extracted CO2 logic to a dedicated route

README.md

@@ -109,7 +109,7 @@ python -m cern_caimira.apps.calculator
 To run with a specific template theme created:
 
 ```
-python -m cern_caimira.apps.calculator --theme=ui/apps/templates/{theme}
+python -m cern_caimira.apps.calculator --theme=cern_caimira/src/cern_caimira/apps/templates/{theme}
 ```
 
 To run the entire app in a different `APPLICATION_ROOT` path:
@@ -168,9 +168,11 @@ Running with Visual Studio Code (VSCode):
 
 ### Running the tests
 
+Make sure you are in the root directory of the project. Then:
+
 ```
-pip install -e .[test]
-pytest ./caimira
+pip install -e .[test] 
+python -m pytest
 ```
 
 ### Running the profiler

caimira/src/caimira/api/app.py

@@ -8,22 +8,25 @@
 from tornado.options import define, options
 import logging
 
-from caimira.api.routes.report_routes import ReportHandler
+from caimira.api.routes.report_routes import VirusReportHandler, CO2ReportHandler
 
 define("port", default=8088, help="Port to listen on", type=int)
 
 logging.basicConfig(format="%(message)s", level=logging.INFO)
 
+
 class Application(tornado.web.Application):
     def __init__(self):
         handlers = [
-            (r"/report", ReportHandler),
+            (r"/co2_report", CO2ReportHandler),
+            (r"/virus_report", VirusReportHandler),
         ]
         settings = dict(
             debug=True,
         )
         super(Application, self).__init__(handlers, **settings)
 
+
 if __name__ == "__main__":
     app = Application()
     app.listen(options.port)

caimira/src/caimira/api/controller/co2_report_controller.py

@@ -0,0 +1,26 @@
+from caimira.calculator.validators.co2.co2_validator import CO2FormData
+from caimira.calculator.store.data_registry import DataRegistry
+
+
+def generate_form_obj(form_data, data_registry):
+    return CO2FormData.from_dict(form_data=form_data, data_registry=data_registry)
+
+
+def generate_model(form_obj, data_registry):
+    sample_size = data_registry.monte_carlo['sample_size']
+    return form_obj.build_model(sample_size=sample_size)
+
+
+def generate_report(model):
+    return dict(model.CO2_fit_params())
+
+
+def submit_CO2_form(form_data):
+    data_registry = DataRegistry()
+
+    form_obj = generate_form_obj(
+        form_data=form_data, data_registry=data_registry)
+    model = generate_model(form_obj=form_obj, data_registry=data_registry)
+    report_data = generate_report(model=model)
+
+    return report_data

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

@@ -0,0 +1,38 @@
+import concurrent.futures
+import functools
+
+from caimira.calculator.validators.virus.virus_validator import VirusFormData
+from caimira.calculator.store.data_registry import DataRegistry
+import caimira.calculator.report.virus_report_data as rg
+
+
+def generate_form_obj(form_data, data_registry):
+    return VirusFormData.from_dict(
+        form_data=form_data,
+        data_registry=data_registry,
+    )
+
+
+def generate_model(form_obj, data_registry):
+    sample_size = data_registry.monte_carlo['sample_size']
+    return form_obj.build_model(sample_size=sample_size)
+
+
+def generate_report_results(form_obj, model):
+    return rg.calculate_report_data(
+        form=form_obj,
+        model=model,
+        executor_factory=functools.partial(
+            concurrent.futures.ThreadPoolExecutor, None,  # TODO define report_parallelism
+        ),
+    )
+
+
+def submit_virus_form(form_data):
+    data_registry = DataRegistry
+
+    form_obj = generate_form_obj(form_data=form_data, data_registry=data_registry)
+    model = generate_model(form_obj=form_obj, data_registry=data_registry)
+    report_data = generate_report_results(form_obj=form_obj, model=model)
+
+    return report_data

caimira/src/caimira/api/routes/report_routes.py

@@ -1,15 +1,24 @@
 import json
 import traceback
 import tornado.web
+import sys
 
-from caimira.api.controller.report_controller import submit_virus_form
+from caimira.api.controller.virus_report_controller import submit_virus_form
+from caimira.api.controller.co2_report_controller import submit_CO2_form
 
-class ReportHandler(tornado.web.RequestHandler):
+
+class BaseReportHandler(tornado.web.RedirectHandler):
     def set_default_headers(self):
         self.set_header("Access-Control-Allow-Origin", "*")
         self.set_header("Access-Control-Allow-Headers", "x-requested-with")
         self.set_header("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
 
+    def write_error(self, status_code, **kwargs):
+        self.set_status(status_code)
+        self.write({"message": kwargs.get('exc_info')[1].__str__()})
+
+
+class VirusReportHandler(BaseReportHandler):
     def post(self):
         try:
             form_data = json.loads(self.request.body)
@@ -24,5 +33,27 @@ def post(self):
             self.write(response_data)
         except Exception as e:
             traceback.print_exc()
-            self.set_status(400)
-            self.write({"message": str(e)})
+            self.write_error(status_code=400, exc_info=sys.exc_info())
+
+
+class CO2ReportHandler(tornado.web.RequestHandler):
+    def set_default_headers(self):
+        self.set_header("Access-Control-Allow-Origin", "*")
+        self.set_header("Access-Control-Allow-Headers", "x-requested-with")
+        self.set_header("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
+
+    def post(self):
+        try:
+            form_data = json.loads(self.request.body)
+            report_data = submit_CO2_form(form_data)
+
+            response_data = {
+                "status": "success",
+                "message": "Results generated successfully",
+                "report_data": report_data,
+            }
+
+            self.write(response_data)
+        except Exception as e:
+            traceback.print_exc()
+            self.write_error(status_code=400, exc_info=sys.exc_info())

caimira/src/caimira/calculator/report/co2_report_data.py

@@ -0,0 +1,60 @@
+from caimira.calculator.validators.co2.co2_validator import CO2FormData
+from caimira.calculator.models.models import CO2DataModel
+
+
+def build_initial_plot(
+        form: CO2FormData,
+) -> dict:
+    '''
+    Initial plot with the suggested ventilation state changes. 
+    This method receives the form input and returns the CO2
+    plot with the respective transition times.
+    '''
+    CO2model: CO2DataModel = form.build_model()
+
+    occupancy_transition_times = list(CO2model.occupancy.transition_times)
+
+    ventilation_transition_times: list = form.find_change_points()
+    # The entire ventilation changes consider the initial and final occupancy state change
+    all_vent_transition_times: list = sorted(
+        [occupancy_transition_times[0]] +
+        [occupancy_transition_times[-1]] +
+        ventilation_transition_times)
+
+    ventilation_plot: str = form.generate_ventilation_plot(
+        ventilation_transition_times=all_vent_transition_times,
+        occupancy_transition_times=occupancy_transition_times
+    )
+
+    context = {
+        'CO2_plot': ventilation_plot,
+        'transition_times': [round(el, 2) for el in all_vent_transition_times],
+    }
+
+    return context
+
+
+def build_fitting_results(
+        form: CO2FormData,
+) -> dict:
+    '''
+    Final fitting results with the respective predictive CO2. 
+    This method receives the form input and returns the fitting results
+    along with the CO2 plot with the predictive CO2.
+    '''
+    CO2model: CO2DataModel = form.build_model()
+
+    # Ventilation times after user manipulation from the suggested ventilation state change times.
+    ventilation_transition_times = list(CO2model.ventilation_transition_times)
+
+    # The result of the following method is a dict with the results of the fitting
+    # algorithm, namely the breathing rate and ACH values. It also returns the
+    # predictive CO2 result based on the fitting results.
+    context = dict(CO2model.CO2_fit_params())
+
+    # Add the transition times and CO2 plot to the results.
+    context['transition_times'] = ventilation_transition_times
+    context['CO2_plot'] = form.generate_ventilation_plot(ventilation_transition_times=ventilation_transition_times[:-1],
+                                                         predictive_CO2=context['predictive_CO2'])
+
+    return context