pingu_98/cara
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

added table for l/s/p results

Browse source
This commit is contained in:
lrdossan 2024-07-09 16:22:52 +02:00
parent bad998c4c3
commit b3f7321a28
5 changed files with 67 additions and 23 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
caimira/apps/calculator/co2_model_generator.py
View file

@ -182,8 +182,7 @@ class CO2FormData(FormData):
return models.CO2DataModel(
data_registry=self.data_registry,
room_volume=self.room_volume,
number=models.IntPiecewiseConstant(transition_times=tuple(all_state_changes), values=tuple(total_people)),
presence=None,
occupancy=models.IntPiecewiseConstant(transition_times=tuple(all_state_changes), values=tuple(total_people)),
ventilation_transition_times=self.ventilation_transition_times(),
times=self.CO2_data['times'],
CO2_concentrations=self.CO2_data['CO2'],

14
caimira/apps/calculator/static/js/co2_form.js
View file

@ -268,16 +268,20 @@ function displayFittingData(json_response) {
" m³/h"
);
let ventilation_table =
"<tr><th>Time (HH:MM)</th><th>ACH value (h⁻¹)</th></tr>";
json_response["ventilation_values"].forEach((val, index) => {
"<tr><th>Time (HH:MM)</th><th>ACH value (h⁻¹)</th><th>Flow rate (L/s/person)</th></tr>";
json_response["ventilation_values"].forEach((CO2_val, index) => {
let transition_times = displayTransitionTimesHourFormat(
json_response["transition_times"][index],
json_response["transition_times"][index + 1]
);
ventilation_table += `<tr><td>${transition_times}</td><td>${val.toPrecision(
2
)}</td></tr>`;
ventilation_table += `<tr>
<td>${transition_times}</td>
<td>${CO2_val.toPrecision(2)}</td>
<td>${json_response['ventilation_lsp_values'][index].toPrecision(2)}</td>
</tr>`;
});
$("#disable_fitting_algorithm").prop("disabled", false);
$("#ventilation_rate_fit").html(ventilation_table);
$("#generate_fitting_data").html("Fit data");

2
caimira/apps/templates/base/calculator.form.html.j2
View file

@ -380,7 +380,7 @@
</div>
</div>
</div>
</div></br>
</div></br>
<div class='sub_title'>HEPA filtration:</div>
<div>
<input type="radio" id="hepa_yes" name="hepa_option" value=1 onclick="require_fields(this)" data-enables="#DIVhepa_amount">

68
caimira/models.py
View file

@ -1532,28 +1532,30 @@ class CO2DataModel:
'''
data_registry: DataRegistry
room_volume: float
number: typing.Union[int, IntPiecewiseConstant]
presence: typing.Optional[Interval]
occupancy: IntPiecewiseConstant
ventilation_transition_times: typing.Tuple[float, ...]
times: typing.Sequence[float]
CO2_concentrations: typing.Sequence[float]
def CO2_concentrations_from_params(self,
exhalation_rate: float,
ventilation_values: typing.Tuple[float, ...]) -> typing.List[_VectorisedFloat]:
CO2_concentrations = CO2ConcentrationModel(
def CO2_concentration_model(self,
exhalation_rate: float,
ventilation_values: typing.Tuple[float, ...]) -> CO2ConcentrationModel:
return CO2ConcentrationModel(
data_registry=self.data_registry,
room=Room(volume=self.room_volume),
ventilation=CustomVentilation(PiecewiseConstant(
self.ventilation_transition_times, ventilation_values)),
CO2_emitters=SimplePopulation(
number=self.number,
presence=self.presence,
number=self.occupancy,
presence=None,
activity=Activity(
exhalation_rate=exhalation_rate, inhalation_rate=exhalation_rate),
)
)
return [CO2_concentrations.concentration(time) for time in self.times]
def CO2_concentrations_from_params(self, CO2_concentration_model: CO2ConcentrationModel) -> typing.List[_VectorisedFloat]:
# Calculate the predictive CO2 concentration
return [CO2_concentration_model.concentration(time) for time in self.times]
def CO2_fit_params(self):
if len(self.times) != len(self.CO2_concentrations):
@ -1566,10 +1568,11 @@ class CO2DataModel:
def fun(x):
exhalation_rate = x[0]
ventilation_values = tuple(x[1:])
the_concentrations = self.CO2_concentrations_from_params(
CO2_concentration_model = self.CO2_concentration_model(
exhalation_rate=exhalation_rate,
ventilation_values=ventilation_values
)
the_concentrations = self.CO2_concentrations_from_params(CO2_concentration_model)
return np.sqrt(np.sum((np.array(self.CO2_concentrations) -
np.array(the_concentrations))**2))
# The goal is to minimize the difference between the two different curves (known concentrations vs. predicted concentrations)
@ -1577,10 +1580,49 @@ class CO2DataModel:
bounds=[(0, None) for _ in range(len(self.ventilation_transition_times))],
options={'xtol': 1e-3})
# Final prediction
exhalation_rate = res_dict['x'][0]
ventilation_values = res_dict['x'][1:]
predictive_CO2 = self.CO2_concentrations_from_params(exhalation_rate=exhalation_rate, ventilation_values=ventilation_values)
return {"exhalation_rate": exhalation_rate, "ventilation_values": list(ventilation_values), 'predictive_CO2': list(predictive_CO2)}
ventilation_values = res_dict['x'][1:] # In ACH
# Final CO2ConcentrationModel with obtained prediction
the_CO2_concentration_model = self.CO2_concentration_model(
exhalation_rate=exhalation_rate,
ventilation_values=ventilation_values
)
the_predictive_CO2 = self.CO2_concentrations_from_params(the_CO2_concentration_model)
# Ventilation in L/s/person
def max_occupancy_in_interval(start: float, stop: float) -> int:
"""
Given a certain ventilation interval, get the maximum number of
people in that period od time.
"""
max_people: int = 0
for i, (people_start, people_stop) in enumerate(zip(self.occupancy.transition_times[:-1],
self.occupancy.transition_times[1:])):
if people_stop <= start or people_start >= stop:
continue
if self.occupancy.values[i] > max_people: max_people = self.occupancy.values[i]
return max_people
vent_volume_liter_person = []
for i, (vent_start, vent_stop) in enumerate(zip(self.ventilation_transition_times[:-1],
self.ventilation_transition_times[1:])):
max_people = max_occupancy_in_interval(vent_start, vent_stop)
if max_people == 0:
# If in a certain interval there are no occupancy, the flow rate per second/person is 0
vent_volume_liter_person.append(0)
else:
vent_volume_liter_person.append(
ventilation_values[i] / 3600 * self.room_volume / max_people * 1000
) # 1m^3 = 1000L
return {
"exhalation_rate": exhalation_rate,
"ventilation_values": list(ventilation_values),
"ventilation_lsp_values": vent_volume_liter_person,
'predictive_CO2': list(the_predictive_CO2)
}
@dataclass(frozen=True)

3
caimira/tests/models/test_fitting_algorithm.py
View file

@ -41,9 +41,8 @@ def test_fitting_algorithm(data_registry, activity_type, ventilation_active, air
data_model = models.CO2DataModel(
data_registry=data_registry,
room_volume=75,
number=models.IntPiecewiseConstant(transition_times=tuple(
occupancy=models.IntPiecewiseConstant(transition_times=tuple(
[8, 12, 13, 17]), values=tuple([2, 1, 2])),
presence=None,
ventilation_transition_times=tuple(ventilation_active),
times=times,
CO2_concentrations=CO2_concentrations