adated docscrings and comments troughout the code following review

caimira/apps/calculator/co2_model_generator.py

@@ -102,8 +102,7 @@ def find_change_points(self) -> list:
         """
         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 contribution
-        of occupancy state changes.
+        Returns a list of the detected ventilation state changes, discarding any occupancy state change.
         """
         times: list = self.CO2_data['times']
         CO2_values: list = self.CO2_data['CO2']
@@ -126,7 +125,7 @@ def find_change_points(self) -> list:
         min_distance_points = max(int((peak_valley_min_interval_in_minutes * 60) // diff), 1)
 
         # Calculate minimum width of datapoints for valley detection
-        width_min_interval_in_minutes = 20 # Minimum time difference for a valley detection
+        width_min_interval_in_minutes = 20 # Minimum duration of a valley
         min_valley_width = max(int((width_min_interval_in_minutes * 60) // diff), 1)
 
         # Find peaks (maxima) in the smoothed data applying the distance factor

caimira/apps/calculator/static/js/co2_form.js

@@ -134,8 +134,8 @@ function uploadFile(endpoint) {
     const max_presence_time = parseFloat((Math.max(elapsed_time_infected, elapsed_time_exposed)).toFixed(2));
     if (firstTimeInData > min_presence_time || lastTimeInData < max_presence_time) {
       $("#upload-error")
-        .text(`The times data of the file should encompass the entire simulation time (from ${min_presence_time/60} to ${max_presence_time/60}). 
-        Got times from ${firstTimeInData/60} to ${lastTimeInData/60}. Either adapt the simulation presence times, or the times data of the file.`).show();
+        .text(`The times of the data file should encompass the entire simulation time (from ${min_presence_time/60} to ${max_presence_time/60}). 
+        Got times from ${firstTimeInData/60} to ${lastTimeInData/60}. Either adapt the simulation presence times, or the times of the data file.`).show();
       return;
     }
 

caimira/tests/models/test_co2_concentration_model.py

@@ -49,16 +49,15 @@ def test_integrated_concentration(simple_co2_conc_model):
 @pytest.mark.parametrize(
     "scenario_data, room_volume, max_total_people, start, finish, state_changes", [
         ["office_scenario_1_sensor_data", 102, 4, "14:00", "17:30", (14.78, 15.1, 15.53, 15.87, 16.52, 16.83)],
-        ["office_scenario_2_sensor_data", 60, 2, "08:38", "17:30", (10.17, 12.45, 14.5)], # Second should be 12.87
+        ["office_scenario_2_sensor_data", 60, 2, "08:38", "17:30", (10.17, 12.45, 14.5)], # Second state change should actually be 12.87 - that's the real time at which the ventilation was changed in the room.
         ["meeting_scenario_1_sensor_data", 83, 3, "09:04", "11:45", (10.37, 11.07)],
         ["meeting_scenario_2_sensor_data", 83, 4, "13:40", "16:40", (14.37, 14.72, 15, 15.33, 15.68, 16.03)]
     ]
 )
 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 using the
-    scipy find_peaks and specific smoothing techniques. Only
-    the ventilation state changes are target for detection.
+    Specific test of the find_change_points method. 
+    Testing the ventilation state changes only.
     '''
     CO2_form_model: CO2FormData = CO2FormData(
         CO2_data=request.getfixturevalue(scenario_data),
@@ -75,17 +74,17 @@ def test_find_change_points(scenario_data, room_volume, max_total_people, start,
 @pytest.mark.parametrize(
     "scenario_data, room_volume, occupancy, presence_interval, all_state_changes", [
         ["office_scenario_1_sensor_data", 102, (4,), (14, 17.5), (14, 14.25, 14.78, 15.1, 15.53, 15.87, 16.52, 16.83, 17.5)],
-        ["office_scenario_2_sensor_data", 60, (2, 0, 2), (8.62, 11.93, 12.42, 17.5), (8.62, 10.17, 12.45, 14.5, 17.5, 20.)],
+        ["office_scenario_2_sensor_data", 60, (2, 0, 2), (8.62, 11.93, 12.42, 17.5), (8.62, 10.17, 12.45, 14.5, 17.5, 20.)], # Third state change should actually be 12.87 - that's the real time at which the ventilation was changed in the room.
         ["meeting_scenario_1_sensor_data", 83, (2, 3, 2, 3), (9.07, 9.32, 9.75, 10.75, 11.75), (9.07, 10.37, 11.07, 11.75)],
         ["meeting_scenario_2_sensor_data", 83, (2, 3, 4), (13.67, 13.75, 15.87, 16.67), (13.67, 14.37, 14.72, 15.00, 15.33, 15.68, 16.03, 16.67)]
     ]
 )
 def test_predictive_model_accuracy(data_registry, scenario_data, room_volume, occupancy, presence_interval, all_state_changes, request):
     '''
-    Specific test corresponding to specific data files of four
-    different occurencies (2 office and 2 meeting room scenarios). 
+    Specific test corresponding to the data files of four
+    different scenarios (2 in an office and 2 in a meeting room). 
     The room volume, number of people and ventilation transition times 
-    correspond to the real occurencies in the simulation days.
+    correspond to the actual state change occurrences during the day.
 
     Note that the last time from the input file is considered as a ventilation
     state change.