Merge branch 'changes/CO2_review' into 'master'

CO2 fitting algorithm post-review changes

See merge request caimira/caimira!516

CHANGELOG.md

@@ -1,3 +1,11 @@
+# 4.17.4 (November 05, 2024)
+
+## Features Added
+- CO2 fitting algorithm post-review changes
+
+## Bug Fixes
+- N/A
+
 # 4.17.3 (October 16, 2024)
 
 ## Features Added

caimira/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "caimira"
-version = "4.17.3"
+version = "4.17.4"
 description = "CAiMIRA - CERN Airborne Model for Indoor Risk Assessment"
 license = { text = "Apache-2.0" }
 authors = [

caimira/src/caimira/calculator/models/monte_carlo/models.py

@@ -129,8 +129,8 @@ _MODEL_CLASSES = [
 
 # Inject the runtime generated MC types into this module.
 for _model in _MODEL_CLASSES:
-    setattr(sys.modules[__name__], _model.__name__, _build_mc_model(_model))
+    setattr(sys.modules[__name__], _model.__name__, _build_mc_model(_model)) # type: ignore
 
 
 # Make sure that each of the models is imported if you do a ``import *``.
-__all__ = [_model.__name__ for _model in _MODEL_CLASSES] + ["MCModelBase"]
+__all__ = [_model.__name__ for _model in _MODEL_CLASSES] + ["MCModelBase"] # type: ignore

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

@@ -21,14 +21,15 @@ def build_initial_plot(
         [occupancy_transition_times[-1]] +
         ventilation_transition_times)
 
-    ventilation_plot: str = form.generate_ventilation_plot(
+    vent_plot_img, vent_plot_data = form.generate_ventilation_plot(
-        ventilation_transition_times=all_vent_transition_times,
+        ventilation_transition_times=ventilation_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],
+        'CO2_plot_img': vent_plot_img,
+        'CO2_plot_data': vent_plot_data
     }
 
     return context
@@ -52,9 +53,12 @@ def build_fitting_results(
     # predictive CO2 result based on the fitting results.
     context = dict(CO2model.CO2_fit_params())
 
+    vent_plot_img, vent_plot_data = form.generate_ventilation_plot(ventilation_transition_times=ventilation_transition_times[:-1],
+                                        predictive_CO2=context['predictive_CO2'])
+
     # 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],
+    context['CO2_plot_img'] = vent_plot_img
-                                                         predictive_CO2=context['predictive_CO2'])
+    context['CO2_plot_data'] = vent_plot_data
 
     return context

caimira/src/caimira/calculator/validators/co2/co2_validator.py

@@ -105,7 +105,7 @@ class CO2FormData(FormData):
         """
         Perform change point detection using scipy library (find_peaks method) with rolling average of data.
         Incorporate existing state change candidates and adjust the result accordingly.
-        Returns a list of the detected ventilation state changes, discarding any occupancy state change.
+        Returns a list of the detected ventilation transition times, discarding any occupancy state change.
         """
         times: list = self.CO2_data['times']
         CO2_values: list = self.CO2_data['CO2']
@@ -147,33 +147,46 @@ class CO2FormData(FormData):
     def generate_ventilation_plot(self,
                                   ventilation_transition_times: typing.Optional[list] = None,
                                   occupancy_transition_times: typing.Optional[list] = None,
-                                  predictive_CO2: typing.Optional[list] = None) -> str:
+                                  predictive_CO2: typing.Optional[list] = None):
             
             # Plot data (x-axis: times; y-axis: CO2 concentrations)
             times_values: list = self.CO2_data['times']
             CO2_values: list = self.CO2_data['CO2']
 
             fig = plt.figure(figsize=(7, 4), dpi=110)
-            plt.plot(times_values, CO2_values, label='Input CO₂')
+            plt.plot(times_values, CO2_values, label='CO₂ Data')
+            
+            # Add predictive CO2
+            if (predictive_CO2):
+                plt.plot(times_values, predictive_CO2, label='Predictive CO₂')
 
-            # Add occupancy state changes:
+            # Add ventilation transition times:
-            if (occupancy_transition_times):
-                for i, time in enumerate(occupancy_transition_times):
-                    plt.axvline(x = time, color = 'grey', linewidth=0.5, linestyle='--', label='Occupancy change (from input)' if i == 0 else None)
-            # Add ventilation state changes:
             if (ventilation_transition_times):
                 for i, time in enumerate(ventilation_transition_times):
                     if i == 0:
-                        label = 'Ventilation change (detected)' if occupancy_transition_times else 'Ventilation state changes'
+                        label = 'Ventilation transition times (suggestion)' if occupancy_transition_times else 'Ventilation transition times'
                     else: label = None
-                    plt.axvline(x = time, color = 'red', linewidth=0.5, linestyle='--', label=label)
+                    plt.axvline(x = time, color = 'red', linewidth=1, linestyle='--', label=label)
+            
+            # Add occupancy changes (UI):
+            if (occupancy_transition_times):
+                for i, time in enumerate(occupancy_transition_times):
+                    plt.axvline(x = time, color = 'grey', linewidth=1, linestyle='--', label='Occupancy change (from UI)' if i == 0 else None)
 
-            if (predictive_CO2):
-                plt.plot(times_values, predictive_CO2, label='Predictive CO₂')
             plt.xlabel('Time of day')
             plt.ylabel('Concentration (ppm)')
             plt.legend()
-            return img2base64(_figure2bytes(fig))
+
+            vent_plot_data = {
+                'plot': img2base64(_figure2bytes(fig)),
+                'times': times_values,
+                'CO2': CO2_values,
+                'occ_trans_time': occupancy_transition_times,
+                'vent_trans_time': ventilation_transition_times,
+                'predictive_CO2': predictive_CO2,
+            }
+
+            return img2base64(_figure2bytes(fig)), vent_plot_data
 
     def population_present_changes(self, infected_presence: models.Interval, exposed_presence: models.Interval) -> typing.List[float]:
         state_change_times = set(infected_presence.transition_times())

caimira/tests/models/test_co2_concentration_model.py

@@ -57,7 +57,7 @@ def test_integrated_concentration(simple_co2_conc_model):
 def test_find_change_points(scenario_data, room_volume, max_total_people, start, finish, state_changes, request):
     '''
     Specific test of the find_change_points method. 
-    Testing the ventilation state changes only.
+    Testing the ventilation transition times only.
     '''
     CO2_form_model: CO2FormData = CO2FormData(
         CO2_data=request.getfixturevalue(scenario_data),

cern_caimira/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "cern-caimira"
-version = "4.17.3"
+version = "4.17.4"
 description = "CAiMIRA - CERN Airborne Model for Indoor Risk Assessment"
 license = { text = "Apache-2.0" }
 authors = [

cern_caimira/src/cern_caimira/apps/calculator/static/js/co2_form.js

@@ -222,7 +222,7 @@ function validateCO2Form() {
         if (parsedValue.length <= 1) {
           insertErrorFor(
             $referenceNode,
-            `'${$ventilationStates.attr('name')}' must have more than one ventilation state change (at least the beggining and end of simulation time).<br />`
+            `'${$ventilationStates.attr('name')}' must have more than one ventilation transition time (at least the beginning and end of simulation time).<br />`
           );
           submit = false;
         }
@@ -297,9 +297,9 @@ function displayTransitionTimesHourFormat(start, stop) {
 
 function displayFittingData(json_response) {
   $("#DIVCO2_fitting_result").show();
-  $("#CO2_data_plot").attr("src", json_response["CO2_plot"]);
+  $("#CO2_data_plot").attr("src", json_response["CO2_plot_img"]);
   // Not needed for the form submission
-  delete json_response["CO2_plot"];
+  delete json_response["CO2_plot_img"];
   delete json_response["predictive_CO2"];
   // Convert nulls to empty strings in the JSON response
   if (json_response["room_capacity"] === null) json_response["room_capacity"] = '';
@@ -381,7 +381,7 @@ function plotCO2Data(url) {
       response
         .json()
         .then((json_response) => {
-          $("#CO2_data_plot").attr("src", json_response["CO2_plot"])
+          $("#CO2_data_plot").attr("src", json_response["CO2_plot_img"])
           $("#fitting_ventilation_states").val(`[${json_response["transition_times"]}]`)
         })
         .then($("#DIVCO2_fitting_to_submit").show())

cern_caimira/src/cern_caimira/apps/templates/base/calculator.form.html.j2

@@ -349,7 +349,7 @@
                   The dashed lines are suggestions for the ventilation transition times<br>
                 </p>
                 <div id="DIVfitting_ventilation" class="form-group mb-0">
-                  <label for="fitting_ventilation_states">Please enter the ventilation state change times, separated by comma - e.g. [8.5, 10, 11.5, 17]. </label>
+                  <label for="fitting_ventilation_states">Please enter the ventilation transition times, separated by comma - e.g. [8.5, 10, 11.5, 17]. </label>
                   <div data-tooltip="Default values indicated below correspond to the dashed lines in the above plot - these are only suggestions and can be changed.">
                       <span class="tooltip_text">?</span>
                   </div>