Merge pull request #490 from amath-idm/fix-webapp-plotting

Fix webapp plotting

CHANGELOG.rst

@@ -3,6 +3,13 @@ What's new
 
 All notable changes to the codebase are documented in this file. Note: in many cases, changes from multiple patch versions are grouped together, so numbering will not be strictly consecutive.
 
+Version 1.0.1 (2020-05-09)
+--------------------------
+- Added argument ``as_date`` for ``sim.date()`` to return a ``datetime`` object instead of a string.
+- Fixed plotting of interventions in the webapp.
+- Removed default 1-hour time limit for simulations.
+- GitHub info: PR `490 <https://github.com/amath-idm/covasim/pull/490>`__, previous head ``9a6c23b``
+
 
 Version 1.0.0 (2020-05-08)
 --------------------------

covasim/README.rst

@@ -2,7 +2,7 @@
 Parameters
 ==========
 
-This file describes the expected behavior of each parameter in the model. Note: the term "overall infection rate" can be explored using ``sim.results['doubling_time']`` and ``sim.results['r_eff']`` (a higher infection rate means lower doubling times and higher *R\_eff*), as well as by simply looking at the epidemic curves.
+This file describes each of the input parameters in Covasim. Note: the overall infection rate can be explored using ``sim.results['doubling_time']`` and ``sim.results['r_eff']`` (a higher infection rate means lower doubling times and higher *R\_eff*), as well as by simply looking at the epidemic curves.
 
 Population parameters
 ---------------------
@@ -33,30 +33,29 @@ Basic disease transmission
 * ``dynam_layer`` = Which layers are dynamic; set below
 * ``beta_layer``  = Transmissibility per layer; set below
 * ``n_imports``   = Average daily number of imported cases (actual number is drawn from Poisson distribution)
-* ``beta_dist``   = Distribution to draw individual level transmissibility
-* ``viral_dist``  = The time varying viral load (transmissibility)
+* ``beta_dist``   = Distribution to draw individual level transmissibility; see https://wellcomeopenresearch.org/articles/5-67
+* ``viral_dist``  = The time varying viral load (transmissibility); estimated from Lescure 2020, Lancet, https://doi.org/10.1016/S1473-3099(20)30200-0
 
 Efficacy of protection measures
 -------------------------------
-* ``asymp_factor`` = Multiply beta by this factor for asymptomatic cases
-* ``diag_factor``  = Multiply beta by this factor for diganosed cases
+* ``asymp_factor`` = Multiply beta by this factor for asymptomatic cases; no statistically significant difference in transmissibility: https://www.sciencedirect.com/science/article/pii/S1201971220302502
+* ``diag_factor``  = Multiply beta by this factor for diganosed cases; based on intervention strength
 * ``quar_eff``     = Quarantine multiplier on transmissibility and susceptibility; set below
-* ``quar_period``  = Number of days to quarantine for
+* ``quar_period``  = Number of days to quarantine for; assumption based on standard policies
 
 Time for disease progression
 ----------------------------
-* ``exp2inf``  = Duration from exposed to infectious
-* ``inf2sym``  = Duration from infectious to symptomatic
-* ``sym2sev``  = Duration from symptomatic to severe symptoms
-* ``sev2crit`` = Duration from severe symptoms to requiring ICU
+* ``exp2inf``  = Duration from exposed to infectious; see Lauer et al., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081172/, subtracting inf2sim duration
+* ``inf2sym``  = Duration from infectious to symptomatic; see Linton et al., https://doi.org/10.3390/jcm9020538
+* ``sym2sev``  = Duration from symptomatic to severe symptoms; see Linton et al., https://doi.org/10.3390/jcm9020538Duration from severe symptoms to requiring ICU; see Wang et al., https://jamanetwork.com/journals/jama/fullarticle/2761044Duration from severe symptoms to requiring ICU
 
 Time for disease recovery
 -------------------------
-* ``asym2rec`` = Duration for asymptomatics to recover
-* ``mild2rec`` = Duration from mild symptoms to recovered
-* ``sev2rec``  = Duration from severe symptoms to recovered
-* ``crit2rec`` = Duration from critical symptoms to recovered
-* ``crit2die`` = Duration from critical symptoms to death
+* ``asym2rec`` = Duration for asymptomatic people to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x
+* ``mild2rec`` = Duration for people with mild symptoms to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x
+* ``sev2rec``  = Duration for people with severe symptoms to recover, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
+* ``crit2rec`` = Duration for people with critical symptoms to recover, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
+* ``crit2die`` = Duration from critical symptoms to death, 17.8 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
 
 Severity parameters
 -------------------
@@ -66,15 +65,15 @@ Severity parameters
 * ``rel_crit_prob``   = Scale factor for proportion of severe cases that become critical
 * ``rel_death_prob``  = Scale factor for proportion of critical cases that result in death
 * ``prog_by_age``     = Whether to set disease progression based on the person's age
-* ``prognoses``       = Populate this later
+* ``prognoses``       = The actual arrays of prognoses by age; this is populated later
 
 Events and interventions
 ------------------------
-* ``interventions`` = List of Intervention instances
+* ``interventions`` = The interventions present in this simulation; populated by the user
 * ``interv_func``   = Custom intervention function
-* ``timelimit``     = Time limit for a simulation (seconds)
+* ``timelimit``     = Time limit for the simulation (seconds)
 * ``stopping_func`` = A function to call to stop the sim partway through
 
 Health system parameters
 --------------------------
-* ``n_beds`` = Baseline assumption is that there's no upper limit on the number of beds i.e. there's enough for everyone
+* ``n_beds`` = The number of beds available for severely/critically ill patients (default is no constraint)

covasim/base.py

@@ -2,11 +2,10 @@
 Base classes for Covasim.
 '''
 
-import datetime as dt
 import numpy as np
-import pylab as pl
 import pandas as pd
 import sciris as sc
+import datetime as dt
 from . import utils as cvu
 from . import misc as cvm
 from . import defaults as cvd
@@ -233,32 +232,40 @@ def day(self, day, *args):
         return days
 
 
-    def date(self, ind, *args, dateformat=None):
+    def date(self, ind, *args, dateformat=None, as_date=False):
         '''
-        Convert an integer or list of integer simulation days to a date/list of dates.
+        Convert one or more integer days of simulation time to a date/list of dates --
+        by default returns a string, or returns a datetime Date object if as_date is True.
 
         Args:
             ind (int, list, or array): the day(s) in simulation time
+            as_date (bool): whether to return as a datetime date instead of a string
 
         Returns:
-            dates (str or list): the date relative to the simulation start day, as an integer
+            dates (str, Date, or list): the date(s) corresponding to the simulation day(s)
 
-        **Example**::
+        **Examples**::
 
-            sim.date(35) # Returns '2020-04-05'
+            sim.date(34) # Returns '2020-04-04'
+            sim.date([34, 54]) # Returns ['2020-04-04', '2020-04-24']
+            sim.date(34, 54, as_dt=True) # Returns [datetime.date(2020, 4, 4), datetime.date(2020, 4, 24)]
         '''
 
+        # Handle inputs
         if sc.isnumber(ind): # If it's a number, convert it to a list
             ind = sc.promotetolist(ind)
         ind.extend(args)
-
         if dateformat is None:
             dateformat = '%Y-%m-%d'
 
+        # Do the conversion
         dates = []
         for i in ind:
-            tmp = self['start_day'] + dt.timedelta(days=int(i))
-            dates.append(tmp.strftime(dateformat))
+            date_obj = self['start_day'] + dt.timedelta(days=int(i))
+            if as_date:
+                dates.append(date_obj)
+            else:
+                dates.append(date_obj.strftime(dateformat))
 
         # Return a string rather than a list if only one provided
         if len(ind)==1:

covasim/interventions.py

@@ -1,8 +1,13 @@
-import inspect
+'''
+Specify the core interventions available in Covasim. Other interventions can be
+defined by the user by inheriting from these classes.
+'''
+
 import numpy as np
 import pandas as pd
 import pylab as pl
 import sciris as sc
+import inspect
 import datetime as dt
 from . import utils as cvu
 from . import base as cvb

covasim/misc.py

@@ -2,10 +2,10 @@
 Miscellaneous functions that do not belong anywhere else
 '''
 
-import datetime as dt
 import numpy as np
 import pandas as pd
 import sciris as sc
+import datetime as dt
 import scipy.stats as sps
 from . import version as cvver
 

covasim/parameters.py

@@ -63,10 +63,10 @@ def make_pars(set_prognoses=False, prog_by_age=True, **kwargs):
     pars['dur']['sev2crit'] = {'dist':'lognormal_int', 'par1':3.0, 'par2':7.4} # Duration from severe symptoms to requiring ICU; see Wang et al., https://jamanetwork.com/journals/jama/fullarticle/2761044
 
     # Duration parameters: time for disease recovery
-    pars['dur']['asym2rec'] = {'dist':'lognormal_int', 'par1':8.0,  'par2':2.0} # Duration for asymptomatics to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x
-    pars['dur']['mild2rec'] = {'dist':'lognormal_int', 'par1':8.0,  'par2':2.0} # Duration from mild symptoms to recovered; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x
-    pars['dur']['sev2rec']  = {'dist':'lognormal_int', 'par1':14.0, 'par2':2.4} # Duration from severe symptoms to recovered, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
-    pars['dur']['crit2rec'] = {'dist':'lognormal_int', 'par1':14.0, 'par2':2.4} # Duration from critical symptoms to recovered, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
+    pars['dur']['asym2rec'] = {'dist':'lognormal_int', 'par1':8.0,  'par2':2.0} # Duration for asymptomatic people to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x
+    pars['dur']['mild2rec'] = {'dist':'lognormal_int', 'par1':8.0,  'par2':2.0} # Duration for people with mild symptoms to recover; see Wölfel et al., https://www.nature.com/articles/s41586-020-2196-x
+    pars['dur']['sev2rec']  = {'dist':'lognormal_int', 'par1':14.0, 'par2':2.4} # Duration for people with severe symptoms to recover, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
+    pars['dur']['crit2rec'] = {'dist':'lognormal_int', 'par1':14.0, 'par2':2.4} # Duration for people with critical symptoms to recover, 22.6 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
     pars['dur']['crit2die'] = {'dist':'lognormal_int', 'par1':6.2,  'par2':1.7} # Duration from critical symptoms to death, 17.8 days total; see Verity et al., https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf
 
     # Severity parameters: probabilities of symptom progression
@@ -76,16 +76,16 @@ def make_pars(set_prognoses=False, prog_by_age=True, **kwargs):
     pars['rel_crit_prob']   = 1.0  # Scale factor for proportion of severe cases that become critical
     pars['rel_death_prob']  = 1.0  # Scale factor for proportion of critical cases that result in death
     pars['prog_by_age']     = prog_by_age # Whether to set disease progression based on the person's age
-    pars['prognoses']       = None # Populate this later
+    pars['prognoses']       = None # The actual arrays of prognoses by age; this is populated later
 
     # Events and interventions
-    pars['interventions'] = []   # List of Intervention instances
+    pars['interventions'] = []   # The interventions present in this simulation; populated by the user
     pars['interv_func']   = None # Custom intervention function
-    pars['timelimit']     = 3600 # Time limit for a simulation (seconds)
+    pars['timelimit']     = None # Time limit for the simulation (seconds)
     pars['stopping_func'] = None # A function to call to stop the sim partway through
 
     # Health system parameters
-    pars['n_beds'] = None  # Baseline assumption is that there's no upper limit on the number of beds i.e. there's enough for everyone
+    pars['n_beds'] = None  # The number of beds available for severely/critically ill patients (default is no constraint)
 
     # Update with any supplied parameter values and generate things that need to be generated
     pars.update(kwargs)

covasim/plotting.py

@@ -589,18 +589,20 @@ def get_individual_states(sim):
 plotly_legend = dict(legend_orientation="h", legend=dict(x=0.0, y=1.18))
 
 
-def plotly_interventions(sim, fig):
+def plotly_interventions(sim, fig, add_to_legend=False):
     ''' Add vertical lines for interventions to the plot '''
     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']:
-                        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 change", showarrow=False)])
+                        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
 
-def plotly_sim(sim):
+def plotly_sim(sim, do_show=False):
     ''' Main simulation results -- parallel of sim.plot() '''
 
     plots = []
@@ -618,10 +620,15 @@ def plotly_sim(sim):
                 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)
+        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
 
 

covasim/population.py

@@ -5,13 +5,13 @@
 #%% Imports
 import numpy as np # Needed for a few things not provided by pl
 import sciris as sc
+from collections import defaultdict
 from . import requirements as cvreq
 from . import utils as cvu
 from . import data as cvdata
 from . import defaults as cvd
 from . import parameters as cvpars
 from . import people as cvppl
-from collections import defaultdict
 
 
 # Specify all externally visible functions this file defines

covasim/sim.py

@@ -473,7 +473,7 @@ def run(self, do_plot=False, until=None, verbose=None, **kwargs):
 
             # Check if we were asked to stop
             elapsed = sc.toc(T, output=True)
-            if elapsed > self['timelimit']:
+            if self['timelimit'] and elapsed > self['timelimit']:
                 sc.printv(f"Time limit ({self['timelimit']} s) exceeded", 1, verbose)
                 break
             elif self['stopping_func'] and self['stopping_func'](self):

covasim/utils.py

@@ -8,7 +8,7 @@
 import numpy  as np # For numerics
 from . import defaults as cvd
 
-# What to import -- not all functions are externally available
+# What functions are externally visible -- note, this gets populated in each section below
 __all__ = []
 
 # Set dtypes

covasim/version.py

@@ -2,6 +2,6 @@
 
 __all__ = ['__version__', '__versiondate__', '__license__']
 
-__version__ = '1.0.0'
-__versiondate__ = '2020-05-08'
+__version__ = '1.0.1'
+__versiondate__ = '2020-05-09'
 __license__ = f'Covasim {__version__} ({__versiondate__}) — © 2020 by IDM'

tests/devtests/plotly_tests.py

@@ -1,10 +1,16 @@
+'''
+Test Plotly plotting outside of the webapp.
+'''
+
 import plotly.io as pio
 import covasim as cv
 
+pio.renderers.default = "browser" # Or can use a Jupyter notebook
 
-pio.renderers.default = "browser"
-
-sim = cv.Sim(pop_size=10e3)
-
+ce = cv.clip_edges(**{'start_day': 10, 'change': 0.5})
+sim = cv.Sim(pop_size=100, n_days=60, datafile='../example_data.csv', interventions=ce, verbose=0)
 sim.run()
-fig = cv.animate_people(sim, do_show=True)
+
+f1list = cv.plotly_sim(sim, do_show=True)
+f2     = cv.plotly_people(sim, do_show=True)
+f3     = cv.plotly_animate(sim, do_show=True)