Merge pull request #1396 from amath-idm/rc3.1.4

Version 3.1.4

.github/workflows/tests.yaml

@@ -25,7 +25,9 @@ jobs:
         run: pip install -r requirements_test.txt
       - name: Run integration tests
         working-directory: ./tests
-        run: pytest -v test_*.py --workers auto --durations=0
-      - name: Run unit tests
-        working-directory: ./tests/unittests
-        run: pytest -v test_*.py --workers auto --durations=0
+        run: pytest -v test_*.py unittests/test_*.py --workers auto --durations=0 --junitxml=test-results.xml
+      - name: Publish test Results
+        uses: EnricoMi/publish-unit-test-result-action@v2
+        if: always()
+        with:
+          junit_files: tests/test-results.xml

CHANGELOG.rst

@@ -28,6 +28,32 @@ Latest versions (3.1.x)
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 
+Version 3.1.4 (2022-10-07)
+--------------------------
+
+Improvements
+^^^^^^^^^^^^
+- Added a new ``isolated`` state to explicitly track people who are in isolation (like quarantine).
+- Switched default parallelizer from ``multiprocess`` to ``concurrent.futures``; the latter is faster but less robust, so will automatically try again using ``multiprocess`` if ``concurrent.futures`` fails.
+- Added ``cv.Fit.summarize()`` to quickly get information about the mismatches.
+
+Bugfixes
+^^^^^^^^
+- Fixed plotting bug that prevented ``MultiSim`` plots from appearing correctly in Jupyter notebooks with (ironically) ``cv.options(jupyter=True)``.
+- Fixed bug with Matplotlib options (e.g. ``cv.options(dpi=150)``) not being set properly.
+- ``cv.Calibration.remove_db()`` now deletes the Optuna study as well. 
+- Fixed several issues with ``cv.git_info()`` pulling information from the wrong frame (i.e. file location).
+- Fixed shrunken sims dropping interventions/analyzers that were functions rather than classes.
+
+Other
+^^^^^
+- Updated Sciris dependency to 2.0.1.
+- Updated license to MIT instead of Creative Commons.
+- Added ``papers.rst`` and ``papers.bib`` that list additional Covasim publications.
+- Added a style guide and draft code linting scripts.
+- *GitHub info*: PR `1396 <https://github.com/amath-idm/covasim/pull/1396>`_
+
+
 Version 3.1.3 (2022-07-19)
 --------------------------
 - Fixed a bug with using ``'seir'`` as a default plot option. (Thanks Rik Belew for finding and fixing.)

README.rst

@@ -34,29 +34,17 @@ GitHub_ page. Full information about Covasim is provided in the documentation_.
 Background
 ==========
 
-Covasim has been used for analyses in over a dozen countries, both to inform policy decisions (including in the US, UK, and Australia), and as part of research studies. Academic papers that have been written using Covasim include:
+Covasim has been used for analyses in over a dozen countries, both to inform policy decisions (including in the US, UK, and Australia), and as part of research studies. Some key papers that have been written using Covasim include:
 
 1. **Controlling COVID-19 via test-trace-quarantine**. Kerr CC, Mistry D, Stuart RM, Rosenfeld R, Hart G, Núñez RC, Selvaraj P, Cohen JA, Abeysuriya RG, George L, Hagedorn B, Jastrzębski M, Fagalde M, Duchin J, Famulare M, Klein DJ (2021). *Nature Communications* 12:2993. doi: https://doi.org/10.1038/s41467-021-23276-9.
 
 2. **Determining the optimal strategy for reopening schools, the impact of test and trace interventions, and the risk of occurrence of a second COVID-19 epidemic wave in the UK: a modelling study**. Panovska-Griffiths J, Kerr CC, Stuart RM, Mistry D, Klein DJ, Viner R, Bonnell C (2020-08-03). *Lancet Child and Adolescent Health* S2352-4642(20) 30250-9. doi: https://doi.org/10.1016/S2352-4642(20)30250-9.
 
 3. **Estimating and mitigating the risk of COVID-19 epidemic rebound associated with reopening of international borders in Vietnam: a modelling study**. Pham QD, Stuart RM, Nguyen TV, Luong QC, Tran DQ, Phan LT, Dang TQ, Tran DN, Mistry D, Klein DJ, Abeysuriya RG, Oron AP, Kerr CC (2021-04-12). *Lancet Global Health* S2214-109X(21) 00103-0; doi: https://doi.org/10.1016/S2214-109X(21)00103-0.
 
-4. **Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting**. Scott N, Palmer A, Delport D, Abeysuriya RG, Stuart RM, Kerr CC, Mistry D, Klein DJ, Sacks-Davis R, Heath K, Hainsworth S, Pedrana A, Stoove M, Wilson DP, Hellard M (in press; accepted 2020-09-02). *Medical Journal of Australia* [`Preprint <https://www.mja.com.au/journal/2020/modelling-impact-reducing-control-measures-covid-19-pandemic-low-transmission-setting>`__]; doi: https://doi.org/10.1101/2020.06.11.20127027.
+A more complete list of papers is given in ``papers.rst``.
 
-5. **The role of masks, testing and contact tracing in preventing COVID-19 resurgences: a case study from New South Wales, Australia**. Stuart RM, Abeysuriya RG, Kerr CC, Mistry D, Klein DJ, Gray R, Hellard M, Scott N (in press; accepted 2021-03-19). *BMJ Open*; doi: https://doi.org/10.1101/2020.09.02.20186742.
-
-6. **The potential contribution of face coverings to the control of SARS-CoV-2 transmission in schools and broader society in the UK: a modelling study**. Panovska-Griffiths J, Kerr CC, Waites W, Stuart RM, Mistry D, Foster D, Klein DJ, Viner R, Bonnell C (in press; accepted 2021-04-08). *Nature Scientific Reports*; doi: https://doi.org/10.1101/2020.09.28.20202937.
-
-7. **Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures**. Cohen JA, Mistry D, Kerr CC, Klein DJ (under review; posted 2020-09-10). *medRxiv* 2020.09.08.20190942; doi: https://doi.org/10.1101/2020.09.08.20190942.
-
-8. **COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support**. Shea K, Borchering RK, Probert WJM, et al. (under review; posted 2020-11-05). *medRxiv* 2020.11.03.20225409; doi: https://doi.org/10.1101/2020.11.03.20225409.
-
-9. **Preventing a cluster from becoming a new wave in settings with zero community COVID-19 cases**. Abeysuriya RG, Delport D, Stuart RM, Sacks-Davis R, Kerr CC, Mistry D, Klein DJ, Hellard M, Scott N (under review; posted 2020-12-22). *medRxiv* 2020.12.21.20248595; doi: https://doi.org/10.1101/2020.12.21.20248595.
-
-10. **Modelling the impact of reopening schools in early 2021 in the presence of the new SARS-CoV-2 variant in the UK**. Panovska-Griffiths J, Kerr CC, Waites W, Stuart RM, Mistry D, Foster D, Klein DJ, Viner R, Bonnell C (under review; posted 2021-02-09). *medRxiv* 2021.02.07.21251287; doi: https://doi.org/10.1101/2021.02.07.21251287.
-
-If you have written a paper or report using Covasim, we'd love to know about it! Please write to us `here <mailto:[email protected]>`__.
+If you've written a paper or report using Covasim, we'd love to know about it! Please write to us `here <mailto:[email protected]>`__.
 
 
 Requirements
@@ -203,21 +191,13 @@ Examples of how to calibrate simulations, including `Optuna`_ (also covered in t
 .. _Weights and Biases: https://www.wandb.com/
 
 
-Licenses
---------
-
-Licensing information and legal notices.
-
-
 Tests
 -----
 
-Integration, development, and unit tests. While not (yet) beautifully curated, these folders contain many usage examples. See the `tests README`_ for more information.
-
-.. _tests README: ./tests
+Integration, development, and unit tests. While not (yet) beautifully curated, these folders contain many usage examples. See README in the tests folder for more information.
 
 
 Disclaimer
 ==========
 
-The code in this repository was developed by IDM to support our research in disease transmission and managing epidemics. We’ve made it publicly available under the Creative Commons Attribution-ShareAlike 4.0 International License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. We make no representations that the code works as intended or that we will provide support, address issues that are found, or accept pull requests. You are welcome to create your own fork and modify the code to suit your own modeling needs as contemplated under the Creative Commons Attribution-ShareAlike 4.0 International License. See the contributing and code of conduct READMEs for more information.
+The code in this repository was developed by IDM, the Burnet Institute, the University of Copenhagen, and other collaborators to support our joint research on COVID. We’ve made it publicly available under the MIT License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. Note that Covasim depends on a number of user-installed Python packages that can be installed automatically via ``pip install``. We make no representations that the code works as intended or that we will provide support, address issues that are found, or accept pull requests. You are welcome to create your own fork and modify the code to suit your own modeling needs as contemplated under the MIT License. See the contributing and code of conduct READMEs for more information.

covasim/analysis.py

@@ -173,7 +173,7 @@ class snapshot(Analyzer):
 
     def __init__(self, days, *args, die=True, **kwargs):
         super().__init__(**kwargs) # Initialize the Analyzer object
-        days = sc.promotetolist(days) # Combine multiple days
+        days = sc.tolist(days) # Combine multiple days
         days.extend(args) # Include additional arguments, if present
         self.days      = days # Converted to integer representations
         self.die       = die  # Whether or not to raise an exception
@@ -297,7 +297,7 @@ def initialize(self, sim):
         # Handle states
         if self.states is None:
             self.states = ['exposed', 'severe', 'dead', 'tested', 'diagnosed']
-        self.states = sc.promotetolist(self.states)
+        self.states = sc.tolist(self.states)
         for s,state in enumerate(self.states):
             self.states[s] = state.replace('date_', '') # Allow keys starting with date_ as input, but strip it off here
 
@@ -1222,6 +1222,18 @@ def compute_mismatch(self, use_median=False):
         return self.mismatch
 
 
+    def summarize(self):
+        ''' Print out results from the fit '''
+        if self.mismatch is not None:
+            print('Mismatch values for:')
+            print(self.mismatches)
+            print('\nTotal mismatch value:')
+            print(self.mismatch)
+        else:
+            print('Mismatch values not yet calculated; please run sim.compute_fit().')
+        return
+
+
     def plot(self, keys=None, width=0.8, fig_args=None, axis_args=None, plot_args=None,
              date_args=None, do_show=None, fig=None, **kwargs):
         '''
@@ -1332,6 +1344,7 @@ def plot(self, keys=None, width=0.8, fig_args=None, axis_args=None, plot_args=No
 
         return cvpl.handle_show_return(fig=fig, do_show=do_show)
 
+
 def import_optuna():
     ''' A helper function to import Optuna, which is an optional dependency '''
     try:
@@ -1341,6 +1354,7 @@ def import_optuna():
         raise ModuleNotFoundError(errormsg)
     return op
 
+
 class Calibration(Analyzer):
     '''
     A class to handle calibration of Covasim simulations. Uses the Optuna hyperparameter
@@ -1356,7 +1370,7 @@ class Calibration(Analyzer):
         sim          (Sim)  : the simulation to calibrate
         calib_pars   (dict) : a dictionary of the parameters to calibrate of the format dict(key1=[best, low, high])
         fit_args     (dict) : a dictionary of options that are passed to sim.compute_fit() to calculate the goodness-of-fit
-        par_samplers (dict) : an optional mapping from parameters to the Optuna sampler to use for choosing new points for each; by default, suggest_uniform
+        par_samplers (dict) : an optional mapping from parameters to the Optuna sampler to use for choosing new points for each; by default, suggest_float
         custom_fn    (func) : a custom function for modifying the simulation; receives the sim and calib_pars as inputs, should return the modified sim
         n_trials     (int)  : the number of trials per worker
         n_workers    (int)  : the number of parallel workers (default: maximum
@@ -1398,7 +1412,7 @@ def __init__(self, sim, calib_pars=None, fit_args=None, custom_fn=None, par_samp
         if db_name   is None: db_name   = f'{name}.db'
         if keep_db   is None: keep_db   = False
         if storage   is None: storage   = f'sqlite:///{db_name}'
-        if total_trials is not None: n_trials = total_trials/n_workers
+        if total_trials is not None: n_trials = np.ceil(total_trials/n_workers)
         self.run_args   = sc.objdict(n_trials=int(n_trials), n_workers=int(n_workers), name=name, db_name=db_name, keep_db=keep_db, storage=storage)
 
         # Handle other inputs
@@ -1462,7 +1476,7 @@ def run_trial(self, trial):
                     errormsg = 'The requested sampler function is not found: ensure it is a valid attribute of an Optuna Trial object'
                     raise AttributeError(errormsg) from E
             else:
-                sampler_fn = trial.suggest_uniform
+                sampler_fn = trial.suggest_float
             pars[key] = sampler_fn(key, low, high) # Sample from values within this range
         mismatch = self.run_sim(pars)
         return mismatch
@@ -1487,20 +1501,28 @@ def run_workers(self):
         else: # Special case: just run one
             output = [self.worker()]
         return output
-
-
+    
+    
     def remove_db(self):
         '''
         Remove the database file if keep_db is false and the path exists.
 
         New in version 3.1.0.
         '''
-        path = self.run_args.db_name
-        if os.path.exists(path):
-            os.remove(path)
+        try:
+            op = import_optuna()
+            op.delete_study(study_name=self.run_args.name, storage=self.run_args.storage)
+            if self.verbose:
+                print(f'Deleted study {self.run_args.name} in {self.run_args.storage}')
+        except Exception as E:
+            print('Could not delete study, skipping...')
+            print(str(E))
+        if os.path.exists(self.run_args.db_name):
+            os.remove(self.run_args.db_name)
             if self.verbose:
-                print(f'Removed existing calibration: {path}')
+                print(f'Removed existing calibration {self.run_args.db_name}')
         return
+
 
 
     def make_study(self):

covasim/base.py

@@ -192,11 +192,11 @@ def npts(self):
         return len(self.values)
 
 
-def set_metadata(obj, **kwargs):
+def set_metadata(obj, frame=4, **kwargs):
     ''' Set standard metadata for an object '''
     obj.created = kwargs.get('created', sc.now())
     obj.version = kwargs.get('version', cvv.__version__)
-    obj.git_info = kwargs.get('git_info', cvm.git_info())
+    obj.git_info = kwargs.get('git_info', cvm.git_info(frame=frame)) # 4 = 2 (default) + base + caller
     return
 
 
@@ -286,7 +286,7 @@ def update_pars(self, pars=None, create=False, **kwargs):
 
     def set_metadata(self, simfile):
         ''' Set the metadata for the simulation -- creation time and filename '''
-        set_metadata(self)
+        set_metadata(self, frame=5)
         if simfile is None:
             self.simfile = 'covasim.sim'
         return
@@ -397,7 +397,7 @@ def date(self, ind, *args, dateformat=None, as_date=False):
 
         # Handle inputs
         if not isinstance(ind, list): # If it's a number, string, or dateobj, convert it to a list
-            ind = sc.promotetolist(ind)
+            ind = sc.tolist(ind)
         ind.extend(args)
         if dateformat is None:
             dateformat = '%Y-%m-%d'
@@ -547,7 +547,7 @@ def to_json(self, filename=None, keys=None, tostring=False, indent=2, verbose=Fa
         # Handle keys
         if keys is None:
             keys = ['results', 'pars', 'summary']
-        keys = sc.promotetolist(keys)
+        keys = sc.tolist(keys)
 
         # Convert to JSON-compatible format
         d = {}
@@ -662,8 +662,14 @@ def shrink(self, skip_attrs=None, in_place=True):
 
         # Shrink interventions and analyzers, with a lot of checking along the way
         for key in ['interventions', 'analyzers']:
-            ias = self.pars[key] # List of interventions or analyzers
-            shrunken_ias = [ia.shrink(in_place=in_place) for ia in ias if isinstance(ia, (cvi.Intervention, cva.Analyzer))]
+            ias = sc.tolist(self.pars[key]) # List of interventions or analyzers
+            shrunken_ias = []
+            for ia in ias:
+                if isinstance(ia, (cvi.Intervention, cva.Analyzer)):
+                    shrunken_ia = ia.shrink(in_place=in_place)
+                else:
+                    shrunken_ia = ia
+                shrunken_ias.append(shrunken_ia)
             self.pars[key] = shrunken_ias # Actually shrink, and re-store
 
         # Don't return if in place
@@ -760,7 +766,7 @@ def _get_ia(self, which, label=None, partial=False, as_list=False, as_inds=False
                 label = np.arange(n_ia)
             if isinstance(label, np.ndarray): # Allow arrays to be provided
                 label = label.tolist()
-            labels = sc.promotetolist(label)
+            labels = sc.tolist(label)
 
             # Calculate the matches
             matches = []
@@ -979,7 +985,7 @@ def _resize_arrays(self, new_size=None, keys=None):
         # Reset sizes
         if keys is None:
             keys = self.keys()
-        keys = sc.promotetolist(keys)
+        keys = sc.tolist(keys)
         for key in keys:
             self[key].resize(new_size, refcheck=False) # Don't worry about cross-references to the arrays
 
@@ -1827,7 +1833,7 @@ def find_contacts(self, inds, as_array=True):
 
         # Check types
         if not isinstance(inds, np.ndarray):
-            inds = sc.promotetoarray(inds)
+            inds = sc.toarray(inds)
         if inds.dtype != np.int64:  # pragma: no cover # This is int64 since indices often come from cv.true(), which returns int64
             inds = np.array(inds, dtype=np.int64)
 

covasim/defaults.py

@@ -70,6 +70,7 @@ def __init__(self):
             'dead',
             'known_contact',
             'quarantined',
+            'isolated',
             'vaccinated',
         ]
 
@@ -110,6 +111,7 @@ def __init__(self):
         self.dates = [f'date_{state}' for state in self.states] # Convert each state into a date
         self.dates.append('date_pos_test') # Store the date when a person tested which will come back positive
         self.dates.append('date_end_quarantine') # Store the date when a person comes out of quarantine
+        self.dates.append('date_end_isolation') # Store the date when a person comes out of isolation
 
         # Duration of different states: these are floats per person -- used in people.py
         self.durs = [
@@ -152,6 +154,7 @@ def __init__(self):
     'diagnosed':   'Number of confirmed cases',
     'known_dead':  'Number of confirmed deaths',
     'quarantined': 'Number in quarantine',
+    'isolated':    'Number in isolation',
     'vaccinated':  'Number of people vaccinated',
 }
 
@@ -174,6 +177,7 @@ def __init__(self):
     'diagnoses':    'diagnoses',
     'known_deaths': 'known deaths',
     'quarantined':  'quarantined people',
+    'isolated':     'isolated people',
     'doses':        'vaccine doses',
     'vaccinated':   'vaccinated people'
 }
@@ -258,6 +262,7 @@ def get_default_colors():
     c.diagnoses             = '#5f5cd2'
     c.diagnosed             = c.diagnoses
     c.quarantined           = '#5c399c'
+    c.isolated              = '#9756ff'
     c.doses                 = c.quarantined # TODO: new color
     c.vaccinated            = c.quarantined
     c.recoveries            = '#9e1149'