'''
Core plotting functions for simulations, multisims, and scenarios.
Also includes Plotly-based plotting functions to supplement the Matplotlib based
ones that are of the Sim and Scenarios objects. Intended mostly for use with the
webapp.
'''
import numpy as np
import pylab as pl
import sciris as sc
import datetime as dt
import matplotlib.ticker as ticker
from . import utils as psu
#%% Defaults
# Specify all externally visible functions this file defines
__all__ = ['get_sim_plots', 'get_scen_plots']
# Define the 'overview plots', i.e. the most useful set of plots to explore different aspects of a simulation
overview_plots = [
'cum_infections',
'cum_sus_para_infs',
'cum_diagnoses',
'new_infections',
'new_sus_para_infs',
'new_diagnoses',
'n_naive',
'new_tests',
'n_symptomatic',
'new_quarantined',
'n_quarantined',
'test_yield',
'r_eff',
]
[docs]def get_sim_plots(which='default'):
'''
Specify which quantities to plot; used in sim.py.
Args:
which (str): either 'default' or 'overview'
'''
if which in [None, 'default']:
plots = sc.odict({
'Total counts': [
'cum_infections',
],
'Daily counts': [
'new_infections',
'new_diagnoses',
],
'Health outcomes': [
'cum_symptomatic',
],
'Programmatic outcomes': [
'n_quarantined',
'cum_diagnoses',
],
})
elif which == 'overview':
plots = sc.dcp(overview_plots)
else:
errormsg = f'The choice which="{which}" is not supported'
raise ValueError(errormsg)
return plots
[docs]def get_scen_plots(which='default'):
''' Default scenario plots -- used in run.py '''
if which in [None, 'default']:
plots = sc.odict({
'Cumulative infections': ['cum_infections'],
'New infections per day': ['new_infections'],
'New paralytic cases': ['new_symptomatic'],
'Cumulative paralytic cases': ['cum_symptomatic'],
'New diagnoses': ['new_diagnoses'],
'Total diagnoses': ['cum_diagnoses'],
'Number quarantined': ['n_quarantined'],
})
elif which == 'overview':
plots = sc.dcp(overview_plots)
else:
errormsg = f'The choice which="{which}" is not supported'
raise ValueError(errormsg)
return plots
__all__ += ['plot_sim', 'plot_scens', 'plot_result', 'plot_compare', 'plot_people']
#%% Plotting helper functions
def handle_args(fig_args=None, plot_args=None, scatter_args=None, axis_args=None, fill_args=None, legend_args=None, show_args=None):
''' Handle input arguments -- merge user input with defaults; see sim.plot for documentation '''
args = sc.objdict()
args.fig = sc.mergedicts({'figsize': (16, 14)}, fig_args)
args.plot = sc.mergedicts({'lw': 3, 'alpha': 0.7}, plot_args)
args.scatter = sc.mergedicts({'s':70, 'marker':'s', 'alpha':0.7, 'zorder':0}, scatter_args)
args.axis = sc.mergedicts({'left': 0.10, 'bottom': 0.05, 'right': 0.95, 'top': 0.97, 'wspace': 0.25, 'hspace': 0.25}, axis_args)
args.fill = sc.mergedicts({'alpha': 0.2}, fill_args)
args.legend = sc.mergedicts({'loc': 'best', 'frameon':False}, legend_args)
args.show = sc.mergedicts({'data':True, 'interventions':True, 'legend':True, }, show_args)
# Handle what to show
show_keys = ['data', 'ticks', 'interventions', 'legend']
args.show = {k:True for k in show_keys}
if show_args in [True, False]: # Handle all on or all off
args.show = {k:show_args for k in show_keys}
else:
args.show = sc.mergedicts(args.show, show_args)
return args
def handle_to_plot(which, to_plot, n_cols, sim):
''' Handle which quantities to plot '''
# If not specified or specified as a string, load defaults
if to_plot is None or isinstance(to_plot, str):
if which == 'sim':
to_plot = get_sim_plots(to_plot)
elif which =='scens':
to_plot = get_scen_plots(to_plot)
else:
errormsg = f'"which" must be "sim" or "scens", not "{which}"'
raise NotImplementedError(errormsg)
# If a list of keys has been supplied
if isinstance(to_plot, list):
to_plot_list = to_plot # Store separately
to_plot = sc.odict() # Create the dict
for reskey in to_plot_list:
to_plot[sim.results[reskey].name] = [reskey] # Use the result name as the key and the reskey as the value
to_plot = sc.odict(sc.dcp(to_plot)) # In case it's supplied as a dict
# Handle rows and columns -- assume 5 is the most rows we would want
n_plots = len(to_plot)
if n_cols is None:
max_rows = 4 # Assumption -- if desired, the user can override this by setting n_cols manually
n_cols = int((n_plots-1)//max_rows + 1) # This gives 1 column for 1-4, 2 for 5-8, etc.
n_rows = int(np.ceil(n_plots/n_cols)) # Number of subplot rows to have
return to_plot, n_cols, n_rows
def create_figs(args, font_size, font_family, sep_figs, fig=None):
''' Create the figures and set overall figure properties '''
if sep_figs:
fig = None
figs = []
else:
if fig is None:
fig = pl.figure(**args.fig) # Create the figure if none is supplied
figs = None
pl.subplots_adjust(**args.axis)
pl.rcParams['font.size'] = font_size
if font_family:
pl.rcParams['font.family'] = font_family
return fig, figs, None # Initialize axis to be None
def create_subplots(figs, fig, shareax, n_rows, n_cols, pnum, fig_args, sep_figs, log_scale, title):
''' Create subplots and set logarithmic scale '''
# Try to find axes by label, if they've already been defined -- this is to avoid the deprecation warning of reusing axes
label = f'ax{pnum+1}'
ax = None
try:
for fig_ax in fig.axes:
if fig_ax.get_label() == label:
ax = fig_ax
break
except:
pass
# Handle separate figs
if sep_figs:
figs.append(pl.figure(**fig_args))
if ax is None:
ax = pl.subplot(111, label=label)
else:
if ax is None:
ax = pl.subplot(n_rows, n_cols, pnum+1, sharex=shareax, label=label)
# Handle log scale
if log_scale:
if isinstance(log_scale, list):
if title in log_scale:
ax.set_yscale('log')
else:
ax.set_yscale('log')
return ax
def plot_data(sim, ax, key, scatter_args):
''' Add data to the plot '''
if sim.data is not None and key in sim.data and len(sim.data[key]):
this_color = sim.results[key].color
data_t = (sim.data.index-sim['start_day'])/np.timedelta64(1,'D') # Convert from data date to model output index based on model start date
ax.scatter(data_t, sim.data[key], c=[this_color], label='Data', **scatter_args)
return
def plot_interventions(sim, ax):
''' Add interventions to the plot '''
for intervention in sim['interventions']:
intervention.plot_intervention(sim, ax)
return
def title_grid_legend(ax, title, grid, commaticks, setylim, legend_args, show_legend=True):
''' Plot styling -- set the plot title, add a legend, and optionally add gridlines'''
# Handle show_legend being in the legend args, since in some cases this is the only way it can get passed
if 'show_legend' in legend_args:
show_legend = legend_args.pop('show_legend')
popped = True
else:
popped = False
# Show the legend
if show_legend:
ax.legend(**legend_args)
# If we removed it from the legend_args dict, put it back now
if popped:
legend_args['show_legend'] = show_legend
# Set the title and gridlines
ax.set_title(title)
ax.grid(grid)
# Set the y axis style
if setylim:
ax.set_ylim(bottom=0)
if commaticks:
ylims = ax.get_ylim()
if ylims[1] >= 1000:
sc.commaticks(ax=ax)
return
def reset_ticks(ax, sim, interval, as_dates, dateformat):
''' Set the tick marks, using dates by default '''
# Set the default -- "Mar-01"
if dateformat is None:
dateformat = '%b-%d'
# Set the x-axis intervals
if interval:
xmin,xmax = ax.get_xlim()
ax.set_xticks(pl.arange(xmin, xmax+1, interval))
# Set xticks as dates
if as_dates:
@ticker.FuncFormatter
def date_formatter(x, pos):
return (sim['start_day'] + dt.timedelta(days=x)).strftime(dateformat)
ax.xaxis.set_major_formatter(date_formatter)
if not interval:
ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
return
def tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show):
''' Handle saving, figure showing, and what value to return '''
# Handle saving
if do_save:
if fig_path is not None: # No figpath provided - see whether do_save is a figpath
fig_path = sc.makefilepath(fig_path) # Ensure it's valid, including creating the folder
psu.savefig(filename=fig_path) # Save the figure
# Show the figure
if do_show:
pl.show()
# Return the figure or figures
if sep_figs:
return figs
else:
return fig
def set_line_options(input_args, reskey, default):
'''From the supplied line argument, usually a color or label, decide what to use '''
if input_args is not None:
if isinstance(input_args, dict): # If it's a dict, pull out this value
output = input_args[reskey]
else: # Otherwise, assume it's the same value for all
output = input_args
else:
output = default # Default value
return output
#%% Core plotting functions
[docs]def plot_sim(sim, 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 single simulation -- see Sim.plot() for documentation '''
# Handle inputs
args = handle_args(fig_args, plot_args, scatter_args, axis_args, fill_args, legend_args, show_args)
to_plot, n_cols, n_rows = handle_to_plot('sim', to_plot, n_cols, sim=sim)
fig, figs, ax = create_figs(args, font_size, font_family, sep_figs, fig)
# Do the plotting
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 reskey in keylabels:
res = sim.results[reskey]
res_t = sim.results['t']
color = set_line_options(colors, reskey, res.color) # Choose the color
label = set_line_options(labels, reskey, 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) # Plot the data
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['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)
[docs]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, 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)
# 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, default_colors[snum]) # Choose the color
label = set_line_options(labels, scenkey, 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) # 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)
[docs]def plot_result(sim, key, fig_args=None, plot_args=None, axis_args=None, scatter_args=None,
font_size=18, font_family=None, grid=False, commaticks=True, setylim=True,
as_dates=True, dateformat=None, interval=None, color=None, label=None, fig=None,
do_show=True, do_save=False, fig_path=None):
''' Plot a single result -- see Sim.plot_result() for documentation '''
# Handle inputs
sep_figs = False # Only one figure
fig_args = sc.mergedicts({'figsize':(16,8)}, fig_args)
axis_args = sc.mergedicts({'top': 0.95}, axis_args)
args = handle_args(fig_args, plot_args, scatter_args, axis_args)
fig, figs, ax = create_figs(args, font_size, font_family, sep_figs, fig)
# Gather results
res = sim.results[key]
res_t = sim.results['t']
if color is None:
color = res.color
# Reuse the figure, if available
try:
if fig.axes[0].get_label() == 'plot_result':
ax = fig.axes[0]
except:
pass
if ax is None: # Otherwise, make a new one
ax = pl.subplot(111, label='plot_result')
# Do the plotting
if label is None:
label = res.name
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, c=color, label=label, **args.plot)
plot_data(sim, ax, key, args.scatter) # Plot the data
plot_interventions(sim, ax) # Plot the interventions
title_grid_legend(ax, res.name, grid, commaticks, setylim, args.legend) # Configure the title, grid, and legend
reset_ticks(ax, sim, interval, as_dates, dateformat) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
return tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show)
[docs]def plot_compare(df, log_scale=True, fig_args=None, plot_args=None, axis_args=None, scatter_args=None,
font_size=18, font_family=None, grid=False, commaticks=True, setylim=True,
as_dates=True, dateformat=None, interval=None, color=None, label=None, fig=None):
''' Plot a MultiSim comparison -- see MultiSim.plot_compare() for documentation '''
# Handle inputs
fig_args = sc.mergedicts({'figsize':(16,16)}, fig_args)
axis_args = sc.mergedicts({'left': 0.16, 'bottom': 0.05, 'right': 0.98, 'top': 0.98, 'wspace': 0.50, 'hspace': 0.10}, axis_args)
args = handle_args(fig_args, plot_args, scatter_args, axis_args)
fig, figs, ax = create_figs(args, font_size, font_family, sep_figs=False, fig=fig)
# Map from results into different categories
mapping = {
'cum': 'Cumulative counts',
'new': 'New counts',
'n': 'Number in state',
'r': 'R_eff',
}
category = []
for v in df.index.values:
v_type = v.split('_')[0]
if v_type in mapping:
category.append(v_type)
else:
category.append('other')
df['category'] = category
# Plot
for i,m in enumerate(mapping):
not_r_eff = m != 'r'
if not_r_eff:
ax = fig.add_subplot(2, 2, i+1)
else:
ax = fig.add_subplot(8, 2, 10)
dfm = df[df['category'] == m]
logx = not_r_eff and log_scale
dfm.plot(ax=ax, kind='barh', logx=logx, legend=False)
if not(not_r_eff):
ax.legend(loc='upper left', bbox_to_anchor=(0,-0.3))
ax.grid(True)
return fig
#%% Other plotting functions
[docs]def plot_people(people, bins=None, width=1.0, font_size=18, alpha=0.6, fig_args=None, axis_args=None, plot_args=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=(30,22)), 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=3, alpha=0.6, markersize=10, c=color, zorder=10), plot_args)
pl.rcParams['font.size'] = font_size
# 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
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)
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*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
return fig