polished cern caimira frontned model related methods

Author: lrdossan

caimira/src/caimira/api/controller/virus_report_controller.py

@@ -18,10 +18,9 @@ def generate_model(form_obj, data_registry):
     return form_obj.build_model(sample_size=sample_size)
 
 
-def generate_report_results(form_obj, model):
+def generate_report_results(form_obj):
     return rg.calculate_report_data(
         form=form_obj,
-        model=model,
         executor_factory=functools.partial(
             concurrent.futures.ThreadPoolExecutor, None,  # TODO define report_parallelism
         ),

caimira/src/caimira/calculator/report/virus_report_data.py

@@ -5,8 +5,6 @@
 import typing
 import numpy as np
 import matplotlib.pyplot as plt
-import urllib
-import zlib
 
 from caimira.calculator.models import models, dataclass_utils, profiler, monte_carlo as mc
 from caimira.calculator.models.enums import ViralLoads
@@ -123,7 +121,9 @@ def _calculate_co2_concentration(CO2_model, time, fn_name=None):
 
 
 @profiler.profile
-def calculate_report_data(form: VirusFormData, model: models.ExposureModel, executor_factory: typing.Callable[[], concurrent.futures.Executor]) -> typing.Dict[str, typing.Any]:
+def calculate_report_data(form: VirusFormData, executor_factory: typing.Callable[[], concurrent.futures.Executor]) -> typing.Dict[str, typing.Any]:
+    model: models.ExposureModel = form.build_model()
+    
     times = interesting_times(model)
     short_range_intervals = [interaction.presence.boundaries()[0]
                              for interaction in model.short_range]
@@ -191,7 +191,7 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec
             uncertainties_plot(prob, conditional_probability_data)))
 
     return {
-        "model_repr": repr(model),
+        "model": model,
         "times": list(times),
         "exposed_presence_intervals": exposed_presence_intervals,
         "short_range_intervals": short_range_intervals,
@@ -330,26 +330,7 @@ def img2base64(img_data) -> str:
     return f'data:image/png;base64,{pic_hash}'
 
 
-def generate_permalink(base_url, get_root_url,  get_root_calculator_url, form: VirusFormData):
-    form_dict = VirusFormData.to_dict(form, strip_defaults=True)
-
-    # Generate the calculator URL arguments that would be needed to re-create this
-    # form.
-    args = urllib.parse.urlencode(form_dict)
-
-    # Then zlib compress + base64 encode the string. To be inverted by the
-    # /_c/ endpoint.
-    compressed_args = base64.b64encode(zlib.compress(args.encode())).decode()
-    qr_url = f"{base_url}{get_root_url()}/_c/{compressed_args}"
-    url = f"{base_url}{get_root_calculator_url()}?{args}"
-
-    return {
-        'link': url,
-        'shortened': qr_url,
-    }
-
-
-def manufacture_viral_load_scenarios_percentiles(model: mc.ExposureModel) -> typing.Dict[str, mc.ExposureModel]:
+def calculate_vl_scenarios_percentiles(model: mc.ExposureModel) -> typing.Dict[str, mc.ExposureModel]:
     viral_load = model.concentration_model.infected.virus.viral_load_in_sputum
     scenarios = {}
     for percentil in (0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99):
@@ -359,7 +340,9 @@ def manufacture_viral_load_scenarios_percentiles(model: mc.ExposureModel) -> typ
                                                               )
         scenarios[str(vl)] = np.mean(
             specific_vl_scenario.infection_probability())
-    return scenarios
+    return {
+        'alternative_viral_load': scenarios,
+    }
 
 
 def manufacture_alternative_scenarios(form: VirusFormData) -> typing.Dict[str, mc.ExposureModel]:
@@ -451,7 +434,6 @@ def comparison_report(
         form: VirusFormData,
         report_data: typing.Dict[str, typing.Any],
         scenarios: typing.Dict[str, mc.ExposureModel],
-        sample_times: typing.List[float],
         executor_factory: typing.Callable[[], concurrent.futures.Executor],
 ):
     if (form.short_range_option == "short_range_no"):
@@ -474,7 +456,7 @@ def comparison_report(
         results = executor.map(
             scenario_statistics,
             scenarios.values(),
-            [sample_times] * len(scenarios),
+            [report_data['times']] * len(scenarios),
             [compute_prob_exposure] * len(scenarios),
             timeout=60,
         )
@@ -485,3 +467,10 @@ def comparison_report(
     return {
         'stats': statistics,
     }
+
+
+def alternative_scenarios_data(form: VirusFormData, report_data: typing.Dict[str, typing.Any], executor_factory: typing.Callable[[], concurrent.futures.Executor]) -> typing.Dict[str, typing.Any]:
+    alternative_scenarios: typing.Dict[str, typing.Any] = manufacture_alternative_scenarios(form=form)
+    return {
+        'alternative_scenarios': comparison_report(form=form, report_data=report_data, scenarios=alternative_scenarios, executor_factory=executor_factory)
+    }

cern_caimira/src/cern_caimira/apps/calculator/__init__.py

@@ -246,8 +246,7 @@ async def post(self) -> None:
             max_workers=self.settings['handler_worker_pool_size'],
             timeout=300,
         )
-        model = virus_report_controller.generate_model(form, data_registry)
-        report_data_task = executor.submit(calculate_report_data, form, model,
+        report_data_task = executor.submit(calculate_report_data, form,
                                            executor_factory=functools.partial(
                                                concurrent.futures.ThreadPoolExecutor,
                                                self.settings['report_generation_parallelism'],

cern_caimira/src/cern_caimira/apps/calculator/report/virus_report.py

@@ -5,13 +5,16 @@
 import json
 import typing
 import jinja2
+import urllib
+import zlib
+import base64
 import numpy as np
 
 from .. import markdown_tools
 
 from caimira.calculator.models import models
 from caimira.calculator.validators.virus.virus_validator import VirusFormData
-from caimira.calculator.report.virus_report_data import calculate_report_data, interesting_times, manufacture_alternative_scenarios, manufacture_viral_load_scenarios_percentiles, comparison_report, generate_permalink
+from caimira.calculator.report.virus_report_data import alternative_scenarios_data, calculate_report_data, calculate_vl_scenarios_percentiles
 
 
 def minutes_to_time(minutes: int) -> str:
@@ -62,6 +65,25 @@ def non_zero_percentage(percentage: int) -> str:
         return "{:0.1f}%".format(percentage)
 
 
+def generate_permalink(base_url, get_root_url,  get_root_calculator_url, form: VirusFormData):
+    form_dict = VirusFormData.to_dict(form, strip_defaults=True)
+
+    # Generate the calculator URL arguments that would be needed to re-create this
+    # form.
+    args = urllib.parse.urlencode(form_dict)
+
+    # Then zlib compress + base64 encode the string. To be inverted by the
+    # /_c/ endpoint.
+    compressed_args = base64.b64encode(zlib.compress(args.encode())).decode()
+    qr_url = f"{base_url}{get_root_url()}/_c/{compressed_args}"
+    url = f"{base_url}{get_root_calculator_url()}?{args}"
+
+    return {
+        'link': url,
+        'shortened': qr_url,
+    }
+
+
 @dataclasses.dataclass
 class VirusReportGenerator:
     jinja_loader: jinja2.BaseLoader
@@ -74,44 +96,53 @@ def build_report(
             form: VirusFormData,
             executor_factory: typing.Callable[[], concurrent.futures.Executor],
     ) -> str:
-        model = form.build_model()
         context = self.prepare_context(
-            base_url, model, form, executor_factory=executor_factory)
+            base_url, form, executor_factory=executor_factory)
         return self.render(context)
 
     def prepare_context(
             self,
             base_url: str,
-            model: models.ExposureModel,
             form: VirusFormData,
             executor_factory: typing.Callable[[], concurrent.futures.Executor],
     ) -> dict:
         now = datetime.utcnow().astimezone()
         time = now.strftime("%Y-%m-%d %H:%M:%S UTC")
 
-        data_registry_version = f"v{model.data_registry.version}" if model.data_registry.version else None
         context = {
-            'model': model,
             'form': form,
             'creation_date': time,
-            'data_registry_version': data_registry_version,
         }
 
-        scenario_sample_times = interesting_times(model)
-        report_data = calculate_report_data(
-            form, model, executor_factory=executor_factory)
+        # Main report data
+        report_data = calculate_report_data(form, executor_factory)
         context.update(report_data)
 
-        alternative_scenarios = manufacture_alternative_scenarios(form)
-        context['alternative_viral_load'] = manufacture_viral_load_scenarios_percentiles(
-            model) if form.conditional_probability_viral_loads else None
-        context['alternative_scenarios'] = comparison_report(
-            form, report_data, alternative_scenarios, scenario_sample_times, executor_factory=executor_factory,
-        )
-        context['permalink'] = generate_permalink(
+        # Model and Data Registry
+        model: models.ExposureModel = report_data['model']
+        data_registry_version: typing.Optional[str] = f"v{model.data_registry.version}" if model.data_registry.version else None
+
+        # Alternative scenarios data
+        alternative_scenarios: typing.Dict[str,typing.Any] = alternative_scenarios_data(form, report_data, executor_factory)
+        context.update(alternative_scenarios)
+
+        # Alternative viral load data
+        if form.conditional_probability_viral_loads:
+            alternative_viral_load: typing.Dict[str,typing.Any] = calculate_vl_scenarios_percentiles(model)
+            context.update(alternative_viral_load)
+
+        # Permalink
+        permalink: typing.Dict[str, str] = generate_permalink(
             base_url, self.get_root_url, self.get_root_calculator_url, form)
-        context['get_url'] = self.get_root_url
-        context['get_calculator_url'] = self.get_root_calculator_url
+
+        # URLs (root, calculator and permalink)
+        context.update({
+            'model_repr': repr(model),
+            'data_registry_version': data_registry_version,
+            'permalink': permalink,
+            'get_url': self.get_root_url,
+            'get_calculator_url': self.get_root_calculator_url,
+        })
 
         return context
 

cern_caimira/tests/test_report_generator.py

@@ -104,23 +104,21 @@ def test_interesting_times_w_temp(exposure_model_w_outside_temp_changes):
     np.testing.assert_allclose(result, expected)
 
 
-def test_expected_new_cases(baseline_form_with_sr: VirusFormData):
-    model = baseline_form_with_sr.build_model()
-    
+def test_expected_new_cases(baseline_form_with_sr: VirusFormData):    
     executor_factory = partial(
         concurrent.futures.ThreadPoolExecutor, 1,
     )
 
     # Short- and Long-range contributions
-    report_data = rep_gen.calculate_report_data(baseline_form_with_sr, model, executor_factory)
+    report_data = rep_gen.calculate_report_data(baseline_form_with_sr, executor_factory)
     sr_lr_expected_new_cases = report_data['expected_new_cases']
     sr_lr_prob_inf = report_data['prob_inf']/100
 
     # Long-range contributions alone
-    scenario_sample_times = rep_gen.interesting_times(model)
+    scenario_sample_times = report_data['times']
     alternative_scenarios = rep_gen.manufacture_alternative_scenarios(baseline_form_with_sr)
     alternative_statistics = rep_gen.comparison_report(
-        baseline_form_with_sr, report_data, alternative_scenarios, scenario_sample_times, executor_factory=executor_factory,
+        baseline_form_with_sr, report_data, alternative_scenarios, executor_factory=executor_factory,
     )
 
     lr_expected_new_cases = alternative_statistics['stats']['Base scenario without short-range interactions']['expected_new_cases']