def plotter(key, sims, ax, label='', ylabel='', low_q=0.05, high_q=0.95, startday=None):
color = cv.get_colors()[key.split('_')[1]]
ys = []
for s in sims:
ys.append(s.results[key].values)
yarr = np.array(ys)
best = pl.median(yarr, axis=0)
low = pl.quantile(yarr, q=low_q, axis=0)
high = pl.quantile(yarr, q=high_q, axis=0)
sim = sims[0]
tvec = np.arange(len(best))
fill_label = None
pl.fill_between(tvec, low, high, facecolor=color, alpha=0.2, label=fill_label)
pl.plot(tvec, best, c=color, label=label, lw=4, alpha=1.0)
sc.setylim()
datemarks = pl.array([sim.day('2020-03-01'),sim.day('2020-05-01'),sim.day('2020-07-01'),
sim.day('2020-09-01')])
ax.set_xticks(datemarks)
pl.ylabel(ylabel)
return
def plotter(key, sims, ax, label='', ylabel='', low_q=0.05, high_q=0.95, subsample=2):
which = key.split('_')[1]
try:
color = cv.get_colors()[which]
except:
color = [0.5,0.5,0.5]
ys = []
for s in sims:
ys.append(s.results[key].values)
yarr = np.array(ys)
best = pl.median(yarr, axis=0)
low = pl.quantile(yarr, q=low_q, axis=0)
high = pl.quantile(yarr, q=high_q, axis=0)
tvec = np.arange(len(best))
# tempsim = cv.Sim(datafile='../UK_Covid_cases_january03.xlsx')
# sim = sims[0]
# if key in tempsim.data:
# data_t = np.array((tempsim.data.index-sim['start_day'])/np.timedelta64(1,'D'))
# inds = np.arange(0, len(data_t), subsample)
# data = tempsim.data[key][inds]
# pl.plot(data_t[inds], data, 'd', c=color, markersize=10, alpha=0.5, label='Data')
fill_label = None
end = None
start = 2 if key == 'r_eff' else 0
pl.fill_between(tvec[start:end], low[start:end], high[start:end], facecolor=color, alpha=0.2, label=fill_label)
pl.plot(tvec[start:end], best[start:end], c=color, label=label, lw=4, alpha=1.0)
sc.setylim()
datemarks = pl.array([sim.day('2020-03-01'),sim.day('2020-06-01'),
sim.day('2020-09-01'),sim.day('2020-09-01'),
sim.day('2020-12-01'),sim.day('2021-03-01'),
sim.day('2021-05-01')])
ax.set_xticks(datemarks)
pl.ylabel(ylabel)
return
def plotter(key, sims, ax, ys=None, calib=False, label='', ylabel='', low_q=0.1, high_q=0.9):
which = key.split('_')[1]
try:
color = cv.get_colors()[which]
except:
color = [0.5,0.5,0.5]
if which == 'deaths':
color = [0.5,0.0,0.0]
if ys is None:
ys = []
for s in sims:
ys.append(s.results[key].values)
yarr = np.array(ys)
best = pl.median(yarr, axis=0) # Changed from median to mean for smoother plots
low = pl.quantile(yarr, q=low_q, axis=0)
high = pl.quantile(yarr, q=high_q, axis=0)
sim = sims[0] # For having a sim to refer to
# Formatting parameters
plot_args = sc.mergedicts({'lw': 3, 'alpha': 0.8})
fill_args = sc.mergedicts({'alpha': 0.2})
tvec = np.arange(len(best))
if calib:
if key == 'r_eff':
end = -2
else:
end = -1
else:
end = None
pl.fill_between(tvec[:end], low[:end], high[:end], facecolor=color, **fill_args)
pl.plot(tvec[:end], best[:end], c=color, label=label, **plot_args)
if key in sim.data:
data_t = np.array((sim.data.index-sim['start_day'])/np.timedelta64(1,'D'))
pl.plot(data_t, sim.data[key], 'o', c=color, markersize=10, label='Data')
if calib:
xlims = pl.xlim()
pl.xlim([13, xlims[1]-1])
else:
pl.xlim([0,94])
sc.setylim()
xmin,xmax = ax.get_xlim()
if calib:
ax.set_xticks(pl.arange(xmin+2, xmax, 7))
else:
ax.set_xticks(pl.arange(xmin+2, xmax, 7))
pl.ylabel(ylabel)
pl.legend(loc='upper left')
return
def plotter(key,
sims,
ax,
ys=None,
calib=False,
label='',
ylabel='',
low_q=0.025,
high_q=0.975,
flabel=True,
startday=None,
subsample=2,
chooseseed=None):
which = key.split('_')[1]
try:
color = cv.get_colors()[which]
except:
color = [0.5, 0.5, 0.5]
if which == 'diagnoses':
color = [0.03137255, 0.37401, 0.63813918, 1.]
elif which == '':
color = [0.82400815, 0., 0., 1.]
if ys is None:
ys = []
for s in sims:
ys.append(s.results[key].values)
yarr = np.array(ys)
if chooseseed is not None:
best = sims[chooseseed].results[key].values
else:
best = pl.median(yarr, axis=0)
low = pl.quantile(yarr, q=low_q, axis=0)
high = pl.quantile(yarr, q=high_q, axis=0)
sim = sims[0] # For having a sim to refer to
tvec = np.arange(len(best))
if key in sim.data:
data_t = np.array(
(sim.data.index - sim['start_day']) / np.timedelta64(1, 'D'))
inds = np.arange(0, len(data_t), subsample)
pl.plot(data_t[inds],
sim.data[key][inds],
'd',
c=color,
markersize=15,
alpha=0.75,
label='Data')
start = None
if startday is not None:
start = sim.day(startday)
end = sim.day(calibration_end)
if flabel:
if which == 'infections':
fill_label = '95% projected interval'
else:
fill_label = '95% projected interval'
else:
fill_label = None
pl.fill_between(tvec[startday:end],
low[startday:end],
high[startday:end],
facecolor=color,
alpha=0.2,
label=fill_label)
pl.plot(tvec[startday:end],
best[startday:end],
c=color,
label=label,
lw=4,
alpha=1.0)
# Print some stats
if key == 'cum_infections':
print(
f'Estimated {which} on July 25: {best[sim.day("2020-07-25")]} (95%: {low[sim.day("2020-07-25")]}-{high[sim.day("2020-07-25")]})'
)
print(
f'Estimated {which} overall: {best[sim.day(calibration_end)]} (95%: {low[sim.day(calibration_end)]}-{high[sim.day(calibration_end)]})'
)
elif key == 'n_infectious':
peakday = sc.findnearest(best, max(best))
peakval = max(best)
print(
f'Estimated peak {which} on {sim.date(peakday)}: {peakval} (95%: {low[peakday]}-{high[peakday]})'
)
print(
f'Estimated {which} on last day: {best[sim.day(calibration_end)]} (95%: {low[sim.day(calibration_end)]}-{high[sim.day(calibration_end)]})'
)
elif key == 'cum_diagnoses':
print(
f'Estimated {which} overall: {best[sim.day(calibration_end)]} (95%: {low[sim.day(calibration_end)]}-{high[sim.day(calibration_end)]})'
)
sc.setylim()
xmin, xmax = ax.get_xlim()
if calib:
ax.set_xticks(pl.arange(xmin + 2, xmax, 28))
else:
ax.set_xticks(pl.arange(xmin + 2, xmax, 28))
pl.ylabel(ylabel)
datemarks = pl.array([
sim.day('2020-07-01'),
sim.day('2020-08-01'),
sim.day('2020-09-01'),
sim.day('2020-10-01')
]) * 1.
ax.set_xticks(datemarks)
return
def plotter(key,
sims,
ax,
ys=None,
calib=False,
label='',
ylabel='',
low_q=0.025,
high_q=0.975,
flabel=True,
subsample=2):
''' Plot a single time series with uncertainty '''
which = key.split('_')[1]
try:
color = cv.get_colors()[which]
except:
color = [0.5, 0.5, 0.5]
if which == 'deaths':
color = [0.5, 0.0, 0.0]
if ys is None:
ys = []
for i, s in enumerate(sims):
if i < sims_cutoff:
ys.append(s.results[key].values)
yarr = np.array(ys)
best = pl.median(yarr,
axis=0) # Changed from median to mean for smoother plots
low = pl.quantile(yarr, q=low_q, axis=0)
high = pl.quantile(yarr, q=high_q, axis=0)
sim = sims[0] # For having a sim to refer to
tvec = np.arange(len(best))
data, data_t = None, None
if key in sim.data:
data_t = np.array(
(sim.data.index - sim['start_day']) / np.timedelta64(1, 'D'))
inds = np.arange(0, len(data_t), subsample)
data = sim.data[key][inds]
pl.plot(data_t[inds],
data,
'd',
c=color,
markersize=10,
alpha=0.5,
label='Data')
end = None
if flabel:
if which == 'infections':
fill_label = '95% predic-\ntion interval'
else:
fill_label = '95% prediction\ninterval'
else:
fill_label = None
# Trim the beginning for r_eff and actually plot
start = 2 if key == 'r_eff' else 0
pl.fill_between(tvec[start:end],
low[start:end],
high[start:end],
facecolor=color,
alpha=0.2,
label=fill_label)
pl.plot(tvec[start:end],
best[start:end],
c=color,
label=label,
lw=4,
alpha=1.0)
sc.setylim()
xmin, xmax = ax.get_xlim()
ax.set_xticks(np.arange(xmin, xmax, day_stride))
pl.ylabel(ylabel)
plotres[key] = sc.objdict(
dict(tvec=tvec,
best=best,
low=low,
high=high,
data=data,
data_t=data_t))
return
def plotter(key,
sims,
ax,
ys=None,
calib=False,
label='',
ylabel='',
low_q=0.025,
high_q=0.975,
flabel=True,
startday=None,
subsample=2,
chooseseed=None):
which = key.split('_')[1]
try:
color = cv.get_colors()[which]
except:
color = [0.5, 0.5, 0.5]
if which == 'diagnoses':
color = [0.03137255, 0.37401, 0.63813918, 1.]
elif which == '':
color = [0.82400815, 0., 0., 1.]
if ys is None:
ys = []
for s in sims:
ys.append(s.results[key].values)
yarr = np.array(ys)
if chooseseed is not None:
best = sims[chooseseed].results[key].values
else:
best = pl.median(yarr, axis=0)
low = pl.quantile(yarr, q=low_q, axis=0)
high = pl.quantile(yarr, q=high_q, axis=0)
sim = sims[0] # For having a sim to refer to
tvec = np.arange(len(best))
if key in sim.data:
data_t = np.array(
(sim.data.index - sim['start_day']) / np.timedelta64(1, 'D'))
inds = np.arange(0, len(data_t), subsample)
pl.plot(data_t[inds],
sim.data[key][inds],
'd',
c=color,
markersize=10,
alpha=0.5,
label='Data')
start = None
if startday is not None:
start = sim.day(startday)
end = sim.day('2021-03-31')
if flabel:
if which == 'infections':
fill_label = '95% projected interval'
else:
fill_label = '95% projected interval'
else:
fill_label = None
pl.fill_between(tvec[startday:end],
low[startday:end],
high[startday:end],
facecolor=color,
alpha=0.2,
label=fill_label)
pl.plot(tvec[startday:end],
best[startday:end],
c=color,
label=label,
lw=4,
alpha=1.0)
#for s in sims:
# pl.plot(tvec[startday:end], s.results[key].values[startday:end], c=[0.4, 0.4, 0.4], label=label, lw=1, alpha=0.7)
#sc.setylim()
datemarks = pl.array([
sim.day('2020-07-01'),
sim.day('2020-09-01'),
sim.day('2020-11-01'),
sim.day('2021-01-01')
]) * 1.
ax.set_xticks(datemarks)
pl.ylabel(ylabel)
return