One of the main reasons people don’t use ABMs is because they can be very slow. While “vanilla Starsim” is quite fast (10,000 agents running for 100 timesteps should take about a second), custom modules, if not properly written, can be quite slow.
The first step of fixing a slow module is to identify the problem. To do this, Starsim includes some built-in profiling tools.
Initializing sim with 10000 agents
Profiling 15 function(s):
<bound method Sim.run of Sim(n=10000; 2000.01.01—2020.01.01; networks=randomnet; diseases=sis)>
<bound method Sim.start_step of Sim(n=10000; 2000.01.01—2020.01.01; networks=randomnet; diseases=sis [...]
<function Module.start_step at 0x7f3dd84985c0>
<function Module.start_step at 0x7f3dd84985c0>
<bound method SIS.step_state of sis(pars=[init_prev, beta, dur_inf, waning, imm_boost, _n_initial_ca [...]
<bound method DynamicNetwork.step of randomnet(n_edges=50000; pars=[n_contacts, dur, beta]; states=[ [...]
<bound method Infection.step of sis(pars=[init_prev, beta, dur_inf, waning, imm_boost, _n_initial_ca [...]
<bound method People.step_die of People(n=10000; age=30.0±17.3)>
<bound method People.update_results of People(n=10000; age=30.0±17.3)>
<bound method Network.update_results of randomnet(n_edges=50000; pars=[n_contacts, dur, beta]; state [...]
<function SIS.update_results at 0x7f3dd7e378a0>
<function Module.finish_step at 0x7f3dd84987d0>
<function Module.finish_step at 0x7f3dd84987d0>
<bound method People.finish_step of People(n=10000; age=30.0±17.3)>
<bound method Sim.finish_step of Sim(n=10000; 2000.01.01—2020.01.01; networks=randomnet; diseases=si [...]
Running 2000.01.01 ( 0/21) (0.00 s) ———————————————————— 5%
Running 2010.01.01 (10/21) (0.82 s) ••••••••••—————————— 52%
Running 2020.01.01 (20/21) (0.90 s) •••••••••••••••••••• 100%
Elapsed time: 0.925 s
——————————————————————————————————————————————————————————————————————
Profile of networks.Network.update_results: 0.000507274 s (0.0548333%)
——————————————————————————————————————————————————————————————————————
Total time: 0.000507274 s
File: /home/runner/work/starsim/starsim/starsim/networks.py
Function: Network.update_results at line 254
Line # Hits Time Per Hit % Time Line Contents
==============================================================
254 def update_results(self):
255 """ Store the number of edges in the network """
256 21 95962.0 4569.6 18.9 super().update_results()
257 21 402789.0 19180.4 79.4 self.results['n_edges'][self.ti] = len(self)
258 21 8523.0 405.9 1.7 return
————————————————————————————————————————————————————————————————
Profile of people.People.finish_step: 0.000916916 s (0.0991131%)
————————————————————————————————————————————————————————————————
Total time: 0.000916916 s
File: /home/runner/work/starsim/starsim/starsim/people.py
Function: People.finish_step at line 490
Line # Hits Time Per Hit % Time Line Contents
==============================================================
490 def finish_step(self):
491 21 596905.0 28424.0 65.1 self.remove_dead()
492 21 312149.0 14864.2 34.0 self.update_post()
493 21 7862.0 374.4 0.9 return
———————————————————————————————————————————————————————————
Profile of people.People.step_die: 0.00205963 s (0.222634%)
———————————————————————————————————————————————————————————
Total time: 0.00205963 s
File: /home/runner/work/starsim/starsim/starsim/people.py
Function: People.step_die at line 452
Line # Hits Time Per Hit % Time Line Contents
==============================================================
452 def step_die(self):
453 """ Carry out any deaths or removals that took place this timestep """
454 21 1753473.0 83498.7 85.1 death_uids = ((self.ti_dead <= self.sim.ti) | (self.ti_removed <= self.sim.ti)).uids
455 21 88495.0 4214.0 4.3 self.alive[death_uids] = False # Whilst not dead, removed agents should not be included in alive totals
456
457 # Execute deaths that took place this timestep (i.e., changing the `alive` state of the agents). This is executed
458 # before analyzers have run so that analyzers are able to inspect and record outcomes for agents that died this timestep
459 42 162739.0 3874.7 7.9 for disease in self.sim.diseases():
460 21 16419.0 781.9 0.8 if isinstance(disease, ss.Disease):
461 21 29626.0 1410.8 1.4 disease.step_die(death_uids)
462
463 21 8877.0 422.7 0.4 return death_uids
————————————————————————————————————————————————————————————————
Profile of people.People.update_results: 0.0035537 s (0.384134%)
————————————————————————————————————————————————————————————————
Total time: 0.0035537 s
File: /home/runner/work/starsim/starsim/starsim/people.py
Function: People.update_results at line 481
Line # Hits Time Per Hit % Time Line Contents
==============================================================
481 def update_results(self):
482 21 33349.0 1588.0 0.9 ti = self.sim.ti
483 21 18127.0 863.2 0.5 res = self.sim.results
484 21 698502.0 33262.0 19.7 res.n_alive[ti] = np.count_nonzero(self.alive)
485 21 1002329.0 47730.0 28.2 res.new_deaths[ti] = np.count_nonzero(self.ti_dead == ti)
486 21 870243.0 41440.1 24.5 res.new_emigrants[ti] = np.count_nonzero(self.ti_removed == ti)
487 21 921353.0 43874.0 25.9 res.cum_deaths[ti] = np.sum(res.new_deaths[:ti]) # TODO: inefficient to compute the cumulative sum on every timestep!
488 21 9798.0 466.6 0.3 return
————————————————————————————————————————————————————————————————
Profile of diseases.SIS.update_results: 0.00360167 s (0.389319%)
————————————————————————————————————————————————————————————————
Total time: 0.00360167 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: SIS.update_results at line 670
Line # Hits Time Per Hit % Time Line Contents
==============================================================
670 @ss.required()
671 def update_results(self):
672 """ Store the population immunity (susceptibility) """
673 21 2642133.0 125815.9 73.4 super().update_results()
674 21 950712.0 45272.0 26.4 self.results['rel_sus'][self.ti] = self.rel_sus.mean()
675 21 8827.0 420.3 0.2 return
——————————————————————————————————————————————————————
Profile of sim.Sim.start_step: 0.00520186 s (0.56229%)
——————————————————————————————————————————————————————
Total time: 0.00520186 s
File: /home/runner/work/starsim/starsim/starsim/sim.py
Function: Sim.start_step at line 438
Line # Hits Time Per Hit % Time Line Contents
==============================================================
438 def start_step(self):
439 """ Start the step -- only print progress; all actual changes happen in the modules """
440
441 # Set the time and if we have reached the end of the simulation, then do nothing
442 21 13372.0 636.8 0.3 if self.complete:
443 errormsg = 'Simulation already complete (call sim.init() to re-run)'
444 raise AlreadyRunError(errormsg)
445
446 # Print out progress if needed
447 21 3533491.0 168261.5 67.9 self.elapsed = self.timer.toc(output=True)
448 21 15081.0 718.1 0.3 if self.verbose: # Print progress
449 21 10800.0 514.3 0.2 t = self.t
450 21 276298.0 13157.0 5.3 simlabel = f'"{self.label}": ' if self.label else ''
451 21 865953.0 41235.9 16.6 string = f' Running {simlabel}{t.now("str")} ({t.ti:2.0f}/{t.npts}) ({self.elapsed:0.2f} s) '
452 21 16472.0 784.4 0.3 if self.verbose >= 1:
453 sc.heading(string)
454 21 11975.0 570.2 0.2 elif self.verbose > 0:
455 21 23254.0 1107.3 0.4 if not (t.ti % int(1.0 / self.verbose)):
456 3 425373.0 141791.0 8.2 sc.progressbar(t.ti + 1, t.npts, label=string, length=20, newline=True)
457 21 9789.0 466.1 0.2 return
————————————————————————————————————————————————————————————
Profile of diseases.SIS.step_state: 0.00579115 s (0.625989%)
————————————————————————————————————————————————————————————
Total time: 0.00579115 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: SIS.step_state at line 631
Line # Hits Time Per Hit % Time Line Contents
==============================================================
631 def step_state(self):
632 """ Progress infectious -> recovered """
633 21 1426865.0 67946.0 24.6 recovered = (self.infected & (self.ti_recovered <= self.ti)).uids
634 21 99272.0 4727.2 1.7 self.infected[recovered] = False
635 21 66092.0 3147.2 1.1 self.susceptible[recovered] = True
636 21 4189542.0 199502.0 72.3 self.update_immunity()
637 21 9377.0 446.5 0.2 return
————————————————————————————————————————————————————————————
Profile of modules.Module.start_step: 0.0133938 s (1.44779%)
————————————————————————————————————————————————————————————
Total time: 0.0133938 s
File: /home/runner/work/starsim/starsim/starsim/modules.py
Function: Module.start_step at line 678
Line # Hits Time Per Hit % Time Line Contents
==============================================================
678 @required()
679 def start_step(self):
680 """ Tasks to perform at the beginning of the step """
681 42 31457.0 749.0 0.2 if self.finalized:
682 errormsg = f'The module {self._debug_name} has already been run. Did you mean to copy it before running it?'
683 raise RuntimeError(errormsg)
684 42 23873.0 568.4 0.2 if self.dists is not None: # Will be None if no distributions are defined
685 42 13317252.0 317077.4 99.4 self.dists.jump_dt() # Advance random number generators forward for calls on this step
686 42 21209.0 505.0 0.2 return
———————————————————————————————————————————————————————————————
Profile of networks.DynamicNetwork.step: 0.0483088 s (5.22189%)
———————————————————————————————————————————————————————————————
Total time: 0.0483088 s
File: /home/runner/work/starsim/starsim/starsim/networks.py
Function: DynamicNetwork.step at line 412
Line # Hits Time Per Hit % Time Line Contents
==============================================================
412 def step(self):
413 21 14340127.0 682863.2 29.7 self.end_pairs()
414 21 33958477.0 1.62e+06 70.3 self.add_pairs()
415 21 10165.0 484.0 0.0 return
—————————————————————————————————————————————————————————
Profile of diseases.Infection.step: 0.817697 s (88.3882%)
—————————————————————————————————————————————————————————
Total time: 0.817697 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: Infection.step at line 208
Line # Hits Time Per Hit % Time Line Contents
==============================================================
208 def step(self):
209 """
210 Perform key infection updates, including infection and setting prognoses
211 """
212 # Create new cases
213 21 801704121.0 3.82e+07 98.0 new_cases, sources, networks = self.infect() # TODO: store outputs in self or use objdict rather than 3 returns
214
215 # Set prognoses
216 21 15229.0 725.2 0.0 if len(new_cases):
217 21 15964366.0 760207.9 2.0 self.set_outcomes(new_cases, sources)
218
219 21 12961.0 617.2 0.0 return new_cases, sources, networks
Figure(672x480)
This graph (which is a shortcut to sim.loop.plot_cpu()) shows us how much time each step in the integration loop takes. We can get line-by-line detail of where each function is taking time, though:
prof.disp(maxentries=5)
——————————————————————————————————————————————————————
Profile of sim.Sim.start_step: 0.00520186 s (0.56229%)
——————————————————————————————————————————————————————
Total time: 0.00520186 s
File: /home/runner/work/starsim/starsim/starsim/sim.py
Function: Sim.start_step at line 438
Line # Hits Time Per Hit % Time Line Contents
==============================================================
438 def start_step(self):
439 """ Start the step -- only print progress; all actual changes happen in the modules """
440
441 # Set the time and if we have reached the end of the simulation, then do nothing
442 21 13372.0 636.8 0.3 if self.complete:
443 errormsg = 'Simulation already complete (call sim.init() to re-run)'
444 raise AlreadyRunError(errormsg)
445
446 # Print out progress if needed
447 21 3533491.0 168261.5 67.9 self.elapsed = self.timer.toc(output=True)
448 21 15081.0 718.1 0.3 if self.verbose: # Print progress
449 21 10800.0 514.3 0.2 t = self.t
450 21 276298.0 13157.0 5.3 simlabel = f'"{self.label}": ' if self.label else ''
451 21 865953.0 41235.9 16.6 string = f' Running {simlabel}{t.now("str")} ({t.ti:2.0f}/{t.npts}) ({self.elapsed:0.2f} s) '
452 21 16472.0 784.4 0.3 if self.verbose >= 1:
453 sc.heading(string)
454 21 11975.0 570.2 0.2 elif self.verbose > 0:
455 21 23254.0 1107.3 0.4 if not (t.ti % int(1.0 / self.verbose)):
456 3 425373.0 141791.0 8.2 sc.progressbar(t.ti + 1, t.npts, label=string, length=20, newline=True)
457 21 9789.0 466.1 0.2 return
————————————————————————————————————————————————————————————
Profile of diseases.SIS.step_state: 0.00579115 s (0.625989%)
————————————————————————————————————————————————————————————
Total time: 0.00579115 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: SIS.step_state at line 631
Line # Hits Time Per Hit % Time Line Contents
==============================================================
631 def step_state(self):
632 """ Progress infectious -> recovered """
633 21 1426865.0 67946.0 24.6 recovered = (self.infected & (self.ti_recovered <= self.ti)).uids
634 21 99272.0 4727.2 1.7 self.infected[recovered] = False
635 21 66092.0 3147.2 1.1 self.susceptible[recovered] = True
636 21 4189542.0 199502.0 72.3 self.update_immunity()
637 21 9377.0 446.5 0.2 return
————————————————————————————————————————————————————————————
Profile of modules.Module.start_step: 0.0133938 s (1.44779%)
————————————————————————————————————————————————————————————
Total time: 0.0133938 s
File: /home/runner/work/starsim/starsim/starsim/modules.py
Function: Module.start_step at line 678
Line # Hits Time Per Hit % Time Line Contents
==============================================================
678 @required()
679 def start_step(self):
680 """ Tasks to perform at the beginning of the step """
681 42 31457.0 749.0 0.2 if self.finalized:
682 errormsg = f'The module {self._debug_name} has already been run. Did you mean to copy it before running it?'
683 raise RuntimeError(errormsg)
684 42 23873.0 568.4 0.2 if self.dists is not None: # Will be None if no distributions are defined
685 42 13317252.0 317077.4 99.4 self.dists.jump_dt() # Advance random number generators forward for calls on this step
686 42 21209.0 505.0 0.2 return
———————————————————————————————————————————————————————————————
Profile of networks.DynamicNetwork.step: 0.0483088 s (5.22189%)
———————————————————————————————————————————————————————————————
Total time: 0.0483088 s
File: /home/runner/work/starsim/starsim/starsim/networks.py
Function: DynamicNetwork.step at line 412
Line # Hits Time Per Hit % Time Line Contents
==============================================================
412 def step(self):
413 21 14340127.0 682863.2 29.7 self.end_pairs()
414 21 33958477.0 1.62e+06 70.3 self.add_pairs()
415 21 10165.0 484.0 0.0 return
—————————————————————————————————————————————————————————
Profile of diseases.Infection.step: 0.817697 s (88.3882%)
—————————————————————————————————————————————————————————
Total time: 0.817697 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: Infection.step at line 208
Line # Hits Time Per Hit % Time Line Contents
==============================================================
208 def step(self):
209 """
210 Perform key infection updates, including infection and setting prognoses
211 """
212 # Create new cases
213 21 801704121.0 3.82e+07 98.0 new_cases, sources, networks = self.infect() # TODO: store outputs in self or use objdict rather than 3 returns
214
215 # Set prognoses
216 21 15229.0 725.2 0.0 if len(new_cases):
217 21 15964366.0 760207.9 2.0 self.set_outcomes(new_cases, sources)
218
219 21 12961.0 617.2 0.0 return new_cases, sources, networks
(Note that the names of the functions here refer to the actual functions called, which may not match the graph above. That’s because, for example, ss.SIS does not define its own step() method, but instead inherits step() from Infection. In the graph, this is shown as sis.step(), but is listed in the table as Infection.step(). This is because it’s referring to the actual code being run, so refers to where those lines of code exist in the codebase; there is no code corresponding to SIS.step() since it’s just inherited from Infection.step().)
If you want more detail, you can also define custom functions to follow. For example, we can see that ss.SIS.infect() takes the most time in ss.SIS.step(), so let’s profile that:
Initializing sim with 10000 agents
Profiling 1 function(s):
<function Infection.infect at 0x7f3dd7e364b0>
Running 2000.01.01 ( 0/21) (0.00 s) ———————————————————— 5%
Running 2010.01.01 (10/21) (0.08 s) ••••••••••—————————— 52%
Running 2020.01.01 (20/21) (0.15 s) •••••••••••••••••••• 100%
Elapsed time: 0.176 s
————————————————————————————————————————————————————————————
Profile of diseases.Infection.infect: 0.0662263 s (37.6094%)
————————————————————————————————————————————————————————————
Total time: 0.0662263 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: Infection.infect at line 230
Line # Hits Time Per Hit % Time Line Contents
==============================================================
230 def infect(self):
231 """ Determine who gets infected on this timestep via transmission on the network """
232 21 14004.0 666.9 0.0 new_cases = []
233 21 7930.0 377.6 0.0 sources = []
234 21 7576.0 360.8 0.0 networks = []
235 21 611924.0 29139.2 0.9 betamap = self.validate_beta()
236
237 21 1368093.0 65147.3 2.1 rel_trans = self.rel_trans.asnew(self.infectious * self.rel_trans)
238 21 1035269.0 49298.5 1.6 rel_sus = self.rel_sus.asnew(self.susceptible * self.rel_sus)
239
240 42 144379.0 3437.6 0.2 for i, (nkey,route) in enumerate(self.sim.networks.items()):
241 21 35106.0 1671.7 0.1 nk = ss.standardize_netkey(nkey)
242
243 # Main use case: networks
244 21 13099.0 623.8 0.0 if isinstance(route, ss.Network):
245 21 253010.0 12048.1 0.4 if len(route): # Skip networks with no edges
246 21 10330.0 491.9 0.0 edges = route.edges
247 21 352238.0 16773.2 0.5 p1p2b0 = [edges.p1, edges.p2, betamap[nk][0]] # Person 1, person 2, beta 0
248 21 334397.0 15923.7 0.5 p2p1b1 = [edges.p2, edges.p1, betamap[nk][1]] # Person 2, person 1, beta 1
249 63 43908.0 697.0 0.1 for src, trg, beta in [p1p2b0, p2p1b1]:
250 42 69989.0 1666.4 0.1 if beta: # Skip networks with no transmission
251 42 1188392.0 28295.0 1.8 disease_beta = beta.to_prob(self.t.dt) if isinstance(beta, ss.Rate) else beta
252 42 1696509.0 40393.1 2.6 beta_per_dt = route.net_beta(disease_beta=disease_beta, disease=self) # Compute beta for this network and timestep
253 42 49291345.0 1.17e+06 74.4 randvals = self.trans_rng.rvs(src, trg) # Generate a new random number based on the two other random numbers
254 42 37272.0 887.4 0.1 args = (src, trg, rel_trans, rel_sus, beta_per_dt, randvals) # Set up the arguments to calculate transmission
255 42 7358028.0 175191.1 11.1 target_uids, source_uids = self.compute_transmission(*args) # Actually calculate it
256 42 27273.0 649.4 0.0 new_cases.append(target_uids)
257 42 21199.0 504.7 0.0 sources.append(source_uids)
258 42 446485.0 10630.6 0.7 networks.append(np.full(len(target_uids), dtype=ss_int, fill_value=i))
259
260 # Handle everything else: mixing pools, environmental transmission, etc.
261 elif isinstance(route, ss.Route):
262 # Mixing pools are unidirectional, only use the first beta value
263 disease_beta = betamap[nk][0].to_prob(self.t.dt) if isinstance(betamap[nk][0], ss.Rate) else betamap[nk][0]
264 target_uids = route.compute_transmission(rel_sus, rel_trans, disease_beta, disease=self)
265 new_cases.append(target_uids)
266 sources.append(np.full(len(target_uids), dtype=ss_float, fill_value=np.nan))
267 networks.append(np.full(len(target_uids), dtype=ss_int, fill_value=i))
268 else:
269 errormsg = f'Cannot compute transmission via route {type(route)}; please subclass ss.Route and define a compute_transmission() method'
270 raise TypeError(errormsg)
271
272 # Finalize
273 21 16585.0 789.8 0.0 if len(new_cases) and len(sources):
274 21 252912.0 12043.4 0.4 new_cases = ss.uids.cat(new_cases)
275 21 1256683.0 59842.0 1.9 new_cases, inds = new_cases.unique(return_index=True)
276 21 216187.0 10294.6 0.3 sources = ss.uids.cat(sources)[inds]
277 21 104458.0 4974.2 0.2 networks = np.concatenate(networks)[inds]
278 else:
279 new_cases = ss.uids()
280 sources = ss.uids()
281 networks = np.empty(0, dtype=ss_int)
282
283 21 11720.0 558.1 0.0 return new_cases, sources, networks
————————————————————————————————————————————————————————————
Profile of diseases.Infection.infect: 0.0662263 s (37.6094%)
————————————————————————————————————————————————————————————
Total time: 0.0662263 s
File: /home/runner/work/starsim/starsim/starsim/diseases.py
Function: Infection.infect at line 230
Line # Hits Time Per Hit % Time Line Contents
==============================================================
230 def infect(self):
231 """ Determine who gets infected on this timestep via transmission on the network """
232 21 14004.0 666.9 0.0 new_cases = []
233 21 7930.0 377.6 0.0 sources = []
234 21 7576.0 360.8 0.0 networks = []
235 21 611924.0 29139.2 0.9 betamap = self.validate_beta()
236
237 21 1368093.0 65147.3 2.1 rel_trans = self.rel_trans.asnew(self.infectious * self.rel_trans)
238 21 1035269.0 49298.5 1.6 rel_sus = self.rel_sus.asnew(self.susceptible * self.rel_sus)
239
240 42 144379.0 3437.6 0.2 for i, (nkey,route) in enumerate(self.sim.networks.items()):
241 21 35106.0 1671.7 0.1 nk = ss.standardize_netkey(nkey)
242
243 # Main use case: networks
244 21 13099.0 623.8 0.0 if isinstance(route, ss.Network):
245 21 253010.0 12048.1 0.4 if len(route): # Skip networks with no edges
246 21 10330.0 491.9 0.0 edges = route.edges
247 21 352238.0 16773.2 0.5 p1p2b0 = [edges.p1, edges.p2, betamap[nk][0]] # Person 1, person 2, beta 0
248 21 334397.0 15923.7 0.5 p2p1b1 = [edges.p2, edges.p1, betamap[nk][1]] # Person 2, person 1, beta 1
249 63 43908.0 697.0 0.1 for src, trg, beta in [p1p2b0, p2p1b1]:
250 42 69989.0 1666.4 0.1 if beta: # Skip networks with no transmission
251 42 1188392.0 28295.0 1.8 disease_beta = beta.to_prob(self.t.dt) if isinstance(beta, ss.Rate) else beta
252 42 1696509.0 40393.1 2.6 beta_per_dt = route.net_beta(disease_beta=disease_beta, disease=self) # Compute beta for this network and timestep
253 42 49291345.0 1.17e+06 74.4 randvals = self.trans_rng.rvs(src, trg) # Generate a new random number based on the two other random numbers
254 42 37272.0 887.4 0.1 args = (src, trg, rel_trans, rel_sus, beta_per_dt, randvals) # Set up the arguments to calculate transmission
255 42 7358028.0 175191.1 11.1 target_uids, source_uids = self.compute_transmission(*args) # Actually calculate it
256 42 27273.0 649.4 0.0 new_cases.append(target_uids)
257 42 21199.0 504.7 0.0 sources.append(source_uids)
258 42 446485.0 10630.6 0.7 networks.append(np.full(len(target_uids), dtype=ss_int, fill_value=i))
259
260 # Handle everything else: mixing pools, environmental transmission, etc.
261 elif isinstance(route, ss.Route):
262 # Mixing pools are unidirectional, only use the first beta value
263 disease_beta = betamap[nk][0].to_prob(self.t.dt) if isinstance(betamap[nk][0], ss.Rate) else betamap[nk][0]
264 target_uids = route.compute_transmission(rel_sus, rel_trans, disease_beta, disease=self)
265 new_cases.append(target_uids)
266 sources.append(np.full(len(target_uids), dtype=ss_float, fill_value=np.nan))
267 networks.append(np.full(len(target_uids), dtype=ss_int, fill_value=i))
268 else:
269 errormsg = f'Cannot compute transmission via route {type(route)}; please subclass ss.Route and define a compute_transmission() method'
270 raise TypeError(errormsg)
271
272 # Finalize
273 21 16585.0 789.8 0.0 if len(new_cases) and len(sources):
274 21 252912.0 12043.4 0.4 new_cases = ss.uids.cat(new_cases)
275 21 1256683.0 59842.0 1.9 new_cases, inds = new_cases.unique(return_index=True)
276 21 216187.0 10294.6 0.3 sources = ss.uids.cat(sources)[inds]
277 21 104458.0 4974.2 0.2 networks = np.concatenate(networks)[inds]
278 else:
279 new_cases = ss.uids()
280 sources = ss.uids()
281 networks = np.empty(0, dtype=ss_int)
282
283 21 11720.0 558.1 0.0 return new_cases, sources, networks
(Note: you can only follow functions that are called as part of sim.run() this way. To follow other functions, such as those run by sim.init(), you can use sc.profile() directly.)
Debugging
When figuring out what your sim is doing – whether it’s doing something it shouldn’t be, or not doing something it should – sim.loop is your friend. It shows everything that will happen in the sim, and in what order:
As you can see, it’s a lot – this is only three timesteps and two modules, and it’s already 41 steps.
The typical way to do debugging is to insert breakpoints or print statements into your modules for custom debugging (e.g., to print a value), or to use analyzers for heavier-lift debugging. Starsim also lets you manually modify the loop by inserting “probes” or other arbitrary functions. For example, if you wanted to check the population size after each time the People object is updated:
Initializing sim with 10000 agents
Running 2000.01.01 ( 0/11) (0.00 s) •——————————————————— 9%
Population size is 10191
Population size is 10264
Population size is 10357
Population size is 10447
Population size is 10557
Population size is 10664
Population size is 10739
Population size is 10817
Population size is 10917
Population size is 11033
Running 2010.01.01 (10/11) (0.08 s) •••••••••••••••••••• 100%
Population size is 11124
Initializing sim with 10000 agents
Running 2000.01.01 ( 0/11) (0.00 s) •——————————————————— 9%
Population size is 10191
Population size is 10264
Population size is 10357
Population size is 10447
Population size is 10557
Population size is 10664
Population size is 10739
Population size is 10817
Population size is 10917
Population size is 11033
Running 2010.01.01 (10/11) (0.08 s) •••••••••••••••••••• 100%
Population size is 11124
However, inserting functions directly in the loop gives you more control over their exact placement, whereas analyzers are always executed last in the timestep.
The loop also has methods for visualizing itself. You can get a simple representation of the loop with loop.plot():
sim.loop.plot()
Figure(672x480)
Or a slightly more detailed one with loop.plot_step_order():
sim.loop.plot_step_order()
Figure(672x480)
This is especially useful if your simulation has modules with different timesteps, e.g.:
Initializing sim with 10000 agents
Figure(672x480)
(Note: this is a 3D plot, so it helps if you can plot it in a separate window interactively to be able to move it around, rather than just in a notebook.)
