From e7a1c831b723209371e937a22eee44c9760c4ec1 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 20 Jul 2021 12:36:21 +0200 Subject: [PATCH 001/133] Concentration profile plot with cumulative dose #fixes164 --- cara/apps/calculator/report_generator.py | 38 ++++++++++++++++++------ 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index a899a18f..4d2b8dc0 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -105,18 +105,20 @@ def img2base64(img_data) -> str: # A src suitable for a tag such as f'. return f'data:image/png;base64,{pic_hash}' - def plot(times, concentrations, model: models.ExposureModel): fig = plt.figure() ax = fig.add_subplot(1, 1, 1) datetimes = [datetime(1970, 1, 1) + timedelta(hours=time) for time in times] - ax.plot(datetimes, concentrations, lw=2, color='#1f77b4', label='Mean concentration') + + #Concentration as mean viral concentration (virion m$^{-3}$) + concentrations = [c * model.concentration_model.virus.quantum_infectious_dose for c in concentrations] + + ax.plot(datetimes, concentrations, lw=2, color='#1f77b4', label='Mean viral concentration') ax.spines['right'].set_visible(False) - ax.spines['top'].set_visible(False) - ax.set_xlabel('Time of day') - ax.set_ylabel('Mean concentration ($q/m^3$)') - ax.set_title('Mean concentration of infectious quanta') + ax.set_xlabel('Time of day', fontsize=14) + ax.set_ylabel('Mean viral concentration\n(virion m$^{-3}$)', fontsize=14) + ax.set_title('Concentration profile') ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) # Plot presence of exposed person @@ -128,13 +130,31 @@ def plot(times, concentrations, model: models.ExposureModel): label="Presence of exposed person(s)" if i == 0 else "" ) + #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose + factor = 0.6 * np.mean(model.exposed.activity.inhalation_rate) * (1 - model.exposed.mask.η_inhale) + present_indexes = np.array([model.exposed.person_present(t) for t in times]) + modified_concentrations = np.array(concentrations) + modified_concentrations[~present_indexes] = 0 + + qds = [np.trapz(modified_concentrations[:i + 1], times[:i + 1]) * factor for i in range(len(times))] + + ax1 = ax.twinx() + ax1.plot(datetimes, qds, label='qD - Mean viral concentration', color='#1f77b4', linestyle='dotted') + ax1.spines["right"].set_linestyle("--") + ax1.spines["right"].set_linestyle((0,(1,5))) + ax1.set_ylabel('Mean cumulative dose\n(virion)', fontsize=14) + ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1]) + ax1.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) + # Place a legend outside of the axes itself. - ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left') - ax.set_ylim(0) + fig.legend(bbox_to_anchor=(1.05, 0.9), loc='upper left') + ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1]) + # Remove top spines + ax.spines['top'].set_visible(False) + ax1.spines['top'].set_visible(False) return fig - def minutes_to_time(minutes: int) -> str: minute_string = str(minutes % 60) minute_string = "0" * (2 - len(minute_string)) + minute_string From e07bee91ac7bd81e5e653cc13c6ff7230497601f Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 20 Jul 2021 15:34:06 +0200 Subject: [PATCH 002/133] Legend correction and order --- cara/apps/calculator/report_generator.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 4d2b8dc0..ddcf1cd9 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -123,7 +123,7 @@ def plot(times, concentrations, model: models.ExposureModel): # Plot presence of exposed person for i, (presence_start, presence_finish) in enumerate(model.exposed.presence.boundaries()): - plt.fill_between( + ax.fill_between( datetimes, concentrations, 0, where=(np.array(times) > presence_start) & (np.array(times) < presence_finish), color="#1f77b4", alpha=0.1, @@ -139,7 +139,7 @@ def plot(times, concentrations, model: models.ExposureModel): qds = [np.trapz(modified_concentrations[:i + 1], times[:i + 1]) * factor for i in range(len(times))] ax1 = ax.twinx() - ax1.plot(datetimes, qds, label='qD - Mean viral concentration', color='#1f77b4', linestyle='dotted') + ax1.plot(datetimes, qds, label='Mean cumulative dose', color='#1f77b4', linestyle='dotted') ax1.spines["right"].set_linestyle("--") ax1.spines["right"].set_linestyle((0,(1,5))) ax1.set_ylabel('Mean cumulative dose\n(virion)', fontsize=14) @@ -147,7 +147,12 @@ def plot(times, concentrations, model: models.ExposureModel): ax1.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) # Place a legend outside of the axes itself. - fig.legend(bbox_to_anchor=(1.05, 0.9), loc='upper left') + ax_handles, ax_labels = ax.get_legend_handles_labels() + ax1_handles, ax1_labels = ax1.get_legend_handles_labels() + handles = ax_handles + ax1_handles + labels = ax_labels + ax1_labels + order = [0, 2, 1] # 0 - Mean viral concentration 1 - Presence of exposed person(s) 2 - Mean cumulative dose + fig.legend(handles = [handles[idx] for idx in order], labels = [labels[idx] for idx in order], bbox_to_anchor=(1.05, 0.9), loc='upper left') ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1]) # Remove top spines ax.spines['top'].set_visible(False) From f7b2f3c567adedd73de7bfbede628c2d6ea73444 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Wed, 21 Jul 2021 09:42:46 +0200 Subject: [PATCH 003/133] Alternative Scenarios with cumulative dose plot --- cara/apps/calculator/report_generator.py | 27 ++++++++++++++++++++---- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index ddcf1cd9..1d8061db 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -238,6 +238,7 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray): fig = plt.figure() ax = fig.add_subplot(1, 1, 1) + ax1 = ax.twinx() dash_styled_scenarios = [ 'Base scenario with FFP2 masks', @@ -245,15 +246,31 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray) 'Base scenario with HEPA and FFP2 masks', ] - sample_dts = [datetime(1970, 1, 1) + timedelta(hours=time) for time in sample_times] + datetimes = [datetime(1970, 1, 1) + timedelta(hours=time) for time in sample_times] + for name, statistics in scenarios.items(): concentrations = statistics['concentrations'] - + factor = statistics['factor'] + present_indexes = statistics['present_indexes'] + + modified_concentrations = np.array(concentrations) + modified_concentrations[~present_indexes] = 0 + qds = [np.trapz(modified_concentrations[:i + 1], sample_times[:i + 1]) * factor for i in range(len(sample_times))] + if name in dash_styled_scenarios: - ax.plot(sample_dts, concentrations, label=name, linestyle='--') + ax.plot(datetimes, concentrations, label=name, linestyle='--') + ax1.plot(datetimes, qds, label='Mean cumulative dose', linestyle='dotted') else: - ax.plot(sample_dts, concentrations, label=name, linestyle='-', alpha=0.5) + ax.plot(datetimes, concentrations, label=name, linestyle='-', alpha=0.5) + ax1.plot(datetimes, qds, label='Mean cumulative dose', linestyle='dotted', alpha=0.5) + + + ax1.spines["right"].set_linestyle("--") + ax1.spines["right"].set_linestyle((0,(1,5))) + ax1.set_ylabel('Mean cumulative dose\n(virion)', fontsize=14) + ax1.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) + # Place a legend outside of the axes itself. ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left') ax.spines['right'].set_visible(False) @@ -276,6 +293,8 @@ def scenario_statistics(mc_model: mc.ExposureModel, sample_times: np.ndarray): np.mean(model.concentration_model.concentration(time)) for time in sample_times ], + 'factor': 0.6 * np.mean(model.exposed.activity.inhalation_rate) * (1 - model.exposed.mask.η_inhale), + 'present_indexes': np.array([model.exposed.person_present(t) for t in sample_times]), } From 85045f02dbaf198b3908618d7e72e7f7b49d2690 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Wed, 21 Jul 2021 11:01:41 +0200 Subject: [PATCH 004/133] Alternative Scenarios with cumulative dose fix #227 --- cara/apps/calculator/report_generator.py | 41 ++++++++++--------- .../templates/base/calculator.report.html.j2 | 4 +- 2 files changed, 24 insertions(+), 21 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 1d8061db..22aaf932 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -249,37 +249,41 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray) datetimes = [datetime(1970, 1, 1) + timedelta(hours=time) for time in sample_times] for name, statistics in scenarios.items(): + model = statistics['model'] concentrations = statistics['concentrations'] - factor = statistics['factor'] - present_indexes = statistics['present_indexes'] - + + #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose + factor = 0.6 * np.mean(model.exposed.activity.inhalation_rate) * (1 - model.exposed.mask.η_inhale) + present_indexes = np.array([model.exposed.person_present(t) for t in sample_times]) + modified_concentrations = np.array(concentrations) modified_concentrations[~present_indexes] = 0 qds = [np.trapz(modified_concentrations[:i + 1], sample_times[:i + 1]) * factor for i in range(len(sample_times))] + # Plot concentrations and cumulative dose if name in dash_styled_scenarios: ax.plot(datetimes, concentrations, label=name, linestyle='--') - ax1.plot(datetimes, qds, label='Mean cumulative dose', linestyle='dotted') + ax1.plot(datetimes, qds, label=f'Mean cumulative dose:\n{name}', linestyle='dotted') else: ax.plot(datetimes, concentrations, label=name, linestyle='-', alpha=0.5) - ax1.plot(datetimes, qds, label='Mean cumulative dose', linestyle='dotted', alpha=0.5) - + ax1.plot(datetimes, qds, label=f'Mean cumulative dose:\n{name}', linestyle='dotted', alpha=0.5) + + # Place a legend outside of the axes itself. + fig.legend(bbox_to_anchor=(1.05, 0.95), loc='upper left') + + ax.spines['right'].set_visible(False) + ax.spines['top'].set_visible(False) + ax.set_xlabel('Time of day', fontsize=14) + ax.set_ylabel('Mean viral concentration\n(virion m$^{-3}$)', fontsize=14) + ax.set_title('Concentration profile') + ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) + ax1.spines['top'].set_visible(False) ax1.spines["right"].set_linestyle("--") ax1.spines["right"].set_linestyle((0,(1,5))) ax1.set_ylabel('Mean cumulative dose\n(virion)', fontsize=14) ax1.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) - - # Place a legend outside of the axes itself. - ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left') - ax.spines['right'].set_visible(False) - ax.spines['top'].set_visible(False) - ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M")) - - ax.set_xlabel('Time of day') - ax.set_ylabel('Mean concentration ($q/m^3$)') - ax.set_title('Mean concentration of infectious quanta') return fig @@ -287,14 +291,13 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray) def scenario_statistics(mc_model: mc.ExposureModel, sample_times: np.ndarray): model = mc_model.build_model(size=_DEFAULT_MC_SAMPLE_SIZE) return { + 'model': model, 'probability_of_infection': np.mean(model.infection_probability()), 'expected_new_cases': np.mean(model.expected_new_cases()), 'concentrations': [ - np.mean(model.concentration_model.concentration(time)) + np.mean(model.concentration_model.concentration(time)) * model.concentration_model.virus.quantum_infectious_dose for time in sample_times ], - 'factor': 0.6 * np.mean(model.exposed.activity.inhalation_rate) * (1 - model.exposed.mask.η_inhale), - 'present_indexes': np.array([model.exposed.person_present(t) for t in sample_times]), } diff --git a/cara/apps/calculator/templates/base/calculator.report.html.j2 b/cara/apps/calculator/templates/base/calculator.report.html.j2 index 64a44cfa..98655d8c 100644 --- a/cara/apps/calculator/templates/base/calculator.report.html.j2 +++ b/cara/apps/calculator/templates/base/calculator.report.html.j2 @@ -219,7 +219,7 @@

Alternative scenarios:

-

+ {% block report_scenarios_summary_table %} @@ -242,7 +242,7 @@ {% endblock report_scenarios_summary_table %} -

+

Notes for alternative scenarios:
From bc38f5ec7a4950ccba4b16c175014e1fef4b9457 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 23 Jul 2021 09:15:25 +0200 Subject: [PATCH 005/133] cumulated_exposure method in models.py --- cara/apps/calculator/report_generator.py | 4 ++-- cara/models.py | 9 ++++++--- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 22aaf932..217e0902 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -131,7 +131,7 @@ def plot(times, concentrations, model: models.ExposureModel): ) #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose - factor = 0.6 * np.mean(model.exposed.activity.inhalation_rate) * (1 - model.exposed.mask.η_inhale) + cumulated_exposure = model.cumulated_exposure() present_indexes = np.array([model.exposed.person_present(t) for t in times]) modified_concentrations = np.array(concentrations) modified_concentrations[~present_indexes] = 0 @@ -139,7 +139,7 @@ def plot(times, concentrations, model: models.ExposureModel): qds = [np.trapz(modified_concentrations[:i + 1], times[:i + 1]) * factor for i in range(len(times))] ax1 = ax.twinx() - ax1.plot(datetimes, qds, label='Mean cumulative dose', color='#1f77b4', linestyle='dotted') + ax1.plot(datetimes, cumulated_exposure, label='Mean cumulative dose', color='#1f77b4', linestyle='dotted') ax1.spines["right"].set_linestyle("--") ax1.spines["right"].set_linestyle((0,(1,5))) ax1.set_ylabel('Mean cumulative dose\n(virion)', fontsize=14) diff --git a/cara/models.py b/cara/models.py index 585fb824..b762b451 100644 --- a/cara/models.py +++ b/cara/models.py @@ -876,15 +876,18 @@ class ExposureModel: return exposure * self.repeats - def infection_probability(self) -> _VectorisedFloat: + def cumulated_exposure(self) -> _VectorisedFloat: exposure = self.quanta_exposure() - - inf_aero = ( + + return ( self.exposed.activity.inhalation_rate * (1 - self.exposed.mask.inhale_efficiency()) * exposure * self.fraction_deposited ) + def infection_probability(self) -> _VectorisedFloat: + inf_aero = self.cumulated_exposure() + # Probability of infection. return (1 - np.exp(-inf_aero)) * 100 From 460c5cf520af94ff2143e83f3d14d2f3c53a4998 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 23 Jul 2021 10:57:52 +0200 Subject: [PATCH 006/133] Cumulative dose calculation code in models fixes #164 --- cara/apps/calculator/report_generator.py | 9 ++------- cara/models.py | 13 +++++++++++++ cara/tests/models/test_exposure_model.py | 22 ++++++++++++++-------- cara/tests/models/test_interval.py | 22 ++++++++++++++++++++++ 4 files changed, 51 insertions(+), 15 deletions(-) create mode 100644 cara/tests/models/test_interval.py diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 217e0902..f2933735 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -131,15 +131,10 @@ def plot(times, concentrations, model: models.ExposureModel): ) #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose - cumulated_exposure = model.cumulated_exposure() - present_indexes = np.array([model.exposed.person_present(t) for t in times]) - modified_concentrations = np.array(concentrations) - modified_concentrations[~present_indexes] = 0 - - qds = [np.trapz(modified_concentrations[:i + 1], times[:i + 1]) * factor for i in range(len(times))] + qds = [np.mean(dataclass_utils.nested_replace(model, {'exposed.presence': model.exposed.presence.generate_truncated_interval(t)}).cumulated_exposure()) for t in times] ax1 = ax.twinx() - ax1.plot(datetimes, cumulated_exposure, label='Mean cumulative dose', color='#1f77b4', linestyle='dotted') + ax1.plot(datetimes, qds, label='Mean cumulative dose', color='#1f77b4', linestyle='dotted') ax1.spines["right"].set_linestyle("--") ax1.spines["right"].set_linestyle((0,(1,5))) ax1.set_ylabel('Mean cumulative dose\n(virion)', fontsize=14) diff --git a/cara/models.py b/cara/models.py index b762b451..b115d2f4 100644 --- a/cara/models.py +++ b/cara/models.py @@ -100,6 +100,19 @@ class Interval: return True return False + def generate_truncated_interval(self, time_stop: float) -> tuple: + truncated_boundaries = [] + for start, end in self.boundaries(): + if start < time_stop <= end: + end = time_stop + truncated_boundaries.append((start, end)) + break + elif time_stop <= start: + break + else: + truncated_boundaries.append((start, end)) + + return SpecificInterval(tuple(truncated_boundaries)) @dataclass(frozen=True) class SpecificInterval(Interval): diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py index 1c6c6d3d..298a9c93 100644 --- a/cara/tests/models/test_exposure_model.py +++ b/cara/tests/models/test_exposure_model.py @@ -1,3 +1,4 @@ +import decimal import typing import numpy as np @@ -55,24 +56,24 @@ populations = [ @pytest.mark.parametrize( - "population, cm, f_dep, expected_exposure, expected_probability",[ + "population, cm, f_dep, expected_exposure, expected_cumulated_exposure, expected_probability",[ [populations[1], KnownConcentrations(lambda t: 1.2), 1., - np.array([14.4, 14.4]), np.array([99.6803184113, 99.5181053773])], + np.array([14.4, 14.4]), np.array([3.44736/0.6, 3.20112/0.6]), np.array([99.6803184113, 99.5181053773])], [populations[2], KnownConcentrations(lambda t: 1.2), 1., - np.array([14.4, 14.4]), np.array([97.4574432074, 98.3493482895])], + np.array([14.4, 14.4]), np.array([2.2032/0.6, 2.4624/0.6]), np.array([97.4574432074, 98.3493482895])], [populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1., - np.array([14.4, 28.8]), np.array([98.3493482895, 99.9727534893])], + np.array([14.4, 28.8]), np.array([2.4624/0.6, 4.9248/0.6]), np.array([98.3493482895, 99.9727534893])], [populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1., - np.array([14.4, 28.8]), np.array([99.6803184113, 99.9976777757])], + np.array([14.4, 28.8]), np.array([3.44736/0.6, 6.40224/0.6]), np.array([99.6803184113, 99.9976777757])], [populations[0], KnownConcentrations(lambda t: 2.4), np.array([0.5, 1.]), - 28.8, np.array([98.3493482895, 99.9727534893])], + 28.8, np.array([4.104, 8.208]), np.array([98.3493482895, 99.9727534893])], ]) -def test_exposure_model_ndarray(population, cm, f_dep, - expected_exposure, expected_probability): +def test_exposure_model_ndarray(population, cm, f_dep, + expected_exposure, expected_cumulated_exposure, expected_probability): model = ExposureModel(cm, population, fraction_deposited = f_dep) np.testing.assert_almost_equal( model.quanta_exposure(), expected_exposure @@ -80,11 +81,16 @@ def test_exposure_model_ndarray(population, cm, f_dep, np.testing.assert_almost_equal( model.infection_probability(), expected_probability, decimal=10 ) + np.testing.assert_almost_equal( + model.cumulated_exposure(), expected_cumulated_exposure, decimal=10 + ) assert isinstance(model.infection_probability(), np.ndarray) assert isinstance(model.expected_new_cases(), np.ndarray) + assert isinstance(model.cumulated_exposure(), np.ndarray) assert model.infection_probability().shape == (2,) assert model.expected_new_cases().shape == (2,) + assert model.cumulated_exposure().shape == (2,) @pytest.mark.parametrize("population", populations) diff --git a/cara/tests/models/test_interval.py b/cara/tests/models/test_interval.py new file mode 100644 index 00000000..5aa73603 --- /dev/null +++ b/cara/tests/models/test_interval.py @@ -0,0 +1,22 @@ +import numpy as np +import pytest + +from cara import models + + +@pytest.mark.parametrize( + "stop_time, expected_boundaries", + [ + [1.05, ((0, 1),)], + [1.8, ((0, 1), (1.1, 1.8))], + [2., ((0, 1), (1.1, 1.999))], + [3., ((0, 1), (1.1, 1.999), (2, 3))], + [-1, ()], + [4, ((0, 1), (1.1, 1.999), (2, 3))], + ], +) +def test_interval_truncation(stop_time, expected_boundaries): + interesting_times = models.SpecificInterval( + ([0, 1], [1.1, 1.999], [2, 3]), ) + assert interesting_times.generate_truncated_interval( + stop_time).boundaries() == expected_boundaries From 680db38cce62da76ed5f4513676f04c8fab09f75 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 23 Jul 2021 12:15:50 +0200 Subject: [PATCH 007/133] pipelined fixed for returning value models.py --- cara/models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cara/models.py b/cara/models.py index b115d2f4..93785d1d 100644 --- a/cara/models.py +++ b/cara/models.py @@ -100,7 +100,7 @@ class Interval: return True return False - def generate_truncated_interval(self, time_stop: float) -> tuple: + def generate_truncated_interval(self, time_stop: float) -> "Interval": truncated_boundaries = [] for start, end in self.boundaries(): if start < time_stop <= end: @@ -112,7 +112,7 @@ class Interval: else: truncated_boundaries.append((start, end)) - return SpecificInterval(tuple(truncated_boundaries)) + return SpecificInterval(present_times = tuple(truncated_boundaries)) @dataclass(frozen=True) class SpecificInterval(Interval): From 56e8714633c25c5bd25d1ca964beee7ba2ab34ba Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 23 Jul 2021 14:31:46 +0200 Subject: [PATCH 008/133] Concentration plot for alternative scenarios fix --- cara/apps/calculator/report_generator.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index f2933735..3a8d3197 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -248,12 +248,7 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray) concentrations = statistics['concentrations'] #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose - factor = 0.6 * np.mean(model.exposed.activity.inhalation_rate) * (1 - model.exposed.mask.η_inhale) - present_indexes = np.array([model.exposed.person_present(t) for t in sample_times]) - - modified_concentrations = np.array(concentrations) - modified_concentrations[~present_indexes] = 0 - qds = [np.trapz(modified_concentrations[:i + 1], sample_times[:i + 1]) * factor for i in range(len(sample_times))] + qds = [np.mean(dataclass_utils.nested_replace(model, {'exposed.presence': model.exposed.presence.generate_truncated_interval(t)}).cumulated_exposure()) for t in sample_times] # Plot concentrations and cumulative dose if name in dash_styled_scenarios: From 7bab71653262e394e98307b8586e28a236f03359 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 23 Jul 2021 15:11:19 +0200 Subject: [PATCH 009/133] added @cached to integrated_concentrations method --- cara/models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/cara/models.py b/cara/models.py index 93785d1d..27bd965a 100644 --- a/cara/models.py +++ b/cara/models.py @@ -839,6 +839,7 @@ class ConcentrationModel: fac = np.exp(-IVRR * delta_time) return concentration_limit * (1 - fac) + concentration_at_last_state_change * fac + @cached() def integrated_concentration(self, start: float, stop: float) -> _VectorisedFloat: """ Get the integrated concentration dose between the times start and stop. From 5983fca92ebcba13745db4f8f004d62d7fcf79ea Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 26 Jul 2021 09:55:56 +0200 Subject: [PATCH 010/133] test exposure model fix --- cara/apps/calculator/report_generator.py | 7 ++-- cara/models.py | 47 ++++++++++++++---------- cara/tests/models/test_exposure_model.py | 29 +++++++++------ cara/tests/models/test_interval.py | 22 ----------- 4 files changed, 48 insertions(+), 57 deletions(-) delete mode 100644 cara/tests/models/test_interval.py diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 3a8d3197..b4db8d28 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -10,6 +10,7 @@ import zlib import loky import jinja2 import matplotlib +from numpy.lib.function_base import quantile matplotlib.use('agg') import matplotlib.pyplot as plt import numpy as np @@ -131,8 +132,8 @@ def plot(times, concentrations, model: models.ExposureModel): ) #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose - qds = [np.mean(dataclass_utils.nested_replace(model, {'exposed.presence': model.exposed.presence.generate_truncated_interval(t)}).cumulated_exposure()) for t in times] - + qds = [np.mean(model.cumulated_exposure_vs_time(t)) for t in times] + ax1 = ax.twinx() ax1.plot(datetimes, qds, label='Mean cumulative dose', color='#1f77b4', linestyle='dotted') ax1.spines["right"].set_linestyle("--") @@ -248,7 +249,7 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray) concentrations = statistics['concentrations'] #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose - qds = [np.mean(dataclass_utils.nested_replace(model, {'exposed.presence': model.exposed.presence.generate_truncated_interval(t)}).cumulated_exposure()) for t in sample_times] + qds = [np.mean(model.cumulated_exposure_vs_time(t)) for t in sample_times] # Plot concentrations and cumulative dose if name in dash_styled_scenarios: diff --git a/cara/models.py b/cara/models.py index 27bd965a..924d91cc 100644 --- a/cara/models.py +++ b/cara/models.py @@ -100,20 +100,6 @@ class Interval: return True return False - def generate_truncated_interval(self, time_stop: float) -> "Interval": - truncated_boundaries = [] - for start, end in self.boundaries(): - if start < time_stop <= end: - end = time_stop - truncated_boundaries.append((start, end)) - break - elif time_stop <= start: - break - else: - truncated_boundaries.append((start, end)) - - return SpecificInterval(present_times = tuple(truncated_boundaries)) - @dataclass(frozen=True) class SpecificInterval(Interval): #: A sequence of times (start, stop), in hours, that the infected person @@ -881,17 +867,32 @@ class ExposureModel: #: The fraction of viruses actually deposited in the respiratory tract fraction_deposited: _VectorisedFloat = 0.6 - def quanta_exposure(self) -> _VectorisedFloat: - """The number of virus quanta per meter^3.""" + def quanta_exposure_vs_time(self, time: float) -> _VectorisedFloat: + """The number of virus quanta per meter^3 integrated until time.""" exposure = 0.0 for start, stop in self.exposed.presence.boundaries(): - exposure += self.concentration_model.integrated_concentration(start, stop) - + if start > time: + break + elif time <= stop: + stop = time + exposure += self.concentration_model.integrated_concentration(start, stop) + break + else: + exposure += self.concentration_model.integrated_concentration(start, stop) + return exposure * self.repeats - def cumulated_exposure(self) -> _VectorisedFloat: - exposure = self.quanta_exposure() + def quanta_exposure(self) -> _VectorisedFloat: + """The number of virus quanta per meter^3 for the full simulation time.""" + if self.exposed.presence.transition_times(): + return self.quanta_exposure_vs_time(max(self.exposed.presence.transition_times())) + else: + return 0 + + def cumulated_exposure_vs_time(self, time: float) -> _VectorisedFloat: + + exposure = self.quanta_exposure_vs_time(time) return ( self.exposed.activity.inhalation_rate * @@ -899,6 +900,12 @@ class ExposureModel: exposure * self.fraction_deposited ) + def cumulated_exposure(self) -> _VectorisedFloat: + if self.exposed.presence.transition_times(): + return self.cumulated_exposure_vs_time(max(self.exposed.presence.transition_times())) + else: + return 0 + def infection_probability(self) -> _VectorisedFloat: inf_aero = self.cumulated_exposure() diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py index 298a9c93..e3e35e87 100644 --- a/cara/tests/models/test_exposure_model.py +++ b/cara/tests/models/test_exposure_model.py @@ -4,26 +4,31 @@ import typing import numpy as np import numpy.testing import pytest +from dataclasses import dataclass from cara import models from cara.models import ExposureModel +@dataclass(frozen=True) class KnownConcentrations(models.ConcentrationModel): """ A ConcentrationModel which is based on pre-known quanta concentrations and which therefore doesn't need other components. Useful for testing. """ - def __init__(self, concentration_function: typing.Callable) -> None: - self._func = concentration_function + #def __init__(self, concentration_function: typing.Callable) -> None: + # self._func = concentration_function + + + concentration_function: typing.Callable def infectious_virus_removal_rate(self, time: float) -> models._VectorisedFloat: # very large decay constant -> same as constant concentration return 1.e50 def _concentration_limit(self, time: float) -> models._VectorisedFloat: - return self._func(time) + return self.concentration_function(time) def state_change_times(self): return [0, 24] @@ -32,7 +37,7 @@ class KnownConcentrations(models.ConcentrationModel): return 24 def concentration(self, time: float) -> models._VectorisedFloat: # noqa - return self._func(time) + return self.concentration_function(time) halftime = models.PeriodicInterval(120, 60) @@ -57,19 +62,19 @@ populations = [ @pytest.mark.parametrize( "population, cm, f_dep, expected_exposure, expected_cumulated_exposure, expected_probability",[ - [populations[1], KnownConcentrations(lambda t: 1.2), 1., + [populations[1], KnownConcentrations(None, None, None, lambda t: 1.2), 1., np.array([14.4, 14.4]), np.array([3.44736/0.6, 3.20112/0.6]), np.array([99.6803184113, 99.5181053773])], - [populations[2], KnownConcentrations(lambda t: 1.2), 1., + [populations[2], KnownConcentrations(None, None, None, lambda t: 1.2), 1., np.array([14.4, 14.4]), np.array([2.2032/0.6, 2.4624/0.6]), np.array([97.4574432074, 98.3493482895])], - [populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1., + [populations[0], KnownConcentrations(None, None, None,lambda t: np.array([1.2, 2.4])), 1., np.array([14.4, 28.8]), np.array([2.4624/0.6, 4.9248/0.6]), np.array([98.3493482895, 99.9727534893])], - [populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1., + [populations[1], KnownConcentrations(None, None, None,lambda t: np.array([1.2, 2.4])), 1., np.array([14.4, 28.8]), np.array([3.44736/0.6, 6.40224/0.6]), np.array([99.6803184113, 99.9976777757])], - [populations[0], KnownConcentrations(lambda t: 2.4), np.array([0.5, 1.]), + [populations[0], KnownConcentrations(None, None, None,lambda t: 2.4), np.array([0.5, 1.]), 28.8, np.array([4.104, 8.208]), np.array([98.3493482895, 99.9727534893])], ]) def test_exposure_model_ndarray(population, cm, f_dep, @@ -95,7 +100,7 @@ def test_exposure_model_ndarray(population, cm, f_dep, @pytest.mark.parametrize("population", populations) def test_exposure_model_ndarray_and_float_mix(population): - cm = KnownConcentrations(lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2])) + cm = KnownConcentrations(None, None, None, lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2])) model = ExposureModel(cm, population) expected_exposure = np.array([14.4, 14.4]) @@ -109,8 +114,8 @@ def test_exposure_model_ndarray_and_float_mix(population): @pytest.mark.parametrize("population", populations) def test_exposure_model_compare_scalar_vector(population): - cm_scalar = KnownConcentrations(lambda t: 1.2) - cm_array = KnownConcentrations(lambda t: np.array([1.2, 1.2])) + cm_scalar = KnownConcentrations(None, None, None,lambda t: 1.2) + cm_array = KnownConcentrations(None, None, None, lambda t: np.array([1.2, 1.2])) model_scalar = ExposureModel(cm_scalar, population) model_array = ExposureModel(cm_array, population) expected_exposure = 14.4 diff --git a/cara/tests/models/test_interval.py b/cara/tests/models/test_interval.py deleted file mode 100644 index 5aa73603..00000000 --- a/cara/tests/models/test_interval.py +++ /dev/null @@ -1,22 +0,0 @@ -import numpy as np -import pytest - -from cara import models - - -@pytest.mark.parametrize( - "stop_time, expected_boundaries", - [ - [1.05, ((0, 1),)], - [1.8, ((0, 1), (1.1, 1.8))], - [2., ((0, 1), (1.1, 1.999))], - [3., ((0, 1), (1.1, 1.999), (2, 3))], - [-1, ()], - [4, ((0, 1), (1.1, 1.999), (2, 3))], - ], -) -def test_interval_truncation(stop_time, expected_boundaries): - interesting_times = models.SpecificInterval( - ([0, 1], [1.1, 1.999], [2, 3]), ) - assert interesting_times.generate_truncated_interval( - stop_time).boundaries() == expected_boundaries From 1803d4e919ee826304deecae9cd670282f5ca549 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 26 Jul 2021 14:16:51 +0200 Subject: [PATCH 011/133] mypy errors - added some dummy variables to test_exposure_model.py --- cara/tests/models/test_exposure_model.py | 32 +++++++++++++++--------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py index e3e35e87..1b06c23f 100644 --- a/cara/tests/models/test_exposure_model.py +++ b/cara/tests/models/test_exposure_model.py @@ -17,10 +17,6 @@ class KnownConcentrations(models.ConcentrationModel): which therefore doesn't need other components. Useful for testing. """ - #def __init__(self, concentration_function: typing.Callable) -> None: - # self._func = concentration_function - - concentration_function: typing.Callable def infectious_virus_removal_rate(self, time: float) -> models._VectorisedFloat: @@ -58,23 +54,35 @@ populations = [ models.Activity(np.array([0.51,0.57]), 0.57), ), ] +dummyRoom = models.Room(50, 0.5) +dummyVentilation = models._VentilationBase() +dummyInfPopulation = models.InfectedPopulation( + number=1, + presence=halftime, + mask=models.Mask.types['Type I'], + activity=models.Activity.types['Standing'], + virus=models.Virus.types['SARS_CoV_2_B117'], + expiration=models.Expiration.types['Talking'] +) +def known_concentrations(func): + return KnownConcentrations(dummyRoom, dummyVentilation, dummyInfPopulation, func) @pytest.mark.parametrize( "population, cm, f_dep, expected_exposure, expected_cumulated_exposure, expected_probability",[ - [populations[1], KnownConcentrations(None, None, None, lambda t: 1.2), 1., + [populations[1], known_concentrations(lambda t: 1.2), 1., np.array([14.4, 14.4]), np.array([3.44736/0.6, 3.20112/0.6]), np.array([99.6803184113, 99.5181053773])], - [populations[2], KnownConcentrations(None, None, None, lambda t: 1.2), 1., + [populations[2], known_concentrations(lambda t: 1.2), 1., np.array([14.4, 14.4]), np.array([2.2032/0.6, 2.4624/0.6]), np.array([97.4574432074, 98.3493482895])], - [populations[0], KnownConcentrations(None, None, None,lambda t: np.array([1.2, 2.4])), 1., + [populations[0], known_concentrations(lambda t: np.array([1.2, 2.4])), 1., np.array([14.4, 28.8]), np.array([2.4624/0.6, 4.9248/0.6]), np.array([98.3493482895, 99.9727534893])], - [populations[1], KnownConcentrations(None, None, None,lambda t: np.array([1.2, 2.4])), 1., + [populations[1], known_concentrations(lambda t: np.array([1.2, 2.4])), 1., np.array([14.4, 28.8]), np.array([3.44736/0.6, 6.40224/0.6]), np.array([99.6803184113, 99.9976777757])], - [populations[0], KnownConcentrations(None, None, None,lambda t: 2.4), np.array([0.5, 1.]), + [populations[0], known_concentrations(lambda t: 2.4), np.array([0.5, 1.]), 28.8, np.array([4.104, 8.208]), np.array([98.3493482895, 99.9727534893])], ]) def test_exposure_model_ndarray(population, cm, f_dep, @@ -100,7 +108,7 @@ def test_exposure_model_ndarray(population, cm, f_dep, @pytest.mark.parametrize("population", populations) def test_exposure_model_ndarray_and_float_mix(population): - cm = KnownConcentrations(None, None, None, lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2])) + cm = known_concentrations(lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2])) model = ExposureModel(cm, population) expected_exposure = np.array([14.4, 14.4]) @@ -114,8 +122,8 @@ def test_exposure_model_ndarray_and_float_mix(population): @pytest.mark.parametrize("population", populations) def test_exposure_model_compare_scalar_vector(population): - cm_scalar = KnownConcentrations(None, None, None,lambda t: 1.2) - cm_array = KnownConcentrations(None, None, None, lambda t: np.array([1.2, 1.2])) + cm_scalar = known_concentrations(lambda t: 1.2) + cm_array = known_concentrations(lambda t: np.array([1.2, 1.2])) model_scalar = ExposureModel(cm_scalar, population) model_array = ExposureModel(cm_array, population) expected_exposure = 14.4 From f329d76e2224c721ae46016bf232458b5e3f24d7 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 26 Jul 2021 14:20:16 +0200 Subject: [PATCH 012/133] Performed some calculations missed on the code --- cara/tests/models/test_exposure_model.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py index 1b06c23f..acf85d3f 100644 --- a/cara/tests/models/test_exposure_model.py +++ b/cara/tests/models/test_exposure_model.py @@ -71,16 +71,16 @@ def known_concentrations(func): @pytest.mark.parametrize( "population, cm, f_dep, expected_exposure, expected_cumulated_exposure, expected_probability",[ [populations[1], known_concentrations(lambda t: 1.2), 1., - np.array([14.4, 14.4]), np.array([3.44736/0.6, 3.20112/0.6]), np.array([99.6803184113, 99.5181053773])], + np.array([14.4, 14.4]), np.array([5.7456, 5.3352]), np.array([99.6803184113, 99.5181053773])], [populations[2], known_concentrations(lambda t: 1.2), 1., - np.array([14.4, 14.4]), np.array([2.2032/0.6, 2.4624/0.6]), np.array([97.4574432074, 98.3493482895])], + np.array([14.4, 14.4]), np.array([3.672, 4.104]), np.array([97.4574432074, 98.3493482895])], [populations[0], known_concentrations(lambda t: np.array([1.2, 2.4])), 1., - np.array([14.4, 28.8]), np.array([2.4624/0.6, 4.9248/0.6]), np.array([98.3493482895, 99.9727534893])], + np.array([14.4, 28.8]), np.array([4.104, 8.208]), np.array([98.3493482895, 99.9727534893])], [populations[1], known_concentrations(lambda t: np.array([1.2, 2.4])), 1., - np.array([14.4, 28.8]), np.array([3.44736/0.6, 6.40224/0.6]), np.array([99.6803184113, 99.9976777757])], + np.array([14.4, 28.8]), np.array([5.7456, 10.6704]), np.array([99.6803184113, 99.9976777757])], [populations[0], known_concentrations(lambda t: 2.4), np.array([0.5, 1.]), 28.8, np.array([4.104, 8.208]), np.array([98.3493482895, 99.9727534893])], From 072c47d70a01cd2fd6b55ce02acdbcf2c6ab13fb Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 17 Aug 2021 15:31:32 +0200 Subject: [PATCH 013/133] updated variable names and type --- cara/apps/calculator/report_generator.py | 7 +++++-- cara/apps/calculator/static/js/report.js | 10 +++++----- .../templates/base/calculator.report.html.j2 | 4 ++-- 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 66d66910..5124f94a 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -44,12 +44,15 @@ def calculate_report_data(model: models.ExposureModel): er = np.array(model.concentration_model.infected.emission_rate_when_present()).mean() exposed_occupants = model.exposed.number expected_new_cases = np.array(model.expected_new_cases()).mean() - cumulative_dose = np.array([model.cumulated_exposure_vs_time(t) for t in times]).mean() + cumulative_doses = [ + np.array(model.cumulated_exposure_vs_time(float(time))).mean() + for time in times + ] return { "times": list(times), "exposed_presence_intervals": [list(interval) for interval in model.exposed.presence.boundaries()], - "cumulative_dose": cumulative_dose, + "cumulative_doses": cumulative_doses, "concentrations": concentrations, "highest_const": highest_const, "prob_inf": prob, diff --git a/cara/apps/calculator/static/js/report.js b/cara/apps/calculator/static/js/report.js index 58ab40ae..69ef2ee2 100644 --- a/cara/apps/calculator/static/js/report.js +++ b/cara/apps/calculator/static/js/report.js @@ -1,5 +1,5 @@ /* Generate the concentration plot using d3 library. */ -function draw_concentration_plot(svg_id, times, concentrations, cumulative_dose, exposed_presence_intervals) { +function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses, exposed_presence_intervals) { var visBoundingBox = d3.select(svg_id) .node() .getBoundingClientRect(); @@ -7,7 +7,7 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_dose, var time_format = d3.timeFormat('%H:%M'); var data = [] - times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index], 'cumulative_dose': cumulative_dose[index] })) + times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index], 'cumulative_doses': cumulative_doses[index] })) var vis = d3.select(svg_id), width = visBoundingBox.width - 400, @@ -20,7 +20,7 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_dose, bisecHour = d3.bisector((d) => { return d.hour; }).left, yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]), - yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_dose)]), + yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_doses)]), xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), yAxis = d3.axisLeft(yRange), @@ -49,9 +49,9 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_dose, // Line representing the cumulative concentration. var lineCumulativeFunc = d3.line() - .defined(d => !isNaN(d.cumulative_dose)) + .defined(d => !isNaN(d.cumulative_doses)) .x(d => xTimeRange(d.time)) - .y(d => yCumulatedRange(d.cumulative_dose)) + .y(d => yCumulatedRange(d.cumulative_doses)) .curve(d3.curveBasis); vis.append('svg:path') diff --git a/cara/apps/calculator/templates/base/calculator.report.html.j2 b/cara/apps/calculator/templates/base/calculator.report.html.j2 index be127ece..b4cf7fb7 100644 --- a/cara/apps/calculator/templates/base/calculator.report.html.j2 +++ b/cara/apps/calculator/templates/base/calculator.report.html.j2 @@ -224,9 +224,9 @@

Alternative scenarios:

From ab181b534543b2ca293daf2ec45a2f1c58335769 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 20 Aug 2021 16:44:27 +0200 Subject: [PATCH 014/133] removed unused import --- cara/tests/models/test_exposure_model.py | 1 - 1 file changed, 1 deletion(-) diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py index 3c32ccd3..67238bc2 100644 --- a/cara/tests/models/test_exposure_model.py +++ b/cara/tests/models/test_exposure_model.py @@ -1,4 +1,3 @@ -import decimal import typing import numpy as np From 67b60442199f52ba811297f2dcbcfa78b92fc217 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 20 Aug 2021 16:45:36 +0200 Subject: [PATCH 015/133] changed virus to virions --- cara/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cara/models.py b/cara/models.py index 319c9782..d48263d0 100644 --- a/cara/models.py +++ b/cara/models.py @@ -894,7 +894,7 @@ class ExposureModel: return exposure * self.repeats def exposure(self) -> _VectorisedFloat: - """The number of virus per meter^3 for the full simulation time.""" + """The number of virions per meter^3 for the full simulation time.""" if self.exposed.presence.transition_times(): return self.exposure_vs_time(max(self.exposed.presence.transition_times())) else: From cb334cbafa16b0832a047edd2493c9bdf712f5c8 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 2 Sep 2021 16:19:32 +0200 Subject: [PATCH 016/133] variable renaming for the exposure and inhaled_exposure --- cara/apps/calculator/report_generator.py | 4 ++-- cara/models.py | 16 ++++++++-------- cara/tests/models/test_exposure_model.py | 10 +++++----- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 93e3f695..8d40cd41 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -114,7 +114,7 @@ def calculate_report_data(model: models.ExposureModel): exposed_occupants = model.exposed.number expected_new_cases = np.array(model.expected_new_cases()).mean() cumulative_doses = [ - np.array(model.cumulated_exposure_vs_time(float(time))).mean() + np.array(model.inhaled_exposure_between_bounds(float(time))).mean() for time in times ] @@ -276,7 +276,7 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: typing.List concentrations = statistics['concentrations'] #See CERN-OPEN-2021-004, p. 15, eq. 16. - Cumulative Dose - qds = [np.mean(model.cumulated_exposure_vs_time(t)) for t in sample_times] + qds = [np.mean(model.inhaled_exposure_between_bounds(t)) for t in sample_times] # Plot concentrations and cumulative dose if name in dash_styled_scenarios: diff --git a/cara/models.py b/cara/models.py index d48263d0..fc165ccb 100644 --- a/cara/models.py +++ b/cara/models.py @@ -877,8 +877,8 @@ class ExposureModel: #: The fraction of viruses actually deposited in the respiratory tract fraction_deposited: _VectorisedFloat = 0.6 - def exposure_vs_time(self, time: float) -> _VectorisedFloat: - """The number of virus per meter^3 integrated until time.""" + def exposure_between_bounds(self, time: float) -> _VectorisedFloat: + """The cumulative number of virions per meter^3 from model start to the given time.""" exposure = 0.0 for start, stop in self.exposed.presence.boundaries(): @@ -896,13 +896,13 @@ class ExposureModel: def exposure(self) -> _VectorisedFloat: """The number of virions per meter^3 for the full simulation time.""" if self.exposed.presence.transition_times(): - return self.exposure_vs_time(max(self.exposed.presence.transition_times())) + return self.exposure_between_bounds(max(self.exposed.presence.transition_times())) else: return 0 - def cumulated_exposure_vs_time(self, time: float) -> _VectorisedFloat: + def inhaled_exposure_between_bounds(self, time: float) -> _VectorisedFloat: - exposure = self.exposure_vs_time(time) + exposure = self.exposure_between_bounds(time) return ( self.exposed.activity.inhalation_rate * @@ -910,14 +910,14 @@ class ExposureModel: exposure * self.fraction_deposited ) - def cumulated_exposure(self) -> _VectorisedFloat: + def inhaled_exposure(self) -> _VectorisedFloat: if self.exposed.presence.transition_times(): - return self.cumulated_exposure_vs_time(max(self.exposed.presence.transition_times())) + return self.inhaled_exposure_between_bounds(max(self.exposed.presence.transition_times())) else: return 0 def infection_probability(self) -> _VectorisedFloat: - inf_aero = self.cumulated_exposure() + inf_aero = self.inhaled_exposure() # Probability of infection. return (1 - np.exp(-(inf_aero/self.concentration_model.virus.infectious_dose))) * 100 diff --git a/cara/tests/models/test_exposure_model.py b/cara/tests/models/test_exposure_model.py index 67238bc2..df530f7e 100644 --- a/cara/tests/models/test_exposure_model.py +++ b/cara/tests/models/test_exposure_model.py @@ -70,7 +70,7 @@ def known_concentrations(func): @pytest.mark.parametrize( - "population, cm, f_dep, expected_exposure, expected_cumulated_exposure, expected_probability",[ + "population, cm, f_dep, expected_exposure, expected_inhaled_exposure, expected_probability",[ [populations[1], known_concentrations(lambda t: 36.), 1., np.array([432, 432]), np.array([172.368, 160.056]), np.array([99.6803184113, 99.5181053773])], @@ -87,7 +87,7 @@ def known_concentrations(func): 864, np.array([123.12, 246.24]), np.array([98.3493482895, 99.9727534893])], ]) def test_exposure_model_ndarray(population, cm, f_dep, - expected_exposure, expected_cumulated_exposure, expected_probability): + expected_exposure, expected_inhaled_exposure, expected_probability): model = ExposureModel(cm, population, fraction_deposited=f_dep) np.testing.assert_almost_equal( model.exposure(), expected_exposure @@ -96,15 +96,15 @@ def test_exposure_model_ndarray(population, cm, f_dep, model.infection_probability(), expected_probability, decimal=10 ) np.testing.assert_almost_equal( - model.cumulated_exposure(), expected_cumulated_exposure, decimal=10 + model.inhaled_exposure(), expected_inhaled_exposure, decimal=10 ) assert isinstance(model.infection_probability(), np.ndarray) assert isinstance(model.expected_new_cases(), np.ndarray) - assert isinstance(model.cumulated_exposure(), np.ndarray) + assert isinstance(model.inhaled_exposure(), np.ndarray) assert model.infection_probability().shape == (2,) assert model.expected_new_cases().shape == (2,) - assert model.cumulated_exposure().shape == (2,) + assert model.inhaled_exposure().shape == (2,) @pytest.mark.parametrize("population", populations) From 485518f3f409c99fa23d30bb80f4011875c5972f Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 3 Sep 2021 09:35:57 +0200 Subject: [PATCH 017/133] new time with breaks included --- cara/apps/calculator/report_generator.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 8d40cd41..5966d6f7 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -10,7 +10,7 @@ import zlib import loky import jinja2 import matplotlib -from numpy.lib.function_base import quantile +from numpy.lib.function_base import append, quantile matplotlib.use('agg') import matplotlib.pyplot as plt import numpy as np @@ -113,6 +113,24 @@ def calculate_report_data(model: models.ExposureModel): er = np.array(model.concentration_model.infected.emission_rate_when_present()).mean() exposed_occupants = model.exposed.number expected_new_cases = np.array(model.expected_new_cases()).mean() + print(len(times)) + times2 = np.array([]) + for start, stop in model.exposed.presence.boundaries(): + times2 = np.concatenate((times2, np.linspace(start, stop, int(len(times)/len(model.exposed.presence.boundaries()))))) + + + # for start, stop in model.exposed.presence.boundaries(): + # if start > time: + # break + # elif time <= stop: + # stop = time + # times2.append(time) + # break + # else: + # times2.append(time) + + + print(len(times2)) cumulative_doses = [ np.array(model.inhaled_exposure_between_bounds(float(time))).mean() for time in times From befa2a51f76c7be8b9fbb5e37788064590d81955 Mon Sep 17 00:00:00 2001 From: Cole Austin Coughlin Date: Fri, 3 Sep 2021 11:42:02 -0500 Subject: [PATCH 018/133] fixed website with new report --- .../templates/calculator.form.html.j2 | 343 ++++++++++------ .../calculator/templates/userguide.html.j2 | 365 +++++++++--------- cara/apps/static/css/style.css | 250 ++++++++++++ cara/apps/templates/about.html.j2 | 27 +- cara/apps/templates/index.html.j2 | 116 ++---- cara/apps/templates/layout.html.j2 | 348 +++-------------- 6 files changed, 781 insertions(+), 668 deletions(-) create mode 100644 cara/apps/static/css/style.css diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index c119d4e5..143202ab 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -17,15 +17,15 @@ {% block main %} -
-
-
v{{ calculator_version }} Please send feedback to CARA-dev@cern.ch -
- -

CARA - COVID Airborne Risk Assessment calculator

-
+{# This should probably be the same as the index logo and title #} +
+
+ +

CARA - COVID Airborne Risk Assessment Calculator

+
+
{% if DEBUG %}
@@ -36,37 +36,45 @@ v{{ calculator_version }} Please sen -
-
-
- +{#
#} +{#
#} +
+
+
+
-
- - +
+ +
-
- - +
+ +
+ Virus data:
? -

+
+ +
+
-
- - +
+ +
+ +
@@ -74,51 +82,58 @@ v{{ calculator_version }} Please sen Room data:
? -

+
+ +
-
- -
+
+
- +
+ +
+ -
-
+
+
  
- +
+ +
-
-
-          +
+
+   
- -

+
+ +
+
+ +
Central heating system in use:       - -
- -
- ? -
- + +
+ +
- - - + + +
@@ -128,8 +143,13 @@ v{{ calculator_version }} Please sen Ventilation data:
? -

+
+ +
+ Ventilation type: +
+    @@ -138,47 +158,70 @@ v{{ calculator_version }} Please sen
- HEPA filtration: + HEPA filtration:
-    +    -    - +    +
Face masks:
? -

- Are masks worn when occupants are at workstations? +
+ +
+ + Are masks worn when occupants are at workstations?
   @@ -189,9 +232,9 @@ v{{ calculator_version }} Please sen
-
-
+
+
Event data: @@ -199,21 +242,26 @@ v{{ calculator_version }} Please sen ?

-
- - +
+ +
-
- -
+
+ +
- Activity type: - @@ -225,16 +273,42 @@ v{{ calculator_version }} Please sen
- Exposed person(s) presence:
- Start:    - Finish:
- Infected person(s) presence:
- Start:    - Finish:
+
+ + +
+
+ Exposed person(s) presence:
+
+
+ Start:    +
+
+ Finish:
+
+
+
+
+ +
+ Infected person(s) presence:
+
+
+ Start:    +
+
+ Finish:
+
+
+
+
+
+
- Which month is the event? - @@ -248,6 +322,7 @@ v{{ calculator_version }} Please sen
+
@@ -258,9 +333,12 @@ v{{ calculator_version }} Please sen
- -
-
+
+ + + +

+
Lunch break:   @@ -268,18 +346,31 @@ v{{ calculator_version }} Please sen   
- +
+
Start:    +
+
Finish:
+
+
+
+
Coffee Breaks:   +
+
     
+
+
+ +
Duration (minutes):
+
+
Start:    +
+
Finish:
+
+
- Coffee Breaks:   +
+
+ Coffee Breaks: +
+
     
+
+
+ +
Duration (minutes):    @@ -379,9 +379,9 @@ v{{ calculator_version }} Please sen
- +
+{% endblock main %} From ff7e8326f96d0b73585fac9005216ddc3d505237 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 8 Oct 2021 14:58:58 +0200 Subject: [PATCH 046/133] Changed indentation of the user guide --- .../calculator/templates/userguide.html.j2 | 330 +++++++++--------- 1 file changed, 165 insertions(+), 165 deletions(-) diff --git a/cara/apps/calculator/templates/userguide.html.j2 b/cara/apps/calculator/templates/userguide.html.j2 index 2902dd26..3be0ef2a 100644 --- a/cara/apps/calculator/templates/userguide.html.j2 +++ b/cara/apps/calculator/templates/userguide.html.j2 @@ -55,173 +55,173 @@
-

How to use this tool


-

Simulation Name & Room number


-

In order to be able to trace back the simulations in your workplace risk assessments, performed with the tool, you can give each one a unique name - for example "Office use on Tuesday mornings". - The simulation name has no bearing on the calculation.

-

A room number is included, if you do not wish to use a formal room number any reference will do - for example "57/2-004"

-

Virus Data


-

Please choose the correct virus strain or any reported Variant of Concern (VOC) from the list. - Changing this setting alters the properties of the virus which are used for the simulation. - This has a significant effect on the probability of infection. - The choices are:

-
    -
  • SARS-CoV-2 (nominal strain), covering typical strains and varaints which are not of concern from an epidemiologic point of view of the virus;
    • -
  • SARS-CoV-2 (Alpha VOC), first identified in the UK at the end of 2020 which is found to be approximately 1.5x more transmissible compared to the non-VOCs;
    • -
  • SARS-CoV-2 (Gamma VOC), first identified in Brazil in January 2021 which is found to be approximately 2.2x more transmissible compared to the non-VOCs.
    • -
  • SARS-CoV-2 (Delta VOC), first identified in India towards the end of 2020 which is found to be approximately 60% more transmissible compared to the ALPHA VOC.
    • -
-

The user can base their choice according to the prevalence of the different variants in the local area. Access to this information can be found here:

- -

N.B. The transmission data for the Gamma variant has been taken from a study data gathered in Manaus, Brazil where the variant was first observed. - The local population in Manaus had very high levels of Covid-19 antibodies (>67%) in recent months. - This factor has been taken into account by the authors of the study, via statistical adjustments to the transmission value (i.e. it has been increased, to account for spread in a population with significant acquired Covid-19 immunity). - However, this value may be revised in the future as more studies of the Gamma VOC transmission in different geographical locations become available.

-

Room Data


-

Please enter either the room volume (in m³) or both the floor area (m²) and the room height (m). - This information is available via GIS Portal (https://gis.cern.ch/gisportal/).

-

Room heating system


-

The use of central heating (e.g. radiators) reduces relative humidity of the indoor air, which can decrease the decay rate of viral infectivity. If your space is heated with such water radiators, select 'Yes'. If your space does not have such heating, or they are not in use in the period of the simulation (e.g. summer), select 'No'.

-

Ventilation type


-

There are three main options:

-

Mechanical ventilation


-

If the room has mechanical ventilation, suppling fresh air from outside (either a local or centralised system), you should select this option. - In order to make an accurate calculation you will need to know either the flow rate of fresh air supplied in the room or th total number of air changes per hour with fresh air.

-

Please bear in mind that any of the two inputs only consider the supply of fresh air. If a portion of air is recirculated, it shall not be accounted for in the inputs.

-

Natural ventilation


-

Natural ventilation refers to rooms which have openable windows. - There are many possibilities to calculate natural ventilation air flows, for simplification this tool assumes a single-sided natural ventilation scheme which is a conservative approach for the purpose of this tool.

-

Please choose the type of window (see illustration below):

-
    -
  • Sliding or side-hung
    • -
  • Top- or bottom-hung -
    • - -
- Window type -

Please enter the number, height and width and opening distance of the windows (in m). - If there are multiple windows of different sizes, you should take an average.

-

The window opening distance (in m) is:

-
    -
  • In case of Top- or Bottom-Hung, the distance between the fixed frame and the movable glazed part when open.
    • -
  • In the case of Sliding or Side-Hung option, the length the window is moved open. - Window opening distance example (image of open window and measuring tape): +

    How to use this tool


    +

    Simulation Name & Room number


    +

    In order to be able to trace back the simulations in your workplace risk assessments, performed with the tool, you can give each one a unique name - for example "Office use on Tuesday mornings". +The simulation name has no bearing on the calculation.

    +

    A room number is included, if you do not wish to use a formal room number any reference will do - for example "57/2-004"

    +

    Virus Data


    +

    Please choose the correct virus strain or any reported Variant of Concern (VOC) from the list. +Changing this setting alters the properties of the virus which are used for the simulation. +This has a significant effect on the probability of infection. +The choices are:

    +
      +
    • SARS-CoV-2 (nominal strain), covering typical strains and varaints which are not of concern from an epidemiologic point of view of the virus;
      • +
    • SARS-CoV-2 (Alpha VOC), first identified in the UK at the end of 2020 which is found to be approximately 1.5x more transmissible compared to the non-VOCs;
      • +
    • SARS-CoV-2 (Gamma VOC), first identified in Brazil in January 2021 which is found to be approximately 2.2x more transmissible compared to the non-VOCs.
      • +
    • SARS-CoV-2 (Delta VOC), first identified in India towards the end of 2020 which is found to be approximately 60% more transmissible compared to the ALPHA VOC.
      • +
    +

    The user can base their choice according to the prevalence of the different variants in the local area. Access to this information can be found here:

    + +

    N.B. The transmission data for the Gamma variant has been taken from a study data gathered in Manaus, Brazil where the variant was first observed. +The local population in Manaus had very high levels of Covid-19 antibodies (>67%) in recent months. +This factor has been taken into account by the authors of the study, via statistical adjustments to the transmission value (i.e. it has been increased, to account for spread in a population with significant acquired Covid-19 immunity). +However, this value may be revised in the future as more studies of the Gamma VOC transmission in different geographical locations become available.

    +

    Room Data


    +

    Please enter either the room volume (in m³) or both the floor area (m²) and the room height (m). +This information is available via GIS Portal (https://gis.cern.ch/gisportal/).

    +

    Room heating system


    +

    The use of central heating (e.g. radiators) reduces relative humidity of the indoor air, which can decrease the decay rate of viral infectivity. If your space is heated with such water radiators, select 'Yes'. If your space does not have such heating, or they are not in use in the period of the simulation (e.g. summer), select 'No'.

    +

    Ventilation type


    +

    There are three main options:

    +

    Mechanical ventilation


    +

    If the room has mechanical ventilation, suppling fresh air from outside (either a local or centralised system), you should select this option. +In order to make an accurate calculation you will need to know either the flow rate of fresh air supplied in the room or th total number of air changes per hour with fresh air.

    +

    Please bear in mind that any of the two inputs only consider the supply of fresh air. If a portion of air is recirculated, it shall not be accounted for in the inputs.

    +

    Natural ventilation


    +

    Natural ventilation refers to rooms which have openable windows. +There are many possibilities to calculate natural ventilation air flows, for simplification this tool assumes a single-sided natural ventilation scheme which is a conservative approach for the purpose of this tool.

    +

    Please choose the type of window (see illustration below):

    +
      +
    • Sliding or side-hung
      • +
    • Top- or bottom-hung
      • - -
    - Window Opening Distance -

    Notes: If you are unsure about the opening distance for the window, it is recommended to choose a conservative value (5 cms, 0.05m or 10cms, 0.10m). - If you open the window at different distances throughout the day, choose an average value.

    -

    When using natural ventilation, the circulation of air is simulated as a function of the difference between the temperature inside the room and the outside air temperature. The average outdoor temperature for each hour of the day has been computed for every month of the year based on historical data for Geneva, Switzerland. - It is therefore very important to enter the correct time and date in the event data section. - Finally, you must specify if the windows are open permanently (at all the times), or periodically (in intervals for a certain duration and frequency - both in minutes) - e.g. open the window for 10 minutes (duration) every 60 minutes (frequency).

    -

    No ventilation


    -

    This option assumes there is neither Mechanical nor Natural ventilation in the simulation.

    -

    HEPA filtration


    -

    A HEPA filter is a high efficiency particulate matter filter, which removes small airborne particles from the air. - They can be very useful for removing particles with viruses from the air in an enclosed space. - The calculator allows you to simulate the installation of a HEPA air filter within the room. - The recommended airflow rate for the HEPA filter should correspond to a total air exchange rate of 3 - 6 ACH (the higher the better, even beyond 6).

    -

    Event Data


    -

    Here we capture the information about the event being simulated. - First enter the number of occupants in the space, if you have a (small) variation in the number of people, please input the average or consider using the expert tool. - Within the number of people occupying the space, you should specify how many are infected.

    -

    As an example, for a shared office with 4 people, where one person is infected, we enter 4 occupants and 1 infected person.

    -

    Activity type


    -

    There are a few predefined activities in the tool at present.

    -
      -
    • Office = All persons seated, talking occasionally (1/3rd of the time, with normal breathing the other 2/3rds of the time). Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Meeting = All persons seated, having a conversation (approximately each occupant is 1/N % of the time talking, where N is the number of occupants). Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Library = All persons seated, breathing only (not talking), all the time.
      • -
    • Call Centre = All persons seated, all talking simultaneously, all the time. This is a conservative profile, i.e. will show an increased P(i) compared to office/meeting activity. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Control Room (day shift) = All persons seated, all talking 50% of the time. This is a conservative profile, i.e. will show an increased P(i) compared to office/meeting activity. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Control Room (night shift) = All persons seated, all talking 10% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Lab = Based on a typical lab or technical working area, all persons are doing light activity and talking 50% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Workshop = Based on a mechanical assembly workshop or equipment installation scenario, all persons are doing moderate activity and talking 50% of the time. This activity is equally applicable to bicycling, or walking on a gradient, in the LHC tunnels. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Training = Based on a typical training course scenario. - One individual (the trainer) is standing and talking, with all other individuals seated and talking quietly (whispering). - In this case it is assumed that the infected person is the trainer, because this is the worst case in terms of viral shedding.
      • -
    • Gym = All persons are doing heavy exercise and breathing (not talking). Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    -

    Timings

    -

    You should enter the time (hours:minutes) for the start and end of the simulation period (i.e. 8:30 to 17:30 for a typical office day). - It is important to enter the correct times for the simulation, in particular when using natural ventilation. - It is possible to specify a different time for the entry and exit of both the exposed and infected person, however for most cases (where we do not know apriori which of the occupants is infected), it is recommended to set these to the same values as the activity start and end.

    -

    When is the event?


    -

    This is included for completeness in all simulations, however it is of particular relevance to those using natural ventilation because of variations in outside air temperature.

    -

    Only the month is used by the model to retrieve the average outdoor air temperatures for the Geneva region.

    -

    Breaks


    -

    Lunch Break


    -

    You have the option to specify a lunch break. - This will be useful if you plan to simulate a typical full working day. - During the lunch break it is assumed that all occupants will leave the simulated space (to go eat lunch somewhere else - restaurant or break room). - If you plan to eat lunch in the same area where you have been working, you should select 'No' even if a lunch break will be taken, since the risk of infection is related to the occupation of the simulated space. - See 'Split Breaks' if the occupants do not break at the same time.

    -

    It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

    -

    Coffee Breaks


    -

    You have the option to choose 0(No breaks), 2 or 4 coffee breaks during the simulated period. - It is assumed that all occupants vacate the space during the break period. - If coffee breaks are taken in-situ, this option should be set to 'No breaks'.

    -

    When enabled, the breaks are spread equally throughout the day - for example if we simulate the period from 9:00 to 18:00, with a lunch break from 13:00 to 14:00 and considering 2 coffee breaks, the tool will schedule the first coffee break around 11:00 and the second around 16:00. - The exact timing of the breaks within the day is not particularly critical to an accurate simulation, so you do not need to be concerned about major differences if you take a coffee break at 10:00 instead of 11:00. - The variation of coffee breaks can be altered in 5 minute increments up to 30 minutes in length. - Note that this doesn't necessarily have to be a coffee break, it can represent any period where the simulated space is vacated. - See 'Split Breaks' if the occupants do not break at the same time.

    -

    It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

    -

    Split breaks


    -

    You have the option to specify whether the exposed and infected person(s) break at the same time. - If not, then you can input separate breaks. This is particularly different when specifying coffee breaks as they are spread evenly throughout the activity times specified.

    -

    If we take an example where the exposed person(s) activity time is from 9:00 to 18:00 and the infected person(s) is from 10:00 to 17:00, with both having a lunch break from 13:00 to 14:00 and have 2 coffee breaks each, we can have two different results:

    -
      -

    1. Specify the default situtaion where both exposed and infected persons(s) have their breaks at the same time: in this case the coffee break times are calculated based on the activity time of the exposed - both will have their first coffee break around 11:00 and the second around 16:00.

      -
      2. -

    2. Specify separate breaks for the infected person(s): in this case the coffee breaks will be calculated based on the different activity times (i.e. exposed from 9:00 to 18:00 and infected from 10:00 to 17:00) - the exposed person(s) will have their first coffee break around 11:00 and the second around 16:00, whereas the infected will have their first coffee break around 11:30 and the second around 15:30.

      -
      3. -
    -

    Face Masks


    -

    The model allows for a simulation with either a continuous wearing of face masks throughout the duration of the event, or have the removed at all times - i.e. all occupants (infected and exposed alike) wear or not masks for the duration of the simulation. - Please bear in mind the user inputs shall be aligned with the current applicable public health & safety instructions. - Please check what are the applicable rules, before deciding which assumptions are used for the simulation.

    -

    If you have selected the Training activity type, this equates to the trainer and all participants either wearing masks throughout the training (Yes), or removing them when seated/standing at their socially distanced positions within the training room (No). - Please confirm what are the applicable rules, before deciding which assumptions are used for the simulation

    -

    For the time being only the Type 1 surgical and FFP2 masks can be selected.

    -

    Generate Report


    -

    When you have entered all the necessary information, please click on the Generate Report button to execute the model. With the implementation of Monte Carlo simulations, the browser might take a few secounds to react.

    -

    Report


    -

    The report will open in your web browser. - It contains a summary of all the input data, which will allow the simulation to be repeated if required in the future as we improve the model.

    -

    Results


    -

    This part of the report shows the P(I) or probability of one exposed person getting infected. - It is estimated based on the emission rate of virus into the simulated volume, and the amount which is inhaled by exposed individuals. - This probability is valid for the simulation duration - i.e. the start and end time. - If you are using the natural ventilation option, the simulation is only valid for the selected month, because the following or preceding month will have a different average temperature profile. - The expected number of new cases for the simulation is calculated based on the probability of infection, multiplied by the number of exposed occupants.

    -

    The graph shows the variation in the concentration of infectious viruses within the simulated volume. - It is determined by:

    -
      -
    • The presence of the infected person, who emits airborne viruses in the volume.
      • -
    • The emission rate is related to the type of activity of the infected person (sitting, light exercise), their level of vocalisation (breathing, talking or shouting).
      • -
    • The accumulation of infectious quanta in the volume, which is driven, among other factors, by ventilation (if applicable).
        -
      • In a mechanical ventilation scenario, the removal rate is constant, based on fresh airflow supply in and out of the simulated space.
        • -
      • Under natural ventilation conditions, the effectiveness of ventilation relies upon the hourly temperature difference between the inside and outside air temperature.
        • -
      • A HEPA filter removes infectious virus from the air at a constant rate and is modelled in the same way as mechanical ventilation, however air passed through a HEPA filter is recycled (i.e. it is not fresh air).
        • -
      -
      • -
    -

    QR code


    -

    At the end of the report you can find a unique QR code / hyperlink for this report. This provides an automatic way to review the calculator form with the corresponding specified parameters. - This allows for:

    -
      -
    • sharing reports by either scanning or clicking on the QR code to obtain a shareable link.
      • -
    • easily regenerating reports with any new versions of the CARA model released in the future.
      • -
    -

    Conclusion


    -

    This tool provides informative comparisons for COVID-19 (long-range) airborne risk only - see Disclaimer - If you have any comments on your experience with the app, or feedback for potential improvements, please share them with the development team Send email.

    +
+Window type +

Please enter the number, height and width and opening distance of the windows (in m). +If there are multiple windows of different sizes, you should take an average.

+

The window opening distance (in m) is:

+
    +
  • In case of Top- or Bottom-Hung, the distance between the fixed frame and the movable glazed part when open.
    • +
  • In the case of Sliding or Side-Hung option, the length the window is moved open. +Window opening distance example (image of open window and measuring tape): +
    • + + +
+Window Opening Distance +

Notes: If you are unsure about the opening distance for the window, it is recommended to choose a conservative value (5 cms, 0.05m or 10cms, 0.10m). +If you open the window at different distances throughout the day, choose an average value.

+

When using natural ventilation, the circulation of air is simulated as a function of the difference between the temperature inside the room and the outside air temperature. The average outdoor temperature for each hour of the day has been computed for every month of the year based on historical data for Geneva, Switzerland. +It is therefore very important to enter the correct time and date in the event data section. +Finally, you must specify if the windows are open permanently (at all the times), or periodically (in intervals for a certain duration and frequency - both in minutes) - e.g. open the window for 10 minutes (duration) every 60 minutes (frequency).

+

No ventilation


+

This option assumes there is neither Mechanical nor Natural ventilation in the simulation.

+

HEPA filtration


+

A HEPA filter is a high efficiency particulate matter filter, which removes small airborne particles from the air. +They can be very useful for removing particles with viruses from the air in an enclosed space. +The calculator allows you to simulate the installation of a HEPA air filter within the room. +The recommended airflow rate for the HEPA filter should correspond to a total air exchange rate of 3 - 6 ACH (the higher the better, even beyond 6).

+

Event Data


+

Here we capture the information about the event being simulated. +First enter the number of occupants in the space, if you have a (small) variation in the number of people, please input the average or consider using the expert tool. +Within the number of people occupying the space, you should specify how many are infected.

+

As an example, for a shared office with 4 people, where one person is infected, we enter 4 occupants and 1 infected person.

+

Activity type


+

There are a few predefined activities in the tool at present.

+
    +
  • Office = All persons seated, talking occasionally (1/3rd of the time, with normal breathing the other 2/3rds of the time). Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Meeting = All persons seated, having a conversation (approximately each occupant is 1/N % of the time talking, where N is the number of occupants). Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Library = All persons seated, breathing only (not talking), all the time.
    • +
  • Call Centre = All persons seated, all talking simultaneously, all the time. This is a conservative profile, i.e. will show an increased P(i) compared to office/meeting activity. Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Control Room (day shift) = All persons seated, all talking 50% of the time. This is a conservative profile, i.e. will show an increased P(i) compared to office/meeting activity. Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Control Room (night shift) = All persons seated, all talking 10% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Lab = Based on a typical lab or technical working area, all persons are doing light activity and talking 50% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Workshop = Based on a mechanical assembly workshop or equipment installation scenario, all persons are doing moderate activity and talking 50% of the time. This activity is equally applicable to bicycling, or walking on a gradient, in the LHC tunnels. Everyone (exposed and infected occupants) is treated the same in this model.
    • +
  • Training = Based on a typical training course scenario. +One individual (the trainer) is standing and talking, with all other individuals seated and talking quietly (whispering). +In this case it is assumed that the infected person is the trainer, because this is the worst case in terms of viral shedding.
    • +
  • Gym = All persons are doing heavy exercise and breathing (not talking). Everyone (exposed and infected occupants) is treated the same in this model.
    • +
+

Timings

+

You should enter the time (hours:minutes) for the start and end of the simulation period (i.e. 8:30 to 17:30 for a typical office day). +It is important to enter the correct times for the simulation, in particular when using natural ventilation. +It is possible to specify a different time for the entry and exit of both the exposed and infected person, however for most cases (where we do not know apriori which of the occupants is infected), it is recommended to set these to the same values as the activity start and end.

+

When is the event?


+

This is included for completeness in all simulations, however it is of particular relevance to those using natural ventilation because of variations in outside air temperature.

+

Only the month is used by the model to retrieve the average outdoor air temperatures for the Geneva region.

+

Breaks


+

Lunch Break


+

You have the option to specify a lunch break. +This will be useful if you plan to simulate a typical full working day. +During the lunch break it is assumed that all occupants will leave the simulated space (to go eat lunch somewhere else - restaurant or break room). +If you plan to eat lunch in the same area where you have been working, you should select 'No' even if a lunch break will be taken, since the risk of infection is related to the occupation of the simulated space. +See 'Split Breaks' if the occupants do not break at the same time.

+

It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

+

Coffee Breaks


+

You have the option to choose 0(No breaks), 2 or 4 coffee breaks during the simulated period. +It is assumed that all occupants vacate the space during the break period. +If coffee breaks are taken in-situ, this option should be set to 'No breaks'.

+

When enabled, the breaks are spread equally throughout the day - for example if we simulate the period from 9:00 to 18:00, with a lunch break from 13:00 to 14:00 and considering 2 coffee breaks, the tool will schedule the first coffee break around 11:00 and the second around 16:00. +The exact timing of the breaks within the day is not particularly critical to an accurate simulation, so you do not need to be concerned about major differences if you take a coffee break at 10:00 instead of 11:00. +The variation of coffee breaks can be altered in 5 minute increments up to 30 minutes in length. +Note that this doesn't necessarily have to be a coffee break, it can represent any period where the simulated space is vacated. +See 'Split Breaks' if the occupants do not break at the same time.

+

It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

+

Split breaks


+

You have the option to specify whether the exposed and infected person(s) break at the same time. +If not, then you can input separate breaks. This is particularly different when specifying coffee breaks as they are spread evenly throughout the activity times specified.

+

If we take an example where the exposed person(s) activity time is from 9:00 to 18:00 and the infected person(s) is from 10:00 to 17:00, with both having a lunch break from 13:00 to 14:00 and have 2 coffee breaks each, we can have two different results:

+
    +

  1. Specify the default situtaion where both exposed and infected persons(s) have their breaks at the same time: in this case the coffee break times are calculated based on the activity time of the exposed - both will have their first coffee break around 11:00 and the second around 16:00.

    +
    2. +

  2. Specify separate breaks for the infected person(s): in this case the coffee breaks will be calculated based on the different activity times (i.e. exposed from 9:00 to 18:00 and infected from 10:00 to 17:00) - the exposed person(s) will have their first coffee break around 11:00 and the second around 16:00, whereas the infected will have their first coffee break around 11:30 and the second around 15:30.

    +
    3. +
+

Face Masks


+

The model allows for a simulation with either a continuous wearing of face masks throughout the duration of the event, or have the removed at all times - i.e. all occupants (infected and exposed alike) wear or not masks for the duration of the simulation. +Please bear in mind the user inputs shall be aligned with the current applicable public health & safety instructions. +Please check what are the applicable rules, before deciding which assumptions are used for the simulation.

+

If you have selected the Training activity type, this equates to the trainer and all participants either wearing masks throughout the training (Yes), or removing them when seated/standing at their socially distanced positions within the training room (No). +Please confirm what are the applicable rules, before deciding which assumptions are used for the simulation

+

For the time being only the Type 1 surgical and FFP2 masks can be selected.

+

Generate Report


+

When you have entered all the necessary information, please click on the Generate Report button to execute the model. With the implementation of Monte Carlo simulations, the browser might take a few secounds to react.

+

Report


+

The report will open in your web browser. +It contains a summary of all the input data, which will allow the simulation to be repeated if required in the future as we improve the model.

+

Results


+

This part of the report shows the P(I) or probability of one exposed person getting infected. +It is estimated based on the emission rate of virus into the simulated volume, and the amount which is inhaled by exposed individuals. +This probability is valid for the simulation duration - i.e. the start and end time. +If you are using the natural ventilation option, the simulation is only valid for the selected month, because the following or preceding month will have a different average temperature profile. +The expected number of new cases for the simulation is calculated based on the probability of infection, multiplied by the number of exposed occupants.

+

The graph shows the variation in the concentration of infectious viruses within the simulated volume. +It is determined by:

+
    +
  • The presence of the infected person, who emits airborne viruses in the volume.
    • +
  • The emission rate is related to the type of activity of the infected person (sitting, light exercise), their level of vocalisation (breathing, talking or shouting).
    • +
  • The accumulation of infectious quanta in the volume, which is driven, among other factors, by ventilation (if applicable).
      +
    • In a mechanical ventilation scenario, the removal rate is constant, based on fresh airflow supply in and out of the simulated space.
      • +
    • Under natural ventilation conditions, the effectiveness of ventilation relies upon the hourly temperature difference between the inside and outside air temperature.
      • +
    • A HEPA filter removes infectious virus from the air at a constant rate and is modelled in the same way as mechanical ventilation, however air passed through a HEPA filter is recycled (i.e. it is not fresh air).
      • +
    +
    • +
+

QR code


+

At the end of the report you can find a unique QR code / hyperlink for this report. This provides an automatic way to review the calculator form with the corresponding specified parameters. +This allows for:

+
    +
  • sharing reports by either scanning or clicking on the QR code to obtain a shareable link.
    • +
  • easily regenerating reports with any new versions of the CARA model released in the future.
    • +
+

Conclusion


+

This tool provides informative comparisons for COVID-19 (long-range) airborne risk only - see Disclaimer +If you have any comments on your experience with the app, or feedback for potential improvements, please share them with the development team Send email.

{% endblock main %} From ed99cff6bd804dbecfa5330bad6b8c89be103c21 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 8 Oct 2021 15:12:21 +0200 Subject: [PATCH 047/133] CRemoved unused concepts --- .../calculator/templates/userguide.html.j2 | 100 ++++++++++++------ 1 file changed, 66 insertions(+), 34 deletions(-) diff --git a/cara/apps/calculator/templates/userguide.html.j2 b/cara/apps/calculator/templates/userguide.html.j2 index 3be0ef2a..4d35c32b 100644 --- a/cara/apps/calculator/templates/userguide.html.j2 +++ b/cara/apps/calculator/templates/userguide.html.j2 @@ -22,7 +22,7 @@ CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions.

- CARA models the concentration profile of potential infectious viruses in enclosed spaces with clear and intuitive graphs. + CARA models the concentration profile of potential virions in enclosed spaces with clear and intuitive graphs. The user can set a number of parameters, including room volume, exposure time, activity type, mask-wearing and ventilation. The report generated indicates how to avoid exceeding critical concentrations and chains of airborne transmission in spaces such as individual offices, meeting rooms and labs.

@@ -50,13 +50,15 @@

CARA has not undergone review, approval or certification by competent authorities, and as a result, it cannot be considered as a fully endorsed and reliable tool, namely in the assessment of potential viral emissions from infected hosts to be modelled. -

+

+

How to use this tool


-

Simulation Name & Room number


+

Simulation Name & Room number

+

In order to be able to trace back the simulations in your workplace risk assessments, performed with the tool, you can give each one a unique name - for example "Office use on Tuesday mornings". The simulation name has no bearing on the calculation.

A room number is included, if you do not wish to use a formal room number any reference will do - for example "57/2-004"

@@ -66,12 +68,12 @@ Changing this setting alters the properties of the virus which are used for the This has a significant effect on the probability of infection. The choices are:

    -
  • SARS-CoV-2 (nominal strain), covering typical strains and varaints which are not of concern from an epidemiologic point of view of the virus;
    • +
  • SARS-CoV-2 (nominal strain), covering typical strains and variants which are not of concern from an epidemiologic point of view of the virus;
  • SARS-CoV-2 (Alpha VOC), first identified in the UK at the end of 2020 which is found to be approximately 1.5x more transmissible compared to the non-VOCs;
  • SARS-CoV-2 (Gamma VOC), first identified in Brazil in January 2021 which is found to be approximately 2.2x more transmissible compared to the non-VOCs.
  • SARS-CoV-2 (Delta VOC), first identified in India towards the end of 2020 which is found to be approximately 60% more transmissible compared to the ALPHA VOC.
-

The user can base their choice according to the prevalence of the different variants in the local area. Access to this information can be found here:

+

The user can modify the selected variant from the default, according to the prevalence of the different variants in the local area. Access to this information can be found here:

  • Geneva: https://www.covid19.admin.ch/fr/epidemiologic/virus-variants?detGeo=GE
  • Ain (France): https://www.santepubliquefrance.fr/dossiers/coronavirus-covid-19/covid-19-cartographie-des-variants-en-france-donnees-par-region-et-par-departement
    • @@ -80,58 +82,71 @@ The choices are:

    The local population in Manaus had very high levels of Covid-19 antibodies (>67%) in recent months. This factor has been taken into account by the authors of the study, via statistical adjustments to the transmission value (i.e. it has been increased, to account for spread in a population with significant acquired Covid-19 immunity). However, this value may be revised in the future as more studies of the Gamma VOC transmission in different geographical locations become available.

    -

    Room Data


    +
    +

    Room Data

    +

    Please enter either the room volume (in m³) or both the floor area (m²) and the room height (m). This information is available via GIS Portal (https://gis.cern.ch/gisportal/).

    -

    Room heating system


    +
    +

    Room heating system

    +

    The use of central heating (e.g. radiators) reduces relative humidity of the indoor air, which can decrease the decay rate of viral infectivity. If your space is heated with such water radiators, select 'Yes'. If your space does not have such heating, or they are not in use in the period of the simulation (e.g. summer), select 'No'.

    -

    Ventilation type


    +
    +

    Ventilation type

    +

    There are three main options:

    -

    Mechanical ventilation


    +
    +

    Mechanical ventilation

    +

    If the room has mechanical ventilation, suppling fresh air from outside (either a local or centralised system), you should select this option. In order to make an accurate calculation you will need to know either the flow rate of fresh air supplied in the room or th total number of air changes per hour with fresh air.

    Please bear in mind that any of the two inputs only consider the supply of fresh air. If a portion of air is recirculated, it shall not be accounted for in the inputs.

    -

    Natural ventilation


    +
    +

    Natural ventilation

    +

    Natural ventilation refers to rooms which have openable windows. There are many possibilities to calculate natural ventilation air flows, for simplification this tool assumes a single-sided natural ventilation scheme which is a conservative approach for the purpose of this tool.

    Please choose the type of window (see illustration below):

    • Sliding or side-hung
    • Top- or bottom-hung -
      • - +Window type
    -Window type

    Please enter the number, height and width and opening distance of the windows (in m). If there are multiple windows of different sizes, you should take an average.

    The window opening distance (in m) is:

      -
    • In case of Top- or Bottom-Hung, the distance between the fixed frame and the movable glazed part when open.
    • In the case of Sliding or Side-Hung option, the length the window is moved open. Window opening distance example (image of open window and measuring tape): -
      • - - +Window Opening Distance +
    • In case of Top- or Bottom-Hung, the distance between the fixed frame and the movable glazed part when open.
    -Window Opening Distance

    Notes: If you are unsure about the opening distance for the window, it is recommended to choose a conservative value (5 cms, 0.05m or 10cms, 0.10m). If you open the window at different distances throughout the day, choose an average value.

    When using natural ventilation, the circulation of air is simulated as a function of the difference between the temperature inside the room and the outside air temperature. The average outdoor temperature for each hour of the day has been computed for every month of the year based on historical data for Geneva, Switzerland. It is therefore very important to enter the correct time and date in the event data section. Finally, you must specify if the windows are open permanently (at all the times), or periodically (in intervals for a certain duration and frequency - both in minutes) - e.g. open the window for 10 minutes (duration) every 60 minutes (frequency).

    -

    No ventilation


    +
    +

    No ventilation

    +

    This option assumes there is neither Mechanical nor Natural ventilation in the simulation.

    -

    HEPA filtration


    +
    +

    HEPA filtration

    +

    A HEPA filter is a high efficiency particulate matter filter, which removes small airborne particles from the air. They can be very useful for removing particles with viruses from the air in an enclosed space. The calculator allows you to simulate the installation of a HEPA air filter within the room. The recommended airflow rate for the HEPA filter should correspond to a total air exchange rate of 3 - 6 ACH (the higher the better, even beyond 6).

    -

    Event Data


    +
    +

    Event Data

    +

    Here we capture the information about the event being simulated. First enter the number of occupants in the space, if you have a (small) variation in the number of people, please input the average or consider using the expert tool. Within the number of people occupying the space, you should specify how many are infected.

    As an example, for a shared office with 4 people, where one person is infected, we enter 4 occupants and 1 infected person.

    -

    Activity type


    +
    +

    Activity type

    +

    There are a few predefined activities in the tool at present.

    • Office = All persons seated, talking occasionally (1/3rd of the time, with normal breathing the other 2/3rds of the time). Everyone (exposed and infected occupants) is treated the same in this model.
      • @@ -154,15 +169,20 @@ It is possible to specify a different time for the entry and exit of both the ex

      When is the event?


      This is included for completeness in all simulations, however it is of particular relevance to those using natural ventilation because of variations in outside air temperature.

      Only the month is used by the model to retrieve the average outdoor air temperatures for the Geneva region.

      -

      Breaks


      -

      Lunch Break


      +
      +

      Breaks

      +
      +

      Lunch Break

      +

      You have the option to specify a lunch break. This will be useful if you plan to simulate a typical full working day. During the lunch break it is assumed that all occupants will leave the simulated space (to go eat lunch somewhere else - restaurant or break room). If you plan to eat lunch in the same area where you have been working, you should select 'No' even if a lunch break will be taken, since the risk of infection is related to the occupation of the simulated space. See 'Split Breaks' if the occupants do not break at the same time.

      It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

      -

      Coffee Breaks


      +
      +

      Coffee Breaks

      +

      You have the option to choose 0(No breaks), 2 or 4 coffee breaks during the simulated period. It is assumed that all occupants vacate the space during the break period. If coffee breaks are taken in-situ, this option should be set to 'No breaks'.

      @@ -172,7 +192,9 @@ The variation of coffee breaks can be altered in 5 minute increments up to 30 mi Note that this doesn't necessarily have to be a coffee break, it can represent any period where the simulated space is vacated. See 'Split Breaks' if the occupants do not break at the same time.

      It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

      -

      Split breaks


      +
      +

      Split breaks

      +

      You have the option to specify whether the exposed and infected person(s) break at the same time. If not, then you can input separate breaks. This is particularly different when specifying coffee breaks as they are spread evenly throughout the activity times specified.

      If we take an example where the exposed person(s) activity time is from 9:00 to 18:00 and the infected person(s) is from 10:00 to 17:00, with both having a lunch break from 13:00 to 14:00 and have 2 coffee breaks each, we can have two different results:

      @@ -182,44 +204,54 @@ If not, then you can input separate breaks. This is particularly different when

    • Specify separate breaks for the infected person(s): in this case the coffee breaks will be calculated based on the different activity times (i.e. exposed from 9:00 to 18:00 and infected from 10:00 to 17:00) - the exposed person(s) will have their first coffee break around 11:00 and the second around 16:00, whereas the infected will have their first coffee break around 11:30 and the second around 15:30.

      • -

      Face Masks


      +
      +

      Face Masks

      +

      The model allows for a simulation with either a continuous wearing of face masks throughout the duration of the event, or have the removed at all times - i.e. all occupants (infected and exposed alike) wear or not masks for the duration of the simulation. Please bear in mind the user inputs shall be aligned with the current applicable public health & safety instructions. Please check what are the applicable rules, before deciding which assumptions are used for the simulation.

      If you have selected the Training activity type, this equates to the trainer and all participants either wearing masks throughout the training (Yes), or removing them when seated/standing at their socially distanced positions within the training room (No). Please confirm what are the applicable rules, before deciding which assumptions are used for the simulation

      For the time being only the Type 1 surgical and FFP2 masks can be selected.

      -

      Generate Report


      +
      +

      Generate Report

      +

      When you have entered all the necessary information, please click on the Generate Report button to execute the model. With the implementation of Monte Carlo simulations, the browser might take a few secounds to react.


      Report


      The report will open in your web browser. It contains a summary of all the input data, which will allow the simulation to be repeated if required in the future as we improve the model.

      -

      Results


      +
      +

      Results

      +

      This part of the report shows the P(I) or probability of one exposed person getting infected. It is estimated based on the emission rate of virus into the simulated volume, and the amount which is inhaled by exposed individuals. This probability is valid for the simulation duration - i.e. the start and end time. If you are using the natural ventilation option, the simulation is only valid for the selected month, because the following or preceding month will have a different average temperature profile. The expected number of new cases for the simulation is calculated based on the probability of infection, multiplied by the number of exposed occupants.

      -

      The graph shows the variation in the concentration of infectious viruses within the simulated volume. +

      The graph shows the variation in the concentration of virions within the simulated volume. It is determined by:

      • The presence of the infected person, who emits airborne viruses in the volume.
      • The emission rate is related to the type of activity of the infected person (sitting, light exercise), their level of vocalisation (breathing, talking or shouting).
        • -
      • The accumulation of infectious quanta in the volume, which is driven, among other factors, by ventilation (if applicable).
          +
        • The accumulation of virions in the volume, which is driven, among other factors, by ventilation (if applicable).
          • In a mechanical ventilation scenario, the removal rate is constant, based on fresh airflow supply in and out of the simulated space.
          • Under natural ventilation conditions, the effectiveness of ventilation relies upon the hourly temperature difference between the inside and outside air temperature.
            • -
          • A HEPA filter removes infectious virus from the air at a constant rate and is modelled in the same way as mechanical ventilation, however air passed through a HEPA filter is recycled (i.e. it is not fresh air).
            • +
          • A HEPA filter removes virions from the air at a constant rate and is modelled in the same way as mechanical ventilation, however air passed through a HEPA filter is recycled (i.e. it is not fresh air).
        -

        QR code


        +
        +

        QR code

        +

        At the end of the report you can find a unique QR code / hyperlink for this report. This provides an automatic way to review the calculator form with the corresponding specified parameters. This allows for:

        • sharing reports by either scanning or clicking on the QR code to obtain a shareable link.
        • easily regenerating reports with any new versions of the CARA model released in the future.
        -

        Conclusion


        +
        +

        Conclusion

        +

        This tool provides informative comparisons for COVID-19 (long-range) airborne risk only - see Disclaimer If you have any comments on your experience with the app, or feedback for potential improvements, please share them with the development team Send email.

From 608f638abfd3f1af2a495c58876ad415f4755307 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 11 Oct 2021 16:53:34 +0200 Subject: [PATCH 048/133] Replaced   to correct padding fixes #200; Size fix for mobile devices --- .../templates/calculator.form.html.j2 | 242 ++++++++++-------- cara/apps/static/css/style.css | 2 +- 2 files changed, 131 insertions(+), 113 deletions(-) diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index 6626dd25..220e7f08 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -59,14 +59,16 @@ v{{ calculator_version }} Please sen ?

-
- - +
+
+
+ +
@@ -92,7 +94,7 @@ v{{ calculator_version }} Please sen
-    +
@@ -100,7 +102,7 @@ v{{ calculator_version }} Please sen
-    +
@@ -110,23 +112,23 @@ v{{ calculator_version }} Please sen
Central heating system in use: - -    - -    + + + +
- - -
- - - - -
+
+
+
+
+ + + +
@@ -142,55 +144,63 @@ v{{ calculator_version }} Please sen
Ventilation type:
-    - -    - + + + +
- -
@@ -522,7 +519,10 @@ v{{ calculator_version }}
- +
From 3ce30558b32a19e95495091d615025bd25d50ce4 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 12 Oct 2021 16:44:58 +0200 Subject: [PATCH 053/133] Updated report mobile version --- cara/apps/calculator/static/js/report.js | 426 +++++++++++------- .../templates/base/calculator.report.html.j2 | 53 ++- .../cern/templates/calculator.report.html.j2 | 16 +- cara/apps/static/css/style.css | 43 +- 4 files changed, 353 insertions(+), 185 deletions(-) diff --git a/cara/apps/calculator/static/js/report.js b/cara/apps/calculator/static/js/report.js index b815da2f..c544f44a 100644 --- a/cara/apps/calculator/static/js/report.js +++ b/cara/apps/calculator/static/js/report.js @@ -1,93 +1,95 @@ /* Generate the concentration plot using d3 library. */ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence_intervals) { - var visBoundingBox = d3.select(svg_id) - .node() - .getBoundingClientRect(); - + var time_format = d3.timeFormat('%H:%M'); + // H:M time format for x axis. var data = [] - times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index] })) + // Prepare data + times.map((time, index) => data.push({'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index] })) - var vis = d3.select(svg_id), - width = visBoundingBox.width - 300, - height = visBoundingBox.height, - margins = { top: 30, right: 20, bottom: 50, left: 50 }, + // Add main SVG element + var plot_div = document.getElementById(svg_id); + var vis = d3.select(plot_div).append('svg'); + + var xRange = d3.scaleTime().domain([data[0].hour, data[data.length - 1].hour]); + var xTimeRange = d3.scaleLinear().domain([data[0].time, data[data.length - 1].time]); + var bisecHour = d3.bisector((d) => { return d.hour; }).left; - // H:M time format for x axis. - xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([data[0].hour, data[data.length - 1].hour]), - xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([data[0].time, data[data.length - 1].time]), - bisecHour = d3.bisector((d) => { return d.hour; }).left, - - yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]), - - xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), - yAxis = d3.axisLeft(yRange); - - // Plot tittle. - plot_title(vis, width, margins.top, 'Mean concentration of virions'); + var yRange = d3.scaleLinear().domain([0., Math.max(...concentrations)]); // Line representing the mean concentration. - plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4'); - - // X axis. - plot_x_axis(vis, height, width, margins, xAxis, 'Time of day'); - - // Y axis - plot_y_axis(vis, height, width, margins, yAxis, 'Mean concentration (virions/m³)') + var lineFunc = d3.line(); + var draw_line = vis.append('svg:path') + .attr('stroke', '#1f77b4') + .attr('stroke-width', 2) + .attr('fill', 'none'); // Area representing the presence of exposed person(s). - exposed_presence_intervals.forEach(b => { - var curveFunc = d3.area() - .x(d => xTimeRange(d.time)) - .y0(height - margins.bottom) - .y1(d => yRange(d.concentration)); - - vis.append('svg:path') - .attr('d', curveFunc(data.filter(d => { - return d.time >= b[0] && d.time <= b[1] - }))) + var exposedArea = {}; + var drawArea = {}; + exposed_presence_intervals.forEach((b, index) => { + exposedArea[index] = d3.area(); + drawArea[index] = vis.append('svg:path') .attr('fill', '#1f77b4') .attr('fill-opacity', '0.1'); - }) + }); + + // Plot tittle. + var plotTitleEl = vis.append('svg:foreignObject') + .attr("background-color", "transparent") + .attr('height', 30) + .style('text-align', 'center') + .html('Mean concentration of virions'); + + // X axis declaration. + var xAxisEl = vis.append('svg:g') + .attr('class', 'x axis'); + + // X axis label. + var xAxisLabelEl = vis.append('text') + .attr('class', 'x label') + .attr('fill', 'black') + .attr('text-anchor', 'middle') + .text('Time of day') + + // Y axis declaration. + var yAxisEl = vis.append('svg:g') + .attr('class', 'y axis'); + + // Y axis label. + var yAxisLabelEl = vis.append('svg:text') + .attr('class', 'y label') + .attr('fill', 'black') + .attr('text-anchor', 'middle') + .text('Mean concentration (virions/m³)'); // Legend for the plot elements - line and area. - var size = 20 - vis.append('rect') - .attr('x', width + size) - .attr('y', margins.top + size) + var legendLineIcon = vis.append('rect') .attr('width', 20) .attr('height', 3) .style('fill', '#1f77b4'); - vis.append('rect') - .attr('x', width + size) - .attr('y', 3 * size) + var legendAreaIcon = vis.append('rect') .attr('width', 20) .attr('height', 20) .attr('fill', '#1f77b4') .attr('fill-opacity', '0.1'); - vis.append('text') - .attr('x', width + 3 * size) - .attr('y', margins.top + size) + var legendLineText = vis.append('text') .text('Mean concentration') .style('font-size', '15px') .attr('alignment-baseline', 'central'); - vis.append('text') - .attr('x', width + 3 * size) - .attr('y', margins.top + 2 * size) + var legendAreaText = vis.append('text') .text('Presence of exposed person(s)') .style('font-size', '15px') .attr('alignment-baseline', 'central'); - // Legend bounding box. - vis.append('rect') + // Legend bounding + var legendBBox = vis.append('rect') .attr('width', 275) .attr('height', 50) - .attr('x', width * 1.005) - .attr('y', margins.top + 5) .attr('stroke', 'lightgrey') .attr('stroke-width', '2') .attr('rx', '5px') @@ -122,15 +124,111 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence .attr('x', 18) .attr('y', 18); - vis.append('rect') + var toolBox = vis.append('rect') .attr('fill', 'none') .attr('pointer-events', 'all') - .attr('width', width - margins.right) - .attr('height', height) .on('mouseover', () => { focus.style('display', null); }) .on('mouseout', () => { focus.style('display', 'none'); }) .on('mousemove', mousemove); + function redraw() { + + // Define width and height according to the screen size. + var div_width = plot_div.clientWidth; + var div_height = plot_div.clientHeight; + var graph_width = div_width; + var graph_height = div_height + if (div_width >= 900) { // For screens with width > 900px legend can be on the graph's right side. + var margins = { top: 30, right: 20, bottom: 50, left: 50 }; + div_width = 900; + graph_width = div_width * (2/3); + } + else { + var margins = { top: 30, right: 5, bottom: 50, left: 20 }; + graph_height = div_height * 0.85; // On mobile screen sizes we want the legend to be on the bottom of the graph. + }; + + // Use the extracted size to set the size of the SVG element. + vis.attr("width", div_width) + .attr('height', div_height); + + // SVG components according to the width and height. + + // Axis ranges. + xRange.range([margins.left, graph_width - margins.right]); + xTimeRange.range([margins.left, graph_width - margins.right]); + yRange.range([graph_height - margins.bottom, margins.top]) + + // Line. + lineFunc.defined(d => !isNaN(d.concentration)) + .x(d => xTimeRange(d.time)) + .y(d => yRange(d.concentration)); + draw_line.attr("d", lineFunc(data)); + + // Area. + exposed_presence_intervals.forEach((b, index) => { + exposedArea[index].x(d => xTimeRange(d.time)) + .y0(graph_height - margins.bottom) + .y1(d => yRange(d.concentration)); + + drawArea[index].attr('d', exposedArea[index](data.filter(d => { + return d.time >= b[0] && d.time <= b[1] + }))); + }); + + // Title. + plotTitleEl.attr('width', graph_width); + + // Axis. + var xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)); + var yAxis = d3.axisLeft(yRange); + + xAxisEl.attr('transform', 'translate(0,' + (graph_height - margins.bottom) + ')') + .call(xAxis); + xAxisLabelEl.attr('x', (graph_width + margins.right) / 2) + .attr('y', graph_height * 0.97); + + yAxisEl.attr('transform', 'translate(' + margins.left + ',0)').call(yAxis); + yAxisLabelEl.attr('x', (graph_height + margins.bottom) / 2) + .attr('y', (graph_height + margins.left) * 0.92) + .attr('transform', 'rotate(-90, 0,' + graph_height + ')'); + + // Legend on right side. + const size = 20; + if (plot_div.clientWidth >= 900) { + legendLineIcon.attr('x', graph_width + size) + .attr('y', margins.top + size); + legendLineText.attr('x', graph_width + 3 * size) + .attr('y', margins.top + size); + legendAreaIcon.attr('x', graph_width + size) + .attr('y', margins.top + 1.5 * size); + legendAreaText.attr('x', graph_width + 3 * size) + .attr('y', margins.top + 2 * size); + legendBBox.attr('x', graph_width * 1.005) + .attr('y', margins.top * 1.2); + } + // Legend on the bottom. + else { + legendLineIcon.attr('x', size * 0.5) + .attr('y', graph_height * 1.05); + legendLineText.attr('x', 2 * size) + .attr('y', graph_height * 1.05); + legendAreaIcon.attr('x', size * 0.50) + .attr('y', graph_height * 1.01 + size); + legendAreaText.attr('x', 2 * size) + .attr('y', graph_height + 1.7 * size); + legendBBox.attr('x', 1) + .attr('y', graph_height * 1.01); + } + + // ToolBox. + toolBox.attr('width', graph_width - margins.right) + .attr('height', graph_height); + } + + // Draw for the first time to initialize. + redraw(); + function mousemove() { var x0 = xRange.invert(d3.pointer(event, this)[0]), i = bisecHour(data, x0, 1), @@ -141,12 +239,17 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence focus.select('#tooltip-time').text('x = ' + time_format(d.hour)); focus.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2)); } + + // Redraw based on the new size whenever the browser window is resized. + window.addEventListener("resize", redraw); + + } // Generate the alternative scenarios plot using d3 library. // 'alternative_scenarios' is a dictionary with all the alternative scenarios // 'times' is a list of times for all the scenarios -function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scenarios, times) { +function draw_alternative_scenarios_plot(concentration_plot_svg_id, alternative_plot_svg_id, times, alternative_scenarios) { // H:M format var time_format = d3.timeFormat('%H:%M'); // D3 array of ten categorical colors represented as RGB hexadecimal strings. @@ -171,148 +274,169 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen // We need one scenario to get the time range var first_scenario = Object.values(data_for_scenarios)[0] - var vis = d3.select(svg_id), - width = width, - height = height, - margins = { top: 30, right: 20, bottom: 50, left: 50 }, + // Add main SVG element + var alternative_plot_div = document.getElementById(alternative_plot_svg_id); + var vis = d3.select(alternative_plot_div).append('svg'); - // H:M time format for x axis. - xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([first_scenario[0].hour, first_scenario[first_scenario.length - 1].hour]), - xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([times[0], times[times.length - 1]]), + var xRange = d3.scaleTime().domain([first_scenario[0].hour, first_scenario[first_scenario.length - 1].hour]); + var xTimeRange = d3.scaleLinear().domain([times[0], times[times.length - 1]]); - yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., highest_concentration]), - - xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), - yAxis = d3.axisLeft(yRange); - - // Plot title. - plot_title(vis, width, margins.top, 'Mean concentration of virions'); + var yRange = d3.scaleLinear().domain([0., highest_concentration]); // Line representing the mean concentration for each scenario. + var lineFuncs = {}, draw_lines = {}, label_icons = {}, label_text = {}; for (const [scenario_name, data] of Object.entries(data_for_scenarios)) { var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name) // Line representing the mean concentration. - plot_scenario_data(vis, data, xTimeRange, yRange, colors[scenario_index]) + lineFuncs[scenario_name] = d3.line(); + + draw_lines[scenario_name] = vis.append('svg:path') + .attr("stroke", colors[scenario_index]) + .attr('stroke-width', 2) + .attr('fill', 'none'); // Legend for the plot elements - lines. - var size = 20 * (scenario_index + 1) - vis.append('rect') - .attr('x', width + 20) - .attr('y', margins.top + size) + label_icons[scenario_name] = vis.append('rect') .attr('width', 20) .attr('height', 3) .style('fill', colors[scenario_index]); - vis.append('text') - .attr('x', width + 3 * 20) - .attr('y', margins.top + size) + label_text[scenario_name] = vis.append('text') .text(scenario_name) .style('font-size', '15px') .attr('alignment-baseline', 'central'); } + // Plot title. + var plotTitleEl = vis.append('svg:foreignObject') + .attr("background-color", "transparent") + .attr('height', 30) + .style('text-align', 'center') + .html('Mean concentration of virions'); + + // X axis. - plot_x_axis(vis, height, width, margins, xAxis, "Time of day"); + var xAxisEl = vis.append('svg:g') + .attr('class', 'x axis'); + + // X axis label. + var xAxisLabelEl = vis.append('text') + .attr('class', 'x label') + .attr('fill', 'black') + .attr('text-anchor', 'middle') + .text('Time of day'); // Y axis declaration. - vis.append('svg:g') - .attr('class', 'y axis') - .attr('transform', 'translate(' + margins.left + ',0)') - .call(yAxis); + var yAxisEl = vis.append('svg:g') + .attr('class', 'y axis'); // Y axis label. - vis.append('svg:text') + var yAxisLabelEl = vis.append('svg:text') .attr('class', 'y label') .attr('fill', 'black') - .attr('transform', 'rotate(-90, 0,' + height + ')') .attr('text-anchor', 'middle') - .attr('x', (height + margins.bottom) / 2) - .attr('y', (height + margins.left) * 0.92) .text('Mean concentration (virions/m³)'); // Legend bounding box. - vis.append('rect') + var legendBBox = vis.append('rect') .attr('width', 275) .attr('height', 25 * (Object.keys(data_for_scenarios).length)) - .attr('x', width * 1.005) - .attr('y', margins.top + 5) .attr('stroke', 'lightgrey') .attr('stroke-width', '2') .attr('rx', '5px') .attr('ry', '5px') .attr('stroke-linejoin', 'round') .attr('fill', 'none'); -} + function redraw() { + // Define width and height according to the screen size. + var div_width = document.getElementById(concentration_plot_svg_id).clientWidth; + var div_height = document.getElementById(concentration_plot_svg_id).clientHeight; + var graph_width = div_width; + var graph_height = div_height + if (div_width >= 900) { // For screens with width > 900px legend can be on the graph's right side. + var margins = { top: 30, right: 20, bottom: 50, left: 50 }; + div_width = 900; + graph_width = div_width * (2/3); + } + else { + var margins = { top: 30, right: 5, bottom: 50, left: 20 }; + graph_height = div_height * 0.8; // On mobile screen sizes we want the legend to be on the bottom of the graph. + }; -// Functions used to build the plots' components + // Use the extracted size to set the size of the SVG element. + vis.attr("width", div_width) + .attr('height', div_height); -function plot_title(vis, width, margin_top, title) { - vis.append('svg:foreignObject') - .attr('width', width) - .attr('height', margin_top) - .attr('fill', 'none') - .append('xhtml:div') - .style('text-align', 'center') - .html(title); + // SVG components according to the width and height. - return vis; -} + // Axis ranges. + xRange.range([margins.left, graph_width - margins.right]); + xTimeRange.range([margins.left, graph_width - margins.right]); + yRange.range([graph_height - margins.bottom, margins.top]); -function plot_x_axis(vis, height, width, margins, xAxis, label) { - // X axis declaration - vis.append('svg:g') - .attr('class', 'x axis') - .attr('transform', 'translate(0,' + (height - margins.bottom) + ')') - .call(xAxis); + for (const [scenario_name, data] of Object.entries(data_for_scenarios)) { + var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name) + // Lines. + lineFuncs[scenario_name].defined(d => !isNaN(d.concentration)) + .x(d => xTimeRange(d.time)) + .y(d => yRange(d.concentration)); + draw_lines[scenario_name].attr("d", lineFuncs[scenario_name](data)); - // X axis label. - vis.append('text') - .attr('class', 'x label') - .attr('fill', 'black') - .attr('text-anchor', 'middle') - .attr('x', (width + margins.right) / 2) - .attr('y', height * 0.97) - .text(label); + // Legend on right side. + var size = 20 * (scenario_index + 1); + if (document.getElementById(concentration_plot_svg_id).clientWidth >= 900) { + label_icons[scenario_name].attr('x', graph_width + 20) + .attr('y', margins.top + size); + label_text[scenario_name].attr('x', graph_width + 3 * 20) + .attr('y', margins.top + size); + } + // Legend on the bottom. + else { + label_icons[scenario_name].attr('x', margins.left * 0.5) + .attr('y', graph_height + size); + label_text[scenario_name].attr('x', margins.left * 1.75) + .attr('y', graph_height + size); + } - return vis; -} + } -function plot_y_axis(vis, height, width, margins, yAxis, label) { - // Y axis declaration. - vis.append('svg:g') - .attr('class', 'y axis') - .attr('transform', 'translate(' + margins.left + ',0)') - .call(yAxis); + // Title. + plotTitleEl.attr('width', graph_width); - // Y axis label. - vis.append('svg:text') - .attr('class', 'y label') - .attr('fill', 'black') - .attr('transform', 'rotate(-90, 0,' + height + ')') - .attr('text-anchor', 'middle') - .attr('x', (height + margins.bottom) / 2) - .attr('y', (height + margins.left) * 0.92) - .text(label); + // Axis. + var xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)); + var yAxis = d3.axisLeft(yRange); - return vis; + xAxisEl.attr('transform', 'translate(0,' + (graph_height - margins.bottom) + ')') + .call(xAxis); + xAxisLabelEl.attr('x', (graph_width + margins.right) / 2) + .attr('y', graph_height * 0.97) -} + yAxisEl.attr('transform', 'translate(' + margins.left + ',0)') + .call(yAxis); + yAxisLabelEl.attr('transform', 'rotate(-90, 0,' + graph_height + ')') + .attr('x', (graph_height + margins.bottom) / 2) + .attr('y', (graph_height + margins.left) * 0.92); -function plot_scenario_data(vis, data, xTimeRange, yRange, line_color) { - var lineFunc = d3.line() - .defined(d => !isNaN(d.concentration)) - .x(d => xTimeRange(d.time)) - .y(d => yRange(d.concentration)) - .curve(d3.curveBasis); + // Legend on right side. + if (document.getElementById(concentration_plot_svg_id).clientWidth >= 900) { + legendBBox.attr('x', graph_width * 1.02) + .attr('y', margins.top * 1.15); + + } + // Legend on the bottom. + else { + legendBBox.attr('x', 1) + .attr('y', graph_height * 1.01) + } + } - vis.append('svg:path') - .attr('d', lineFunc(data)) - .attr("stroke", line_color) - .attr('stroke-width', 2) - .attr('fill', 'none'); + // Draw for the first time to initialize. + redraw(); - return vis; + // Redraw based on the new size whenever the browser window is resized. + window.addEventListener("resize", redraw); } \ No newline at end of file diff --git a/cara/apps/calculator/templates/base/calculator.report.html.j2 b/cara/apps/calculator/templates/base/calculator.report.html.j2 index d06a21bc..3c9252ad 100644 --- a/cara/apps/calculator/templates/base/calculator.report.html.j2 +++ b/cara/apps/calculator/templates/base/calculator.report.html.j2 @@ -8,6 +8,7 @@ + @@ -17,14 +18,14 @@ {% block report_header %} -
- -
-

CARA - CALCULATOR REPORT

-

Created {{ creation_date }} using CARA calculator version v{{ form.calculator_version }}

+
+ +
+

CARA - CALCULATOR REPORT

+

Created {{ creation_date }} using CARA calculator version v{{ form.calculator_version }}

- - + +
{% endblock report_header %} @@ -59,8 +60,8 @@

- -
+
+
Probability of infection (%)
{% block warning_animation %} @@ -74,23 +75,26 @@ {% endblock warning_animation %}
- - {% block report_summary %} - - {% endblock report_summary %} +
+ {% block report_summary %} + + {% endblock report_summary %} +
+
{% block report_summary_footnote %} {% endblock report_summary_footnote %}

* The results are based on the parameters and assumptions published in the CERN Open Report CERN-OPEN-2021-004.

- + +

@@ -107,12 +111,13 @@
- +
+
{% block report_scenarios_summary_table %} @@ -160,13 +165,13 @@
-
-
- +
+
+

Click the QR code to regenerate the report and get a shareable link.
Alternatively, scan to regenerate the report.

- +
@@ -390,7 +395,7 @@ {% block disclaimer_container %}


-
+
{% block disclaimer %}

Disclaimer:

diff --git a/cara/apps/calculator/themes/cern/templates/calculator.report.html.j2 b/cara/apps/calculator/themes/cern/templates/calculator.report.html.j2 index 3871c6b9..e7bbe887 100644 --- a/cara/apps/calculator/themes/cern/templates/calculator.report.html.j2 +++ b/cara/apps/calculator/themes/cern/templates/calculator.report.html.j2 @@ -22,7 +22,7 @@ {% endblock warning_animation %} {% block report_summary %} -
+
{% if ((prob_inf > 15) or (expected_new_cases >= 1)) %} From 342be899b964d5e5f715f2ea77d1fb51d3dd8d78 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 18 Oct 2021 10:25:17 +0200 Subject: [PATCH 070/133] Dropdown list items update --- cara/apps/templates/layout.html.j2 | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/cara/apps/templates/layout.html.j2 b/cara/apps/templates/layout.html.j2 index 887ffdee..d05e3fb8 100644 --- a/cara/apps/templates/layout.html.j2 +++ b/cara/apps/templates/layout.html.j2 @@ -38,16 +38,17 @@
From 1355096d0ac30837d018ae724c7eefaebccdd92a Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 18 Oct 2021 14:03:59 +0200 Subject: [PATCH 071/133] Add image to CARA home page closes #204 --- cara/apps/static/css/style.css | 9 +++ cara/apps/static/images/CARA_1_Vs3_Colour.jpg | Bin 0 -> 976583 bytes cara/apps/templates/index.html.j2 | 65 ++++++++++-------- 3 files changed, 44 insertions(+), 30 deletions(-) create mode 100644 cara/apps/static/images/CARA_1_Vs3_Colour.jpg diff --git a/cara/apps/static/css/style.css b/cara/apps/static/css/style.css index 2b8bb197..0be2ab78 100644 --- a/cara/apps/static/css/style.css +++ b/cara/apps/static/css/style.css @@ -89,6 +89,12 @@ body { height: 6em; } +.cara_home_image { + height: 18rem; + padding: 1rem; + border-radius: 1.25rem!important; +} + .container { margin-inline: auto; width: min(90%, 70.5rem); @@ -195,6 +201,9 @@ footer img { .logo { float: left; } + .cara_home_image { + float: right; + } } /* Devices under 768px (md) */ /* @media 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-

Introduction


-
-

- CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions. - It does this by simulating the long-range airborne spread SARS-CoV-2 virus in a finite volume, assuming homogenous mixing, and it estimates the risk of COVID-19 infection therein. - Please see the About page for more details on the methodology, assumptions and limitations of CARA. -

-

- The full CARA source code can be accessed freely under an Apache 2.0 open source license from our code repository. - It includes detailed instructions on how to run your own version of this tool. -

+
+
+

Introduction


+
+

+ CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions. + It does this by simulating the long-range airborne spread SARS-CoV-2 virus in a finite volume, assuming homogenous mixing, and it estimates the risk of COVID-19 infection therein. + Please see the About page for more details on the methodology, assumptions and limitations of CARA. +

+

+ The full CARA source code can be accessed freely under an Apache 2.0 open source license from our code repository. + It includes detailed instructions on how to run your own version of this tool. +

+
+
- -
-

CARA @ CERN


-
-

- CARA has been developed by CERN with the intention of allowing members of personnel with roles related to supervision, health & safety or space management to simulate the concerned workplaces on CERN sites. - A hosted CERN version of the CARA Covid Calculator is available on this site to members of the CERN personnel. -

+
+
- -
-

Acknowledgements


-

- We wish to thank CERN’s HSE Unit, Beams Department, Experimental Physics Department, Information Technology Department, Industry, Procurement and Knowledge Transfer Department and International Relations Sector for their support to the study. - Thanks to Doris Forkel-Wirth, Benoit Delille, Walid Fadel, Olga Beltramello, Letizia Di Giulio, Evelyne Dho, Wayne Salter, Benoit Salvant and colleagues from the COVID working group for providing expert advice and extensively testing the model. - Finally, we wish to thank Fabienne Landua and the design service for preparing the illustrations and Alessandro Raimondo, Ana Padua and Manuela Cirilli from the Knowledge Transfer Group for their continuous support. - Our compliments towards the work and research performed by world leading scientists in this domain: Prof. Manuel Gameiro, Prof. Shelly Miller, Prof. Linsey Marr, Prof. Jose Jimenez, Dr. Lidia Morawska, Prof Yuguo Li et al. – their scientific contribution was indispensable for this project. - -

-
+

CARA @ CERN


+
+

+ CARA has been developed by CERN with the intention of allowing members of personnel with roles related to supervision, health & safety or space management to simulate the concerned workplaces on CERN sites. + A hosted CERN version of the CARA Covid Calculator is available on this site to members of the CERN personnel. +

+
+ +
+

Acknowledgements


+

+ We wish to thank CERN’s HSE Unit, Beams Department, Experimental Physics Department, Information Technology Department, Industry, Procurement and Knowledge Transfer Department and International Relations Sector for their support to the study. + Thanks to Doris Forkel-Wirth, Benoit Delille, Walid Fadel, Olga Beltramello, Letizia Di Giulio, Evelyne Dho, Wayne Salter, Benoit Salvant and colleagues from the COVID working group for providing expert advice and extensively testing the model. + Finally, we wish to thank Fabienne Landua and the design service for preparing the illustrations and Alessandro Raimondo, Ana Padua and Manuela Cirilli from the Knowledge Transfer Group for their continuous support. + Our compliments towards the work and research performed by world leading scientists in this domain: Prof. Manuel Gameiro, Prof. Shelly Miller, Prof. Linsey Marr, Prof. Jose Jimenez, Dr. Lidia Morawska, Prof Yuguo Li et al. – their scientific contribution was indispensable for this project. + +

+
{% endblock main %} From 719e274a54656ea0aa5c770231c267f816bf2b9f Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 19 Oct 2021 11:53:12 +0200 Subject: [PATCH 072/133] Updated graph's legend and y axis domain --- cara/apps/calculator/static/js/report.js | 27 ++++++++++++------------ 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/cara/apps/calculator/static/js/report.js b/cara/apps/calculator/static/js/report.js index 6266a09b..6c9177da 100644 --- a/cara/apps/calculator/static/js/report.js +++ b/cara/apps/calculator/static/js/report.js @@ -20,7 +20,7 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses bisecHour = d3.bisector((d) => { return d.hour; }).left, yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]), - yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_doses)]), + yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_doses)*1.1]), xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), yAxis = d3.axisLeft(yRange), @@ -81,23 +81,23 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses .attr('height', 3) .style('fill', '#1f77b4'); + vis.append('line') + .attr("x1", width + size + 50) + .attr("x2", width + 2 * size + 52) + .attr("y1", 3.5 * size) + .attr("y2", 3.5 * size) + .style("stroke-dasharray", "5 5") //dashed array for line + .attr('stroke-width', '2') + .style("stroke", '#1f77b4'); + vis.append('rect') .attr('x', width + size + 50) - .attr('y', 3 * size) + .attr('y', 4 * size) .attr('width', 20) .attr('height', 20) .attr('fill', '#1f77b4') .attr('fill-opacity', '0.1'); - vis.append('line') - .attr("x1", width + size + 50) - .attr("x2", width + 2 * size + 52) - .attr("y1", margins.top + 3.1 * size) - .attr("y2", margins.top + 3.1 * size) - .style("stroke-dasharray", "5 5") //dashed array for line - .attr('stroke-width', '2') - .style("stroke", '#1f77b4'); - vis.append('text') .attr('x', width + 3 * size + 50) .attr('y', margins.top + size) @@ -108,17 +108,18 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses vis.append('text') .attr('x', width + 3 * size + 50) .attr('y', margins.top + 2 * size) - .text('Presence of exposed person(s)') + .text('Mean cumulative dose') .style('font-size', '15px') .attr('alignment-baseline', 'central'); vis.append('text') .attr('x', width + 3 * size + 50) .attr('y', margins.top + 3 * size) - .text('Mean viral concentration') + .text('Presence of exposed person(s)') .style('font-size', '15px') .attr('alignment-baseline', 'central'); + // Legend bounding box. vis.append('rect') .attr('width', 270) From 2dddbd1897e43d90bd3b7636861ea99a534388dc Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 19 Oct 2021 13:49:08 +0200 Subject: [PATCH 073/133] Removed graphs' titles --- cara/apps/calculator/static/js/report.js | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/cara/apps/calculator/static/js/report.js b/cara/apps/calculator/static/js/report.js index 6c9177da..e7d3ff4e 100644 --- a/cara/apps/calculator/static/js/report.js +++ b/cara/apps/calculator/static/js/report.js @@ -26,9 +26,6 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses yAxis = d3.axisLeft(yRange), yCumulatedAxis = d3.axisRight(yCumulatedRange); - // Plot tittle. - plot_title(vis, width, margins.top, 'Mean concentration of virions'); - // Line representing the mean concentration. plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4'); // Line representing the cumulative concentration. @@ -223,9 +220,6 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), yAxis = d3.axisLeft(yRange); - // Plot title. - plot_title(vis, width, margins.top, 'Mean concentration of virions'); - // Line representing the mean concentration for each scenario. for (const [scenario_name, data] of Object.entries(data_for_scenarios)) { var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name) @@ -287,18 +281,6 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen // Functions used to build the plots' components -function plot_title(vis, width, margin_top, title) { - vis.append('svg:foreignObject') - .attr('width', width) - .attr('height', margin_top) - .attr('fill', 'none') - .append('xhtml:div') - .style('text-align', 'center') - .html(title); - - return vis; -} - function plot_x_axis(vis, height, width, margins, xAxis, label) { // X axis declaration vis.append('svg:g') From b4cdd674d507e20697c287c66c3e023237e1edc6 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 19 Oct 2021 16:23:04 +0200 Subject: [PATCH 074/133] New label, axis ticks and toolbox --- cara/apps/calculator/static/js/report.js | 148 ++++++++++++++--------- 1 file changed, 93 insertions(+), 55 deletions(-) diff --git a/cara/apps/calculator/static/js/report.js b/cara/apps/calculator/static/js/report.js index e7d3ff4e..36e08e0d 100644 --- a/cara/apps/calculator/static/js/report.js +++ b/cara/apps/calculator/static/js/report.js @@ -1,13 +1,18 @@ /* Generate the concentration plot using d3 library. */ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses, exposed_presence_intervals) { + var visBoundingBox = d3.select(svg_id) .node() .getBoundingClientRect(); var time_format = d3.timeFormat('%H:%M'); - var data = [] - times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index], 'cumulative_doses': cumulative_doses[index] })) + var data_for_graphs = { + 'concentrations': [], + 'cumulative_doses': [], + } + times.map((time, index) => data_for_graphs.concentrations.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index]})); + times.map((time, index) => data_for_graphs.cumulative_doses.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': cumulative_doses[index]})); var vis = d3.select(svg_id), width = visBoundingBox.width - 400, @@ -15,21 +20,21 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses margins = { top: 30, right: 20, bottom: 50, left: 50 }, // H:M time format for x axis. - xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([data[0].hour, data[data.length - 1].hour]), - xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([data[0].time, data[data.length - 1].time]), + xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([data_for_graphs.concentrations[0].hour, data_for_graphs.concentrations[data_for_graphs.concentrations.length - 1].hour]), + xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([data_for_graphs.concentrations[0].time, data_for_graphs.concentrations[data_for_graphs.concentrations.length - 1].time]), bisecHour = d3.bisector((d) => { return d.hour; }).left, yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]), yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_doses)*1.1]), xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), - yAxis = d3.axisLeft(yRange), - yCumulatedAxis = d3.axisRight(yCumulatedRange); + yAxis = d3.axisLeft(yRange).ticks(4), + yCumulatedAxis = d3.axisRight(yCumulatedRange).ticks(4); // Line representing the mean concentration. - plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4'); + plot_scenario_data(vis, data_for_graphs.concentrations, xTimeRange, yRange, '#1f77b4'); // Line representing the cumulative concentration. - plot_cumulative_data(vis, data, xTimeRange, yCumulatedRange, '#1f77b4'); + plot_cumulative_data(vis, data_for_graphs.cumulative_doses, xTimeRange, yCumulatedRange, '#1f77b4'); // X axis. plot_x_axis(vis, height, width, margins, xAxis, 'Time of day'); @@ -40,6 +45,7 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses // Y cumulative concentration axis declaration. vis.append('svg:g') .attr('class', 'y axis') + .style('font-size', 14) .style("stroke-dasharray", "5 5") .attr('transform', 'translate(' + (width - margins.right) + ',0)') .call(yCumulatedAxis); @@ -62,7 +68,7 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses .y1(d => yRange(d.concentration)); vis.append('svg:path') - .attr('d', curveFunc(data.filter(d => { + .attr('d', curveFunc(data_for_graphs.concentrations.filter(d => { return d.time >= b[0] && d.time <= b[1] }))) .attr('fill', '#1f77b4') @@ -98,14 +104,14 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses vis.append('text') .attr('x', width + 3 * size + 50) .attr('y', margins.top + size) - .text('Mean concentration') + .text('Viral concentration') .style('font-size', '15px') .attr('alignment-baseline', 'central'); vis.append('text') .attr('x', width + 3 * size + 50) .attr('y', margins.top + 2 * size) - .text('Mean cumulative dose') + .text('Cumulative dose') .style('font-size', '15px') .attr('alignment-baseline', 'central'); @@ -131,50 +137,80 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses .attr('fill', 'none'); // Tooltip. - var focus = vis.append('svg:g') - .style('display', 'none'); + var focus = {}, tooltip_rect = {}, tooltip_time = {}, tooltip_concentration = {}, toolBox = {}; + for (const [concentration, data] of Object.entries(data_for_graphs)) { - focus.append('circle') - .attr('r', 3); + focus[concentration] = vis.append('svg:g') + .style('display', 'none'); - focus.append('rect') - .attr('fill', 'white') - .attr('stroke', '#000') - .attr('width', 85) - .attr('height', 50) - .attr('x', 10) - .attr('y', -22) - .attr('rx', 4) - .attr('ry', 4); + focus[concentration].append('circle') + .attr('r', 3); - focus.append('text') - .attr('id', 'tooltip-time') - .attr('x', 18) - .attr('y', -2); + tooltip_rect[concentration] = focus[concentration].append('rect') + .attr('fill', 'white') + .attr('stroke', '#000') + .attr('width', 85) + .attr('height', 50) + .attr('x', 10) + .attr('y', -22) + .attr('rx', 4) + .attr('ry', 4); - focus.append('text') - .attr('id', 'tooltip-concentration') - .attr('x', 18) - .attr('y', 18); + tooltip_time[concentration] = focus[concentration].append('text') + .attr('id', 'tooltip-time') + .attr('x', 18) + .attr('y', -2); - vis.append('rect') - .attr('fill', 'none') - .attr('pointer-events', 'all') - .attr('width', width - margins.right) - .attr('height', height) - .on('mouseover', () => { focus.style('display', null); }) - .on('mouseout', () => { focus.style('display', 'none'); }) - .on('mousemove', mousemove); + tooltip_concentration[concentration] = focus[concentration].append('text') + .attr('id', 'tooltip-concentration') + .attr('x', 18) + .attr('y', 18); + + toolBox[concentration] = vis.append('rect') + .attr('fill', 'none') + .attr('pointer-events', 'all') + .attr('width', width - margins.right) + .attr('height', height) + .on('mouseover', () => { for (const [concentration, data] of Object.entries(focus)) focus[concentration].style('display', null); }) + .on('mouseout', () => { for (const [concentration, data] of Object.entries(focus)) focus[concentration].style('display', 'none'); }) + .on('mousemove', mousemove); + } function mousemove() { + for (const [scenario, data] of Object.entries(data_for_graphs)) { + if (d3.pointer(event)[0] < width / 2) { + tooltip_rect[scenario].attr('x', 10) + tooltip_time[scenario].attr('x', 18) + tooltip_concentration[scenario].attr('x', 18); + } + else { + tooltip_rect[scenario].attr('x', -90) + tooltip_time[scenario].attr('x', -82) + tooltip_concentration[scenario].attr('x', -82) + } + } + // Concentration line var x0 = xRange.invert(d3.pointer(event, this)[0]), - i = bisecHour(data, x0, 1), - d0 = data[i - 1], - d1 = data[i], - d = x0 - d0.hour > d1.hour - x0 ? d1 : d0; - focus.attr('transform', 'translate(' + xRange(d.hour) + ',' + yRange(d.concentration) + ')'); - focus.select('#tooltip-time').text('x = ' + time_format(d.hour)); - focus.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2)); + i = bisecHour(data_for_graphs.concentrations, x0, 1), + d0 = data_for_graphs.concentrations[i - 1], + d1 = data_for_graphs.concentrations[i]; + if (d1) { + var d = x0 - d0.hour > d1.hour - x0 ? d1 : d0; + focus.concentrations.attr('transform', 'translate(' + xRange(d.hour) + ',' + yRange(d.concentration) + ')'); + focus.concentrations.select('#tooltip-time').text('x = ' + time_format(d.hour)); + focus.concentrations.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2)); + } + // Cumulative line + var x0 = xRange.invert(d3.pointer(event, this)[0]), + i = bisecHour(data_for_graphs.cumulative_doses, x0, 1), + d0 = data_for_graphs.cumulative_doses[i - 1], + d1 = data_for_graphs.cumulative_doses[i]; + if (d1 && d1.concentration) { + var d = x0 - d0.hour > d1.hour - x0 ? d1 : d0; + focus.cumulative_doses.attr('transform', 'translate(' + xRange(d.hour) + ',' + yCumulatedRange(d.concentration) + ')'); + focus.cumulative_doses.select('#tooltip-time').text('x = ' + time_format(d.hour)); + focus.cumulative_doses.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2)); + } } } @@ -190,7 +226,7 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen // Variable for the highest concentration for all the scenarios var highest_concentration = 0. - var data_for_scenarios = {} + var data_for_graphs = {} for (scenario in alternative_scenarios) { scenario_concentrations = alternative_scenarios[scenario].concentrations @@ -200,11 +236,11 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': scenario_concentrations[index] })) // Add data into lines dictionary - data_for_scenarios[scenario] = data + data_for_graphs[scenario] = data } // We need one scenario to get the time range - var first_scenario = Object.values(data_for_scenarios)[0] + var first_scenario = Object.values(data_for_graphs)[0] var vis = d3.select(svg_id), width = width, @@ -221,8 +257,8 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen yAxis = d3.axisLeft(yRange); // Line representing the mean concentration for each scenario. - for (const [scenario_name, data] of Object.entries(data_for_scenarios)) { - var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name) + for (const [scenario_name, data] of Object.entries(data_for_graphs)) { + var scenario_index = Object.keys(data_for_graphs).indexOf(scenario_name) // Line representing the mean concentration. plot_scenario_data(vis, data, xTimeRange, yRange, colors[scenario_index]) @@ -267,7 +303,7 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen // Legend bounding box. vis.append('rect') .attr('width', 275) - .attr('height', 25 * (Object.keys(data_for_scenarios).length)) + .attr('height', 25 * (Object.keys(data_for_graphs).length)) .attr('x', width * 1.005) .attr('y', margins.top + 5) .attr('stroke', 'lightgrey') @@ -285,6 +321,7 @@ function plot_x_axis(vis, height, width, margins, xAxis, label) { // X axis declaration vis.append('svg:g') .attr('class', 'x axis') + .style('font-size', 14) .attr('transform', 'translate(0,' + (height - margins.bottom) + ')') .call(xAxis); @@ -304,6 +341,7 @@ function plot_y_axis(vis, height, width, margins, yAxis, label) { // Y axis declaration. vis.append('svg:g') .attr('class', 'y axis') + .style('font-size', 14) .attr('transform', 'translate(' + margins.left + ',0)') .call(yAxis); @@ -339,9 +377,9 @@ function plot_scenario_data(vis, data, xTimeRange, yRange, line_color) { function plot_cumulative_data(vis, data, xTimeRange, yCumulativeRange, line_color) { var lineCumulativeFunc = d3.line() - .defined(d => !isNaN(d.cumulative_doses)) + .defined(d => !isNaN(d.concentration)) .x(d => xTimeRange(d.time)) - .y(d => yCumulativeRange(d.cumulative_doses)) + .y(d => yCumulativeRange(d.concentration)) .curve(d3.curveBasis); vis.append('svg:path') From f9dcbfa3101f8dadc7dd92d19fa8db9d0d5155eb Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 19 Oct 2021 16:38:16 +0200 Subject: [PATCH 075/133] Changed ipympl version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 6be0d0f9..e5a28303 100644 --- a/setup.py +++ b/setup.py @@ -20,7 +20,7 @@ REQUIREMENTS: dict = { 'core': [ 'dataclasses; python_version < "3.7"', 'ipykernel', - 'ipympl != 0.8.0', + 'ipympl == 0.7.0', 'ipywidgets', 'Jinja2', 'loky', From 5e7d237e16609075557455ca1887811debdc6a3b Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 19 Oct 2021 16:45:45 +0200 Subject: [PATCH 076/133] Updated ipympl compatible version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 704f923c..e8136836 100644 --- a/setup.py +++ b/setup.py @@ -20,7 +20,7 @@ REQUIREMENTS: dict = { 'core': [ 'dataclasses; python_version < "3.7"', 'ipykernel', - 'ipympl != 0.8.0', + 'ipympl != 0.8.0, != 0.8.1', 'ipywidgets', 'Jinja2', 'loky', From e66ebfc7459a7887ddb19a5559296aaae445fd93 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 25 Oct 2021 11:39:51 +0200 Subject: [PATCH 077/133] Updated nav-items padding for mobile screens --- cara/apps/templates/layout.html.j2 | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cara/apps/templates/layout.html.j2 b/cara/apps/templates/layout.html.j2 index d05e3fb8..3ceeb6db 100644 --- a/cara/apps/templates/layout.html.j2 +++ b/cara/apps/templates/layout.html.j2 @@ -37,8 +37,8 @@
From a0f642e46c2ad4df77d810879f638ee490a983c2 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Wed, 17 Nov 2021 14:01:15 +0100 Subject: [PATCH 119/133] Changed location label and placeholder --- cara/apps/calculator/static/js/form.js | 2 +- cara/apps/calculator/templates/calculator.form.html.j2 | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cara/apps/calculator/static/js/form.js b/cara/apps/calculator/static/js/form.js index 88cf09e3..71510e8c 100644 --- a/cara/apps/calculator/static/js/form.js +++ b/cara/apps/calculator/static/js/form.js @@ -611,7 +611,7 @@ $(document).ready(function () { }, cache: true }, - placeholder: 'Search for a location', + placeholder: 'Geneva, CHE', minimumInputLength: 1, templateResult: formatlocation, templateSelection: formatLocationSelection diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index 0b871e68..9637e7bf 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -122,8 +122,8 @@

-
-
+
+
- - - + + +
From ae4c09fda5c34a4bb1f6ae6b09118e484df87210 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 22 Nov 2021 09:10:31 +0100 Subject: [PATCH 121/133] Report title changed --- cara/apps/calculator/templates/base/calculator.report.html.j2 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cara/apps/calculator/templates/base/calculator.report.html.j2 b/cara/apps/calculator/templates/base/calculator.report.html.j2 index d6d1e893..fdccca1f 100644 --- a/cara/apps/calculator/templates/base/calculator.report.html.j2 +++ b/cara/apps/calculator/templates/base/calculator.report.html.j2 @@ -21,7 +21,7 @@
-

CARA - CALCULATOR REPORT

+

REPORT

Created {{ creation_date }} using CARA calculator version v{{ form.calculator_version }}

From 38c4476bbff9b8af14092593005481598a008678 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 29 Nov 2021 11:41:29 +0100 Subject: [PATCH 122/133] Fix input placeholders --- .../calculator/templates/calculator.form.html.j2 | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index 75cbbe5b..a1ecd874 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -188,7 +188,7 @@
-
+
@@ -197,7 +197,7 @@
-
+
@@ -205,11 +205,11 @@
@@ -282,7 +282,7 @@
-
+
From 75ce9f29c53d87dfde5c953a6b47aa2958c31448 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 25 Nov 2021 17:27:16 +0100 Subject: [PATCH 123/133] Hide mask types when the option is "no mask" --- cara/apps/calculator/static/js/form.js | 14 ++++++++++++++ .../calculator/templates/calculator.form.html.j2 | 15 +++++++++------ 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/cara/apps/calculator/static/js/form.js b/cara/apps/calculator/static/js/form.js index 71510e8c..12086af1 100644 --- a/cara/apps/calculator/static/js/form.js +++ b/cara/apps/calculator/static/js/form.js @@ -236,6 +236,14 @@ function on_ventilation_type_change() { }); } +function on_wearing_mask_change() { + wearing_mask = $('input[type=radio][name=mask_wearing_option]') + wearing_mask.each(function (index) { + if (this.checked) getChildElement($(this)).show(); + else getChildElement($(this)).hide(); + }) +} + /* -------UI------- */ function show_disclaimer() { @@ -558,6 +566,12 @@ $(document).ready(function () { // Call the function now to handle forward/back button presses in the browser. on_ventilation_type_change(); + // When the mask_wearing_option changes we want to make its respective + // children show/hide. + $("input[type=radio][name=mask_wearing_option]").change(on_wearing_mask_change); + // Call the function now to handle forward/back button presses in the browser. + on_wearing_mask_change(); + // Setup the maximum number of people at page load (to handle back/forward), // and update it when total people is changed. setMaxInfectedPeople(); diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index 75cbbe5b..92cd6b5e 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -260,15 +260,18 @@
Are masks worn when occupants are at workstations?
- +
- Type of masks used: - - - -
+ +
From b32e81b052bd42ca5093fb00c945e2ff2b19f59b Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 29 Nov 2021 14:06:03 +0100 Subject: [PATCH 124/133] Moved required fields logic to js --- cara/apps/calculator/static/js/form.js | 10 ++++++++-- cara/apps/calculator/templates/calculator.form.html.j2 | 4 ++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/cara/apps/calculator/static/js/form.js b/cara/apps/calculator/static/js/form.js index 12086af1..5a20f025 100644 --- a/cara/apps/calculator/static/js/form.js +++ b/cara/apps/calculator/static/js/form.js @@ -239,8 +239,14 @@ function on_ventilation_type_change() { function on_wearing_mask_change() { wearing_mask = $('input[type=radio][name=mask_wearing_option]') wearing_mask.each(function (index) { - if (this.checked) getChildElement($(this)).show(); - else getChildElement($(this)).hide(); + if (this.checked) { + getChildElement($(this)).show(); + require_fields(this); + } + else { + getChildElement($(this)).hide(); + require_fields(this); + } }) } diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index 92cd6b5e..9b73d9fd 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -260,9 +260,9 @@
Are masks worn when occupants are at workstations?
- + - +
- +
@@ -308,7 +308,7 @@ - +
@@ -531,8 +531,8 @@
  • Library = all seated, no talking, just breathing,
  • Laboratory = light physical activity, talking 50% of the time,
  • Workshop = moderate physical activity, talking 50% of the time,
    • -
  • Training = trainer standing and talking, rest seated and talking quietly. - Trainer assumed infected (worst case scenario),
    • +
  • Conference/Training = speaker/trainer standing and talking, rest seated and talking quietly. + Speaker/Trainer assumed infected (worst case scenario),
  • Gym = heavy exercise, no talking, just breathing.
    • Activity breaks:
    diff --git a/cara/apps/calculator/templates/userguide.html.j2 b/cara/apps/calculator/templates/userguide.html.j2 index a7f99c9e..5a4cb102 100644 --- a/cara/apps/calculator/templates/userguide.html.j2 +++ b/cara/apps/calculator/templates/userguide.html.j2 @@ -157,9 +157,9 @@ Within the number of people occupying the space, you should specify how many are
  • Control Room (night shift) = All persons seated, all talking 10% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
  • Lab = Based on a typical lab or technical working area, all persons are doing light activity and talking 50% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
  • Workshop = Based on a mechanical assembly workshop or equipment installation scenario, all persons are doing moderate activity and talking 50% of the time. This activity is equally applicable to bicycling, or walking on a gradient, in the LHC tunnels. Everyone (exposed and infected occupants) is treated the same in this model.
    • -
  • Training = Based on a typical training course scenario. -One individual (the trainer) is standing and talking, with all other individuals seated and talking quietly (whispering). -In this case it is assumed that the infected person is the trainer, because this is the worst case in terms of viral shedding.
    • +
  • Conference/Training = Based on a typical conference/training course scenario. +One individual (the speaker/trainer) is standing and talking, with all other individuals seated and talking quietly (whispering). +In this case it is assumed that the infected person is the speaker/trainer, because this is the worst case in terms of viral shedding.
  • Gym = All persons are doing heavy exercise and breathing (not talking). Everyone (exposed and infected occupants) is treated the same in this model.

    • Timings

    @@ -210,7 +210,7 @@ If not, then you can input separate breaks. This is particularly different when

    The model allows for a simulation with either a continuous wearing of face masks throughout the duration of the event, or have the removed at all times - i.e. all occupants (infected and exposed alike) wear or not masks for the duration of the simulation. Please bear in mind the user inputs shall be aligned with the current applicable public health & safety instructions. Please check what are the applicable rules, before deciding which assumptions are used for the simulation.

    -

    If you have selected the Training activity type, this equates to the trainer and all participants either wearing masks throughout the training (Yes), or removing them when seated/standing at their socially distanced positions within the training room (No). +

    If you have selected the Conference/Training activity type, this equates to the speakr/trainer and all participants either wearing masks throughout the conference/training (Yes), or removing them when seated/standing at their socially distanced positions within the conference/training room (No). Please confirm what are the applicable rules, before deciding which assumptions are used for the simulation

    For the time being only the Type 1 surgical and FFP2 masks can be selected.


    From 5c7fd32b1426bb5fa845d6fd0c07725cc4c27834 Mon Sep 17 00:00:00 2001 From: Nicola Tarocco Date: Thu, 2 Dec 2021 16:38:31 +0100 Subject: [PATCH 126/133] Change URL for OpenShift test-cara after migration to OKD4 --- .gitlab-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 8e3ddd0b..332f6fe2 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -127,7 +127,7 @@ deploy_to_test: rules: - if: '$CI_COMMIT_BRANCH == "live/test-cara" && $OPENSHIFT_TEST_BUILD_WEBHOOK_SECRET' script: - - curl -X POST -k https://openshift-dev.cern.ch:443/apis/build.openshift.io/v1/namespaces/test-cara/buildconfigs/cara-router/webhooks/${OPENSHIFT_TEST_BUILD_WEBHOOK_SECRET}/generic + - curl -X POST -k https://api.paas.okd.cern.ch/apis/build.openshift.io/v1/namespaces/test-cara/buildconfigs/cara-router/webhooks/${OPENSHIFT_TEST_BUILD_WEBHOOK_SECRET}/generic oci_calculator: From a5b28998ec4d78661b1f5e3df832f4fc7512d80d Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 3 Dec 2021 11:30:46 +0100 Subject: [PATCH 127/133] Added mask icons fixes #223 --- .../calculator/templates/calculator.form.html.j2 | 6 ++++-- cara/apps/static/css/style.css | 4 ++++ cara/apps/static/images/masks/ffp2.png | Bin 0 -> 3714 bytes cara/apps/static/images/masks/t1.png | Bin 0 -> 3395 bytes 4 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 cara/apps/static/images/masks/ffp2.png create mode 100644 cara/apps/static/images/masks/t1.png diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index 57adeeff..d6d7fbd9 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -266,11 +266,13 @@
    diff --git a/cara/apps/static/css/style.css b/cara/apps/static/css/style.css index 008f833a..7e701ebc 100644 --- a/cara/apps/static/css/style.css +++ b/cara/apps/static/css/style.css @@ -170,6 +170,10 @@ body { display: block; } +.mask_icons { + height: 4em; +} + /*===== FIXED BACKGROUND IMG =====*/ .fixed-background { diff --git a/cara/apps/static/images/masks/ffp2.png b/cara/apps/static/images/masks/ffp2.png new file mode 100644 index 0000000000000000000000000000000000000000..57de5fc1422dc87f5e23cdc1bca74310525d8573 GIT binary patch literal 3714 zcmZ`+owUqiYD_Xht_8jY>#6X(R^H-CY8Lz~~YL zQD1(4zgtB)#2& zY`Vlrf_OOoKMX^#o?BADExP?c^Z?8`IT5S69>EM3D0vC*-#tT2bS5YHKks3WE2lM0# z%BjViwlh%emEFD>PHb`Ow)1WjzS&!SreJ&z)-i@hFOJR`FGEFEGMh<=d#%)r-cc|Y zzNaZkVbrM;h=+E-je>^ZgRXi;2|;&H#7h&rPc{q`JN5YMSlz~wlrWzKO2*K#Ix&T^ zcp?Aw+V6GLoWSx7Mz>nw$<$x3!wNivZMPr)Ay7{8`EA|r2`F!y%MX>af;o-4-!s3F9mzaCr_mD6Ubc)s8BzUnJl8D2Mzc@tq-{6 z)Y!W80O=OAkkN z{+vmZ`+K_M7r{qOu-?OIjxMgL%Z`Ci} z*4FOn-HYp*3QXC@sNyU8jC;-3)YfraC7UP{5weAF0*S9qD?+y+v4Y88w`{gp-KSy@ z^Jtl1@Gm{C7O|MJJyKP^rhb;P07w}VDKu}gBp8}$DI<)S!YX+)z@|auMw+9fA0762 z7BjKmxU~gYe+?B#vX?z;sks!0(CvO(iNIoYF~v1;5epyhNSXxUO8A%J0@vZZCmRck zabP`uK5zW){gE={54v%?WI2+Du!HkUc_O?3Dj|s9X1eJ(O7gF+8rERGmG1#;i z)enZ>2Al>)zI>@N{QH}e0stgIU>nF^ITIA*+im+RzaFp z<0|B$n2S#=^*jkzH=TD^cOZc~12bvzLeQWLHq#)?!fyCG?wLB}Gfqz#dq(^*W^>cG+M{9eKnv8cOU~e~_t1wSJudX@% zLnXxh$)3G^qktde;WI+=eMhDsC-0c?dD?)45L2xri|_m~@~6sY{Rw(*8j>c4Nw!o? 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    From 8e9123462874af93b558f07fbdf5a46f690d5db1 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 6 Dec 2021 14:23:35 +0100 Subject: [PATCH 129/133] COVID level scale update #225 --- cara/apps/calculator/report_generator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index a4d88bd9..59ab1302 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -308,9 +308,9 @@ class ReportGenerator: context['permalink'] = generate_permalink(base_url, self.calculator_prefix, form) context['calculator_prefix'] = self.calculator_prefix context['scale_warning'] = { - 'level': 'yellow-2', - 'incidence_rate': 'lower than 25 new cases per 100 000 inhabitants', - 'onsite_access': 'of about 8000', + 'level': 'orange-3', + 'incidence_rate': 'somewhere in between 25 and 100 new cases per 100 000 inhabitants', + 'onsite_access': 'of about 5000', 'threshold': '' } return context From 08432e2a0ea0bb4c0d5b68529faf5a9eaeb9383d Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 7 Dec 2021 17:08:58 +0100 Subject: [PATCH 130/133] Changed FFP2 label --- .../templates/calculator.form.html.j2 | 26 ++++++++++++------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/cara/apps/calculator/templates/calculator.form.html.j2 b/cara/apps/calculator/templates/calculator.form.html.j2 index ab163e1c..bf936c63 100644 --- a/cara/apps/calculator/templates/calculator.form.html.j2 +++ b/cara/apps/calculator/templates/calculator.form.html.j2 @@ -267,16 +267,22 @@
    From 79f51c641160c43c34d2b75b7f5f97a1790833ae Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 9 Dec 2021 14:26:29 +0100 Subject: [PATCH 131/133] Consideration on the time the first person (either infected or exposed) arrives at the enclosed space --- cara/apps/calculator/model_generator.py | 2 +- cara/models.py | 5 ++++- cara/tests/apps/calculator/test_model_generator.py | 6 +++--- 3 files changed, 8 insertions(+), 5 deletions(-) diff --git a/cara/apps/calculator/model_generator.py b/cara/apps/calculator/model_generator.py index 06c29229..aa161dbc 100644 --- a/cara/apps/calculator/model_generator.py +++ b/cara/apps/calculator/model_generator.py @@ -303,7 +303,7 @@ class FormData: # Initializes a ventilation instance as a window if 'natural_ventilation' is selected, or as a HEPA-filter otherwise if self.ventilation_type == 'natural_ventilation': if self.window_opening_regime == 'windows_open_periodically': - window_interval = models.PeriodicInterval(self.windows_frequency, self.windows_duration) + window_interval = models.PeriodicInterval(self.windows_frequency, self.windows_duration, min(self.infected_start, self.exposed_start)) else: window_interval = always_on diff --git a/cara/models.py b/cara/models.py index 272b6b47..012e453e 100644 --- a/cara/models.py +++ b/cara/models.py @@ -123,11 +123,14 @@ class PeriodicInterval(Interval): #: occurring, a value of 0 signifies that the event never happens. duration: float + #: Time at which the first person (infected or exposed) arrives at the enclosed space. + start: float = 0.0 + def boundaries(self) -> BoundarySequence_t: if self.period == 0 or self.duration == 0: return tuple() result = [] - for i in np.arange(0, 24, self.period / 60): + for i in np.arange(self.start, 24, self.period / 60): # NOTE: It is important that the time type is float, not np.float, in # order to allow hashability (for caching). result.append((float(i), float(i+self.duration/60))) diff --git a/cara/tests/apps/calculator/test_model_generator.py b/cara/tests/apps/calculator/test_model_generator.py index 6ee821c0..dd635946 100644 --- a/cara/tests/apps/calculator/test_model_generator.py +++ b/cara/tests/apps/calculator/test_model_generator.py @@ -48,7 +48,7 @@ def test_ventilation_slidingwindow(baseline_form: model_generator.FormData): assert isinstance(baseline_window, models.SlidingWindow) window = models.SlidingWindow( - active=models.PeriodicInterval(period=120, duration=10), + active=models.PeriodicInterval(period=120, duration=10, start=minutes_since_midnight(9 * 60)), inside_temp=models.PiecewiseConstant((0, 24), (293,)), outside_temp=baseline_window.outside_temp, window_height=1.6, opening_length=0.6, @@ -80,7 +80,7 @@ def test_ventilation_hingedwindow(baseline_form: model_generator.FormData): assert isinstance(baseline_window, models.HingedWindow) window = models.HingedWindow( - active=models.PeriodicInterval(period=120, duration=10), + active=models.PeriodicInterval(period=120, duration=10, start=minutes_since_midnight(9 * 60)), inside_temp=models.PiecewiseConstant((0, 24), (293,)), outside_temp=baseline_window.outside_temp, window_height=1.6, window_width=1., opening_length=0.6, @@ -141,7 +141,7 @@ def test_ventilation_window_hepa(baseline_form: model_generator.FormData): # Now build the equivalent ventilation instance directly, and compare. window = models.SlidingWindow( - active=models.PeriodicInterval(period=120, duration=10), + active=models.PeriodicInterval(period=120, duration=10, start=minutes_since_midnight(9 * 60)), inside_temp=models.PiecewiseConstant((0, 24), (293,)), outside_temp=baseline_window.outside_temp, window_height=1.6, opening_length=0.6, From f44817307867b282ec9e95ed90081de006fe565e Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 6 Dec 2021 11:55:58 +0100 Subject: [PATCH 132/133] Added locust test file and README --- simulation-tests/README.md | 18 ++++++++++++++++++ simulation-tests/locust.py | 10 ++++++++++ 2 files changed, 28 insertions(+) create mode 100644 simulation-tests/README.md create mode 100644 simulation-tests/locust.py diff --git a/simulation-tests/README.md b/simulation-tests/README.md new file mode 100644 index 00000000..59052dd2 --- /dev/null +++ b/simulation-tests/README.md @@ -0,0 +1,18 @@ +# locust + +A simple open source load testing tool that allows to define user behavior. + +In order to set it up for the first time, we followed the documentation at https://locust.io/. In particular, we: + +* Defined a class for the users that will be simulating. +* Defined a ``wait_time`` variable that will make the simulated users wait between the specified seconds after each task executed. +* Decorated our method with ``@Task`` that creates a micro-thread that calls this method. +* Defined the ``self.client`` attribute that makes it possible to make HTTP calls that will be logged by Locust. + +To use, open the terminal on this folder and run the following command: + +``locust -f locust.py --host https://cara.web.cern.ch`` + +Then, open up a browser and point it to http://localhost:8089. +By default we pointed out the test to our own web server. +``Start swarming`` will trigger the simulation. \ No newline at end of file diff --git a/simulation-tests/locust.py b/simulation-tests/locust.py new file mode 100644 index 00000000..c697a39b --- /dev/null +++ b/simulation-tests/locust.py @@ -0,0 +1,10 @@ +import time +from locust import HttpUser, task, between + +class WebsiteUser(HttpUser): + '''Time (in seconds) between the execution of each task.''' + wait_time = between(10, 20) + + @task + def baseline_model(self): + self.client.get(url="/calculator-open/baseline-model/result") \ No newline at end of file From 4a45e9c2631dfa3fe6fa4063ffe970d26b814f7e Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 13 Dec 2021 10:39:27 +0000 Subject: [PATCH 133/133] Renamed files and added docstrings on each method. --- .../README.md | 2 +- server-performance-tests/locust.py | 42 +++++++++++++++++++ simulation-tests/locust.py | 10 ----- 3 files changed, 43 insertions(+), 11 deletions(-) rename {simulation-tests => server-performance-tests}/README.md (86%) create mode 100644 server-performance-tests/locust.py delete mode 100644 simulation-tests/locust.py diff --git a/simulation-tests/README.md b/server-performance-tests/README.md similarity index 86% rename from simulation-tests/README.md rename to server-performance-tests/README.md index 59052dd2..7bf5e43b 100644 --- a/simulation-tests/README.md +++ b/server-performance-tests/README.md @@ -9,7 +9,7 @@ In order to set it up for the first time, we followed the documentation at https * Decorated our method with ``@Task`` that creates a micro-thread that calls this method. * Defined the ``self.client`` attribute that makes it possible to make HTTP calls that will be logged by Locust. -To use, open the terminal on this folder and run the following command: +To use, uncomment the desired method on ``lucust.py``` file, open the terminal on this folder and run the following command: ``locust -f locust.py --host https://cara.web.cern.ch`` diff --git a/server-performance-tests/locust.py b/server-performance-tests/locust.py new file mode 100644 index 00000000..1cee4ae0 --- /dev/null +++ b/server-performance-tests/locust.py @@ -0,0 +1,42 @@ +from locust import HttpUser, task, between +from gevent.pool import Group +import time + +''' +Method no. 1 - Simulation with each single user +running x requests in parallel. +Specify the desired number of parallel requests in +"num_of_parallel_requests" variable (35 by default). + +This method was used in simulations with one single +user perfoming 35 requests in parallel. +''' +# num_of_parallel_requests = 35 +# class User(HttpUser): +# wait_time = between(0.05, 0.1) + +# @task(1) +# def test_api(self): +# group = Group() +# for i in range(0, num_of_parallel_requests): +# group.spawn(lambda:self.client.get("/calculator-open/baseline-model/result")) +# group.join() +# while(1): +# time.sleep(1) + +''' +Method no. 2 - Simulation with different users +running x requests concurrently. +With this method, each user is intended to +perform one single request. + +This method was used in simulations with different +number of users requesting once at the same time. +''' +# class User(HttpUser): + +# @task(1) +# def test_api(self): +# self.client.get("/calculator-open/baseline-model/result") +# while(1): +# time.sleep(1) \ No newline at end of file diff --git a/simulation-tests/locust.py b/simulation-tests/locust.py deleted file mode 100644 index c697a39b..00000000 --- a/simulation-tests/locust.py +++ /dev/null @@ -1,10 +0,0 @@ -import time -from locust import HttpUser, task, between - -class WebsiteUser(HttpUser): - '''Time (in seconds) between the execution of each task.''' - wait_time = between(10, 20) - - @task - def baseline_model(self): - self.client.get(url="/calculator-open/baseline-model/result") \ No newline at end of file