}'
dates = self.sim_results['date'][days] # Show these with dates, rather than days, as a reference point
ax.bar(dates, data, width=width, bottom=bottom[i][self.inds.sim[key]], color=colors[k], label=f'{key}')
if i == 0:
bottom.daily[self.inds.sim[key]] += self.losses[key]
else:
bottom.cumul = np.cumsum(bottom.daily)
if k == len(self.keys)-1:
ax.set_xlabel('Date')
ax.set_ylabel(ylabel)
ax.set_title(title)
ax.legend()
pl.subplot(n_rows, n_keys, k+1*n_keys+1)
pl.plot(days, self.pair[key].data, c='k', label='Data', **plot_args)
pl.plot(days, self.pair[key].sim, c=colors[k], label='Simulation', **plot_args)
pl.title(key)
if k == 0:
pl.ylabel('Time series (counts)')
pl.legend()
pl.subplot(n_rows, n_keys, k+2*n_keys+1)
pl.bar(days, self.diffs[key], width=width, color=colors[k], label='Difference')
pl.axhline(0, c='k')
if k == 0:
pl.ylabel('Differences (counts)')
pl.legend()
loss_ax = pl.subplot(n_rows, n_keys, k+3*n_keys+1, sharey=loss_ax)
pl.bar(days, self.losses[key], width=width, color=colors[k], label='Losses')
pl.xlabel(daylabel)
pl.title(f'Total loss: {self.losses[key].sum():0.3f}')
if k == 0:
pl.ylabel('Losses')
pl.legend()
return figs
def plot(self,
do_save=None,
fig_args=None,
plot_args=None,
scatter_args=None,
axis_args=None,
as_days=True,
font_size=18,
use_grid=True,
verbose=None):
'''
Plot the results -- can supply arguments for both the figure and the plots.
Parameters
----------
do_save : bool or str
Whether or not to save the figure. If a string, save to that filename.
fig_args : dict
Dictionary of kwargs to be passed to pl.figure()
plot_args : dict
Dictionary of kwargs to be passed to pl.plot()
as_days : bool
Whether to plot the x-axis as days or time points
Returns
-------
Figure handle
'''
if verbose is None:
verbose = self['verbose']
if verbose:
print('Plotting...')
if fig_args is None: fig_args = {'figsize': (26, 16)}
if plot_args is None: plot_args = {'lw': 3, 'alpha': 0.7}
if scatter_args is None: scatter_args = {'s': 150, 'marker': 's'}
if axis_args is None:
axis_args = {
'left': 0.1,
'bottom': 0.05,
'right': 0.9,
'top': 0.97,
'wspace': 0.2,
'hspace': 0.25
}
fig = pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
pl.rcParams['font.size'] = font_size
res = self.results # Shorten since heavily used
# Plot everything
colors = sc.gridcolors(5)
to_plot = sc.odict({ # TODO
'Total counts': sc.odict({'n_susceptible':'Number susceptible',
'n_exposed':'Number exposed',
'n_infectious':'Number infectious',
'cum_diagnosed':'Number diagnosed',
}),
'Daily counts': sc.odict({'infections':'New infections',
'tests':'Number of tests',
'diagnoses':'New diagnoses',
}),
})
data_mapping = {
'cum_diagnosed': pl.cumsum(self.data['new_positives']),
'tests': self.data['new_tests'],
'diagnoses': self.data['new_positives'],
}
for p, title, keylabels in to_plot.enumitems():
pl.subplot(2, 1, p + 1)
for i, key, label in keylabels.enumitems():
this_color = colors[i + p]
y = res[key]
pl.plot(res['t'], y, label=label, **plot_args, c=this_color)
if key in data_mapping:
pl.scatter(self.data['day'],
data_mapping[key],
c=[this_color],
**scatter_args)
pl.scatter(pl.nan,
pl.nan,
c=[(0, 0, 0)],
label='Data',
**scatter_args)
pl.grid(use_grid)
cv.fixaxis(self)
pl.ylabel('Count')
pl.xlabel('Days since index case')
pl.title(title)
# Ensure the figure actually renders or saves
if do_save:
if isinstance(do_save, str):
filename = do_save # It's a string, assume it's a filename
else:
filename = 'covid_abm_results.png' # Just give it a default name
pl.savefig(filename)
pl.show()
return fig
# Fonts and sizes
font_size = 36
font_family = 'Libertinus Sans'
pl.rcParams['font.size'] = font_size
pl.rcParams['font.family'] = font_family
fig = pl.figure(figsize=(24,10))
ax = pl.axes([0.1, 0.11, 0.85, 0.85])
msim = sc.loadobj(f'{resfolder}/uk_sim_FNL.obj')
sim = msim.base_sim
tt = sim.make_transtree()
#tt = sc.loadobj(f'{resfolder}/tt.obj')
layer_keys = list(sim.people.layer_keys())
layer_mapping = {k:i for i,k in enumerate(layer_keys)}
n_layers = len(layer_keys)
colors = sc.gridcolors(n_layers)
layer_counts = np.zeros((sim.npts, n_layers))
for source_ind, target_ind in tt.transmissions:
dd = tt.detailed[target_ind]
date = dd['date']
layer_num = layer_mapping[dd['layer']]
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])
def plot(self, which=None, n=None, axsize=None, figsize=None):
'''
Create a bar plot of the top causes of burden. By default, plots the top
10 causes of DALYs.
Version: 2018sep27
'''
# Set labels
titles = {
'dalys': 'Top causes of DALYs',
'deaths': 'Top causes of mortality',
'prevalence': 'Most prevalent conditions'
}
# Handle options
if which is None: which = list(titles.keys())
if n is None: n = 10
if axsize is None: axsize = (0.65, 0.15, 0.3, 0.8)
if figsize is None: figsize = (7, 4)
barw = 0.8
# Pull out data
df = sc.dcp(self.data)
nburdens = df.nrows
colors = sc.gridcolors(nburdens + 2, asarray=True)[2:]
colordict = sc.odict()
for c, cause in enumerate(df[self.colnames['cause']]):
colordict[cause] = colors[c]
# Convert to list
if not isinstance(which, list):
asarray = False
whichlist = sc.promotetolist(which)
else:
asarray = True
whichlist = which
# Loop over each option (may only be one)
figs = []
for which in whichlist:
colname = self.colnames[which]
try:
thistitle = titles[which]
thisxlabel = colname
except Exception as E:
errormsg = '"%s" not found, "which" must be one of %s (%s)' % (
which, ', '.join(list(titles.keys())), str(E))
raise Exception(errormsg)
# Process data
df.sort(col=colname, reverse=True)
topdata = df[:n]
try:
barvals = hp.arr(topdata[colname])
except Exception as E:
for r in range(topdata.nrows):
try:
float(topdata[colname, r])
except Exception as E2:
if topdata[colname, r] in ['', None]:
errormsg = 'For cause "%s", the "%s" value is missing or empty' % (
topdata[self.colnames['cause'], r], colname)
else:
errormsg = 'For cause "%s", could not convert "%s" value "%s" to number: %s' % (
topdata[self.colnames['cause'], r], colname,
topdata[colname, r], str(E2))
raise Exception(errormsg)
errormsg = 'An exception was encountered, but could not be reproduced: %s' % str(
E)
raise Exception(errormsg)
barlabels = topdata[self.colnames['cause']].tolist()
# Figure out the units
largestval = barvals[0]
if largestval > 1e6:
barvals /= 1e6
unitstr = ' (millions)'
elif largestval > 1e3:
barvals /= 1e3
unitstr = ' (thousands)'
else:
unitstr = ''
# Create plot
fig = pl.figure(facecolor='none', figsize=figsize)
ax = fig.add_axes(axsize)
ax.set_facecolor('none')
yaxis = pl.arange(n, 0, -1)
for i in range(n):
thiscause = topdata[self.colnames['cause'], i]
color = colordict[thiscause]
pl.barh(yaxis[i],
barvals[i],
height=barw,
facecolor=color,
edgecolor='none')
ax.set_yticks(pl.arange(10, 0, -1))
ax.set_yticklabels(barlabels)
sc.SIticks(ax=ax, axis='x')
ax.set_xlabel(thisxlabel + unitstr)
ax.set_title(thistitle)
sc.boxoff()
figs.append(fig)
if asarray: return figs
else: return figs[0]
def plot_people(people,
bins=None,
width=1.0,
alpha=0.6,
fig_args=None,
axis_args=None,
plot_args=None,
do_show=None,
fig=None):
''' Plot statistics of a population -- see People.plot() for documentation '''
# Handle inputs
if bins is None:
bins = np.arange(0, 101)
# Set defaults
color = [0.1, 0.1, 0.1] # Color for the age distribution
n_rows = 4 # Number of rows of plots
offset = 0.5 # For ensuring the full bars show up
gridspace = 10 # Spacing of gridlines
zorder = 10 # So plots appear on top of gridlines
# Handle other arguments
fig_args = sc.mergedicts(dict(figsize=(18, 11)), fig_args)
axis_args = sc.mergedicts(
dict(left=0.05,
right=0.95,
bottom=0.05,
top=0.95,
wspace=0.3,
hspace=0.35), axis_args)
plot_args = sc.mergedicts(dict(lw=1.5, alpha=0.6, c=color, zorder=10),
plot_args)
# Compute statistics
min_age = min(bins)
max_age = max(bins)
edges = np.append(
bins, np.inf) # Add an extra bin to end to turn them into edges
age_counts = np.histogram(people.age, edges)[0]
# Create the figure
if fig is None:
fig = pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
# Plot age histogram
pl.subplot(n_rows, 2, 1)
pl.bar(bins,
age_counts,
color=color,
alpha=alpha,
width=width,
zorder=zorder)
pl.xlim([min_age - offset, max_age + offset])
pl.xticks(np.arange(0, max_age + 1, gridspace))
pl.grid(True)
pl.xlabel('Age')
pl.ylabel('Number of people')
pl.title(f'Age distribution ({len(people):n} people total)')
# Plot cumulative distribution
pl.subplot(n_rows, 2, 2)
age_sorted = sorted(people.age)
y = np.linspace(0, 100, len(age_sorted)) # Percentage, not hard-coded!
pl.plot(age_sorted, y, '-', **plot_args)
pl.xlim([0, max_age])
pl.ylim([0, 100]) # Percentage
pl.xticks(np.arange(0, max_age + 1, gridspace))
pl.yticks(np.arange(0, 101, gridspace)) # Percentage
pl.grid(True)
pl.xlabel('Age')
pl.ylabel('Cumulative proportion (%)')
pl.title(
f'Cumulative age distribution (mean age: {people.age.mean():0.2f} years)'
)
# Calculate contacts
lkeys = people.layer_keys()
n_layers = len(lkeys)
contact_counts = sc.objdict()
for lk in lkeys:
layer = people.contacts[lk]
p1ages = people.age[layer['p1']]
p2ages = people.age[layer['p2']]
contact_counts[lk] = np.histogram(p1ages, edges)[0] + np.histogram(
p2ages, edges)[0]
# Plot contacts
layer_colors = sc.gridcolors(n_layers)
share_ax = None
for w, w_type in enumerate(['total', 'percapita', 'weighted'
]): # Plot contacts in different ways
for i, lk in enumerate(lkeys):
if w_type == 'total':
weight = 1
total_contacts = 2 * len(
people.contacts[lk]) # x2 since each contact is undirected
ylabel = 'Number of contacts'
title = f'Total contacts for layer "{lk}": {total_contacts:n}'
elif w_type == 'percapita':
weight = np.divide(1.0, age_counts, where=age_counts > 0)
mean_contacts = 2 * len(people.contacts[lk]) / len(
people) # Factor of 2 since edges are bi-directional
ylabel = 'Per capita number of contacts'
title = f'Mean contacts for layer "{lk}": {mean_contacts:0.2f}'
elif w_type == 'weighted':
weight = people.pars['beta_layer'][lk] * people.pars['beta']
total_weight = np.round(weight * 2 * len(people.contacts[lk]))
ylabel = 'Weighted number of contacts'
title = f'Total weight for layer "{lk}": {total_weight:n}'
ax = pl.subplot(n_rows,
n_layers,
n_layers * (w + 1) + i + 1,
sharey=share_ax)
pl.bar(bins,
contact_counts[lk] * weight,
color=layer_colors[i],
width=width,
zorder=zorder,
alpha=alpha)
pl.xlim([min_age - offset, max_age + offset])
pl.xticks(np.arange(0, max_age + 1, gridspace))
pl.grid(True)
pl.xlabel('Age')
pl.ylabel(ylabel)
pl.title(title)
if w_type == 'weighted':
share_ax = ax # Update shared axis
cvset.handle_show(do_show)
return fig
def plot():
fig = pl.figure(num='Fig. 2: Transmission dynamics', figsize=(20,14))
piey, tsy, r3y = 0.68, 0.50, 0.07
piedx, tsdx, r3dx = 0.2, 0.9, 0.25
piedy, tsdy, r3dy = 0.2, 0.47, 0.35
pie1x, pie2x = 0.12, 0.65
tsx = 0.07
dispx, cumx, sympx = tsx, 0.33+tsx, 0.66+tsx
ts_ax = pl.axes([tsx, tsy, tsdx, tsdy])
pie_ax1 = pl.axes([pie1x, piey, piedx, piedy])
pie_ax2 = pl.axes([pie2x, piey, piedx, piedy])
symp_ax = pl.axes([sympx, r3y, r3dx, r3dy])
disp_ax = pl.axes([dispx, r3y, r3dx, r3dy])
cum_ax = pl.axes([cumx, r3y, r3dx, r3dy])
off = 0.06
txtdispx, txtcumx, txtsympx = dispx-off, cumx-off, sympx-off+0.02
tsytxt = tsy+tsdy
r3ytxt = r3y+r3dy
labelsize = 40-wf
pl.figtext(txtdispx, tsytxt, 'a', fontsize=labelsize)
pl.figtext(txtdispx, r3ytxt, 'b', fontsize=labelsize)
pl.figtext(txtcumx, r3ytxt, 'c', fontsize=labelsize)
pl.figtext(txtsympx, r3ytxt, 'd', fontsize=labelsize)
#%% Fig. 2A -- Time series plot
layer_keys = list(sim.layer_keys())
layer_mapping = {k:i for i,k in enumerate(layer_keys)}
n_layers = len(layer_keys)
colors = sc.gridcolors(n_layers)
layer_counts = np.zeros((sim.npts, n_layers))
for source_ind, target_ind in tt.count_transmissions():
dd = tt.detailed[target_ind]
date = dd['date']
layer_num = layer_mapping[dd['layer']]
layer_counts[date, layer_num] += sim.rescale_vec[date]
mar12 = cv.date('2020-03-12')
mar23 = cv.date('2020-03-23')
mar12d = sim.day(mar12)
mar23d = sim.day(mar23)
labels = ['Household', 'School', 'Workplace', 'Community', 'LTCF']
for l in range(n_layers):
ts_ax.plot(sim.datevec, layer_counts[:,l], c=colors[l], lw=3, label=labels[l])
sc.setylim(ax=ts_ax)
sc.boxoff(ax=ts_ax)
ts_ax.set_ylabel('Transmissions per day')
ts_ax.set_xlim([sc.readdate('2020-01-18'), sc.readdate('2020-06-09')])
ts_ax.xaxis.set_major_formatter(mdates.DateFormatter('%b-%d'))
ts_ax.set_xticks([sim.date(d, as_date=True) for d in np.arange(0, sim.day('2020-06-09'), 14)])
ts_ax.legend(frameon=False, bbox_to_anchor=(0.85,0.1))
color = [0.2, 0.2, 0.2]
ts_ax.axvline(mar12, c=color, linestyle='--', alpha=0.4, lw=3)
ts_ax.axvline(mar23, c=color, linestyle='--', alpha=0.4, lw=3)
yl = ts_ax.get_ylim()
labely = yl[1]*1.015
ts_ax.text(mar12, labely, 'Schools close ', color=color, alpha=0.9, style='italic', horizontalalignment='center')
ts_ax.text(mar23, labely, ' Stay-at-home', color=color, alpha=0.9, style='italic', horizontalalignment='center')
#%% Fig. 2A inset -- Pie charts
pre_counts = layer_counts[0:mar12d, :].sum(axis=0)
post_counts = layer_counts[mar23d:, :].sum(axis=0)
pre_counts = pre_counts/pre_counts.sum()*100
post_counts = post_counts/post_counts.sum()*100
lpre = [
f'Household\n{pre_counts[0]:0.1f}%',
f'School\n{pre_counts[1]:0.1f}% ',
f'Workplace\n{pre_counts[2]:0.1f}% ',
f'Community\n{pre_counts[3]:0.1f}%',
f'LTCF\n{pre_counts[4]:0.1f}%',
]
lpost = [
f'Household\n{post_counts[0]:0.1f}%',
f'School\n{post_counts[1]:0.1f}%',
f'Workplace\n{post_counts[2]:0.1f}%',
f'Community\n{post_counts[3]:0.1f}%',
f'LTCF\n{post_counts[4]:0.1f}%',
]
pie_ax1.pie(pre_counts, colors=colors, labels=lpre, **pieargs)
pie_ax2.pie(post_counts, colors=colors, labels=lpost, **pieargs)
pie_ax1.text(0, 1.75, 'Transmissions by layer\nbefore schools closed', style='italic', horizontalalignment='center')
pie_ax2.text(0, 1.75, 'Transmissions by layer\nafter stay-at-home', style='italic', horizontalalignment='center')
#%% Fig. 2B -- histogram by overdispersion
# Process targets
n_targets = tt.count_targets(end_day=mar12)
# Handle bins
max_infections = n_targets.max()
edges = np.arange(0, max_infections+2)
# Analysis
counts = np.histogram(n_targets, edges)[0]
bins = edges[:-1] # Remove last bin since it's an edge
norm_counts = counts/counts.sum()
raw_counts = counts*bins
total_counts = raw_counts/raw_counts.sum()*100
n_bins = len(bins)
index = np.linspace(0, 100, len(n_targets))
sorted_arr = np.sort(n_targets)
sorted_sum = np.cumsum(sorted_arr)
sorted_sum = sorted_sum/sorted_sum.max()*100
change_inds = sc.findinds(np.diff(sorted_arr) != 0)
pl.set_cmap('Spectral_r')
sscolors = sc.vectocolor(n_bins)
width = 1.0
for i in range(n_bins):
disp_ax.bar(bins[i], total_counts[i], width=width, facecolor=sscolors[i])
disp_ax.set_xlabel('Number of transmissions per case')
disp_ax.set_ylabel('Proportion of transmissions (%)')
sc.boxoff()
disp_ax.set_xlim([0.5, 32.5])
disp_ax.set_xticks(np.arange(0, 32.5, 4))
sc.boxoff(ax=disp_ax)
dpie_ax = pl.axes([dispx+0.05, 0.20, 0.2, 0.2])
trans1 = total_counts[1:3].sum()
trans2 = total_counts[3:5].sum()
trans3 = total_counts[5:8].sum()
trans4 = total_counts[8:].sum()
labels = [
f'1-2:\n{trans1:0.0f}%',
f' 3-4:\n {trans2:0.0f}%',
f'5-7: \n{trans3:0.0f}%\n',
f'>7: \n{trans4:0.0f}%\n',
]
dpie_args = sc.mergedicts(pieargs, dict(labeldistance=1.2)) # Slightly smaller label distance
dpie_ax.pie([trans1, trans2, trans3, trans4], labels=labels, colors=sscolors[[0,4,7,12]], **dpie_args)
#%% Fig. 2C -- cumulative distribution function
rev_ind = 100 - index
n_change_inds = len(change_inds)
change_bins = bins[counts>0][1:]
for i in range(n_change_inds):
ib = int(change_bins[i])
ci = change_inds[i]
ici = index[ci]
sci = sorted_sum[ci]
color = sscolors[ib]
if i>0:
cim1 = change_inds[i-1]
icim1 = index[cim1]
scim1 = sorted_sum[cim1]
cum_ax.plot([icim1, ici], [scim1, sci], lw=4, c=color)
cum_ax.scatter([ici], [sci], s=150, zorder=50-i, c=[color], edgecolor='w', linewidth=0.2)
if ib<=6 or ib in [8, 10, 25]:
xoff = 5 - 2*(ib==1) + 3*(ib>=10) + 1*(ib>=20)
yoff = 2*(ib==1)
cum_ax.text(ici-xoff, sci+yoff, ib, fontsize=18-wf, color=color)
cum_ax.set_xlabel('Proportion of primary infections (%)')
cum_ax.set_ylabel('Proportion of transmissions (%)')
xmin = -2
ymin = -2
cum_ax.set_xlim([xmin, 102])
cum_ax.set_ylim([ymin, 102])
sc.boxoff(ax=cum_ax)
# Draw horizontal lines and annotations
ancol1 = [0.2, 0.2, 0.2]
ancol2 = sscolors[0]
ancol3 = sscolors[6]
i01 = sc.findlast(sorted_sum==0)
i20 = sc.findlast(sorted_sum<=20)
i50 = sc.findlast(sorted_sum<=50)
cum_ax.plot([xmin, index[i01]], [0, 0], '--', lw=2, c=ancol1)
cum_ax.plot([xmin, index[i20], index[i20]], [20, 20, ymin], '--', lw=2, c=ancol2)
cum_ax.plot([xmin, index[i50], index[i50]], [50, 50, ymin], '--', lw=2, c=ancol3)
# Compute mean number of transmissions for 80% and 50% thresholds
q80 = sc.findfirst(np.cumsum(total_counts)>20) # Count corresponding to 80% of cumulative infections (100-80)
q50 = sc.findfirst(np.cumsum(total_counts)>50) # Count corresponding to 50% of cumulative infections
n80, n50 = [sum(bins[q:]*norm_counts[q:]/norm_counts[q:].sum()) for q in [q80, q50]]
# Plot annotations
kw = dict(bbox=dict(facecolor='w', alpha=0.9, lw=0), fontsize=20-wf)
cum_ax.text(2, 3, f'{index[i01]:0.0f}% of infections\ndo not transmit', c=ancol1, **kw)
cum_ax.text(8, 23, f'{rev_ind[i20]:0.0f}% of infections cause\n80% of transmissions\n(mean: {n80:0.1f} per infection)', c=ancol2, **kw)
cum_ax.text(14, 53, f'{rev_ind[i50]:0.0f}% of infections cause\n50% of transmissions\n(mean: {n50:0.1f} per infection)', c=ancol3, **kw)
#%% Fig. 2D -- histogram by date of symptom onset
# Calculate
asymp_count = 0
symp_counts = {}
minind = -5
maxind = 15
for _, target_ind in tt.transmissions:
dd = tt.detailed[target_ind]
date = dd['date']
delta = sim.rescale_vec[date] # Increment counts by this much
if dd['s']:
if tt.detailed[dd['source']]['date'] <= date: # Skip dynamical scaling reinfections
sdate = dd['s']['date_symptomatic']
if np.isnan(sdate):
asymp_count += delta
else:
ind = int(date - sdate)
if ind not in symp_counts:
symp_counts[ind] = 0
symp_counts[ind] += delta
# Convert to an array
xax = np.arange(minind-1, maxind+1)
sympcounts = np.zeros(len(xax))
for i,val in symp_counts.items():
if i<minind:
ind = 0
elif i>maxind:
ind = -1
else:
ind = sc.findinds(xax==i)[0]
sympcounts[ind] += val
# Plot
total_count = asymp_count + sympcounts.sum()
sympcounts = sympcounts/total_count*100
presymp = sc.findinds(xax<=0)[-1]
colors = ['#eed15b', '#ee943a', '#c3211a']
asymp_frac = asymp_count/total_count*100
pre_frac = sympcounts[:presymp].sum()
symp_frac = sympcounts[presymp:].sum()
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