update docs

CHANGELOG.rst

@@ -29,6 +29,7 @@ Version 3.0.3 (2021-05-17)
 --------------------------
 - Added a new class, ``cv.Calibration``, that can perform automatic calibration. Simplest usage is ``sim.calibrate(calib_pars)``. Note: this requires Optuna, which is not installed by default; please install separately via ``pip install optuna``. See the updated calibration tutorial for more information.
 - Added a new result, ``known_deaths``, which counts only deaths among people who have been diagnosed.
+- Updated several vaccine and variant parameters (e.g., B1.351 and B117 cross-immunity).
 - ``sim.compute_fit()`` now returns the fit by default, and creates ``sim.fit`` (previously, this was stored in ``sim.results.fit``).
 - *Regression information*: Calls to ``sim.results.fit`` should be replaced with ``sim.fit``. The ``output`` parameter for ``sim.compute_fit()`` has been removed since it now always outputs the ``Fit`` object.
 - *GitHub info*: PR `1047 <https://github.com/amath-idm/covasim/pull/1047>`__

covasim/analysis.py

@@ -1288,6 +1288,13 @@ def __init__(self, sim, calib_pars=None, custom_fn=None, n_trials=None, n_worker
         self.custom_fn  = custom_fn
         self.verbose    = verbose
         self.calibrated = False
+
+        # Handle if the sim has already been run
+        if self.sim.complete:
+            print('Warning: sim has already been run; re-initializing, but in future, use a sim that has not been run')
+            self.sim = self.sim.copy()
+            self.sim.initialize()
+
         return
 
 
@@ -1316,7 +1323,7 @@ def run_trial(self, trial):
         ''' Define the objective for Optuna '''
         pars = {}
         for key, (best,low,high) in self.calib_pars.items():
-            pars[key] = trial.suggest_uniform(key, low, high) # Sample from beta values within this range
+            pars[key] = trial.suggest_uniform(key, low, high) # Sample from values within this range
         mismatch = self.run_sim(pars)
         return mismatch
 
@@ -1353,6 +1360,7 @@ def calibrate(self, calib_pars=None, verbose=True, **kwargs):
 
         Args:
             calib_pars (dict): if supplied, overwrite stored calib_pars
+            verbose (bool): whether to print output from each trial
             kwargs (dict): if supplied, overwrite stored run_args (n_trials, n_workers, etc.)
         '''
 
@@ -1368,8 +1376,8 @@ def calibrate(self, calib_pars=None, verbose=True, **kwargs):
         t0 = sc.tic()
         self.make_study()
         self.run_workers()
-        study = op.load_study(storage=self.run_args.storage, study_name=self.run_args.name)
-        self.best_pars = sc.objdict(study.best_params)
+        self.study = op.load_study(storage=self.run_args.storage, study_name=self.run_args.name)
+        self.best_pars = sc.objdict(self.study.best_params)
         self.elapsed = sc.toc(t0, output=True)
 
         # Compare the results

docs/tutorials/tut_calibration.ipynb

@@ -131,7 +131,7 @@
     "    mismatch = np.mean(mismatches)\n",
     "    return mismatch\n",
     "\n",
-    "guess = [0.01, 1] # Initial guess of parameters -- beta and relative death probability\n",
+    "guess = [0.015, 1] # Initial guess of parameters -- beta and relative death probability\n",
     "pars = scipy.optimize.minimize(objective, x0=guess, method='nelder-mead') # Run the optimization\n",
     "```"
    ]
@@ -216,7 +216,6 @@
    "outputs": [],
    "source": [
     "# Plot the results\n",
-    "calib.summarize()\n",
     "calib.plot(to_plot=['cum_tests', 'cum_diagnoses', 'cum_deaths'])"
    ]
   },

examples/t07_optuna_calibration.py

@@ -30,6 +30,5 @@
     n_workers = 4
     calib = sim.calibrate(calib_pars=calib_pars, n_trials=n_trials, n_workers=n_workers)
 
-    # Summarize and plot the results
-    calib.summarize()
+    # Plot the results
     calib.plot(to_plot=['cum_tests', 'cum_diagnoses', 'cum_deaths'])