added new input "room_capacity" and adapted the calculations of the flow rate (l/s/p) accordingly
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lrdossan
parent
ada64d6acc
commit
53b3a52513
4 changed files with 63 additions and 28 deletions
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@ -22,6 +22,7 @@ class CO2FormData(FormData):
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CO2_data: dict
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fitting_ventilation_states: list
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fitting_ventilation_type: str
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room_capacity: int
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#: The default values for undefined fields. Note that the defaults here
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#: and the defaults in the html form must not be contradictory.
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@ -45,6 +46,7 @@ class CO2FormData(FormData):
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'infected_lunch_start': '12:30',
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'infected_people': 1,
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'infected_start': '08:30',
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'room_capacity': 10,
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'room_volume': NO_DEFAULT,
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'specific_breaks': '{}',
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'total_people': NO_DEFAULT,
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@ -62,6 +64,14 @@ class CO2FormData(FormData):
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# Validate population parameters
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self.validate_population_parameters()
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# Validate room capacity
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if type(self.room_capacity) is not int:
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raise TypeError(f'The room capacity should be a valid integer (> 0). Got {type(self.room_capacity)}.')
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if self.room_capacity <= 0:
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raise TypeError(f'The room capacity should be a valid integer (> 0). Got {self.room_capacity}.')
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if self.room_capacity < self.total_people:
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raise TypeError(f'The room capacity should be higher than the total people in the room. Got {self.room_capacity}.')
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# Validate specific inputs - breaks (exposed and infected)
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if self.specific_breaks != {}:
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if type(self.specific_breaks) is not dict:
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@ -181,6 +191,7 @@ class CO2FormData(FormData):
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return models.CO2DataModel(
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data_registry=self.data_registry,
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room_capacity=self.room_capacity,
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room_volume=self.room_volume,
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occupancy=models.IntPiecewiseConstant(transition_times=tuple(all_state_changes), values=tuple(total_people)),
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ventilation_transition_times=self.ventilation_transition_times(),
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@ -19,6 +19,7 @@ const CO2_data_form = [
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"infected_lunch_start",
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"infected_people",
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"infected_start",
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"room_capacity",
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"room_volume",
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"specific_breaks",
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"total_people",
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@ -137,6 +138,7 @@ function generateJSONStructure(endpoint, jsonData) {
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$("#generate_fitting_data").prop("disabled", false);
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$("#fitting_ventilation_states").prop("disabled", false);
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$("[name=fitting_ventilation_type]").prop("disabled", false);
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$("#room_capacity").prop("disabled", false);
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plotCO2Data(endpoint);
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}
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}
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@ -152,7 +154,9 @@ function validateFormInputs(obj) {
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const $referenceNode = $("#DIVCO2_data_dialog");
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for (let i = 0; i < CO2_data_form.length; i++) {
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const $requiredElement = $(`[name=${CO2_data_form[i]}]`).first();
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if ($requiredElement.attr('name') !== "fitting_ventilation_states" && $requiredElement.val() === "") {
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if ($requiredElement.attr('name') !== "fitting_ventilation_states" &&
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$requiredElement.attr('name') !== "room_capacity" &&
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$requiredElement.val() === "") {
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insertErrorFor(
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$referenceNode,
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`'${$requiredElement.attr('name')}' must be defined.<br />`
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@ -236,6 +240,36 @@ function validateCO2Form() {
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);
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submit = false;
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}
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// Validate room capacity
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const roomCapacity = $fittingToSubmit.find("input[name=room_capacity]");
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const roomCapacityVal = roomCapacity.val();
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if (roomCapacityVal !== "") {
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const roomCapacityNumber = Number(roomCapacityVal);
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const totalPeopleNumber = Number($("#total_people").val());
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if (!Number.isInteger(roomCapacityNumber) || roomCapacityNumber <= 0) {
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insertErrorFor(
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$referenceNode,
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`'${roomCapacity.attr('name')}' must be a valid integer (> 0).</br>`
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);
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submit = false;
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}
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else if (roomCapacityNumber < totalPeopleNumber){
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insertErrorFor(
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$referenceNode,
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`'${roomCapacity.attr('name')}' must be higher than the total people.</br>`
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);
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submit = false;
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}
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console.log(roomCapacityNumber)
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console.log(totalPeopleNumber)
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}
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else {
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insertErrorFor(
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$referenceNode,
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`'${roomCapacity.attr('name')}' must be defined.</br>`
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);
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submit = false;
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}
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}
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return submit;
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@ -341,6 +375,11 @@ function submitFittingAlgorithm(url) {
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"disabled",
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true
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);
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// Disable room capacity input
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$("#room_capacity").prop(
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"disabled",
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true
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);
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// Prepare data for submission
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const CO2_mapping = formatCO2DataForm(CO2_data_form);
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@ -363,6 +363,11 @@
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</div>
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<input type="text" class="form-control" id="fitting_ventilation_states" name="fitting_ventilation_states" placeholder="e.g. [8.5, 10, 11.5, 17]" form="not-submitted"><br>
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</div>
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<strong>Room data:</strong>
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<div class="form-group row">
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<div class="col-sm-5"><label class="col-form-label">Maximum number of occupants:</label></div>
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<div class="col-sm-7 align-self-center"><input type="number" id="room_capacity" class="form-control" name="room_capacity" placeholder="Number" min=1 form="not-submitted"></div>
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</div>
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</div>
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<div id="DIVCO2_fitting_result" style="display: none">
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@ -1526,11 +1526,13 @@ class ShortRangeModel:
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@dataclass(frozen=True)
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class CO2DataModel:
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'''
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The CO2DataModel class models CO2 data based on room volume, ventilation transition times, and people presence.
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It uses optimization techniques to fit the model's parameters and estimate the exhalation rate and ventilation
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values that best match the measured CO2 concentrations.
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The CO2DataModel class models CO2 data based on room volume and capacity,
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ventilation transition times, and people presence.
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It uses optimization techniques to fit the model's parameters and estimate the
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exhalation rate and ventilation values that best match the measured CO2 concentrations.
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'''
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data_registry: DataRegistry
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room_capacity: int
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room_volume: float
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occupancy: IntPiecewiseConstant
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ventilation_transition_times: typing.Tuple[float, ...]
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@ -1592,30 +1594,8 @@ class CO2DataModel:
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the_predictive_CO2 = self.CO2_concentrations_from_params(the_CO2_concentration_model)
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# Ventilation in L/s/person
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def max_occupancy_in_interval(start: float, stop: float) -> int:
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"""
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Given a certain ventilation interval, get the maximum number of
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people in that period of time.
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"""
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max_people: int = 0
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for i, (people_start, people_stop) in enumerate(zip(self.occupancy.transition_times[:-1],
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self.occupancy.transition_times[1:])):
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if people_stop <= start or people_start >= stop:
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continue
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if self.occupancy.values[i] > max_people: max_people = self.occupancy.values[i]
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return max_people
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vent_volume_liter_person = []
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for i, (vent_start, vent_stop) in enumerate(zip(self.ventilation_transition_times[:-1],
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self.ventilation_transition_times[1:])):
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max_people = max_occupancy_in_interval(vent_start, vent_stop)
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if max_people == 0:
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# If in a certain interval there are no occupancy, the flow rate per second/person is 0
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vent_volume_liter_person.append(0)
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else:
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vent_volume_liter_person.append(
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ventilation_values[i] / 3600 * self.room_volume / max_people * 1000
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) # 1m^3 = 1000L
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vent_volume_liter_person = [vent / 3600 * self.room_volume / self.room_capacity * 1000
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for vent in ventilation_values] # 1m^3 = 1000L
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return {
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"exhalation_rate": exhalation_rate,
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