Source code for rsvsim.plotting

'''
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 misc as cvm
from . import defaults as cvd
from . import settings as cvset


__all__ = ['date_formatter', '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, mpl_args=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))
    defaults.plot    = sc.objdict(lw=1.5, alpha= 0.7)
    defaults.scatter = sc.objdict(s=20, marker='s', alpha=0.7, zorder=0)
    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, interval=None, rotation=None, start_day=None, end_day=None)
    defaults.show    = sc.objdict(data=True, ticks=True, interventions=True, legend=True)
    defaults.mpl     = sc.objdict(dpi=None, fontsize=None, fontfamily=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)

    # 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,    fill_args)
    args.show    = sc.mergedicts(defaults.show,    show_args)
    args.mpl     = sc.mergedicts(defaults.mpl,     mpl_args)

    # If unused keyword arguments remain, raise an error
    if len(kwargs):
        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)

    # Handle what to show
    show_keys = defaults.show.keys()
    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)

    # Handle global Matplotlib arguments
    args.mpl_orig = sc.objdict()
    for key,value in args.mpl.items():
        if value is not None:
            args.mpl_orig[key] = cvset.options.get(key)
            cvset.options.set(key, value)

    return args


def handle_to_plot(kind, to_plot, n_cols, sim, check_ready=True):
    ''' Handle which quantities to plot '''

    # 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 not specified or specified as a 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
    if isinstance(to_plot, list):
        to_plot_list = to_plot # Store separately
        to_plot = sc.odict() # Create the dict
        reskeys = sim.result_keys()
        for reskey in to_plot_list:
            name = sim.results[reskey].name if reskey in reskeys else sim.results['genotype'][reskey].name
            to_plot[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,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
        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=[color], label='Data', **scatter_args)
    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_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


[docs]def date_formatter(start_day=None, dateformat=None, interval=None, start=None, end=None, ax=None, sim=None): ''' Create an automatic date formatter based on a number of days and a start day. Wrapper for Matplotlib's date formatter. Note, start_day is not required if the axis uses dates already. To be used in conjunction with setting the x-axis tick label formatter. Args: start_day (str/date): the start day, either as a string or date object dateformat (str): the date format (default '%b-%d') interval (int): if supplied, the interval between ticks (must supply an axis also to take effect) start (str/int): if supplied, the lower limit of the axis end (str/int): if supplied, the upper limit of the axis ax (axes): if supplied, automatically set the x-axis formatter for this axis sim (Sim): if supplied, get the start day from this **Examples**:: # Automatically configure the axis with default option cv.date_formatter(sim=sim, ax=ax) # Manually configure ax = pl.subplot(111) ax.plot(np.arange(60), np.random.random(60)) formatter = cv.date_formatter(start_day='2020-04-04', interval=7, start='2020-05-01', end=50, dateformat='%Y-%m-%d', ax=ax) ax.xaxis.set_major_formatter(formatter) ''' # Set the default -- "Mar-01" if dateformat is None: dateformat = '%b-%d' # Convert to a date object if start_day is None and sim is not None: start_day = sim['start_day'] if start_day is None: errormsg = 'If not supplying a start day, you must supply a sim object' raise ValueError(errormsg) start_day = sc.date(start_day) @ticker.FuncFormatter def mpl_formatter(x, pos): return (start_day + dt.timedelta(days=int(x))).strftime(dateformat) # Set initial tick marks (intervals and limits) if ax is not None: # Handle limits xmin, xmax = ax.get_xlim() if start: xmin = sc.day(start, start_day=start_day) if end: xmax = sc.day(end, start_day=start_day) ax.set_xlim((xmin, xmax)) # Set the x-axis intervals if interval: ax.set_xticks(np.arange(xmin, xmax+1, interval)) # Set the formatter ax.xaxis.set_major_formatter(mpl_formatter) return mpl_formatter
def reset_ticks(ax, sim=None, date_args=None, start_day=None): ''' 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'] # Handle start and end days xmin,xmax = ax.get_xlim() if date_args.start_day: xmin = float(sc.day(date_args.start_day, start_day=start_day)) # Keep original type (float) if date_args.end_day: xmax = float(sc.day(date_args.end_day, start_day=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)) # Set xticks as dates if date_args.as_dates: date_formatter(start_day=start_day, dateformat=date_args.dateformat, ax=ax) if not date_args.interval: ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True)) # Handle rotation if date_args.rotation: ax.tick_params(axis='x', labelrotation=date_args.rotation) return def tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show, args): ''' 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 cvm.savefig(filename=fig_path) # Save the figure # Show the figure, or close it do_show = cvset.handle_show(do_show) if cvset.options.close and not do_show: if sep_figs: for fig in figs: pl.close(fig) else: pl.close(fig) # Reset Matplotlib defaults for key,value in args.mpl_orig.items(): cvset.options.set(key, value) # Return the figure or figures if sep_figs: return figs else: return fig 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, 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 genotype_keys = sim.result_keys('genotype') 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['timestep'] if reskey in genotype_keys: res = sim.results['genotype'][reskey] ns = sim['n_genotypes'] genotype_colors = sc.gridcolors(ns) for genotype in range(ns): color = genotype_colors[genotype] # Choose the color label = sim['genotypes'][genotype].label if res.low is not None and res.high is not None: ax.fill_between(res_t, res.low[genotype,:], res.high[genotype,:], color=color, **args.fill) # Create the uncertainty bound ax.plot(res_t, res.values[genotype,:], 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)
[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, 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 genotype_keys = sim.result_keys('genotype') if reskey in genotype_keys: ns = sim['n_genotypes'] genotype_colors = sc.gridcolors(ns) for genotype in range(ns): res_y = scendata.best[genotype,:] color = genotype_colors[genotype] # Choose the color label = 'groupA' if genotype == 0 else sim['genotypes'][genotype - 1].label ax.fill_between(scens.tsvec, scendata.low[genotype,:], scendata.high[genotype,:], color=color, **args.fill) # Create the uncertainty bound ax.plot(scens.tsvec, 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.tsvec, scendata.low, scendata.high, color=color, **args.fill) # Create the uncertainty bound ax.plot(scens.tsvec, 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)
[docs]def plot_result(key, sim=None, fig_args=None, plot_args=None, axis_args=None, scatter_args=None, date_args=None, mpl_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 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, mpl_args=mpl_args, **kwargs) fig, figs = create_figs(args, sep_figs, fig, ax) # Gather results res = sim.results[key] res_t = sim.results['timestep'] if color is None: color = res.color # 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') # 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, color=color) # 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, args.date) # Optionally reset tick marks (useful for e.g. plotting weeks/months) return tidy_up(fig, figs, sep_figs, do_save, fig_path, do_show, args)
[docs]def plot_compare(df, log_scale=True, fig_args=None, axis_args=None, mpl_args=None, grid=False, commaticks=True, setylim=True, color=None, label=None, fig=None, **kwargs): ''' Plot a MultiSim comparison -- see MultiSim.plot_compare() for documentation ''' # Handle inputs 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, mpl_args=mpl_args, **kwargs) fig, figs = create_figs(args, 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, 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): 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.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
#%% 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): ''' 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): ''' 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): ''' 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): ''' 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): ''' 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