'''
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
from . import misc as cvm
from . import defaults as cvd
from .settings import options as cvo
__all__ = ['plot_sim', 'plot_scens', 'plot_result', 'plot_compare', 'plot_people', 'plotly_sim', 'plotly_people', 'plotly_animate']
#%% Plotting helper functions
def handle_args(fig_args=None, plot_args=None, scatter_args=None, axis_args=None, fill_args=None,
legend_args=None, date_args=None, show_args=None, style_args=None, do_show=None, **kwargs):
''' Handle input arguments -- merge user input with defaults; see sim.plot for documentation '''
# Set defaults
defaults = sc.objdict()
defaults.fig = sc.objdict(figsize=(10, 8), num=None)
defaults.plot = sc.objdict(lw=1.5, alpha= 0.7)
defaults.scatter = sc.objdict(s=20, marker='s', alpha=0.7, zorder=1.75, datastride=1) # NB: 1.75 is above grid lines but below plots
defaults.axis = sc.objdict(left=0.10, bottom=0.08, right=0.95, top=0.95, wspace=0.30, hspace=0.30)
defaults.fill = sc.objdict(alpha=0.2)
defaults.legend = sc.objdict(loc='best', frameon=False)
defaults.date = sc.objdict(as_dates=True, dateformat=None, rotation=None, start=None, end=None)
defaults.show = sc.objdict(data=True, ticks=True, interventions=True, legend=True, outer=False, tight=False, maximize=False, annotations=None, do_show=do_show, returnfig=cvo.returnfig)
defaults.style = sc.objdict(style=None, dpi=None, font=None, fontsize=None, grid=None, facecolor=None) # Use Covasim global defaults
# Handle directly supplied kwargs
for dkey,default in defaults.items():
keys = list(kwargs.keys())
for kw in keys:
if kw in default.keys():
default[kw] = kwargs.pop(kw)
# Handle what to show
if show_args is not None:
annotations = show_args.get('annotations', None)
if annotations in [True, False]: # Handle all on or all off
show_keys = ['data', 'ticks', 'interventions', 'legend']
for k in show_keys:
show_args[k] = annotations
# Merge arguments together
args = sc.objdict()
args.fig = sc.mergedicts(defaults.fig, fig_args)
args.plot = sc.mergedicts(defaults.plot, plot_args)
args.scatter = sc.mergedicts(defaults.scatter, scatter_args)
args.axis = sc.mergedicts(defaults.axis, axis_args)
args.fill = sc.mergedicts(defaults.fill, fill_args)
args.legend = sc.mergedicts(defaults.legend, legend_args)
args.date = sc.mergedicts(defaults.date, date_args)
args.show = sc.mergedicts(defaults.show, show_args)
args.style = sc.mergedicts(defaults.style, style_args)
# Handle potential rcParams keys
keys = list(kwargs.keys())
for key in keys:
if key in pl.rcParams:
args.style[key] = kwargs.pop(key)
# If unused keyword arguments remain, parse or raise an error
if len(kwargs):
# Everything remaining is not found
notfound = sc.strjoin(kwargs.keys())
valid = sc.strjoin(sorted(set([k for d in defaults.values() for k in d.keys()]))) # Remove duplicates and order
errormsg = f'The following keywords could not be processed:\n{notfound}\n\n'
errormsg += f'Valid keywords are:\n{valid}\n\n'
errormsg += 'For more precise plotting control, use fig_args, plot_args, etc.'
raise sc.KeyNotFoundError(errormsg)
return args
def handle_show_return(do_show=None, returnfig=None, fig=None, figs=None):
''' Helper function to handle both show and what to return -- a nothing if Jupyter, else a figure '''
if do_show is None:
do_show = cvo.show
if returnfig is None:
returnfig = cvo.returnfig
figlist = sc.mergelists(fig, figs) # Usually just one figure, but here for completeness
# Decide whether to show the figure or not
backend = pl.get_backend()
if backend == 'agg': # Cannot show plots for a non-interactive backend
do_show = False
if do_show: # Now check whether to show, and atually do it
pl.show()
# Show the figure, or close it
if cvo.close and not do_show:
for f in figlist:
pl.close(f)
# Return the figure or figures unless we're in Jupyter
if not returnfig:
return
else:
if figs is not None:
return figlist
else:
return fig
def handle_to_plot(kind, to_plot, n_cols, sim, check_ready=True):
''' Handle which quantities to plot '''
# Allow default kind to be overwritten by to_plot -- used by msim.plot()
if isinstance(to_plot, tuple):
kind, to_plot = to_plot # Split the tuple
# Check that results are ready
if check_ready and not sim.results_ready:
errormsg = 'Cannot plot since results are not ready yet -- did you run the sim?'
raise RuntimeError(errormsg)
# If it matches a result key, convert to a list
reskeys = sim.result_keys('main')
varkeys = sim.result_keys('variant')
allkeys = reskeys + varkeys
if to_plot in allkeys:
to_plot = sc.tolist(to_plot)
# If not specified or specified as another string, load defaults
if to_plot is None or isinstance(to_plot, str):
to_plot = cvd.get_default_plots(to_plot, kind=kind, sim=sim)
# If a list of keys has been supplied or constructed
if isinstance(to_plot, list):
to_plot_list = to_plot # Store separately
to_plot = sc.odict() # Create the dict
invalid = sc.autolist()
for reskey in to_plot_list:
if reskey in allkeys:
name = sim.results[reskey].name if reskey in reskeys else sim.results['variant'][reskey].name
to_plot[name] = [reskey] # Use the result name as the key and the reskey as the value
else:
invalid += reskey
if len(invalid):
errormsg = f'The following key(s) are invalid:\n{sc.strjoin(invalid)}\n\nValid main keys are:\n{sc.strjoin(reskeys)}\n\nValid variant keys are:\n{sc.strjoin(varkeys)}'
raise sc.KeyNotFoundError(errormsg)
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 = 5 # 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,n_cols = sc.get_rows_cols(n_plots, ncols=n_cols) # Inconsistent naming due to Covasim/Matplotlib conventions
return to_plot, n_cols, n_rows
def create_figs(args, sep_figs, fig=None, ax=None):
'''
Create the figures and set overall figure properties. If a figure is supplied,
reset the axes labels for automatic use by other plotting functions (i.e. ax1, ax2, etc.)
'''
if sep_figs:
fig = None
figs = []
else:
if fig is None:
if ax is None:
fig = pl.figure(**args.fig) # Create the figure if none is supplied
else:
fig = ax.figure
else:
for i,fax in enumerate(fig.axes):
fax.set_label(f'ax{i+1}')
figs = None
pl.subplots_adjust(**args.axis)
return fig, figs
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, color=None):
''' Add data to the plot '''
if sim.data is not None and key in sim.data and len(sim.data[key]):
if color is None:
color = sim.results[key].color
datastride = scatter_args.pop('datastride', 1) # Temporarily pop so other arguments pass correctly to ax.scatter()
x = np.array(sim.data.index)[::datastride]
y = np.array(sim.data[key])[::datastride]
ax.scatter(x, y, c=[color], label='Data', **scatter_args)
scatter_args['datastride'] = datastride # Restore
return
def plot_interventions(sim, ax):
''' Add interventions to the plot '''
for intervention in sim['interventions']:
if hasattr(intervention, 'plot_intervention'): # Don't plot e.g. functions
intervention.plot_intervention(sim, ax)
return
def title_grid_legend(ax, title, grid, commaticks, setylim, legend_args, show_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 and show_args['legend']: # It's pretty ugly, but there are multiple ways of controlling whether the legend shows
# Remove duplicate entries
handles, labels = ax.get_legend_handles_labels()
unique_inds = np.sort(np.unique(labels, return_index=True)[1])
handles = [handles[u] for u in unique_inds]
labels = [labels[u] for u in unique_inds]
# Actually make legend
ax.legend(handles=handles, labels=labels, **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, gridlines, and color
ax.set_title(title)
# Set the y axis style
if setylim and ax.yaxis.get_scale() != 'log':
ax.set_ylim(bottom=0)
if commaticks:
ylims = ax.get_ylim()
if ylims[1] >= 1000:
sc.commaticks(ax=ax)
# Optionally remove x-axis labels except on bottom plots -- don't use ax.label_outer() since we need to keep the y-labels
if show_args['outer']:
lastrow = ax.get_subplotspec().is_last_row()
if not lastrow:
for label in ax.get_xticklabels(which='both'):
label.set_visible(False)
ax.set_xlabel('')
return
def reset_ticks(ax, sim=None, date_args=None, start_day=None, n_cols=1):
''' Set the tick marks, using dates by default '''
# Handle options
date_args = sc.objdict(date_args) # Ensure it's not a regular dict
if start_day is None and sim is not None:
start_day = sim['start_day']
# Set xticks as dates
d_args = {k:date_args.pop(k) for k in ['as_dates', 'dateformat']} # Pop these to handle separately
if d_args['as_dates']:
if d_args['dateformat'] is None and n_cols >= 3: # Change default date format if more than 2 columns are shown
d_args['dateformat'] = 'concise'
if d_args['dateformat'] in ['covasim', 'sciris', 'auto', 'matplotlib', 'concise', 'brief']: # Handle date formatter rather than date format
style, dateformat = d_args['dateformat'], None # Swap argument order
style = style.replace('covasim', 'sciris') # In case any users are confused about what "default" is
else:
dateformat, style = d_args['dateformat'], 'sciris' # Otherwise, treat dateformat as a date format
sc.dateformatter(ax=ax, style=style, dateformat=dateformat, **date_args) # Actually format the axis with dates, rotation, etc.
else:
# Handle start and end days
xmin,xmax = ax.get_xlim()
if date_args.start:
xmin = float(sc.day(date_args.start, start_date=start_day)) # Keep original type (float)
if date_args.end:
xmax = float(sc.day(date_args.end, start_date=start_day))
ax.set_xlim([xmin, xmax])
# Set the x-axis intervals
if date_args.interval:
ax.set_xticks(np.arange(xmin, xmax+1, date_args.interval))
# Restore date args
date_args.update(d_args)
return
def tidy_up(fig, figs, sep_figs, do_save, fig_path, args):
''' Handle saving, figure showing, and what value to return '''
figlist = sc.mergelists(fig, figs) # Usually just one figure, but here for completeness
# Optionally maximize -- does not work on all systems
if args.show.maximize:
for f in figlist:
sc.maximize(fig=f)
pl.pause(0.01) # Force refresh
# Use tight layout for all figures
if args.show.tight:
for f in figlist:
sc.figlayout(fig=f)
# Handle saving
if do_save:
if isinstance(fig_path, str): # No figpath provided - see whether do_save is a figpath
fig_path = sc.makefilepath(fig_path) # Ensure it's valid, including creating the folder
cvm.savefig(fig=figlist, filename=fig_path) # Save the figure
return handle_show_return(do_show=args.show.do_show, returnfig=args.show.returnfig, fig=fig, figs=figs)
def set_line_options(input_args, reskey, resnum, 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]
elif isinstance(input_args, list): # If it's a list, ditto
output = input_args[resnum]
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(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, style_args=None, n_cols=None, grid=True, 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, style_args=style_args, do_show=do_show, **kwargs)
to_plot, n_cols, n_rows = handle_to_plot('sim', to_plot, n_cols, sim=sim)
# Do the plotting
with cvo.with_style(args.style):
fig, figs = create_figs(args, sep_figs, fig, ax)
variant_keys = sim.result_keys('variant')
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['date']
if reskey in variant_keys:
res = sim.results['variant'][reskey]
ns = sim['n_variants']
variant_colors = sc.gridcolors(ns)
for variant in range(ns):
# Colors and labels
v_color = variant_colors[variant]
v_label = 'wild type' if variant == 0 else sim['variants'][variant-1].label
color = set_line_options(colors, reskey, resnum, v_color) # Choose the color
label = set_line_options(labels, reskey, resnum, '') # Choose the label
if label: label += f' - {v_label}'
else: label = v_label
# Plotting
if res.low is not None and res.high is not None:
ax.fill_between(res_t, res.low[variant,:], res.high[variant,:], color=color, **args.fill) # Create the uncertainty bound
ax.plot(res_t, res.values[variant,:], 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, n_cols=n_cols) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
if args.show['interventions']:
plot_interventions(sim, ax) # Plot the interventions
title_grid_legend(ax, title, grid, commaticks, setylim, args.legend, args.show) # Configure the title, grid, and legend
output = tidy_up(fig, figs, sep_figs, do_save, fig_path, args)
return output
[docs]
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, style_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, style_args=style_args, do_show=do_show, **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
# Do the plotting
with cvo.with_style(args.style):
fig, figs = create_figs(args, sep_figs, fig, ax)
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.tolist(reskeys) # In case it's a string
for reskey in reskeys:
res_t = scens.datevec
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
variant_keys = sim.result_keys('variant')
if reskey in variant_keys:
ns = sim['n_variants']
variant_colors = sc.gridcolors(ns)
for variant in range(ns):
res_y = scendata.best[variant,:]
color = variant_colors[variant] # Choose the color
label = 'wild type' if variant == 0 else sim['variants'][variant - 1].label
ax.fill_between(res_t, scendata.low[variant,:], scendata.high[variant,:], color=color, **args.fill) # Create the uncertainty bound
ax.plot(res_t, 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(res_t, scendata.low, scendata.high, color=color, **args.fill) # Create the uncertainty bound
ax.plot(res_t, 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, args.show, pnum==0) # Configure the title, grid, and legend -- only show legend for first
return tidy_up(fig, figs, sep_figs, do_save, fig_path, args)
[docs]
def plot_result(key, sim=None, fig_args=None, plot_args=None, axis_args=None, scatter_args=None,
date_args=None, style_args=None, grid=False, commaticks=True, setylim=True, color=None, label=None,
do_show=None, do_save=False, fig_path=None, fig=None, ax=None, **kwargs):
''' Plot a single result -- see ``cv.Sim.plot_result()`` for documentation '''
# Handle inputs
sep_figs = False # Only one figure
fig_args = sc.mergedicts({'figsize':(8,5)}, fig_args)
axis_args = sc.mergedicts({'top': 0.95}, axis_args)
args = handle_args(fig_args=fig_args, plot_args=plot_args, scatter_args=scatter_args, axis_args=axis_args,
date_args=date_args, style_args=style_args, do_show=do_show, **kwargs)
# Gather results
res = sim.results[key]
res_t = sim.results['date']
if color is None:
color = res.color
# Do the plotting
with cvo.with_style(args.style):
fig, figs = create_figs(args, sep_figs, fig, ax)
# Reuse the figure, if available
if ax is None: # Otherwise, make a new one
try:
ax = fig.axes[0]
except:
ax = fig.add_subplot(111, label='ax1')
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, color=color) # Plot the data
plot_interventions(sim, ax) # Plot the interventions
title_grid_legend(ax, res.name, grid, commaticks, setylim, args.legend, args.show) # Configure the title, grid, and legend
reset_ticks(ax, sim, args.date) # Optionally reset tick marks (useful for e.g. plotting weeks/months)
return tidy_up(fig, figs, sep_figs, do_save, fig_path, args)
[docs]
def plot_compare(df, log_scale=True, fig_args=None, axis_args=None, style_args=None, grid=False,
commaticks=True, setylim=True, color=None, label=None, fig=None,
do_save=None, do_show=None, fig_path=None, **kwargs):
''' Plot a MultiSim comparison -- see MultiSim.plot_compare() for documentation '''
# Handle inputs
sep_figs = False
fig_args = sc.mergedicts({'figsize':(8,8)}, 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=fig_args, axis_args=axis_args, style_args=style_args, do_show=do_show, **kwargs)
# 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
with cvo.with_style(args.style):
fig, figs = create_figs(args, sep_figs=False, fig=fig)
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 tidy_up(fig, figs, sep_figs, do_save, fig_path, args)
#%% Other plotting functions
[docs]
def plot_people(people, bins=None, width=1.0, alpha=0.6, fig_args=None, axis_args=None,
plot_args=None, style_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)
style_args = sc.mergedicts(style_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]
with cvo.with_style(style_args):
# 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.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.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):
contacts_lk = people.contacts[lk]
members_lk = contacts_lk.members
n_contacts = len(contacts_lk)
n_members = len(members_lk)
if w_type == 'total':
weight = 1
total_contacts = 2*n_contacts # x2 since each contact is undirected
ylabel = 'Number of contacts'
participation = n_members/len(people) # Proportion of people that have contacts in this layer
title = f'Total contacts for layer "{lk}": {total_contacts:n}\n({participation*100:.0f}% participation)'
elif w_type == 'percapita':
age_counts_within_layer = np.histogram(people.age[members_lk], edges)[0]
weight = np.divide(1.0, age_counts_within_layer, where=age_counts_within_layer>0)
mean_contacts_within_layer = 2*n_contacts/n_members if n_members else 0 # Factor of 2 since edges are bi-directional
ylabel = 'Per capita number of contacts'
title = f'Mean contacts for layer "{lk}": {mean_contacts_within_layer:0.2f}'
elif w_type == 'weighted':
weight = people.pars['beta_layer'][lk]*people.pars['beta']
total_weight = np.round(weight*2*n_contacts)
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.xlabel('Age')
pl.ylabel(ylabel)
pl.title(title)
if w_type == 'weighted':
share_ax = ax # Update shared axis
return handle_show_return(fig=fig, do_show=do_show)
#%% Plotly functions
def import_plotly():
''' Try to import Plotly, but fail quietly if not available '''
# Try to import Plotly normally
try:
import plotly.graph_objects as go
return go
# If that failed, handle it gracefully
except Exception as E:
class PlotlyImportFailed(object):
''' Define a micro-class to give a helpful error message if the import failed '''
def __init__(self, E):
self.E = E
def __getattr__(self, attr):
errormsg = f'Plotly import failed: {str(self.E)}. Plotly plotting is not available. Please install Plotly first.'
raise ImportError(errormsg)
go = PlotlyImportFailed(E)
return go
def get_individual_states(sim): # pragma: no cover
''' Helper function to convert people into integers '''
people = sim.people
states = [
{'name': 'Healthy',
'quantity': None,
'color': '#a6cee3',
'value': 0
},
{'name': 'Exposed',
'quantity': 'date_exposed',
'color': '#ff7f00',
'value': 2
},
{'name': 'Infectious',
'quantity': 'date_infectious',
'color': '#e33d3e',
'value': 3
},
{'name': 'Recovered',
'quantity': 'date_recovered',
'color': '#3e89bc',
'value': 4
},
{'name': 'Dead',
'quantity': 'date_dead',
'color': '#000000',
'value': 5
},
]
z = np.zeros((len(people), sim.npts))
for state in states:
date = state['quantity']
if date is not None:
inds = sim.people.defined(date)
for ind in inds:
z[ind, int(people[date][ind]):] = state['value']
return z, states
# Default settings for the Plotly legend
plotly_legend = dict(legend_orientation='h', legend=dict(x=0.0, y=1.18))
def plotly_interventions(sim, fig, add_to_legend=False): # pragma: no cover
''' Add vertical lines for interventions to the plot '''
go = import_plotly() # Load Plotly
if sim['interventions']:
for interv in sim['interventions']:
if hasattr(interv, 'days'):
for interv_day in interv.days:
if interv_day and interv_day < sim['n_days']:
interv_date = sim.date(interv_day, as_date=True)
fig.add_shape(dict(type='line', xref='x', yref='paper', x0=interv_date, x1=interv_date, y0=0, y1=1, line=dict(width=0.5, dash='dash')))
if add_to_legend:
fig.add_trace(go.Scatter(x=[interv_date], y=[0], mode='lines', name='Intervention change', line=dict(width=0.5, dash='dash')))
return
[docs]
def plotly_sim(sim, do_show=False): # pragma: no cover
''' Main simulation results -- parallel of sim.plot() '''
go = import_plotly() # Load Plotly
plots = []
to_plot = cvd.get_default_plots()
for p,title,keylabels in to_plot.enumitems():
fig = go.Figure()
for key in keylabels:
label = sim.results[key].name
this_color = sim.results[key].color
x = sim.results['date'][:]
y = sim.results[key][:]
fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name=label, line_color=this_color))
if sim.data is not None and key in sim.data:
xdata = sim.data['date']
ydata = sim.data[key]
fig.add_trace(go.Scatter(x=xdata, y=ydata, mode='markers', name=label + ' (data)', line_color=this_color))
plotly_interventions(sim, fig, add_to_legend=(p==0)) # Only add the intervention label to the legend for the first plot
fig.update_layout(title={'text':title}, yaxis_title='Count', autosize=True, **plotly_legend)
plots.append(fig)
if do_show:
for fig in plots:
fig.show()
return plots
[docs]
def plotly_people(sim, do_show=False): # pragma: no cover
''' Plot a "cascade" of people moving through different states '''
go = import_plotly() # Load Plotly
z, states = get_individual_states(sim)
fig = go.Figure()
for state in states[::-1]: # Reverse order for plotting
x = sim.results['date'][:]
y = (z == state['value']).sum(axis=0)
fig.add_trace(go.Scatter(
x=x, y=y,
stackgroup='one',
line=dict(width=0.5, color=state['color']),
fillcolor=state['color'],
hoverinfo='y+name',
name=state['name']
))
plotly_interventions(sim, fig)
fig.update_layout(yaxis_range=(0, sim.n))
fig.update_layout(title={'text': 'Numbers of people by health state'}, yaxis_title='People', autosize=True, **plotly_legend)
if do_show:
fig.show()
return fig
[docs]
def plotly_animate(sim, do_show=False): # pragma: no cover
''' Plot an animation of each person in the sim '''
go = import_plotly() # Load Plotly
z, states = get_individual_states(sim)
min_color = min(states, key=lambda x: x['value'])['value']
max_color = max(states, key=lambda x: x['value'])['value']
colorscale = [[x['value'] / max_color, x['color']] for x in states]
aspect = 5
y_size = int(np.ceil((z.shape[0] / aspect) ** 0.5))
x_size = int(np.ceil(aspect * y_size))
z = np.pad(z, ((0, x_size * y_size - z.shape[0]), (0, 0)), mode='constant', constant_values=np.nan)
days = sim.tvec
fig_dict = {
'data': [],
'layout': {},
'frames': []
}
fig_dict['layout']['updatemenus'] = [
{
'buttons': [
{
'args': [None, {'frame': {'duration': 200, 'redraw': True},
'fromcurrent': True}],
'label': 'Play',
'method': 'animate'
},
{
'args': [[None], {'frame': {'duration': 0, 'redraw': True},
'mode': 'immediate',
'transition': {'duration': 0}}],
'label': 'Pause',
'method': 'animate'
}
],
'direction': 'left',
'pad': {'r': 10, 't': 87},
'showactive': False,
'type': 'buttons',
'x': 0.1,
'xanchor': 'right',
'y': 0,
'yanchor': 'top'
}
]
sliders_dict = {
'active': 0,
'yanchor': 'top',
'xanchor': 'left',
'currentvalue': {
'font': {'size': 16},
'prefix': 'Day: ',
'visible': True,
'xanchor': 'right'
},
'transition': {'duration': 200},
'pad': {'b': 10, 't': 50},
'len': 0.9,
'x': 0.1,
'y': 0,
'steps': []
}
# make data
fig_dict['data'] = [go.Heatmap(z=np.reshape(z[:, 0], (y_size, x_size)),
zmin=min_color,
zmax=max_color,
colorscale=colorscale,
showscale=False,
)]
for state in states:
fig_dict['data'].append(go.Scatter(x=[None], y=[None], mode='markers',
marker=dict(size=10, color=state['color']),
showlegend=True, name=state['name']))
# make frames
for i, day in enumerate(days):
frame = {'data': [go.Heatmap(z=np.reshape(z[:, i], (y_size, x_size)))],
'name': i}
fig_dict['frames'].append(frame)
slider_step = {'args': [
[i],
{'frame': {'duration': 5, 'redraw': True},
'mode': 'immediate', }
],
'label': i,
'method': 'animate'}
sliders_dict['steps'].append(slider_step)
fig_dict['layout']['sliders'] = [sliders_dict]
fig = go.Figure(fig_dict)
fig.update_layout(
autosize=True,
xaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
),
yaxis=dict(
automargin=True,
showgrid=False,
showline=False,
showticklabels=False,
),
)
fig.update_layout(title={'text': 'Epidemic over time'}, **plotly_legend)
if do_show:
fig.show()
return fig
