Backend separation
- extract, isolate and package it in a completely independent Python module, versioned and in a way that allows releases on PyPI.org - fixed error in placeholder for secondary school (data registry defaults) - added restriction in pytest version to install - expected number of new cases fix - data registry update (schema v2.1.1) - github update - deprecate ExpertApplication and CO2Application - changes to reflect schema update 2.0.2 - version update - Fixed error with f_inf (short-range) - new folder layout - Conditional probability data update - General fixes - Fitting results in L/S/person - CO2 fitting algorithm refinement
This commit is contained in:
lrdossan
parent
6f77b6e725
commit
20b0467f89
181 changed files with 2094 additions and 866 deletions
1
.github/workflows/tests.yml
vendored
1
.github/workflows/tests.yml
vendored
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@ -24,6 +24,7 @@ jobs:
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env:
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PROJECT_ROOT: ./
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PROJECT_NAME: caimira
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CAIMIRA_TESTS_CALCULATOR_TIMEOUT: 30
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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@ -15,24 +15,55 @@ variables:
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# ###################################################################################################
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# Test code
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# Test code - CAiMIRA (model) and CERN CAiMIRA (CERN's UI)
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# A full installation of CAiMIRA, tested with pytest.
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# A full installation of CAiMIRA, tested with pytest.
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test_install:
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extends: .acc_py_full_test
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variables:
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project_root: ./caimira
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project_name: caimira
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# A development installation of CAiMIRA tested with pytest.
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test_dev:
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extends: .acc_py_dev_test
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variables:
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project_root: ./caimira
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project_name: caimira
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# A development installation of CAiMIRA tested with pytest.
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test_dev-39:
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variables:
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PY_VERSION: "3.9"
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project_root: ./caimira
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project_name: caimira
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extends: .acc_py_dev_test
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# A full installation of CERN CAiMIRA, tested with pytest.
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test_install_cern_caimira:
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extends: .acc_py_full_test
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variables:
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project_root: ./_cern_caimira
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project_name: cern_caimira
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# A development installation of CERN CAiMIRA tested with pytest.
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test_dev_cern_caimira:
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extends: .acc_py_dev_test
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variables:
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project_root: ./_cern_caimira
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project_name: cern_caimira
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# A development installation of CERN CAiMIRA tested with pytest.
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test_dev-39_cern_caimira:
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variables:
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PY_VERSION: "3.9"
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project_root: ./_cern_caimira
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project_name: cern_caimira
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extends: .acc_py_dev_test
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# ###################################################################################################
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# Test OpenShift config
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|
|
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409
README.md
409
README.md
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@ -103,25 +103,426 @@ pip install -e . # At the root of the repository
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### Running the Calculator app in development mode
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```
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python -m caimira.apps.calculator
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python -m ui.apps.calculator
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```
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To run with a specific template theme created:
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```
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python -m caimira.apps.calculator --theme=caimira/apps/templates/{theme}
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python -m ui.apps.calculator --theme=ui/apps/templates/{theme}
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```
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To run the entire app in a different `APPLICATION_ROOT` path:
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|
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```
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python -m caimira.apps.calculator --app_root=/myroot
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python -m ui.apps.calculator --app_root=/myroot
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```
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To run the calculator on a different URL path:
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|
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```
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python -m caimira.apps.calculator --prefix=/mycalc
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python -m ui.apps.calculator --prefix=/mycalc
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```
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Each of these commands will start a local version of CAiMIRA, which can be visited at http://localhost:8080/.
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|
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### How to compile and read the documentation
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|
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In order to generate the documentation, CAiMIRA must be installed first with the `doc` dependencies:
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|
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```
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pip install -e .[doc]
|
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```
|
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|
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To generate the HTML documentation page, the command `make html` should be executed in the `caimira/docs` directory.
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If any of the `.rst` files under the `caimira/docs` folder is changed, this command should be executed again.
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|
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Then, right click on `caimira/docs/_build/html/index.html` and select `Open with` your preferred web browser.
|
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|
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### Running the CAiMIRA Expert-App or CO2-App apps in development mode
|
||||
|
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#### Disclaimer
|
||||
|
||||
The `ExpertApplication` and `CO2Application` are no longer actively maintained but will remain in the codebase for legacy purposes.
|
||||
Please note that the functionality of these applications might be compromised due to deprecation issues.
|
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|
||||
#### Running the Applications
|
||||
|
||||
These applications only work within Jupyter notebooks. Attempting to run them outside of a Jupyter environment may result in errors or degraded functionality.
|
||||
|
||||
##### Prerequisites
|
||||
|
||||
Make sure you have the needed dependencies intalled:
|
||||
|
||||
```
|
||||
pip install notebook jupyterlab
|
||||
```
|
||||
|
||||
Running with Visual Studio Code (VSCode):
|
||||
|
||||
1. Ensure you have the following extensions installed in VSCode: `Jupyter` and `Python`.
|
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|
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2. Open VSCode and navigate to the directory containing the notebook.
|
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|
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3. Open the notebook (e.g. `caimira/apps/expert/caimira.ipynb`) and run the cells by clicking the `run` button next to each cell.
|
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|
||||
### Running the tests
|
||||
|
||||
```
|
||||
pip install -e .[test]
|
||||
pytest ./caimira
|
||||
```
|
||||
|
||||
### Running the profiler
|
||||
|
||||
The profiler is enabled when the environment variable `CAIMIRA_PROFILER_ENABLED` is set to 1.
|
||||
|
||||
When visiting http://localhost:8080/profiler, you can start a new session and choose between [PyInstrument](https://github.com/joerick/pyinstrument) or [cProfile](https://docs.python.org/3/library/profile.html#module-cProfile). The app includes two different profilers, mainly because they can give different information.
|
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|
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Keep the profiler page open. Then, in another window, navigate to any page in CAiMIRA, for example generate a new report. Refresh the profiler page, and click on the `Report` link to see the profiler output.
|
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|
||||
The sessions are stored in a local file in the `/tmp` folder. To share it across multiple web nodes, a shared storage should be added to all web nodes. The folder can be customized via the environment variable `CAIMIRA_PROFILER_CACHE_DIR`.
|
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|
||||
### Building the whole environment for local development
|
||||
|
||||
**Simulate the docker build that takes place on openshift with:**
|
||||
|
||||
```
|
||||
s2i build file://$(pwd) --copy --keep-symlinks --context-dir ./app-config/nginx/ centos/nginx-112-centos7 caimira-nginx-app
|
||||
docker build . -f ./app-config/calculator-app/Dockerfile -t calculator-app
|
||||
docker build ./app-config/auth-service -t auth-service
|
||||
```
|
||||
|
||||
Get the client secret from the CERN Application portal for the `caimira-test` app. See [CERN-SSO-integration](#cern-sso-integration) for more info.
|
||||
```
|
||||
read CLIENT_SECRET
|
||||
```
|
||||
|
||||
Define some env vars (copy/paste):
|
||||
```
|
||||
export COOKIE_SECRET=$(openssl rand -hex 50)
|
||||
export OIDC_SERVER=https://auth.cern.ch/auth
|
||||
export OIDC_REALM=CERN
|
||||
export CLIENT_ID=caimira-test
|
||||
```
|
||||
|
||||
Run docker-compose:
|
||||
```
|
||||
cd app-config
|
||||
CURRENT_UID=$(id -u):$(id -g) docker-compose up
|
||||
```
|
||||
|
||||
Then visit http://localhost:8080/.
|
||||
|
||||
### Setting up the application on openshift
|
||||
|
||||
The https://cern.ch/caimira application is running on CERN's OpenShift platform. In order to set it up for the first time, we followed the documentation at https://paas.docs.cern.ch/. In particular we:
|
||||
|
||||
* Added the OpenShift application deploy key to the GitLab repository
|
||||
* Created a Python 3.6 (the highest possible at the time of writing) application in OpenShift
|
||||
* Configured a generic webhook on OpenShift, and call that from the CI of the GitLab repository
|
||||
|
||||
### Updating the caimira-test.web.cern.ch instance
|
||||
|
||||
We have a replica of https://caimira.web.cern.ch running on http://caimira-test.web.cern.ch. Its purpose is to simulate what will happen when
|
||||
a feature is merged. To push your changes to caimira-test, simply push your branch to `live/caimira-test` and the CI pipeline will trigger the
|
||||
deployment. To push to this branch, there is a good chance that you will need to force push - you should always force push with care and
|
||||
understanding why you are doing it. Syntactically, it will look something like (assuming that you have "upstream" as your remote name,
|
||||
but it may be origin if you haven't configured it differently):
|
||||
|
||||
git push --force upstream name-of-local-branch:live/caimira-test
|
||||
|
||||
|
||||
## OpenShift templates
|
||||
|
||||
### First setup
|
||||
|
||||
First, get the [oc](https://docs.okd.io/3.11/cli_reference/get_started_cli.html) client and then login:
|
||||
|
||||
```console
|
||||
$ oc login https://api.paas.okd.cern.ch
|
||||
```
|
||||
|
||||
Then, switch to the project that you want to update:
|
||||
|
||||
```console
|
||||
$ oc project caimira-test
|
||||
```
|
||||
|
||||
Create a new service account in OpenShift to use GitLab container registry:
|
||||
|
||||
```console
|
||||
$ oc create serviceaccount gitlabci-deployer
|
||||
serviceaccount "gitlabci-deployer" created
|
||||
|
||||
$ oc policy add-role-to-user registry-editor -z gitlabci-deployer
|
||||
|
||||
# We will refer to the output of this command as `test-token`
|
||||
$ oc serviceaccounts get-token gitlabci-deployer
|
||||
<...test-token...>
|
||||
```
|
||||
|
||||
Add the token to GitLab to allow GitLab to access OpenShift and define/change image stream tags. Go to `Settings` -> `CI / CD` -> `Variables` -> click on `Expand` button and create the variable `OPENSHIFT_CAIMIRA_TEST_DEPLOY_TOKEN`: insert the token `<...test-token...>`.
|
||||
|
||||
Then, create the webhook secret to be able to trigger automatic builds from GitLab.
|
||||
|
||||
Create and store the secret. Copy the secret above and add it to the GitLab project under `CI /CD` -> `Variables` with the name `OPENSHIFT_CAIMIRA_TEST_WEBHOOK_SECRET`.
|
||||
|
||||
```console
|
||||
$ WEBHOOKSECRET=$(openssl rand -hex 50)
|
||||
$ oc create secret generic \
|
||||
--from-literal="WebHookSecretKey=$WEBHOOKSECRET" \
|
||||
gitlab-caimira-webhook-secret
|
||||
```
|
||||
|
||||
For CI usage, we also suggest creating a service account:
|
||||
|
||||
```console
|
||||
oc create sa gitlab-config-checker
|
||||
```
|
||||
|
||||
Under ``User Management`` -> ``RoleBindings`` create a new `RoleBinding` to grant `View` access to the `gitlab-config-checker` service account:
|
||||
|
||||
* name: `gitlab-config-checker-view-role`
|
||||
* role name: `view`
|
||||
* service account: `gitlab-config-checker`
|
||||
|
||||
To get this new user's authentication token go to ``User Management`` -> ``Service Accounts`` -> `gitlab-config-checker` and locate the token in the newly created secret associated with the user (in this case ``gitlab-config-checker-token-XXXX``). Copy the `token` value from `Data`.
|
||||
|
||||
Create the various configurations:
|
||||
|
||||
```console
|
||||
$ cd app-config/openshift
|
||||
|
||||
$ oc process -f configmap.yaml | oc create -f -
|
||||
$ oc process -f services.yaml | oc create -f -
|
||||
$ oc process -f imagestreams.yaml | oc create -f -
|
||||
$ oc process -f buildconfig.yaml --param GIT_BRANCH='live/caimira-test' | oc create -f -
|
||||
$ oc process -f deploymentconfig.yaml --param PROJECT_NAME='caimira-test' | oc create -f -
|
||||
```
|
||||
|
||||
Manually create the **route** to access the website, see `routes.example.yaml`.
|
||||
After having created the route, make sure that you extend the HTTP request timeout annotation: the
|
||||
report generation can take more time than the default 30 seconds.
|
||||
|
||||
```
|
||||
$ oc annotate route caimira-route --overwrite haproxy.router.openshift.io/timeout=60s
|
||||
```
|
||||
|
||||
### CERN SSO integration
|
||||
|
||||
The SSO integration uses OpenID credentials configured in [CERN Applications portal](https://application-portal.web.cern.ch/).
|
||||
How to configure the application:
|
||||
|
||||
* Application Identifier: `caimira-test`
|
||||
* Homepage: `https://caimira-test.web.cern.ch`
|
||||
* Administrators: `caimira-dev`
|
||||
* SSO Registration:
|
||||
* Protocol: `OpenID (OIDC)`
|
||||
* Redirect URI: `https://caimira-test.web.cern.ch/auth/authorize`
|
||||
* Leave unchecked all the other checkboxes
|
||||
* Define new roles:
|
||||
* Name: `CERN Users`
|
||||
* Role Identifier: `external-users`
|
||||
* Leave unchecked checkboxes
|
||||
* Minimum Level Of Assurance: `CERN (highest)`
|
||||
* Assign role to groups: `cern-accounts-primary` e-group
|
||||
* Name: `External accounts`
|
||||
* Role Identifier: `admin`
|
||||
* Leave unchecked checkboxes
|
||||
* Minimum Level Of Assurance: `Any (no restrictions)`
|
||||
* Assign role to groups: `caimira-app-external-access` e-group
|
||||
* Name: `Allowed users`
|
||||
* Role Identifier: `allowed-users`
|
||||
* Check `This role is required to access my application`
|
||||
* Minimum Level Of Assurance:`Any (no restrictions)`
|
||||
* Assign role to groups: `cern-accounts-primary` and `caimira-app-external-access` e-groups
|
||||
|
||||
Copy the client id and client secret and use it below.
|
||||
|
||||
```console
|
||||
$ COOKIE_SECRET=$(openssl rand -hex 50)
|
||||
$ oc create secret generic \
|
||||
--from-literal="CLIENT_ID=$CLIENT_ID" \
|
||||
--from-literal="CLIENT_SECRET=$CLIENT_SECRET" \
|
||||
--from-literal="COOKIE_SECRET=$COOKIE_SECRET" \
|
||||
auth-service-secrets
|
||||
```
|
||||
|
||||
### External APIs
|
||||
|
||||
- **Geographical location:**
|
||||
There is one external API call to fetch required information related to the geographical location inserted by a user.
|
||||
The documentation for this geocoding service is available at https://developers.arcgis.com/rest/geocode/api-reference/geocoding-suggest.htm .
|
||||
Please note that there is no need for keys on this API call. It is **free-of-charge**.
|
||||
|
||||
- **Humidity and Inside Temperature:**
|
||||
There is the possibility of using one external API call to fetch information related to a location specified in the UI. The data is related to the inside temperature and humidity taken from an indoor measurement device. Note that the API currently used from ARVE is only available for the `CERN theme` as the authorised sensors are installed at CERN."
|
||||
|
||||
- **ARVE:**
|
||||
|
||||
The ARVE Swiss Air Quality System provides trusted air data for commercial buildings in real-time and analyzes it with the help of AI and machine learning algorithms to create actionable insights.
|
||||
|
||||
Create secret:
|
||||
|
||||
```console
|
||||
$ read ARVE_CLIENT_ID
|
||||
$ read ARVE_CLIENT_SECRET
|
||||
$ read ARVE_API_KEY
|
||||
$ oc create secret generic \
|
||||
--from-literal="ARVE_CLIENT_ID=$ARVE_CLIENT_ID" \
|
||||
--from-literal="ARVE_CLIENT_SECRET=$ARVE_CLIENT_SECRET" \
|
||||
--from-literal="ARVE_API_KEY=$ARVE_API_KEY" \
|
||||
arve-api
|
||||
```
|
||||
|
||||
- **CERN Data Service:**
|
||||
|
||||
The CERN data service collects data from various sources and expose them via a REST API endpoint.
|
||||
|
||||
The service is enabled when the environment variable `DATA_SERVICE_ENABLED` is set to 1.
|
||||
|
||||
## Update configuration
|
||||
|
||||
If you need to **update** existing configuration, then modify this repository and after having logged in, run:
|
||||
|
||||
```console
|
||||
$ cd app-config/openshift
|
||||
|
||||
|
||||
$ oc process -f configmap.yaml | oc replace -f -
|
||||
$ oc process -f services.yaml | oc replace -f -
|
||||
$ oc process -f imagestreams.yaml | oc replace -f -
|
||||
$ oc process -f buildconfig.yaml --param GIT_BRANCH='live/caimira-test' | oc replace -f -
|
||||
$ oc process -f deploymentconfig.yaml --param PROJECT_NAME='caimira-test' | oc replace -f -
|
||||
```
|
||||
|
||||
Be aware that if you create/recreate the environment you must manually create a **route** in OpenShift,
|
||||
specifying the respective annotation to be exposed outside CERN.
|
||||
# CAiMIRA - CERN Airborne Model for Risk Assessment
|
||||
|
||||
CAiMIRA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions.
|
||||
|
||||
CAiMIRA models the concentration profile of potential virions in enclosed spaces , both as background (room) concentration and during close-proximity interations, 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.
|
||||
|
||||
The risk assessment tool simulates the airborne spread SARS-CoV-2 virus in a finite volume, assuming a homogenous mixture and a two-stage exhaled jet model, and estimates the risk of COVID-19 infection therein.
|
||||
The results DO NOT include the other known modes of SARS-CoV-2 transmission, such as fomite or blood-bound.
|
||||
Hence, the output from this model is only valid when the other recommended public health & safety instructions are observed, such as good hand hygiene and other barrier measures.
|
||||
|
||||
The model used is based on scientific publications relating to airborne transmission of infectious diseases, dose-response exposures and aerosol science, as of February 2022.
|
||||
It can be used to compare the effectiveness of different airborne-related risk mitigation measures.
|
||||
|
||||
Note that this model applies a deterministic approach, i.e., it is assumed at least one person is infected and shedding viruses into the simulated volume.
|
||||
Nonetheless, it is also important to understand that the absolute risk of infection is uncertain, as it will depend on the probability that someone infected attends the event.
|
||||
The model is most useful for comparing the impact and effectiveness of different mitigation measures such as ventilation, filtration, exposure time, physical activity, amount and nature of close-range interactions and
|
||||
the size of the room, considering both long- and short-range airborne transmission modes of COVID-19 in indoor settings.
|
||||
|
||||
This tool is designed to be informative, allowing the user to adapt different settings and model the relative impact on the estimated infection probabilities.
|
||||
The objective is to facilitate targeted decision-making and investment through comparisons, rather than a singular determination of absolute risk.
|
||||
While the SARS-CoV-2 virus is in circulation among the population, the notion of 'zero risk' or 'completely safe scenario' does not exist.
|
||||
Each event modelled is unique, and the results generated therein are only as accurate as the inputs and assumptions.
|
||||
|
||||
## Authors
|
||||
CAiMIRA was developed by following members of CERN - European Council for Nuclear Research (visit https://home.cern/):
|
||||
|
||||
Andre Henriques<sup>1</sup>, Luis Aleixo<sup>1</sup>, Marco Andreini<sup>1</sup>, Gabriella Azzopardi<sup>2</sup>, James Devine<sup>3</sup>, Philip Elson<sup>4</sup>, Nicolas Mounet<sup>2</sup>, Markus Kongstein Rognlien<sup>2,6</sup>, Nicola Tarocco<sup>5</sup>
|
||||
|
||||
<sup>1</sup>HSE Unit, Occupational Health & Safety Group, CERN<br>
|
||||
<sup>2</sup>Beams Department, Accelerators and Beam Physics Group, CERN<br>
|
||||
<sup>3</sup>Experimental Physics Department, Safety Office, CERN<br>
|
||||
<sup>4</sup>Beams Department, Controls Group, CERN<br>
|
||||
<sup>5</sup>Information Technology Department, Collaboration, Devices & Applications Group, CERN<br>
|
||||
<sup>6</sup>Norwegian University of Science and Technology (NTNU)<br>
|
||||
|
||||
### Reference and Citation
|
||||
|
||||
**For the use of the CAiMIRA web app**
|
||||
|
||||
CAiMIRA – CERN Airborne Model for Indoor Risk Assessment tool
|
||||
|
||||
[](https://doi.org/10.5281/zenodo.6520431)
|
||||
|
||||
© Copyright 2020-2021 CERN. All rights not expressly granted are reserved.
|
||||
|
||||
**For use of the CAiMIRA model**
|
||||
|
||||
Henriques A, Mounet N, Aleixo L, Elson P, Devine J, Azzopardi G, Andreini M, Rognlien M, Tarocco N, Tang J. (2022). Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces. _Interface Focus 20210076_. https://doi.org/10.1098/rsfs.2021.0076
|
||||
|
||||
Reference on the Short-range expiratory jet model from:
|
||||
Jia W, Wei J, Cheng P, Wang Q, Li Y. (2022). Exposure and respiratory infection risk via the short-range airborne route. _Building and Environment_ *219*: 109166.
|
||||
https://doi.org/10.1016/j.buildenv.2022.109166
|
||||
|
||||
***Open Source Acknowledgments***
|
||||
|
||||
For a detailed list of the open-source dependencies used in this project along with their respective licenses, please refer to [License Information](open-source-licences/README.md). This includes both the core dependencies specified in the project's requirements and their transitive dependencies.
|
||||
|
||||
The information also features a distribution diagram of licenses and a brief description of each of them.
|
||||
|
||||
## Applications
|
||||
|
||||
### Calculator
|
||||
|
||||
A risk assessment tool which simulates the airborne spread of the SARS-CoV-2 virus for space managers.
|
||||
|
||||
|
||||
### CAiMIRA Expert App and CO₂ App
|
||||
|
||||
A tool to interact with various parameters of the CAiMIRA model.
|
||||
|
||||
|
||||
## Disclaimer
|
||||
|
||||
CAiMIRA 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.
|
||||
|
||||
The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and non-infringement.
|
||||
In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.
|
||||
|
||||
|
||||
## Running CAiMIRA locally
|
||||
|
||||
The easiest way to run a version of CAiMIRA Calculator is to use docker. A pre-built
|
||||
image of CAiMIRA is made available at https://gitlab.cern.ch/caimira/caimira/container_registry.
|
||||
In order to run CAiMIRA locally with docker, run the following:
|
||||
|
||||
$ docker run -it -p 8080:8080 gitlab-registry.cern.ch/caimira/caimira/calculator
|
||||
|
||||
This will start a local version of CAiMIRA, which can be visited at http://localhost:8080/.
|
||||
|
||||
|
||||
## Development guide
|
||||
|
||||
CAiMIRA is also mirrored to Github if you wish to collaborate on development and can be found at: https://github.com/CERN/caimira
|
||||
|
||||
### Installing CAiMIRA in editable mode
|
||||
|
||||
```
|
||||
pip install -e . # At the root of the repository
|
||||
```
|
||||
|
||||
### Running the Calculator app in development mode
|
||||
|
||||
```
|
||||
python -m cern_caimira.apps.calculator
|
||||
```
|
||||
|
||||
To run with a specific template theme created:
|
||||
|
||||
```
|
||||
python -m cern_caimira.apps.calculator --theme=cern_caimira/apps/templates/{theme}
|
||||
```
|
||||
|
||||
To run the entire app in a different `APPLICATION_ROOT` path:
|
||||
|
||||
```
|
||||
python -m cern_caimira.apps.calculator --app_root=/myroot
|
||||
```
|
||||
|
||||
To run the calculator on a different URL path:
|
||||
|
||||
```
|
||||
python -m cern_caimira.apps.calculator --prefix=/mycalc
|
||||
```
|
||||
|
||||
Each of these commands will start a local version of CAiMIRA, which can be visited at http://localhost:8080/.
|
||||
|
|
|
|||
|
|
@ -7,4 +7,4 @@ nginx -c /opt/caimira/nginx.conf
|
|||
|
||||
cd /opt/caimira/src/caimira
|
||||
# Run the calculator in the foreground.
|
||||
/opt/caimira/app/bin/python -m caimira.apps.calculator --port 8081 --no-debug
|
||||
/opt/caimira/app/bin/python -m ui.apps.calculator --port 8081 --no-debug
|
||||
|
|
|
|||
|
|
@ -26,8 +26,8 @@ if [[ "$APP_NAME" == "calculator-app" ]]; then
|
|||
export "DATA_SERVICE_ENABLED"="${DATA_SERVICE_ENABLED:=0}"
|
||||
export "CAIMIRA_PROFILER_ENABLED"="${CAIMIRA_PROFILER_ENABLED:=0}"
|
||||
|
||||
echo "Starting the caimira webservice with: python -m caimira.apps.calculator ${args[@]}"
|
||||
python -m caimira.apps.calculator "${args[@]}"
|
||||
echo "Starting the caimira webservice with: python -m ui.apps.calculator ${args[@]}"
|
||||
python -m ui.apps.calculator "${args[@]}"
|
||||
|
||||
else
|
||||
echo "No APP_NAME specified"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
version: "3.8"
|
||||
services:
|
||||
|
||||
calculator-app:
|
||||
image: calculator-app
|
||||
environment:
|
||||
|
|
@ -8,7 +7,7 @@ services:
|
|||
- APP_NAME=calculator-app
|
||||
- APPLICATION_ROOT=/
|
||||
- CAIMIRA_CALCULATOR_PREFIX=/calculator-cern
|
||||
- CAIMIRA_THEME=caimira/apps/templates/cern
|
||||
- CAIMIRA_THEME=ui/apps/templates/cern
|
||||
- DATA_SERVICE_ENABLED=0
|
||||
- CAIMIRA_PROFILER_ENABLED=0
|
||||
user: ${CURRENT_UID}
|
||||
|
|
|
|||
|
|
@ -303,3 +303,4 @@
|
|||
- name: PROJECT_NAME
|
||||
description: The name of this project, e.g. caimira-test
|
||||
required: true
|
||||
|
||||
|
|
@ -14,4 +14,4 @@ sphinx:
|
|||
|
||||
python:
|
||||
install:
|
||||
- requirements: caimira/docs/requirements.txt
|
||||
- requirements: caimira/docs/requirements.txt
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
from .expert import ExpertApplication
|
||||
from .expert_co2 import CO2Application
|
||||
|
||||
__all__ = ['ExpertApplication', 'CO2Application']
|
||||
110
caimira/pyproject.toml
Normal file
110
caimira/pyproject.toml
Normal file
|
|
@ -0,0 +1,110 @@
|
|||
[build-system]
|
||||
requires = ["setuptools", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "caimira"
|
||||
version = "2.0.0"
|
||||
description = "CAiMIRA - CERN Airborne Model for Indoor Risk Assessment"
|
||||
readme = "README.md"
|
||||
license = { text = "Apache-2.0" }
|
||||
authors = [
|
||||
{ name = "Andre Henriques", email = "andre.henriques@cern.ch" }
|
||||
]
|
||||
classifiers = [
|
||||
"Programming Language :: Python :: 3",
|
||||
"Operating System :: OS Independent",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
]
|
||||
requires-python = ">=3.9"
|
||||
dependencies = [
|
||||
"ipykernel",
|
||||
"ipympl>=0.9.0",
|
||||
"ipywidgets<8.0",
|
||||
"Jinja2",
|
||||
"loky",
|
||||
"matplotlib",
|
||||
"memoization",
|
||||
"mistune",
|
||||
"numpy",
|
||||
"pandas",
|
||||
"psutil",
|
||||
"pyinstrument",
|
||||
"pyjwt",
|
||||
"python-dateutil",
|
||||
"retry",
|
||||
"ruptures",
|
||||
"scipy",
|
||||
"scikit-learn",
|
||||
"timezonefinder",
|
||||
"tornado",
|
||||
"types-retry",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = []
|
||||
test = [
|
||||
"pytest",
|
||||
"pytest-mypy >= 0.10.3",
|
||||
"mypy >= 1.0.0",
|
||||
"pytest-tornasync",
|
||||
"numpy-stubs @ git+https://github.com/numpy/numpy-stubs.git",
|
||||
"types-dataclasses",
|
||||
"types-python-dateutil",
|
||||
"types-requests"
|
||||
]
|
||||
doc = [
|
||||
"sphinx",
|
||||
"sphinx_rtd_theme"
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://github.com/cern/caimira"
|
||||
|
||||
[tool.setuptools]
|
||||
packages = ["caimira"]
|
||||
package-dir = {"" = "src"}
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
addopts = "--mypy"
|
||||
|
||||
[tool.mypy]
|
||||
no_warn_no_return = true
|
||||
exclude = "caimira/profiler.py"
|
||||
ignore_missing_imports = true # TODO what to do here?
|
||||
|
||||
[tool.mypy-loky]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-ipympl]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-ipywidgets]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-matplotlib]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-mistune]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-qrcode]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-scipy]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-timezonefinder]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-pandas]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-pstats]
|
||||
follow_imports = "skip"
|
||||
|
||||
[tool.mypy-tabulate]
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.mypy-ruptures]
|
||||
ignore_missing_imports = true
|
||||
2
caimira/setup.cfg
Normal file
2
caimira/setup.cfg
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
[tool:pytest]
|
||||
addopts = --mypy
|
||||
3
caimira/src/caimira/__init__.py
Normal file
3
caimira/src/caimira/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
import importlib.metadata
|
||||
|
||||
__version__ = importlib.metadata.version(__package__ or __name__)
|
||||
0
caimira/src/caimira/api/__init__.py
Normal file
0
caimira/src/caimira/api/__init__.py
Normal file
31
caimira/src/caimira/api/app.py
Normal file
31
caimira/src/caimira/api/app.py
Normal file
|
|
@ -0,0 +1,31 @@
|
|||
# """
|
||||
# Entry point for the CAiMIRA application
|
||||
# """
|
||||
|
||||
import tornado.ioloop
|
||||
import tornado.web
|
||||
import tornado.log
|
||||
from tornado.options import define, options
|
||||
import logging
|
||||
|
||||
from caimira.api.routes.report_routes import ReportHandler
|
||||
|
||||
define("port", default=8088, help="Port to listen on", type=int)
|
||||
|
||||
logging.basicConfig(format="%(message)s", level=logging.INFO)
|
||||
|
||||
class Application(tornado.web.Application):
|
||||
def __init__(self):
|
||||
handlers = [
|
||||
(r"/report", ReportHandler),
|
||||
]
|
||||
settings = dict(
|
||||
debug=True,
|
||||
)
|
||||
super(Application, self).__init__(handlers, **settings)
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = Application()
|
||||
app.listen(options.port)
|
||||
logging.info(f"Tornado server is running on port {options.port}")
|
||||
tornado.ioloop.IOLoop.current().start()
|
||||
0
caimira/src/caimira/api/controller/__init__.py
Normal file
0
caimira/src/caimira/api/controller/__init__.py
Normal file
30
caimira/src/caimira/api/controller/report_controller.py
Normal file
30
caimira/src/caimira/api/controller/report_controller.py
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
import concurrent.futures
|
||||
import functools
|
||||
|
||||
from caimira.calculator.validators.virus.virus_validator import VirusFormData
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
import caimira.calculator.report.report_generator as rg
|
||||
|
||||
|
||||
def generate_form_obj(form_data, data_registry):
|
||||
return VirusFormData.from_dict(form_data, data_registry)
|
||||
|
||||
|
||||
def generate_model(form_obj):
|
||||
return form_obj.build_model(250_000)
|
||||
|
||||
|
||||
def generate_report_results(form_obj, model):
|
||||
return rg.calculate_report_data(form=form_obj, model=model, executor_factory=functools.partial(
|
||||
concurrent.futures.ThreadPoolExecutor, None, # TODO define report_parallelism
|
||||
),)
|
||||
|
||||
|
||||
def submit_virus_form(form_data):
|
||||
data_registry = DataRegistry
|
||||
|
||||
form_obj = generate_form_obj(form_data, data_registry)
|
||||
model = generate_model(form_obj)
|
||||
report_data = generate_report_results(form_obj, model=model)
|
||||
|
||||
return report_data
|
||||
0
caimira/src/caimira/api/routes/__init__.py
Normal file
0
caimira/src/caimira/api/routes/__init__.py
Normal file
28
caimira/src/caimira/api/routes/report_routes.py
Normal file
28
caimira/src/caimira/api/routes/report_routes.py
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
import json
|
||||
import traceback
|
||||
import tornado.web
|
||||
|
||||
from caimira.api.controller.report_controller import submit_virus_form
|
||||
|
||||
class ReportHandler(tornado.web.RequestHandler):
|
||||
def set_default_headers(self):
|
||||
self.set_header("Access-Control-Allow-Origin", "*")
|
||||
self.set_header("Access-Control-Allow-Headers", "x-requested-with")
|
||||
self.set_header("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
|
||||
|
||||
def post(self):
|
||||
try:
|
||||
form_data = json.loads(self.request.body)
|
||||
report_data = submit_virus_form(form_data)
|
||||
|
||||
response_data = {
|
||||
"status": "success",
|
||||
"message": "Results generated successfully",
|
||||
"report_data": report_data,
|
||||
}
|
||||
|
||||
self.write(response_data)
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
self.set_status(400)
|
||||
self.write({"message": str(e)})
|
||||
0
caimira/src/caimira/calculator/__init__.py
Normal file
0
caimira/src/caimira/calculator/__init__.py
Normal file
|
Before Width: | Height: | Size: 805 KiB After Width: | Height: | Size: 805 KiB |
|
|
@ -3,4 +3,4 @@ sphinx-rtd-theme==1.2.2
|
|||
pillow==5.4.1
|
||||
mock==1.0.1
|
||||
commonmark==0.9.1
|
||||
recommonmark==0.5.0
|
||||
recommonmark==0.5.0
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
import numpy as np
|
||||
from caimira import models
|
||||
from caimira.data.weather import wx_data, nearest_wx_station
|
||||
from caimira.calculator.models import models
|
||||
from .weather import wx_data, nearest_wx_station
|
||||
|
||||
MONTH_NAMES = [
|
||||
'January', 'February', 'March', 'April', 'May', 'June', 'July',
|
||||
|
|
@ -40,7 +40,7 @@ from scipy.interpolate import interp1d
|
|||
import scipy.stats as sct
|
||||
from scipy.optimize import minimize
|
||||
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
if not typing.TYPE_CHECKING:
|
||||
from memoization import cached
|
||||
|
|
@ -7,11 +7,11 @@ import numpy as np
|
|||
from scipy import special as sp
|
||||
from scipy.stats import weibull_min
|
||||
|
||||
from caimira.enums import ViralLoads
|
||||
from ..enums import ViralLoads
|
||||
|
||||
import caimira.monte_carlo.models as mc
|
||||
from caimira.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCustomKernel, CustomKernel, Uniform, Custom
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
import caimira.calculator.models.monte_carlo.models as mc
|
||||
from caimira.calculator.models.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCustomKernel, CustomKernel, Uniform, Custom
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
|
||||
def evaluate_vl(root: typing.Dict, value: str, data_registry: DataRegistry):
|
||||
|
|
@ -3,7 +3,7 @@ import dataclasses
|
|||
import sys
|
||||
import typing
|
||||
|
||||
import caimira.models
|
||||
from caimira.calculator.models import models
|
||||
|
||||
from .sampleable import SampleableDistribution, _VectorisedFloatOrSampleable
|
||||
|
||||
|
|
@ -57,7 +57,7 @@ def _build_mc_model(model: dataclass_instance) -> typing.Type[MCModelBase[_Model
|
|||
# Note: deepcopy not needed here as we aren't mutating entities beyond
|
||||
# the top level.
|
||||
new_field = copy.copy(field)
|
||||
if field.type is caimira.models._VectorisedFloat: # noqa
|
||||
if field.type is models._VectorisedFloat: # noqa
|
||||
new_field.type = _VectorisedFloatOrSampleable # type: ignore
|
||||
|
||||
field_type: typing.Any = new_field.type
|
||||
|
|
@ -65,30 +65,30 @@ def _build_mc_model(model: dataclass_instance) -> typing.Type[MCModelBase[_Model
|
|||
if getattr(field_type, '__origin__', None) in [typing.Union, typing.Tuple]:
|
||||
# It is challenging to generalise this code, so we provide specific transformations,
|
||||
# and raise for unforseen cases.
|
||||
if new_field.type == typing.Tuple[caimira.models._VentilationBase, ...]:
|
||||
if new_field.type == typing.Tuple[models._VentilationBase, ...]:
|
||||
VB = getattr(sys.modules[__name__], "_VentilationBase")
|
||||
field_type = typing.Tuple[typing.Union[caimira.models._VentilationBase, VB], ...]
|
||||
elif new_field.type == typing.Tuple[caimira.models._ExpirationBase, ...]:
|
||||
field_type = typing.Tuple[typing.Union[models._VentilationBase, VB], ...]
|
||||
elif new_field.type == typing.Tuple[models._ExpirationBase, ...]:
|
||||
EB = getattr(sys.modules[__name__], "_ExpirationBase")
|
||||
field_type = typing.Tuple[typing.Union[caimira.models._ExpirationBase, EB], ...]
|
||||
elif new_field.type == typing.Tuple[caimira.models.SpecificInterval, ...]:
|
||||
field_type = typing.Tuple[typing.Union[models._ExpirationBase, EB], ...]
|
||||
elif new_field.type == typing.Tuple[models.SpecificInterval, ...]:
|
||||
SI = getattr(sys.modules[__name__], "SpecificInterval")
|
||||
field_type = typing.Tuple[typing.Union[caimira.models.SpecificInterval, SI], ...]
|
||||
field_type = typing.Tuple[typing.Union[models.SpecificInterval, SI], ...]
|
||||
|
||||
elif new_field.type == typing.Union[int, caimira.models.IntPiecewiseConstant]:
|
||||
elif new_field.type == typing.Union[int, models.IntPiecewiseConstant]:
|
||||
IPC = getattr(sys.modules[__name__], "IntPiecewiseConstant")
|
||||
field_type = typing.Union[int, caimira.models.IntPiecewiseConstant, IPC]
|
||||
elif new_field.type == typing.Union[caimira.models.Interval, None]:
|
||||
field_type = typing.Union[int, models.IntPiecewiseConstant, IPC]
|
||||
elif new_field.type == typing.Union[models.Interval, None]:
|
||||
I = getattr(sys.modules[__name__], "Interval")
|
||||
field_type = typing.Union[None, caimira.models.Interval, I]
|
||||
field_type = typing.Union[None, models.Interval, I]
|
||||
|
||||
else:
|
||||
# Check that we don't need to do anything with this type.
|
||||
for item in new_field.type.__args__:
|
||||
if getattr(item, '__module__', None) == 'caimira.models':
|
||||
if getattr(item, '__module__', None) == 'source.models.models':
|
||||
raise ValueError(
|
||||
f"unsupported type annotation transformation required for {new_field.type}")
|
||||
elif field_type.__module__ == 'caimira.models':
|
||||
elif field_type.__module__ == 'source.models.models':
|
||||
mc_model = getattr(sys.modules[__name__], new_field.type.__name__)
|
||||
field_type = typing.Union[new_field.type, mc_model]
|
||||
|
||||
|
|
@ -119,7 +119,7 @@ def _build_mc_model(model: dataclass_instance) -> typing.Type[MCModelBase[_Model
|
|||
|
||||
|
||||
_MODEL_CLASSES = [
|
||||
cls for cls in vars(caimira.models).values()
|
||||
cls for cls in vars(models).values()
|
||||
if dataclasses.is_dataclass(cls)
|
||||
]
|
||||
|
||||
|
|
@ -3,7 +3,7 @@ import typing
|
|||
import numpy as np
|
||||
from sklearn.neighbors import KernelDensity # type: ignore
|
||||
|
||||
import caimira.models
|
||||
from caimira.calculator.models import models
|
||||
|
||||
# Declare a float array type of a given size.
|
||||
# There is no better way to declare this currently, unfortunately.
|
||||
|
|
@ -158,5 +158,5 @@ class LogCustomKernel(SampleableDistribution):
|
|||
|
||||
|
||||
_VectorisedFloatOrSampleable = typing.Union[
|
||||
SampleableDistribution, caimira.models._VectorisedFloat,
|
||||
SampleableDistribution, models._VectorisedFloat,
|
||||
]
|
||||
0
caimira/src/caimira/calculator/report/__init__.py
Normal file
0
caimira/src/caimira/calculator/report/__init__.py
Normal file
|
|
@ -0,0 +1 @@
|
|||
# Move here the backend logic.
|
||||
|
|
@ -1,26 +1,17 @@
|
|||
import concurrent.futures
|
||||
import base64
|
||||
import dataclasses
|
||||
from datetime import datetime
|
||||
import io
|
||||
import json
|
||||
import typing
|
||||
import urllib
|
||||
import zlib
|
||||
|
||||
import jinja2
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from caimira import models
|
||||
from caimira.apps.calculator import markdown_tools
|
||||
from caimira.profiler import profile
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from ... import monte_carlo as mc
|
||||
from .model_generator import VirusFormData
|
||||
from ... import dataclass_utils
|
||||
from caimira.enums import ViralLoads
|
||||
|
||||
from caimira.calculator.models import models
|
||||
# from caimira.apps.calculator import markdown_tools
|
||||
# from caimira.profiler import profile
|
||||
from caimira.calculator.validators.virus.virus_validator import VirusFormData
|
||||
from caimira.calculator.models import dataclass_utils
|
||||
from caimira.calculator.models.enums import ViralLoads
|
||||
|
||||
def model_start_end(model: models.ExposureModel):
|
||||
t_start = min(model.exposed.presence_interval().boundaries()[0][0],
|
||||
|
|
@ -82,7 +73,6 @@ def non_temp_transition_times(model: models.ExposureModel):
|
|||
# such as PeriodicIntervals, which extend beyond the model itself).
|
||||
return sorted(time for time in change_times if (t_start <= time <= t_end))
|
||||
|
||||
|
||||
def interesting_times(model: models.ExposureModel, approx_n_pts: typing.Optional[int] = None) -> typing.List[float]:
|
||||
"""
|
||||
Pick approximately ``approx_n_pts`` time points which are interesting for the
|
||||
|
|
@ -104,6 +94,7 @@ def interesting_times(model: models.ExposureModel, approx_n_pts: typing.Optional
|
|||
return nice_times
|
||||
|
||||
|
||||
|
||||
def concentrations_with_sr_breathing(form: VirusFormData, model: models.ExposureModel, times: typing.List[float], short_range_intervals: typing.List) -> typing.List[float]:
|
||||
lower_concentrations = []
|
||||
for time in times:
|
||||
|
|
@ -115,16 +106,20 @@ def concentrations_with_sr_breathing(form: VirusFormData, model: models.Exposure
|
|||
lower_concentrations.append(np.array(model.concentration_model.concentration(float(time))).mean())
|
||||
return lower_concentrations
|
||||
|
||||
|
||||
def _calculate_deposited_exposure(model, time1, time2, fn_name=None):
|
||||
return np.array(model.deposited_exposure_between_bounds(float(time1), float(time2))).mean(),fn_name
|
||||
|
||||
|
||||
def _calculate_long_range_deposited_exposure(model, time1, time2, fn_name=None):
|
||||
return np.array(model.long_range_deposited_exposure_between_bounds(float(time1), float(time2))).mean(), fn_name
|
||||
|
||||
|
||||
def _calculate_co2_concentration(CO2_model, time, fn_name=None):
|
||||
return np.array(CO2_model.concentration(float(time))).mean(), fn_name
|
||||
|
||||
@profile
|
||||
|
||||
# @profile
|
||||
def calculate_report_data(form: VirusFormData, model: models.ExposureModel, executor_factory: typing.Callable[[], concurrent.futures.Executor]) -> typing.Dict[str, typing.Any]:
|
||||
times = interesting_times(model)
|
||||
short_range_intervals = [interaction.presence.boundaries()[0] for interaction in model.short_range]
|
||||
|
|
@ -203,25 +198,6 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec
|
|||
}
|
||||
|
||||
|
||||
def generate_permalink(base_url, get_root_url, get_root_calculator_url, form: VirusFormData):
|
||||
form_dict = VirusFormData.to_dict(form, strip_defaults=True)
|
||||
|
||||
# Generate the calculator URL arguments that would be needed to re-create this
|
||||
# form.
|
||||
args = urllib.parse.urlencode(form_dict)
|
||||
|
||||
# Then zlib compress + base64 encode the string. To be inverted by the
|
||||
# /_c/ endpoint.
|
||||
compressed_args = base64.b64encode(zlib.compress(args.encode())).decode()
|
||||
qr_url = f"{base_url}{get_root_url()}/_c/{compressed_args}"
|
||||
url = f"{base_url}{get_root_calculator_url()}?{args}"
|
||||
|
||||
return {
|
||||
'link': url,
|
||||
'shortened': qr_url,
|
||||
}
|
||||
|
||||
|
||||
def conditional_prob_inf_given_vl_dist(
|
||||
infection_probability: models._VectorisedFloat,
|
||||
viral_loads: np.ndarray,
|
||||
|
|
@ -319,13 +295,6 @@ def uncertainties_plot(infection_probability: models._VectorisedFloat,
|
|||
return fig
|
||||
|
||||
|
||||
def _img2bytes(figure):
|
||||
# Draw the image
|
||||
img_data = io.BytesIO()
|
||||
figure.save(img_data, format='png', bbox_inches="tight")
|
||||
return img_data
|
||||
|
||||
|
||||
def _figure2bytes(figure):
|
||||
# Draw the image
|
||||
img_data = io.BytesIO()
|
||||
|
|
@ -338,249 +307,3 @@ def img2base64(img_data) -> str:
|
|||
pic_hash = base64.b64encode(img_data.read()).decode('ascii')
|
||||
# A src suitable for a tag such as f'<img id="scenario_concentration_plot" src="{result}">.
|
||||
return f'data:image/png;base64,{pic_hash}'
|
||||
|
||||
|
||||
def minutes_to_time(minutes: int) -> str:
|
||||
minute_string = str(minutes % 60)
|
||||
minute_string = "0" * (2 - len(minute_string)) + minute_string
|
||||
hour_string = str(minutes // 60)
|
||||
hour_string = "0" * (2 - len(hour_string)) + hour_string
|
||||
|
||||
return f"{hour_string}:{minute_string}"
|
||||
|
||||
|
||||
def readable_minutes(minutes: int) -> str:
|
||||
time = float(minutes)
|
||||
unit = " minute"
|
||||
if time % 60 == 0:
|
||||
time = minutes/60
|
||||
unit = " hour"
|
||||
if time != 1:
|
||||
unit += "s"
|
||||
|
||||
if time.is_integer():
|
||||
time_str = "{:0.0f}".format(time)
|
||||
else:
|
||||
time_str = "{0:.2f}".format(time)
|
||||
|
||||
return time_str + unit
|
||||
|
||||
|
||||
def hour_format(hour: float) -> str:
|
||||
# Convert float hour to HH:MM format
|
||||
hours = f"{int(hour):02}"
|
||||
minutes = f"{int(hour % 1 * 60):02}"
|
||||
return f"{hours}:{minutes}"
|
||||
|
||||
|
||||
def percentage(absolute: float) -> float:
|
||||
return absolute * 100
|
||||
|
||||
|
||||
def non_zero_percentage(percentage: int) -> str:
|
||||
if percentage < 0.01:
|
||||
return "<0.01%"
|
||||
elif percentage < 1:
|
||||
return "{:0.2f}%".format(percentage)
|
||||
elif percentage > 99.9 or np.isnan(percentage):
|
||||
return ">99.9%"
|
||||
else:
|
||||
return "{:0.1f}%".format(percentage)
|
||||
|
||||
|
||||
def manufacture_viral_load_scenarios_percentiles(model: mc.ExposureModel) -> typing.Dict[str, mc.ExposureModel]:
|
||||
viral_load = model.concentration_model.infected.virus.viral_load_in_sputum
|
||||
scenarios = {}
|
||||
for percentil in (0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99):
|
||||
vl = np.quantile(viral_load, percentil)
|
||||
specific_vl_scenario = dataclass_utils.nested_replace(model,
|
||||
{'concentration_model.infected.virus.viral_load_in_sputum': vl}
|
||||
)
|
||||
scenarios[str(vl)] = np.mean(specific_vl_scenario.infection_probability())
|
||||
return scenarios
|
||||
|
||||
|
||||
def manufacture_alternative_scenarios(form: VirusFormData) -> typing.Dict[str, mc.ExposureModel]:
|
||||
scenarios = {}
|
||||
if (form.short_range_option == "short_range_no"):
|
||||
# Two special option cases - HEPA and/or FFP2 masks.
|
||||
FFP2_being_worn = bool(form.mask_wearing_option == 'mask_on' and form.mask_type == 'FFP2')
|
||||
if FFP2_being_worn and form.hepa_option:
|
||||
FFP2andHEPAalternative = dataclass_utils.replace(form, mask_type='Type I')
|
||||
if not (form.hepa_option and form.mask_wearing_option == 'mask_on' and form.mask_type == 'Type I'):
|
||||
scenarios['Base scenario with HEPA filter and Type I masks'] = FFP2andHEPAalternative.build_mc_model()
|
||||
if not FFP2_being_worn and form.hepa_option:
|
||||
noHEPAalternative = dataclass_utils.replace(form, mask_type = 'FFP2')
|
||||
noHEPAalternative = dataclass_utils.replace(noHEPAalternative, mask_wearing_option = 'mask_on')
|
||||
noHEPAalternative = dataclass_utils.replace(noHEPAalternative, hepa_option=False)
|
||||
if not (not form.hepa_option and FFP2_being_worn):
|
||||
scenarios['Base scenario without HEPA filter, with FFP2 masks'] = noHEPAalternative.build_mc_model()
|
||||
|
||||
# The remaining scenarios are based on Type I masks (possibly not worn)
|
||||
# and no HEPA filtration.
|
||||
form = dataclass_utils.replace(form, mask_type='Type I')
|
||||
if form.hepa_option:
|
||||
form = dataclass_utils.replace(form, hepa_option=False)
|
||||
|
||||
with_mask = dataclass_utils.replace(form, mask_wearing_option='mask_on')
|
||||
without_mask = dataclass_utils.replace(form, mask_wearing_option='mask_off')
|
||||
|
||||
if form.ventilation_type == 'mechanical_ventilation':
|
||||
#scenarios['Mechanical ventilation with Type I masks'] = with_mask.build_mc_model()
|
||||
if not (form.mask_wearing_option == 'mask_off'):
|
||||
scenarios['Mechanical ventilation without masks'] = without_mask.build_mc_model()
|
||||
|
||||
elif form.ventilation_type == 'natural_ventilation':
|
||||
#scenarios['Windows open with Type I masks'] = with_mask.build_mc_model()
|
||||
if not (form.mask_wearing_option == 'mask_off'):
|
||||
scenarios['Windows open without masks'] = without_mask.build_mc_model()
|
||||
|
||||
# No matter the ventilation scheme, we include scenarios which don't have any ventilation.
|
||||
with_mask_no_vent = dataclass_utils.replace(with_mask, ventilation_type='no_ventilation')
|
||||
without_mask_or_vent = dataclass_utils.replace(without_mask, ventilation_type='no_ventilation')
|
||||
|
||||
if not (form.mask_wearing_option == 'mask_on' and form.mask_type == 'Type I' and form.ventilation_type == 'no_ventilation'):
|
||||
scenarios['No ventilation with Type I masks'] = with_mask_no_vent.build_mc_model()
|
||||
if not (form.mask_wearing_option == 'mask_off' and form.ventilation_type == 'no_ventilation'):
|
||||
scenarios['Neither ventilation nor masks'] = without_mask_or_vent.build_mc_model()
|
||||
|
||||
else:
|
||||
no_short_range_alternative = dataclass_utils.replace(form, short_range_interactions=[], total_people=form.total_people - form.short_range_occupants)
|
||||
scenarios['Base scenario without short-range interactions'] = no_short_range_alternative.build_mc_model()
|
||||
|
||||
return scenarios
|
||||
|
||||
|
||||
def scenario_statistics(
|
||||
mc_model: mc.ExposureModel,
|
||||
sample_times: typing.List[float],
|
||||
compute_prob_exposure: bool
|
||||
):
|
||||
model = mc_model.build_model(size=mc_model.data_registry.monte_carlo['sample_size'])
|
||||
if (compute_prob_exposure):
|
||||
# It means we have data to calculate the total_probability_rule
|
||||
prob_probabilistic_exposure = model.total_probability_rule()
|
||||
else:
|
||||
prob_probabilistic_exposure = 0.
|
||||
|
||||
return {
|
||||
'probability_of_infection': np.mean(model.infection_probability()),
|
||||
'expected_new_cases': np.mean(model.expected_new_cases()),
|
||||
'concentrations': [
|
||||
np.mean(model.concentration(time))
|
||||
for time in sample_times
|
||||
],
|
||||
'prob_probabilistic_exposure': prob_probabilistic_exposure,
|
||||
}
|
||||
|
||||
|
||||
def comparison_report(
|
||||
form: VirusFormData,
|
||||
report_data: typing.Dict[str, typing.Any],
|
||||
scenarios: typing.Dict[str, mc.ExposureModel],
|
||||
sample_times: typing.List[float],
|
||||
executor_factory: typing.Callable[[], concurrent.futures.Executor],
|
||||
):
|
||||
if (form.short_range_option == "short_range_no"):
|
||||
statistics = {
|
||||
'Current scenario' : {
|
||||
'probability_of_infection': report_data['prob_inf'],
|
||||
'expected_new_cases': report_data['expected_new_cases'],
|
||||
'concentrations': report_data['concentrations'],
|
||||
}
|
||||
}
|
||||
else:
|
||||
statistics = {}
|
||||
|
||||
if (form.short_range_option == "short_range_yes" and form.exposure_option == "p_probabilistic_exposure"):
|
||||
compute_prob_exposure = True
|
||||
else:
|
||||
compute_prob_exposure = False
|
||||
|
||||
with executor_factory() as executor:
|
||||
results = executor.map(
|
||||
scenario_statistics,
|
||||
scenarios.values(),
|
||||
[sample_times] * len(scenarios),
|
||||
[compute_prob_exposure] * len(scenarios),
|
||||
timeout=60,
|
||||
)
|
||||
|
||||
for (name, model), model_stats in zip(scenarios.items(), results):
|
||||
statistics[name] = model_stats
|
||||
|
||||
return {
|
||||
'stats': statistics,
|
||||
}
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class ReportGenerator:
|
||||
jinja_loader: jinja2.BaseLoader
|
||||
get_root_url: typing.Any
|
||||
get_root_calculator_url: typing.Any
|
||||
|
||||
def build_report(
|
||||
self,
|
||||
base_url: str,
|
||||
form: VirusFormData,
|
||||
executor_factory: typing.Callable[[], concurrent.futures.Executor],
|
||||
) -> str:
|
||||
model = form.build_model()
|
||||
context = self.prepare_context(base_url, model, form, executor_factory=executor_factory)
|
||||
return self.render(context)
|
||||
|
||||
def prepare_context(
|
||||
self,
|
||||
base_url: str,
|
||||
model: models.ExposureModel,
|
||||
form: VirusFormData,
|
||||
executor_factory: typing.Callable[[], concurrent.futures.Executor],
|
||||
) -> dict:
|
||||
now = datetime.utcnow().astimezone()
|
||||
time = now.strftime("%Y-%m-%d %H:%M:%S UTC")
|
||||
|
||||
data_registry_version = f"v{model.data_registry.version}" if model.data_registry.version else None
|
||||
context = {
|
||||
'model': model,
|
||||
'form': form,
|
||||
'creation_date': time,
|
||||
'data_registry_version': data_registry_version,
|
||||
}
|
||||
|
||||
scenario_sample_times = interesting_times(model)
|
||||
report_data = calculate_report_data(form, model, executor_factory=executor_factory)
|
||||
context.update(report_data)
|
||||
|
||||
alternative_scenarios = manufacture_alternative_scenarios(form)
|
||||
context['alternative_viral_load'] = manufacture_viral_load_scenarios_percentiles(model) if form.conditional_probability_viral_loads else None
|
||||
context['alternative_scenarios'] = comparison_report(
|
||||
form, report_data, alternative_scenarios, scenario_sample_times, executor_factory=executor_factory,
|
||||
)
|
||||
context['permalink'] = generate_permalink(base_url, self.get_root_url, self.get_root_calculator_url, form)
|
||||
context['get_url'] = self.get_root_url
|
||||
context['get_calculator_url'] = self.get_root_calculator_url
|
||||
|
||||
return context
|
||||
|
||||
def _template_environment(self) -> jinja2.Environment:
|
||||
env = jinja2.Environment(
|
||||
loader=self.jinja_loader,
|
||||
undefined=jinja2.StrictUndefined,
|
||||
)
|
||||
env.globals["common_text"] = markdown_tools.extract_rendered_markdown_blocks(
|
||||
env.get_template('common_text.md.j2')
|
||||
)
|
||||
env.filters['non_zero_percentage'] = non_zero_percentage
|
||||
env.filters['readable_minutes'] = readable_minutes
|
||||
env.filters['minutes_to_time'] = minutes_to_time
|
||||
env.filters['hour_format'] = hour_format
|
||||
env.filters['float_format'] = "{0:.2f}".format
|
||||
env.filters['int_format'] = "{:0.0f}".format
|
||||
env.filters['percentage'] = percentage
|
||||
env.filters['JSONify'] = json.dumps
|
||||
return env
|
||||
|
||||
def render(self, context: dict) -> str:
|
||||
template = self._template_environment().get_template("calculator.report.html.j2")
|
||||
return template.render(**context, text_blocks=template.globals["common_text"])
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
from caimira.enums import ViralLoads
|
||||
from ..models.enums import ViralLoads
|
||||
|
||||
|
||||
class DataRegistry:
|
||||
|
|
@ -2,7 +2,7 @@ import logging
|
|||
import typing
|
||||
import requests
|
||||
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from ..store.data_registry import DataRegistry
|
||||
|
||||
logger = logging.getLogger("DATA")
|
||||
|
||||
0
caimira/src/caimira/calculator/validators/__init__.py
Normal file
0
caimira/src/caimira/calculator/validators/__init__.py
Normal file
|
|
@ -7,11 +7,11 @@ from scipy.signal import find_peaks
|
|||
import pandas as pd
|
||||
import re
|
||||
|
||||
from caimira import models
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from .form_data import FormData, cast_class_fields
|
||||
from .defaults import NO_DEFAULT
|
||||
from .report_generator import img2base64, _figure2bytes
|
||||
from ..form_validator import FormData, cast_class_fields
|
||||
from ..defaults import NO_DEFAULT
|
||||
from ...store.data_registry import DataRegistry
|
||||
from ...models import models
|
||||
from ...report.report_generator import img2base64, _figure2bytes
|
||||
|
||||
minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
|
||||
|
||||
|
|
@ -7,9 +7,9 @@ import json
|
|||
|
||||
import numpy as np
|
||||
|
||||
from caimira import models
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from .defaults import DEFAULTS, NO_DEFAULT, COFFEE_OPTIONS_INT
|
||||
from ..models import models
|
||||
from ..store.data_registry import DataRegistry
|
||||
|
||||
LOG = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -26,13 +26,16 @@ class FormData:
|
|||
exposed_lunch_option: bool
|
||||
exposed_lunch_start: minutes_since_midnight
|
||||
exposed_start: minutes_since_midnight
|
||||
infected_coffee_break_option: str #Used if infected_dont_have_breaks_with_exposed
|
||||
infected_coffee_duration: int #Used if infected_dont_have_breaks_with_exposed
|
||||
# Used if infected_dont_have_breaks_with_exposed
|
||||
infected_coffee_break_option: str
|
||||
infected_coffee_duration: int # Used if infected_dont_have_breaks_with_exposed
|
||||
infected_dont_have_breaks_with_exposed: bool
|
||||
infected_finish: minutes_since_midnight
|
||||
infected_lunch_finish: minutes_since_midnight #Used if infected_dont_have_breaks_with_exposed
|
||||
infected_lunch_option: bool #Used if infected_dont_have_breaks_with_exposed
|
||||
infected_lunch_start: minutes_since_midnight #Used if infected_dont_have_breaks_with_exposed
|
||||
# Used if infected_dont_have_breaks_with_exposed
|
||||
infected_lunch_finish: minutes_since_midnight
|
||||
infected_lunch_option: bool # Used if infected_dont_have_breaks_with_exposed
|
||||
# Used if infected_dont_have_breaks_with_exposed
|
||||
infected_lunch_start: minutes_since_midnight
|
||||
infected_people: int
|
||||
infected_start: minutes_since_midnight
|
||||
room_volume: float
|
||||
|
|
@ -47,7 +50,6 @@ class FormData:
|
|||
# Take a copy of the form data so that we can mutate it.
|
||||
form_data = form_data.copy()
|
||||
form_data.pop('_xsrf', None)
|
||||
|
||||
# Don't let arbitrary unescaped HTML through the net.
|
||||
for key, value in form_data.items():
|
||||
if isinstance(value, str):
|
||||
|
|
@ -64,7 +66,8 @@ class FormData:
|
|||
form_data[key] = _CAST_RULES_FORM_ARG_TO_NATIVE[key](value)
|
||||
|
||||
if key not in cls._DEFAULTS:
|
||||
raise ValueError(f'Invalid argument "{html.escape(key)}" given')
|
||||
raise ValueError(
|
||||
f'Invalid argument "{html.escape(key)}" given')
|
||||
|
||||
instance = cls(**form_data, data_registry=data_registry)
|
||||
instance.validate()
|
||||
|
|
@ -93,7 +96,8 @@ class FormData:
|
|||
def validate_population_parameters(self):
|
||||
# Validate number of infected <= number of total people
|
||||
if self.infected_people >= self.total_people:
|
||||
raise ValueError('Number of infected people cannot be greater or equal to the number of total people.')
|
||||
raise ValueError(
|
||||
'Number of infected people cannot be greater or equal to the number of total people.')
|
||||
|
||||
# Validate time intervals selected by user
|
||||
time_intervals = [
|
||||
|
|
@ -101,9 +105,11 @@ class FormData:
|
|||
['infected_start', 'infected_finish'],
|
||||
]
|
||||
if self.exposed_lunch_option:
|
||||
time_intervals.append(['exposed_lunch_start', 'exposed_lunch_finish'])
|
||||
time_intervals.append(
|
||||
['exposed_lunch_start', 'exposed_lunch_finish'])
|
||||
if self.infected_dont_have_breaks_with_exposed and self.infected_lunch_option:
|
||||
time_intervals.append(['infected_lunch_start', 'infected_lunch_finish'])
|
||||
time_intervals.append(
|
||||
['infected_lunch_start', 'infected_lunch_finish'])
|
||||
|
||||
for start_name, end_name in time_intervals:
|
||||
start = getattr(self, start_name)
|
||||
|
|
@ -116,29 +122,33 @@ class FormData:
|
|||
lunch_start = getattr(self, f'{population}_lunch_start')
|
||||
lunch_finish = getattr(self, f'{population}_lunch_finish')
|
||||
return (start <= lunch_start <= finish and
|
||||
start <= lunch_finish <= finish)
|
||||
start <= lunch_finish <= finish)
|
||||
|
||||
def get_lunch_mins(population):
|
||||
lunch_mins = 0
|
||||
if getattr(self, f'{population}_lunch_option'):
|
||||
lunch_mins = getattr(self, f'{population}_lunch_finish') - getattr(self, f'{population}_lunch_start')
|
||||
lunch_mins = getattr(
|
||||
self, f'{population}_lunch_finish') - getattr(self, f'{population}_lunch_start')
|
||||
return lunch_mins
|
||||
|
||||
def get_coffee_mins(population):
|
||||
coffee_mins = 0
|
||||
if getattr(self, f'{population}_coffee_break_option') != 'coffee_break_0':
|
||||
coffee_mins = COFFEE_OPTIONS_INT[getattr(self, f'{population}_coffee_break_option')] * getattr(self, f'{population}_coffee_duration')
|
||||
coffee_mins = COFFEE_OPTIONS_INT[getattr(
|
||||
self, f'{population}_coffee_break_option')] * getattr(self, f'{population}_coffee_duration')
|
||||
return coffee_mins
|
||||
|
||||
def get_activity_mins(population):
|
||||
return getattr(self, f'{population}_finish') - getattr(self, f'{population}_start')
|
||||
|
||||
populations = ['exposed', 'infected'] if self.infected_dont_have_breaks_with_exposed else ['exposed']
|
||||
populations = [
|
||||
'exposed', 'infected'] if self.infected_dont_have_breaks_with_exposed else ['exposed']
|
||||
for population in populations:
|
||||
# Validate lunch time within the activity times.
|
||||
if (getattr(self, f'{population}_lunch_option') and
|
||||
not validate_lunch(getattr(self, f'{population}_start'), getattr(self, f'{population}_finish'))
|
||||
):
|
||||
not validate_lunch(getattr(self, f'{population}_start'), getattr(
|
||||
self, f'{population}_finish'))
|
||||
):
|
||||
raise ValueError(
|
||||
f"{population} lunch break must be within presence times."
|
||||
)
|
||||
|
|
@ -152,7 +162,8 @@ class FormData:
|
|||
for attr_name, valid_set in [('exposed_coffee_break_option', COFFEE_OPTIONS_INT),
|
||||
('infected_coffee_break_option', COFFEE_OPTIONS_INT)]:
|
||||
if getattr(self, attr_name) not in valid_set:
|
||||
raise ValueError(f"{getattr(self, attr_name)} is not a valid value for {attr_name}")
|
||||
raise ValueError(
|
||||
f"{getattr(self, attr_name)} is not a valid value for {attr_name}")
|
||||
|
||||
def validate(self):
|
||||
raise NotImplementedError("Subclass must implement")
|
||||
|
|
@ -161,7 +172,8 @@ class FormData:
|
|||
raise NotImplementedError("Subclass must implement")
|
||||
|
||||
def _compute_breaks_in_interval(self, start, finish, n_breaks, duration) -> models.BoundarySequence_t:
|
||||
break_delay = ((finish - start) - (n_breaks * duration)) // (n_breaks+1)
|
||||
break_delay = ((finish - start) -
|
||||
(n_breaks * duration)) // (n_breaks+1)
|
||||
break_times = []
|
||||
end = start
|
||||
for n in range(n_breaks):
|
||||
|
|
@ -173,14 +185,16 @@ class FormData:
|
|||
def exposed_lunch_break_times(self) -> models.BoundarySequence_t:
|
||||
result = []
|
||||
if self.exposed_lunch_option:
|
||||
result.append((self.exposed_lunch_start, self.exposed_lunch_finish))
|
||||
result.append((self.exposed_lunch_start,
|
||||
self.exposed_lunch_finish))
|
||||
return tuple(result)
|
||||
|
||||
def infected_lunch_break_times(self) -> models.BoundarySequence_t:
|
||||
if self.infected_dont_have_breaks_with_exposed:
|
||||
result = []
|
||||
if self.infected_lunch_option:
|
||||
result.append((self.infected_lunch_start, self.infected_lunch_finish))
|
||||
result.append((self.infected_lunch_start,
|
||||
self.infected_lunch_finish))
|
||||
return tuple(result)
|
||||
else:
|
||||
return self.exposed_lunch_break_times()
|
||||
|
|
@ -194,7 +208,8 @@ class FormData:
|
|||
def _coffee_break_times(self, activity_start, activity_finish, coffee_breaks, coffee_duration, lunch_start, lunch_finish) -> models.BoundarySequence_t:
|
||||
time_before_lunch = lunch_start - activity_start
|
||||
time_after_lunch = activity_finish - lunch_finish
|
||||
before_lunch_frac = time_before_lunch / (time_before_lunch + time_after_lunch)
|
||||
before_lunch_frac = time_before_lunch / \
|
||||
(time_before_lunch + time_after_lunch)
|
||||
n_morning_breaks = round(coffee_breaks * before_lunch_frac)
|
||||
breaks = (
|
||||
self._compute_breaks_in_interval(
|
||||
|
|
@ -211,9 +226,11 @@ class FormData:
|
|||
if exposed_coffee_breaks == 0:
|
||||
return ()
|
||||
if self.exposed_lunch_option:
|
||||
breaks = self._coffee_break_times(self.exposed_start, self.exposed_finish, exposed_coffee_breaks, self.exposed_coffee_duration, self.exposed_lunch_start, self.exposed_lunch_finish)
|
||||
breaks = self._coffee_break_times(self.exposed_start, self.exposed_finish, exposed_coffee_breaks,
|
||||
self.exposed_coffee_duration, self.exposed_lunch_start, self.exposed_lunch_finish)
|
||||
else:
|
||||
breaks = self._compute_breaks_in_interval(self.exposed_start, self.exposed_finish, exposed_coffee_breaks, self.exposed_coffee_duration)
|
||||
breaks = self._compute_breaks_in_interval(
|
||||
self.exposed_start, self.exposed_finish, exposed_coffee_breaks, self.exposed_coffee_duration)
|
||||
return breaks
|
||||
|
||||
def infected_coffee_break_times(self) -> models.BoundarySequence_t:
|
||||
|
|
@ -222,9 +239,11 @@ class FormData:
|
|||
if infected_coffee_breaks == 0:
|
||||
return ()
|
||||
if self.infected_lunch_option:
|
||||
breaks = self._coffee_break_times(self.infected_start, self.infected_finish, infected_coffee_breaks, self.infected_coffee_duration, self.infected_lunch_start, self.infected_lunch_finish)
|
||||
breaks = self._coffee_break_times(self.infected_start, self.infected_finish, infected_coffee_breaks,
|
||||
self.infected_coffee_duration, self.infected_lunch_start, self.infected_lunch_finish)
|
||||
else:
|
||||
breaks = self._compute_breaks_in_interval(self.infected_start, self.infected_finish, infected_coffee_breaks, self.infected_coffee_duration)
|
||||
breaks = self._compute_breaks_in_interval(
|
||||
self.infected_start, self.infected_finish, infected_coffee_breaks, self.infected_coffee_duration)
|
||||
return breaks
|
||||
else:
|
||||
return self.exposed_coffee_break_times()
|
||||
|
|
@ -232,13 +251,14 @@ class FormData:
|
|||
def generate_specific_break_times(self, breaks_dict: dict, target: str) -> models.BoundarySequence_t:
|
||||
break_times = []
|
||||
for n in breaks_dict[f'{target}_breaks']:
|
||||
# Parse break times.
|
||||
# Parse break times.
|
||||
begin = time_string_to_minutes(n["start_time"])
|
||||
end = time_string_to_minutes(n["finish_time"])
|
||||
for time in [begin, end]:
|
||||
# For a specific break, the infected and exposed presence is the same.
|
||||
if not getattr(self, f'{target}_start') < time < getattr(self, f'{target}_finish'):
|
||||
raise ValueError(f'All breaks should be within the simulation time. Got {time_minutes_to_string(time)}.')
|
||||
raise ValueError(
|
||||
f'All breaks should be within the simulation time. Got {time_minutes_to_string(time)}.')
|
||||
|
||||
break_times.append((begin, end))
|
||||
return tuple(break_times)
|
||||
|
|
@ -260,7 +280,8 @@ class FormData:
|
|||
|
||||
# Order the breaks by their start-time, and ensure that they are monotonic
|
||||
# and that the start of one break happens after the end of another.
|
||||
break_boundaries: models.BoundarySequence_t = tuple(sorted(breaks, key=lambda break_pair: break_pair[0]))
|
||||
break_boundaries: models.BoundarySequence_t = tuple(
|
||||
sorted(breaks, key=lambda break_pair: break_pair[0]))
|
||||
|
||||
for break_start, break_end in break_boundaries:
|
||||
if break_start >= break_end:
|
||||
|
|
@ -269,13 +290,15 @@ class FormData:
|
|||
prev_break_end = break_boundaries[0][1]
|
||||
for break_start, break_end in break_boundaries[1:]:
|
||||
if prev_break_end >= break_start:
|
||||
raise ValueError(f"A break starts before another ends ({break_start}, {break_end}, {prev_break_end}).")
|
||||
raise ValueError(
|
||||
f"A break starts before another ends ({break_start}, {break_end}, {prev_break_end}).")
|
||||
prev_break_end = break_end
|
||||
|
||||
present_intervals = []
|
||||
|
||||
current_time = start
|
||||
LOG.debug(f"starting time march at {_hours2timestring(current_time/60)} to {_hours2timestring(finish/60)}")
|
||||
LOG.debug(
|
||||
f"starting time march at {_hours2timestring(current_time/60)} to {_hours2timestring(finish/60)}")
|
||||
|
||||
# As we step through the breaks. For each break there are 6 important cases
|
||||
# we must cover. Let S=start; E=end; Bs=Break start; Be=Break end:
|
||||
|
|
@ -336,8 +359,9 @@ class FormData:
|
|||
return models.SpecificInterval(tuple(present_intervals))
|
||||
|
||||
def infected_present_interval(self) -> models.Interval:
|
||||
if self.specific_breaks != {}: # It means the breaks are specific and not predefined
|
||||
breaks = self.generate_specific_break_times(breaks_dict=self.specific_breaks, target='exposed')
|
||||
if self.specific_breaks != {}: # It means the breaks are specific and not predefined
|
||||
breaks = self.generate_specific_break_times(
|
||||
breaks_dict=self.specific_breaks, target='exposed')
|
||||
else:
|
||||
breaks = self.infected_lunch_break_times() + self.infected_coffee_break_times()
|
||||
return self.present_interval(
|
||||
|
|
@ -346,14 +370,17 @@ class FormData:
|
|||
)
|
||||
|
||||
def population_present_interval(self) -> models.Interval:
|
||||
state_change_times = set(self.infected_present_interval().transition_times())
|
||||
state_change_times.update(self.exposed_present_interval().transition_times())
|
||||
state_change_times = set(
|
||||
self.infected_present_interval().transition_times())
|
||||
state_change_times.update(
|
||||
self.exposed_present_interval().transition_times())
|
||||
all_state_changes = sorted(state_change_times)
|
||||
return models.SpecificInterval(tuple(zip(all_state_changes[:-1], all_state_changes[1:])))
|
||||
|
||||
def exposed_present_interval(self) -> models.Interval:
|
||||
if self.specific_breaks != {}: # It means the breaks are specific and not predefined
|
||||
breaks = self.generate_specific_break_times(breaks_dict=self.specific_breaks, target='exposed')
|
||||
if self.specific_breaks != {}: # It means the breaks are specific and not predefined
|
||||
breaks = self.generate_specific_break_times(
|
||||
breaks_dict=self.specific_breaks, target='exposed')
|
||||
else:
|
||||
breaks = self.exposed_lunch_break_times() + self.exposed_coffee_break_times()
|
||||
return self.present_interval(
|
||||
|
|
@ -382,7 +409,7 @@ def time_minutes_to_string(time: int) -> str:
|
|||
:param time: The number of minutes between 'time' and 00:00
|
||||
:return: A string of the form "HH:MM" representing a time of day
|
||||
"""
|
||||
return "{0:0=2d}".format(int(time/60)) + ":" + "{0:0=2d}".format(time%60)
|
||||
return "{0:0=2d}".format(int(time/60)) + ":" + "{0:0=2d}".format(time % 60)
|
||||
|
||||
|
||||
def string_to_list(s: str) -> list:
|
||||
|
|
@ -426,6 +453,7 @@ _CAST_RULES_FORM_ARG_TO_NATIVE: typing.Dict[str, typing.Callable] = {}
|
|||
#: that can be encoded to URL arguments.
|
||||
_CAST_RULES_NATIVE_TO_FORM_ARG: typing.Dict[str, typing.Callable] = {}
|
||||
|
||||
|
||||
def cast_class_fields(cls):
|
||||
for _field in dataclasses.fields(cls):
|
||||
if _field.type is minutes_since_midnight:
|
||||
|
|
@ -447,4 +475,5 @@ def cast_class_fields(cls):
|
|||
_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = string_to_dict
|
||||
_CAST_RULES_NATIVE_TO_FORM_ARG[_field.name] = dict_to_string
|
||||
|
||||
|
||||
cast_class_fields(FormData)
|
||||
|
|
@ -6,17 +6,13 @@ import re
|
|||
|
||||
import numpy as np
|
||||
|
||||
from caimira import models
|
||||
from caimira import data
|
||||
import caimira.data.weather
|
||||
import caimira.monte_carlo as mc
|
||||
from .. import calculator
|
||||
from .form_data import FormData, cast_class_fields, time_string_to_minutes
|
||||
from caimira.monte_carlo.data import activity_distributions, virus_distributions, mask_distributions, short_range_distances
|
||||
from caimira.monte_carlo.data import expiration_distribution, expiration_BLO_factors, expiration_distributions, short_range_expiration_distributions
|
||||
from .defaults import (DEFAULTS, CONFIDENCE_LEVEL_OPTIONS,
|
||||
MECHANICAL_VENTILATION_TYPES, MASK_WEARING_OPTIONS, MONTH_NAMES, VACCINE_BOOSTER_TYPE, VACCINE_TYPE,
|
||||
VENTILATION_TYPES, VOLUME_TYPES, WINDOWS_OPENING_REGIMES, WINDOWS_TYPES)
|
||||
from ..form_validator import FormData, cast_class_fields, time_string_to_minutes
|
||||
from ..defaults import (DEFAULTS, CONFIDENCE_LEVEL_OPTIONS,
|
||||
MECHANICAL_VENTILATION_TYPES, MASK_WEARING_OPTIONS, MONTH_NAMES, VACCINE_BOOSTER_TYPE, VACCINE_TYPE,
|
||||
VENTILATION_TYPES, VOLUME_TYPES, WINDOWS_OPENING_REGIMES, WINDOWS_TYPES)
|
||||
from ...models import models, data, monte_carlo as mc
|
||||
from ...models.monte_carlo.data import activity_distributions, virus_distributions, mask_distributions, short_range_distances
|
||||
from ...models.monte_carlo.data import expiration_distribution, expiration_BLO_factors, expiration_distributions, short_range_expiration_distributions
|
||||
|
||||
LOG = logging.getLogger("MODEL")
|
||||
|
||||
|
|
@ -76,15 +72,17 @@ class VirusFormData(FormData):
|
|||
_DEFAULTS: typing.ClassVar[typing.Dict[str, typing.Any]] = DEFAULTS
|
||||
|
||||
def validate(self):
|
||||
# Validate population parameters
|
||||
self.validate_population_parameters()
|
||||
|
||||
validation_tuples = [('activity_type', self.data_registry.population_scenario_activity.keys()),
|
||||
('mechanical_ventilation_type', MECHANICAL_VENTILATION_TYPES),
|
||||
('mask_type', list(mask_distributions(self.data_registry).keys())),
|
||||
('mechanical_ventilation_type',
|
||||
MECHANICAL_VENTILATION_TYPES),
|
||||
('mask_type', list(mask_distributions(
|
||||
self.data_registry).keys())),
|
||||
('mask_wearing_option', MASK_WEARING_OPTIONS),
|
||||
('ventilation_type', VENTILATION_TYPES),
|
||||
('virus_type', list(virus_distributions(self.data_registry).keys())),
|
||||
('virus_type', list(virus_distributions(
|
||||
self.data_registry).keys())),
|
||||
('volume_type', VOLUME_TYPES),
|
||||
('window_opening_regime', WINDOWS_OPENING_REGIMES),
|
||||
('window_type', WINDOWS_TYPES),
|
||||
|
|
@ -95,11 +93,13 @@ class VirusFormData(FormData):
|
|||
|
||||
for attr_name, valid_set in validation_tuples:
|
||||
if getattr(self, attr_name) not in valid_set:
|
||||
raise ValueError(f"{getattr(self, attr_name)} is not a valid value for {attr_name}")
|
||||
raise ValueError(
|
||||
f"{getattr(self, attr_name)} is not a valid value for {attr_name}")
|
||||
|
||||
# Validate number of infected people == 1 when activity is Conference/Training.
|
||||
if self.activity_type == 'training' and self.infected_people > 1:
|
||||
raise ValueError('Conference/Training activities are limited to 1 infected.')
|
||||
raise ValueError(
|
||||
'Conference/Training activities are limited to 1 infected.')
|
||||
|
||||
# Validate ventilation parameters
|
||||
if self.ventilation_type == 'natural_ventilation':
|
||||
|
|
@ -114,7 +114,7 @@ class VirusFormData(FormData):
|
|||
"ventilation_type is 'natural_ventilation'"
|
||||
)
|
||||
if (self.window_opening_regime == 'windows_open_periodically' and
|
||||
self.windows_duration > self.windows_frequency):
|
||||
self.windows_duration > self.windows_frequency):
|
||||
raise ValueError(
|
||||
'Duration cannot be bigger than frequency.'
|
||||
)
|
||||
|
|
@ -127,61 +127,78 @@ class VirusFormData(FormData):
|
|||
# Validate specific inputs - breaks (exposed and infected)
|
||||
if self.specific_breaks != {}:
|
||||
if type(self.specific_breaks) is not dict:
|
||||
raise TypeError('The specific breaks should be in a dictionary.')
|
||||
raise TypeError(
|
||||
'The specific breaks should be in a dictionary.')
|
||||
|
||||
dict_keys = list(self.specific_breaks.keys())
|
||||
if "exposed_breaks" not in dict_keys:
|
||||
raise TypeError(f'Unable to fetch "exposed_breaks" key. Got "{dict_keys[0]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "exposed_breaks" key. Got "{dict_keys[0]}".')
|
||||
if "infected_breaks" not in dict_keys:
|
||||
raise TypeError(f'Unable to fetch "infected_breaks" key. Got "{dict_keys[1]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "infected_breaks" key. Got "{dict_keys[1]}".')
|
||||
|
||||
for population_breaks in ['exposed_breaks', 'infected_breaks']:
|
||||
if self.specific_breaks[population_breaks] != []:
|
||||
if type(self.specific_breaks[population_breaks]) is not list:
|
||||
raise TypeError(f'All breaks should be in a list. Got {type(self.specific_breaks[population_breaks])}.')
|
||||
raise TypeError(
|
||||
f'All breaks should be in a list. Got {type(self.specific_breaks[population_breaks])}.')
|
||||
for input_break in self.specific_breaks[population_breaks]:
|
||||
# Input validations.
|
||||
if type(input_break) is not dict:
|
||||
raise TypeError(f'Each break should be a dictionary. Got {type(input_break)}.')
|
||||
raise TypeError(
|
||||
f'Each break should be a dictionary. Got {type(input_break)}.')
|
||||
dict_keys = list(input_break.keys())
|
||||
if "start_time" not in input_break:
|
||||
raise TypeError(f'Unable to fetch "start_time" key. Got "{dict_keys[0]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "start_time" key. Got "{dict_keys[0]}".')
|
||||
if "finish_time" not in input_break:
|
||||
raise TypeError(f'Unable to fetch "finish_time" key. Got "{dict_keys[1]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "finish_time" key. Got "{dict_keys[1]}".')
|
||||
for time in input_break.values():
|
||||
if not re.compile("^(2[0-3]|[01]?[0-9]):([0-5]?[0-9])$").match(time):
|
||||
raise TypeError(f'Wrong time format - "HH:MM". Got "{time}".')
|
||||
raise TypeError(
|
||||
f'Wrong time format - "HH:MM". Got "{time}".')
|
||||
|
||||
# Validate specific inputs - precise activity
|
||||
if self.precise_activity != {}:
|
||||
if type(self.precise_activity) is not dict:
|
||||
raise TypeError('The precise activities should be in a dictionary.')
|
||||
raise TypeError(
|
||||
'The precise activities should be in a dictionary.')
|
||||
|
||||
dict_keys = list(self.precise_activity.keys())
|
||||
if "physical_activity" not in dict_keys:
|
||||
raise TypeError(f'Unable to fetch "physical_activity" key. Got "{dict_keys[0]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "physical_activity" key. Got "{dict_keys[0]}".')
|
||||
if "respiratory_activity" not in dict_keys:
|
||||
raise TypeError(f'Unable to fetch "respiratory_activity" key. Got "{dict_keys[1]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "respiratory_activity" key. Got "{dict_keys[1]}".')
|
||||
|
||||
if type(self.precise_activity['physical_activity']) is not str:
|
||||
raise TypeError('The physical activities should be a single string.')
|
||||
raise TypeError(
|
||||
'The physical activities should be a single string.')
|
||||
|
||||
if type(self.precise_activity['respiratory_activity']) is not list:
|
||||
raise TypeError('The respiratory activities should be in a list.')
|
||||
raise TypeError(
|
||||
'The respiratory activities should be in a list.')
|
||||
|
||||
total_percentage = 0
|
||||
for respiratory_activity in self.precise_activity['respiratory_activity']:
|
||||
if type(respiratory_activity) is not dict:
|
||||
raise TypeError('Each respiratory activity should be defined in a dictionary.')
|
||||
raise TypeError(
|
||||
'Each respiratory activity should be defined in a dictionary.')
|
||||
dict_keys = list(respiratory_activity.keys())
|
||||
if "type" not in dict_keys:
|
||||
raise TypeError(f'Unable to fetch "type" key. Got "{dict_keys[0]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "type" key. Got "{dict_keys[0]}".')
|
||||
if "percentage" not in dict_keys:
|
||||
raise TypeError(f'Unable to fetch "percentage" key. Got "{dict_keys[1]}".')
|
||||
raise TypeError(
|
||||
f'Unable to fetch "percentage" key. Got "{dict_keys[1]}".')
|
||||
total_percentage += respiratory_activity['percentage']
|
||||
|
||||
if total_percentage != 100:
|
||||
raise ValueError(f'The sum of all respiratory activities should be 100. Got {total_percentage}.')
|
||||
raise ValueError(
|
||||
f'The sum of all respiratory activities should be 100. Got {total_percentage}.')
|
||||
|
||||
# Validate number of people with short-range interactions
|
||||
max_occupants_for_sr = self.total_people - self.infected_people
|
||||
|
|
@ -218,7 +235,8 @@ class VirusFormData(FormData):
|
|||
for interaction in self.short_range_interactions:
|
||||
short_range.append(mc.ShortRangeModel(
|
||||
data_registry=self.data_registry,
|
||||
expiration=short_range_expiration_distributions(self.data_registry)[interaction['expiration']],
|
||||
expiration=short_range_expiration_distributions(
|
||||
self.data_registry)[interaction['expiration']],
|
||||
activity=infected_population.activity,
|
||||
presence=self.short_range_interval(interaction),
|
||||
distance=short_range_distances(self.data_registry),
|
||||
|
|
@ -233,7 +251,7 @@ class VirusFormData(FormData):
|
|||
infected=infected_population,
|
||||
evaporation_factor=0.3,
|
||||
),
|
||||
short_range = tuple(short_range),
|
||||
short_range=tuple(short_range),
|
||||
exposed=self.exposed_population(),
|
||||
geographical_data=mc.Cases(
|
||||
geographic_population=self.geographic_population,
|
||||
|
|
@ -249,11 +267,14 @@ class VirusFormData(FormData):
|
|||
|
||||
def build_CO2_model(self, sample_size=None) -> models.CO2ConcentrationModel:
|
||||
sample_size = sample_size or self.data_registry.monte_carlo['sample_size']
|
||||
infected_population: models.InfectedPopulation = self.infected_population().build_model(sample_size)
|
||||
infected_population: models.InfectedPopulation = self.infected_population(
|
||||
).build_model(sample_size)
|
||||
exposed_population: models.Population = self.exposed_population().build_model(sample_size)
|
||||
|
||||
state_change_times = set(infected_population.presence_interval().transition_times())
|
||||
state_change_times.update(exposed_population.presence_interval().transition_times())
|
||||
state_change_times = set(
|
||||
infected_population.presence_interval().transition_times())
|
||||
state_change_times.update(
|
||||
exposed_population.presence_interval().transition_times())
|
||||
transition_times = sorted(state_change_times)
|
||||
|
||||
total_people = [infected_population.people_present(stop) + exposed_population.people_present(stop)
|
||||
|
|
@ -262,10 +283,12 @@ class VirusFormData(FormData):
|
|||
if (self.activity_type == 'precise'):
|
||||
activity_defn, _ = self.generate_precise_activity_expiration()
|
||||
else:
|
||||
activity_defn = self.data_registry.population_scenario_activity[self.activity_type]['activity']
|
||||
activity_defn = self.data_registry.population_scenario_activity[
|
||||
self.activity_type]['activity']
|
||||
|
||||
population = mc.SimplePopulation(
|
||||
number=models.IntPiecewiseConstant(transition_times=tuple(transition_times), values=tuple(total_people)),
|
||||
number=models.IntPiecewiseConstant(transition_times=tuple(
|
||||
transition_times), values=tuple(total_people)),
|
||||
presence=None,
|
||||
activity=activity_distributions(self.data_registry)[activity_defn],
|
||||
)
|
||||
|
|
@ -285,7 +308,7 @@ class VirusFormData(FormData):
|
|||
|
||||
"""
|
||||
month = MONTH_NAMES.index(self.event_month) + 1
|
||||
timezone = caimira.data.weather.timezone_at(
|
||||
timezone = data.weather.timezone_at(
|
||||
latitude=self.location_latitude, longitude=self.location_longitude,
|
||||
)
|
||||
# We choose the first of the month for the current year.
|
||||
|
|
@ -306,7 +329,8 @@ class VirusFormData(FormData):
|
|||
month = MONTH_NAMES.index(self.event_month) + 1
|
||||
|
||||
wx_station = self.nearest_weather_station()
|
||||
temp_profile = caimira.data.weather.mean_hourly_temperatures(wx_station = wx_station[0], month = MONTH_NAMES.index(self.event_month) + 1)
|
||||
temp_profile = data.weather.mean_hourly_temperatures(
|
||||
wx_station=wx_station[0], month=MONTH_NAMES.index(self.event_month) + 1)
|
||||
|
||||
_, utc_offset = self.tz_name_and_utc_offset()
|
||||
|
||||
|
|
@ -314,13 +338,14 @@ class VirusFormData(FormData):
|
|||
# result the first data value may no longer be a midnight, and the hours
|
||||
# no longer ordered modulo 24).
|
||||
source_times = np.arange(24) + utc_offset
|
||||
times, temp_profile = caimira.data.weather.refine_hourly_data(
|
||||
times, temp_profile = data.weather.refine_hourly_data(
|
||||
source_times,
|
||||
temp_profile,
|
||||
npts=24*10, # 10 steps per hour => 6 min steps
|
||||
)
|
||||
outside_temp = models.PiecewiseConstant(
|
||||
tuple(float(t) for t in times), tuple(float(t) for t in temp_profile),
|
||||
tuple(float(t) for t in times), tuple(float(t)
|
||||
for t in temp_profile),
|
||||
)
|
||||
return outside_temp
|
||||
|
||||
|
|
@ -333,7 +358,7 @@ class VirusFormData(FormData):
|
|||
transition_times = self.CO2_fitting_result['transition_times']
|
||||
for index, (start, stop) in enumerate(zip(transition_times[:-1], transition_times[1:])):
|
||||
ventilations.append(models.AirChange(active=models.SpecificInterval(present_times=((start, stop), )),
|
||||
air_exch=self.CO2_fitting_result['ventilation_values'][index]))
|
||||
air_exch=self.CO2_fitting_result['ventilation_values'][index]))
|
||||
return models.MultipleVentilation(tuple(ventilations))
|
||||
|
||||
# Initializes a ventilation instance as a window if 'natural_ventilation' is selected, or as a HEPA-filter otherwise
|
||||
|
|
@ -369,7 +394,8 @@ class VirusFormData(FormData):
|
|||
ventilation = models.AirChange(active=always_on, air_exch=0.)
|
||||
else:
|
||||
if self.mechanical_ventilation_type == 'mech_type_air_changes':
|
||||
ventilation = models.AirChange(active=always_on, air_exch=self.air_changes)
|
||||
ventilation = models.AirChange(
|
||||
active=always_on, air_exch=self.air_changes)
|
||||
else:
|
||||
ventilation = models.HVACMechanical(
|
||||
active=always_on, q_air_mech=self.air_supply)
|
||||
|
|
@ -378,16 +404,18 @@ class VirusFormData(FormData):
|
|||
# to the air infiltration from the outside.
|
||||
# See CERN-OPEN-2021-004, p. 12.
|
||||
residual_vent: float = self.data_registry.ventilation['infiltration_ventilation'] # type: ignore
|
||||
infiltration_ventilation = models.AirChange(active=always_on, air_exch=residual_vent)
|
||||
infiltration_ventilation = models.AirChange(
|
||||
active=always_on, air_exch=residual_vent)
|
||||
if self.hepa_option:
|
||||
hepa = models.HEPAFilter(active=always_on, q_air_mech=self.hepa_amount)
|
||||
hepa = models.HEPAFilter(
|
||||
active=always_on, q_air_mech=self.hepa_amount)
|
||||
return models.MultipleVentilation((ventilation, hepa, infiltration_ventilation))
|
||||
else:
|
||||
return models.MultipleVentilation((ventilation, infiltration_ventilation))
|
||||
|
||||
def nearest_weather_station(self) -> caimira.data.weather.WxStationRecordType:
|
||||
def nearest_weather_station(self) -> data.weather.WxStationRecordType:
|
||||
"""Return the nearest weather station (which has valid data) for this form"""
|
||||
return caimira.data.weather.nearest_wx_station(
|
||||
return data.weather.nearest_wx_station(
|
||||
longitude=self.location_longitude, latitude=self.location_latitude
|
||||
)
|
||||
|
||||
|
|
@ -401,11 +429,13 @@ class VirusFormData(FormData):
|
|||
return mask
|
||||
|
||||
def generate_precise_activity_expiration(self) -> typing.Tuple[typing.Any, ...]:
|
||||
if self.precise_activity == {}: # It means the precise activity is not defined by a specific input.
|
||||
# It means the precise activity is not defined by a specific input.
|
||||
if self.precise_activity == {}:
|
||||
return ()
|
||||
respiratory_dict = {}
|
||||
for respiratory_activity in self.precise_activity['respiratory_activity']:
|
||||
respiratory_dict[respiratory_activity['type']] = respiratory_activity['percentage']
|
||||
respiratory_dict[respiratory_activity['type']
|
||||
] = respiratory_activity['percentage']
|
||||
|
||||
return (self.precise_activity['physical_activity'], respiratory_dict)
|
||||
|
||||
|
|
@ -413,11 +443,14 @@ class VirusFormData(FormData):
|
|||
# Initializes the virus
|
||||
virus = virus_distributions(self.data_registry)[self.virus_type]
|
||||
|
||||
activity_defn = self.data_registry.population_scenario_activity[self.activity_type]['activity']
|
||||
expiration_defn = self.data_registry.population_scenario_activity[self.activity_type]['expiration']
|
||||
activity_defn = self.data_registry.population_scenario_activity[
|
||||
self.activity_type]['activity']
|
||||
expiration_defn = self.data_registry.population_scenario_activity[
|
||||
self.activity_type]['expiration']
|
||||
if (self.activity_type == 'smallmeeting'):
|
||||
# Conversation of N people is approximately 1/N% of the time speaking.
|
||||
expiration_defn = {'Speaking': 1, 'Breathing': self.total_people - 1}
|
||||
expiration_defn = {'Speaking': 1,
|
||||
'Breathing': self.total_people - 1}
|
||||
elif (self.activity_type == 'precise'):
|
||||
activity_defn, expiration_defn = self.generate_precise_activity_expiration()
|
||||
|
||||
|
|
@ -434,7 +467,8 @@ class VirusFormData(FormData):
|
|||
mask=self.mask(),
|
||||
activity=activity,
|
||||
expiration=expiration,
|
||||
host_immunity=0., # Vaccination status does not affect the infected population (for now)
|
||||
# Vaccination status does not affect the infected population (for now)
|
||||
host_immunity=0.,
|
||||
)
|
||||
return infected
|
||||
|
||||
|
|
@ -452,8 +486,8 @@ class VirusFormData(FormData):
|
|||
if (self.vaccine_option):
|
||||
if (self.vaccine_booster_option and self.vaccine_booster_type != 'Other'):
|
||||
host_immunity = [vaccine['VE'] for vaccine in data.vaccine_booster_host_immunity if
|
||||
vaccine['primary series vaccine'] == self.vaccine_type and
|
||||
vaccine['booster vaccine'] == self.vaccine_booster_type][0]
|
||||
vaccine['primary series vaccine'] == self.vaccine_type and
|
||||
vaccine['booster vaccine'] == self.vaccine_booster_type][0]
|
||||
else:
|
||||
host_immunity = data.vaccine_primary_host_immunity[self.vaccine_type]
|
||||
else:
|
||||
|
|
@ -480,9 +514,10 @@ def build_expiration(data_registry, expiration_definition) -> mc._ExpirationBase
|
|||
elif isinstance(expiration_definition, dict):
|
||||
total_weight = sum(expiration_definition.values())
|
||||
BLO_factors = np.sum([
|
||||
np.array(expiration_BLO_factors(data_registry)[exp_type]) * weight/total_weight
|
||||
np.array(expiration_BLO_factors(data_registry)
|
||||
[exp_type]) * weight/total_weight
|
||||
for exp_type, weight in expiration_definition.items()
|
||||
], axis=0)
|
||||
], axis=0)
|
||||
return expiration_distribution(data_registry=data_registry, BLO_factors=tuple(BLO_factors))
|
||||
|
||||
|
||||
|
|
@ -524,7 +559,7 @@ def baseline_raw_form_data() -> typing.Dict[str, typing.Union[str, float]]:
|
|||
'mask_type': 'Type I',
|
||||
'mask_wearing_option': 'mask_off',
|
||||
'mechanical_ventilation_type': '',
|
||||
'calculator_version': calculator.__version__,
|
||||
'calculator_version': '4.17.0', #TODO different version for API and calculator form?
|
||||
'opening_distance': '0.2',
|
||||
'event_month': 'January',
|
||||
'room_heating_option': '0',
|
||||
|
|
@ -550,4 +585,5 @@ def baseline_raw_form_data() -> typing.Dict[str, typing.Union[str, float]]:
|
|||
'short_range_interactions': '[]',
|
||||
}
|
||||
|
||||
|
||||
cast_class_fields(VirusFormData)
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
import pandas as pd
|
||||
from tabulate import tabulate
|
||||
# import pandas as pd
|
||||
# from tabulate import tabulate
|
||||
|
||||
'''
|
||||
Script file to generate the vaccine effectiveness values.
|
||||
|
|
@ -1,22 +1,13 @@
|
|||
import pytest
|
||||
|
||||
from caimira.apps.calculator import model_generator
|
||||
from caimira.calculator.validators.virus import virus_validator
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def baseline_form_data():
|
||||
return model_generator.baseline_raw_form_data()
|
||||
return virus_validator.baseline_raw_form_data()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def baseline_form(baseline_form_data, data_registry):
|
||||
return model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def baseline_form_with_sr(baseline_form_data, data_registry):
|
||||
form_data_sr = baseline_form_data
|
||||
form_data_sr['short_range_option'] = 'short_range_yes'
|
||||
form_data_sr['short_range_interactions'] = '[{"expiration": "Shouting", "start_time": "10:30", "duration": "30"}]'
|
||||
form_data_sr['short_range_occupants'] = 5
|
||||
return model_generator.VirusFormData.from_dict(form_data_sr, data_registry)
|
||||
return virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
|
|
|||
|
|
@ -6,24 +6,24 @@ import numpy.testing as npt
|
|||
import pytest
|
||||
from retry import retry
|
||||
|
||||
from caimira.apps.calculator import model_generator
|
||||
from caimira.apps.calculator.form_data import (_hours2timestring, minutes_since_midnight,
|
||||
from caimira.calculator.validators.virus import virus_validator
|
||||
from caimira.calculator.validators.form_validator import (_hours2timestring, minutes_since_midnight,
|
||||
_CAST_RULES_FORM_ARG_TO_NATIVE, _CAST_RULES_NATIVE_TO_FORM_ARG)
|
||||
from caimira import models
|
||||
from caimira.monte_carlo.data import expiration_distributions
|
||||
from caimira.apps.calculator.defaults import NO_DEFAULT
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models.monte_carlo.data import expiration_distributions
|
||||
from caimira.calculator.validators.defaults import NO_DEFAULT
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
|
||||
def test_model_from_dict(baseline_form_data, data_registry):
|
||||
form = model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
form = virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
assert isinstance(form.build_model(), models.ExposureModel)
|
||||
|
||||
|
||||
def test_model_from_dict_invalid(baseline_form_data, data_registry):
|
||||
baseline_form_data['invalid_item'] = 'foobar'
|
||||
with pytest.raises(ValueError, match='Invalid argument "invalid_item" given'):
|
||||
model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
@retry(tries=10)
|
||||
|
|
@ -39,14 +39,14 @@ def test_blend_expiration(data_registry, mask_type):
|
|||
SAMPLE_SIZE = 250000
|
||||
TOLERANCE = 0.02
|
||||
blend = {'Breathing': 2, 'Speaking': 1}
|
||||
r = model_generator.build_expiration(data_registry, blend).build_model(SAMPLE_SIZE)
|
||||
r = virus_validator.build_expiration(data_registry, blend).build_model(SAMPLE_SIZE)
|
||||
mask = models.Mask.types[mask_type]
|
||||
expected = (expiration_distributions(data_registry)['Breathing'].build_model(SAMPLE_SIZE).aerosols(mask).mean()*2/3. +
|
||||
expiration_distributions(data_registry)['Speaking'].build_model(SAMPLE_SIZE).aerosols(mask).mean()/3.)
|
||||
npt.assert_allclose(r.aerosols(mask).mean(), expected, rtol=TOLERANCE)
|
||||
|
||||
|
||||
def test_ventilation_slidingwindow(data_registry: DataRegistry, baseline_form: model_generator.VirusFormData):
|
||||
def test_ventilation_slidingwindow(data_registry: DataRegistry, baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.ventilation_type = 'natural_ventilation'
|
||||
baseline_form.windows_duration = 10
|
||||
baseline_form.windows_frequency = 120
|
||||
|
|
@ -77,7 +77,7 @@ def test_ventilation_slidingwindow(data_registry: DataRegistry, baseline_form: m
|
|||
assert ventilation == baseline_vent
|
||||
|
||||
|
||||
def test_ventilation_hingedwindow(baseline_form: model_generator.VirusFormData):
|
||||
def test_ventilation_hingedwindow(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.ventilation_type = 'natural_ventilation'
|
||||
baseline_form.windows_duration = 10
|
||||
baseline_form.windows_frequency = 120
|
||||
|
|
@ -107,7 +107,7 @@ def test_ventilation_hingedwindow(baseline_form: model_generator.VirusFormData):
|
|||
assert ventilation == baseline_vent
|
||||
|
||||
|
||||
def test_ventilation_mechanical(baseline_form: model_generator.VirusFormData):
|
||||
def test_ventilation_mechanical(baseline_form: virus_validator.VirusFormData):
|
||||
room = models.Room(volume=75, inside_temp=models.PiecewiseConstant((0, 24), (293,)))
|
||||
mech = models.HVACMechanical(
|
||||
active=models.PeriodicInterval(period=120, duration=120),
|
||||
|
|
@ -122,7 +122,7 @@ def test_ventilation_mechanical(baseline_form: model_generator.VirusFormData):
|
|||
np.array([baseline_form.ventilation().air_exchange(room, t) for t in ts]))
|
||||
|
||||
|
||||
def test_ventilation_airchanges(baseline_form: model_generator.VirusFormData):
|
||||
def test_ventilation_airchanges(baseline_form: virus_validator.VirusFormData):
|
||||
room = models.Room(75, inside_temp=models.PiecewiseConstant((0, 24), (293,)))
|
||||
airchange = models.AirChange(
|
||||
active=models.PeriodicInterval(period=120, duration=120),
|
||||
|
|
@ -137,7 +137,7 @@ def test_ventilation_airchanges(baseline_form: model_generator.VirusFormData):
|
|||
np.array([baseline_form.ventilation().air_exchange(room, t) for t in ts]))
|
||||
|
||||
|
||||
def test_ventilation_window_hepa(data_registry: DataRegistry, baseline_form: model_generator.VirusFormData):
|
||||
def test_ventilation_window_hepa(data_registry: DataRegistry, baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.ventilation_type = 'natural_ventilation'
|
||||
baseline_form.windows_duration = 10
|
||||
baseline_form.windows_frequency = 120
|
||||
|
|
@ -181,7 +181,7 @@ def test_ventilation_window_hepa(data_registry: DataRegistry, baseline_form: mod
|
|||
]
|
||||
)
|
||||
def test_infected_less_than_total_people(activity, total_people, infected_people, error,
|
||||
baseline_form: model_generator.VirusFormData,
|
||||
baseline_form: virus_validator.VirusFormData,
|
||||
data_registry: DataRegistry):
|
||||
baseline_form.activity_type = activity
|
||||
baseline_form.total_people = total_people
|
||||
|
|
@ -195,7 +195,7 @@ def present_times(interval: models.Interval) -> models.BoundarySequence_t:
|
|||
return interval.present_times
|
||||
|
||||
|
||||
def test_infected_present_intervals(baseline_form: model_generator.VirusFormData):
|
||||
def test_infected_present_intervals(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.infected_dont_have_breaks_with_exposed = False
|
||||
baseline_form.exposed_coffee_duration = 15
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
|
|
@ -209,7 +209,7 @@ def test_infected_present_intervals(baseline_form: model_generator.VirusFormData
|
|||
assert present_times(baseline_form.infected_present_interval()) == correct
|
||||
|
||||
|
||||
def test_exposed_present_intervals(baseline_form: model_generator.VirusFormData):
|
||||
def test_exposed_present_intervals(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_duration = 15
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
|
|
@ -220,7 +220,7 @@ def test_exposed_present_intervals(baseline_form: model_generator.VirusFormData)
|
|||
assert present_times(baseline_form.exposed_present_interval()) == correct
|
||||
|
||||
|
||||
def test_present_intervals_common_breaks(baseline_form: model_generator.VirusFormData):
|
||||
def test_present_intervals_common_breaks(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.infected_dont_have_breaks_with_exposed = False
|
||||
baseline_form.infected_coffee_duration = baseline_form.exposed_coffee_duration = 15
|
||||
baseline_form.infected_coffee_break_option = baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
|
|
@ -236,7 +236,7 @@ def test_present_intervals_common_breaks(baseline_form: model_generator.VirusFor
|
|||
assert present_times(baseline_form.infected_present_interval()) == correct_infected
|
||||
|
||||
|
||||
def test_present_intervals_split_breaks(baseline_form: model_generator.VirusFormData):
|
||||
def test_present_intervals_split_breaks(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.infected_dont_have_breaks_with_exposed = True
|
||||
baseline_form.infected_coffee_duration = baseline_form.exposed_coffee_duration = 15
|
||||
baseline_form.infected_coffee_break_option = baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
|
|
@ -252,7 +252,7 @@ def test_present_intervals_split_breaks(baseline_form: model_generator.VirusForm
|
|||
assert present_times(baseline_form.infected_present_interval()) == correct_infected
|
||||
|
||||
|
||||
def test_exposed_present_intervals_starting_with_lunch(baseline_form: model_generator.VirusFormData):
|
||||
def test_exposed_present_intervals_starting_with_lunch(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_0'
|
||||
baseline_form.exposed_start = baseline_form.exposed_lunch_start = minutes_since_midnight(13 * 60)
|
||||
baseline_form.exposed_finish = minutes_since_midnight(18 * 60)
|
||||
|
|
@ -261,7 +261,7 @@ def test_exposed_present_intervals_starting_with_lunch(baseline_form: model_gene
|
|||
assert present_times(baseline_form.exposed_present_interval()) == correct
|
||||
|
||||
|
||||
def test_exposed_present_intervals_ending_with_lunch(baseline_form: model_generator.VirusFormData):
|
||||
def test_exposed_present_intervals_ending_with_lunch(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_0'
|
||||
baseline_form.exposed_start = minutes_since_midnight(11 * 60)
|
||||
baseline_form.exposed_finish = baseline_form.exposed_lunch_start = minutes_since_midnight(13 * 60)
|
||||
|
|
@ -270,7 +270,7 @@ def test_exposed_present_intervals_ending_with_lunch(baseline_form: model_genera
|
|||
assert present_times(baseline_form.exposed_present_interval()) == correct
|
||||
|
||||
|
||||
def test_exposed_present_lunch_end_before_beginning(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_exposed_present_lunch_end_before_beginning(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_0'
|
||||
baseline_form.exposed_lunch_start = minutes_since_midnight(14 * 60)
|
||||
baseline_form.exposed_lunch_finish = minutes_since_midnight(13 * 60)
|
||||
|
|
@ -287,7 +287,7 @@ def test_exposed_present_lunch_end_before_beginning(baseline_form: model_generat
|
|||
[9, 20], # lunch_finish after the presence finishing
|
||||
],
|
||||
)
|
||||
def test_exposed_presence_lunch_break(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry, exposed_lunch_start, exposed_lunch_finish):
|
||||
def test_exposed_presence_lunch_break(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry, exposed_lunch_start, exposed_lunch_finish):
|
||||
baseline_form.exposed_lunch_start = minutes_since_midnight(exposed_lunch_start * 60)
|
||||
baseline_form.exposed_lunch_finish = minutes_since_midnight(exposed_lunch_finish * 60)
|
||||
with pytest.raises(ValueError, match='exposed lunch break must be within presence times.'):
|
||||
|
|
@ -303,14 +303,14 @@ def test_exposed_presence_lunch_break(baseline_form: model_generator.VirusFormDa
|
|||
[9, 20], # lunch_finish after the presence finishing
|
||||
],
|
||||
)
|
||||
def test_infected_presence_lunch_break(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry, infected_lunch_start, infected_lunch_finish):
|
||||
def test_infected_presence_lunch_break(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry, infected_lunch_start, infected_lunch_finish):
|
||||
baseline_form.infected_lunch_start = minutes_since_midnight(infected_lunch_start * 60)
|
||||
baseline_form.infected_lunch_finish = minutes_since_midnight(infected_lunch_finish * 60)
|
||||
with pytest.raises(ValueError, match='infected lunch break must be within presence times.'):
|
||||
baseline_form.validate()
|
||||
|
||||
|
||||
def test_exposed_breaks_length(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_exposed_breaks_length(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_4'
|
||||
baseline_form.exposed_coffee_duration = 30
|
||||
baseline_form.exposed_start = minutes_since_midnight(10 * 60)
|
||||
|
|
@ -320,7 +320,7 @@ def test_exposed_breaks_length(baseline_form: model_generator.VirusFormData, dat
|
|||
baseline_form.validate()
|
||||
|
||||
|
||||
def test_infected_breaks_length(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_infected_breaks_length(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.infected_start = minutes_since_midnight(9 * 60)
|
||||
baseline_form.infected_finish = minutes_since_midnight(12 * 60)
|
||||
baseline_form.infected_lunch_start = minutes_since_midnight(10 * 60)
|
||||
|
|
@ -332,7 +332,7 @@ def test_infected_breaks_length(baseline_form: model_generator.VirusFormData, da
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def coffee_break_between_1045_and_1115(baseline_form: model_generator.VirusFormData):
|
||||
def coffee_break_between_1045_and_1115(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_1'
|
||||
baseline_form.exposed_coffee_duration = 30
|
||||
baseline_form.exposed_start = minutes_since_midnight(10 * 60)
|
||||
|
|
@ -390,7 +390,7 @@ def assert_boundaries(interval, boundaries_in_time_string_form):
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def breaks_every_25_mins_for_20_mins(baseline_form: model_generator.VirusFormData):
|
||||
def breaks_every_25_mins_for_20_mins(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_4'
|
||||
baseline_form.exposed_coffee_duration = 20
|
||||
baseline_form.exposed_start = time2mins("10:00")
|
||||
|
|
@ -435,7 +435,7 @@ def test_present_only_during_second_break(breaks_every_25_mins_for_20_mins):
|
|||
assert_boundaries(interval, [])
|
||||
|
||||
|
||||
def test_valid_no_lunch(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_valid_no_lunch(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
# Check that it is valid to have a 0 length lunch if no lunch is selected.
|
||||
baseline_form.exposed_lunch_option = False
|
||||
baseline_form.exposed_lunch_start = minutes_since_midnight(0)
|
||||
|
|
@ -443,7 +443,7 @@ def test_valid_no_lunch(baseline_form: model_generator.VirusFormData, data_regis
|
|||
assert baseline_form.validate() is None
|
||||
|
||||
|
||||
def test_no_breaks(baseline_form: model_generator.VirusFormData):
|
||||
def test_no_breaks(baseline_form: virus_validator.VirusFormData):
|
||||
# Check that the times are correct in the absence of breaks.
|
||||
baseline_form.infected_dont_have_breaks_with_exposed = False
|
||||
baseline_form.exposed_lunch_option = False
|
||||
|
|
@ -458,7 +458,7 @@ def test_no_breaks(baseline_form: model_generator.VirusFormData):
|
|||
assert present_times(baseline_form.infected_present_interval()) == infected_correct
|
||||
|
||||
|
||||
def test_coffee_lunch_breaks(baseline_form: model_generator.VirusFormData):
|
||||
def test_coffee_lunch_breaks(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_duration = 30
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_4'
|
||||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
|
|
@ -470,7 +470,7 @@ def test_coffee_lunch_breaks(baseline_form: model_generator.VirusFormData):
|
|||
np.testing.assert_allclose(present_times(baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
|
||||
|
||||
def test_coffee_lunch_breaks_unbalance(baseline_form: model_generator.VirusFormData):
|
||||
def test_coffee_lunch_breaks_unbalance(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_duration = 30
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
|
|
@ -481,7 +481,7 @@ def test_coffee_lunch_breaks_unbalance(baseline_form: model_generator.VirusFormD
|
|||
np.testing.assert_allclose(present_times(baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
|
||||
|
||||
def test_coffee_breaks(baseline_form: model_generator.VirusFormData):
|
||||
def test_coffee_breaks(baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.exposed_coffee_duration = 10
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_4'
|
||||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
|
|
@ -494,24 +494,24 @@ def test_coffee_breaks(baseline_form: model_generator.VirusFormData):
|
|||
def test_key_validation(baseline_form_data, data_registry):
|
||||
baseline_form_data['activity_type'] = 'invalid key'
|
||||
with pytest.raises(ValueError):
|
||||
model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
def test_key_validation_natural_ventilation_window_type_na(baseline_form_data, data_registry):
|
||||
baseline_form_data['ventilation_type'] = 'natural_ventilation'
|
||||
baseline_form_data['window_type'] = 'not-applicable'
|
||||
with pytest.raises(ValueError, match='window_type cannot be \'not-applicable\''):
|
||||
model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
def test_key_validation_natural_ventilation_window_opening_regime_na(baseline_form_data, data_registry):
|
||||
baseline_form_data['ventilation_type'] = 'natural_ventilation'
|
||||
baseline_form_data['window_opening_regime'] = 'not-applicable'
|
||||
with pytest.raises(ValueError, match='window_opening_regime cannot be \'not-applicable\''):
|
||||
model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
def test_natural_ventilation_window_opening_periodically(baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_natural_ventilation_window_opening_periodically(baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.window_opening_regime = 'windows_open_periodically'
|
||||
baseline_form.windows_duration = 20
|
||||
baseline_form.windows_frequency = 10
|
||||
|
|
@ -523,20 +523,20 @@ def test_key_validation_mech_ventilation_type_na(baseline_form_data, data_regist
|
|||
baseline_form_data['ventilation_type'] = 'mechanical_ventilation'
|
||||
baseline_form_data['mechanical_ventilation_type'] = 'not-applicable'
|
||||
with pytest.raises(ValueError, match='mechanical_ventilation_type cannot be \'not-applicable\''):
|
||||
model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
def test_key_validation_event_month(baseline_form_data, data_registry):
|
||||
baseline_form_data['event_month'] = 'invalid month'
|
||||
with pytest.raises(ValueError, match='invalid month is not a valid value for event_month'):
|
||||
model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
|
||||
|
||||
def test_default_types():
|
||||
# Validate that VirusFormData._DEFAULTS are complete and of the correct type.
|
||||
# Validate that we have the right types and matching attributes to the DEFAULTS.
|
||||
fields = {field.name: field for field in dataclasses.fields(model_generator.VirusFormData)}
|
||||
for field, value in model_generator.VirusFormData._DEFAULTS.items():
|
||||
fields = {field.name: field for field in dataclasses.fields(virus_validator.VirusFormData)}
|
||||
for field, value in virus_validator.VirusFormData._DEFAULTS.items():
|
||||
if field not in fields:
|
||||
raise ValueError(f"Unmatched default {field}")
|
||||
|
||||
|
|
@ -557,7 +557,7 @@ def test_default_types():
|
|||
for field in fields.values():
|
||||
if field.name == "data_registry":
|
||||
continue # Skip the assertion for the "data_registry" field
|
||||
assert field.name in model_generator.VirusFormData._DEFAULTS, f"No default set for field name {field.name}"
|
||||
assert field.name in virus_validator.VirusFormData._DEFAULTS, f"No default set for field name {field.name}"
|
||||
|
||||
|
||||
def test_form_to_dict(baseline_form):
|
||||
|
|
@ -566,7 +566,7 @@ def test_form_to_dict(baseline_form):
|
|||
assert 1 < len(stripped) < len(full)
|
||||
assert 'exposed_coffee_break_option' in stripped
|
||||
# If we set the value to the default one, it should no longer turn up in the dictionary.
|
||||
baseline_form.exposed_coffee_break_option = model_generator.VirusFormData._DEFAULTS['exposed_coffee_break_option']
|
||||
baseline_form.exposed_coffee_break_option = virus_validator.VirusFormData._DEFAULTS['exposed_coffee_break_option']
|
||||
assert 'exposed_coffee_break_option' not in baseline_form.to_dict(baseline_form, strip_defaults=True)
|
||||
|
||||
|
||||
|
|
@ -584,7 +584,7 @@ def test_form_timezone(baseline_form_data, data_registry, longitude, latitude, m
|
|||
baseline_form_data['location_latitude'] = latitude
|
||||
baseline_form_data['location_longitude'] = longitude
|
||||
baseline_form_data['event_month'] = month
|
||||
form = model_generator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
form = virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
|
||||
name, offset = form.tz_name_and_utc_offset()
|
||||
assert name == expected_tz_name
|
||||
assert offset == expected_offset
|
||||
|
|
|
|||
|
|
@ -1,32 +0,0 @@
|
|||
import json
|
||||
|
||||
import tornado.testing
|
||||
|
||||
import caimira.apps.calculator
|
||||
from caimira.apps.calculator import model_generator
|
||||
|
||||
_TIMEOUT = 40.
|
||||
|
||||
|
||||
class TestCalculatorJsonResponse(tornado.testing.AsyncHTTPTestCase):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.http_client.defaults['request_timeout'] = _TIMEOUT
|
||||
|
||||
def get_app(self):
|
||||
return caimira.apps.calculator.make_app()
|
||||
|
||||
@tornado.testing.gen_test(timeout=_TIMEOUT)
|
||||
def test_json_response(self):
|
||||
response = yield self.http_client.fetch(
|
||||
request=self.get_url("/calculator/report-json"),
|
||||
method="POST",
|
||||
headers={'content-type': 'application/json'},
|
||||
body=json.dumps(model_generator.baseline_raw_form_data())
|
||||
)
|
||||
self.assertEqual(response.code, 200)
|
||||
|
||||
data = json.loads(response.body)
|
||||
self.assertIsInstance(data['prob_inf'], float)
|
||||
self.assertIsInstance(data['expected_new_cases'], float)
|
||||
|
||||
|
|
@ -2,8 +2,8 @@ from typing import Type
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from caimira.apps.calculator import model_generator
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from caimira.calculator.validators.virus import virus_validator
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -14,7 +14,7 @@ from caimira.store.data_registry import DataRegistry
|
|||
[{"exposed_breaks": [], "ifected_breaks": []}, 'Unable to fetch "infected_breaks" key. Got "ifected_breaks".'],
|
||||
]
|
||||
)
|
||||
def test_specific_break_structure(break_input, error, baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_specific_break_structure(break_input, error, baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.specific_breaks = break_input
|
||||
with pytest.raises(TypeError, match=error):
|
||||
baseline_form.validate()
|
||||
|
|
@ -31,7 +31,7 @@ def test_specific_break_structure(break_input, error, baseline_form: model_gener
|
|||
[[{"start_time": "10:00", "finish_time": "11"}], 'Wrong time format - "HH:MM". Got "11".'],
|
||||
]
|
||||
)
|
||||
def test_specific_population_break_data_structure(population_break_input, error, baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_specific_population_break_data_structure(population_break_input, error, baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.specific_breaks = {'exposed_breaks': population_break_input, 'infected_breaks': population_break_input}
|
||||
with pytest.raises(TypeError, match=error):
|
||||
baseline_form.validate()
|
||||
|
|
@ -46,7 +46,7 @@ def test_specific_population_break_data_structure(population_break_input, error,
|
|||
[{'exposed_breaks': [], 'infected_breaks': [{"start_time": "08:00", "finish_time": "11:00"}, {"start_time": "14:00", "finish_time": "15:00"}, ]}, "All breaks should be within the simulation time. Got 08:00."],
|
||||
]
|
||||
)
|
||||
def test_specific_break_time(break_input, error, baseline_form: model_generator.VirusFormData):
|
||||
def test_specific_break_time(break_input, error, baseline_form: virus_validator.VirusFormData):
|
||||
with pytest.raises(ValueError, match=error):
|
||||
baseline_form.generate_specific_break_times(breaks_dict=break_input, target='exposed')
|
||||
baseline_form.generate_specific_break_times(breaks_dict=break_input, target='infected')
|
||||
|
|
@ -65,7 +65,7 @@ def test_specific_break_time(break_input, error, baseline_form: model_generator.
|
|||
[{"physical_activity": "Light activity", "respiratory_activity": [{"type": "Breathing", "percentag": 50}, {"type": "Speaking", "percentage": 50}]}, 'Unable to fetch "percentage" key. Got "percentag".'],
|
||||
]
|
||||
)
|
||||
def test_precise_activity_structure(precise_activity_input, error, baseline_form: model_generator.VirusFormData, data_registry: DataRegistry):
|
||||
def test_precise_activity_structure(precise_activity_input, error, baseline_form: virus_validator.VirusFormData, data_registry: DataRegistry):
|
||||
baseline_form.precise_activity = precise_activity_input
|
||||
with pytest.raises(TypeError, match=error):
|
||||
baseline_form.validate()
|
||||
|
|
@ -80,7 +80,7 @@ def test_precise_activity_structure(precise_activity_input, error, baseline_form
|
|||
[{"physical_activity": "Light activity", "respiratory_activity": [{"type": "Breathing", "percentage": 50}]}, 'The sum of all respiratory activities should be 100. Got 50.'],
|
||||
]
|
||||
)
|
||||
def test_sum_precise_activity(precise_activity_input, error, baseline_form: model_generator.VirusFormData):
|
||||
def test_sum_precise_activity(precise_activity_input, error, baseline_form: virus_validator.VirusFormData):
|
||||
baseline_form.precise_activity = precise_activity_input
|
||||
with pytest.raises(ValueError, match=error):
|
||||
baseline_form.validate()
|
||||
|
|
|
|||
|
|
@ -1,11 +1,9 @@
|
|||
from caimira import models
|
||||
import caimira.data
|
||||
import caimira.dataclass_utils
|
||||
|
||||
import pytest
|
||||
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
|
||||
from caimira.calculator.models import models
|
||||
import caimira.calculator.models.data
|
||||
import caimira.calculator.models.dataclass_utils
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
@pytest.fixture
|
||||
def data_registry():
|
||||
|
|
@ -61,12 +59,12 @@ def baseline_exposure_model(data_registry, baseline_concentration_model, baselin
|
|||
|
||||
@pytest.fixture
|
||||
def exposure_model_w_outside_temp_changes(data_registry, baseline_exposure_model: models.ExposureModel):
|
||||
exp_model = caimira.dataclass_utils.nested_replace(
|
||||
exp_model = caimira.calculator.models.dataclass_utils.nested_replace(
|
||||
baseline_exposure_model, {
|
||||
'concentration_model.ventilation': models.SlidingWindow(
|
||||
data_registry=data_registry,
|
||||
active=models.PeriodicInterval(2.2 * 60, 1.8 * 60),
|
||||
outside_temp=caimira.data.GenevaTemperatures['Jan'],
|
||||
outside_temp=caimira.calculator.models.data.GenevaTemperatures['Jan'],
|
||||
window_height=1.6,
|
||||
opening_length=0.6,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ import numpy as np
|
|||
import numpy.testing
|
||||
import pytest
|
||||
|
||||
import caimira.data.weather as wx
|
||||
import caimira.calculator.models.data.weather as wx
|
||||
|
||||
|
||||
def test_nearest_wx_station():
|
||||
|
|
|
|||
|
|
@ -3,8 +3,8 @@ import numpy as np
|
|||
import typing
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
from caimira.apps.calculator.co2_model_generator import CO2FormData
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.validators.co2.co2_validator import CO2FormData
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -5,8 +5,8 @@ import numpy.testing as npt
|
|||
import pytest
|
||||
from dataclasses import dataclass
|
||||
|
||||
from caimira import models
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class KnownConcentrationModelBase(models._ConcentrationModelBase):
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@ import numpy as np
|
|||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
import caimira.dataclass_utils as dc_utils
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models import dataclass_utils as dc_utils
|
||||
|
||||
@pytest.fixture
|
||||
def full_exposure_model(data_registry):
|
||||
|
|
|
|||
|
|
@ -5,11 +5,11 @@ import numpy.testing
|
|||
import pytest
|
||||
from dataclasses import dataclass
|
||||
|
||||
from caimira import models
|
||||
from caimira.models import ExposureModel
|
||||
from caimira.dataclass_utils import replace
|
||||
from caimira.monte_carlo.data import expiration_distributions
|
||||
from caimira.store.data_registry import DataRegistry
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models.models import ExposureModel
|
||||
from caimira.calculator.models.dataclass_utils import replace
|
||||
from caimira.calculator.models.monte_carlo.data import expiration_distributions
|
||||
from caimira.calculator.store.data_registry import DataRegistry
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class KnownNormedconcentration(models.ConcentrationModel):
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import numpy as np
|
|||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
from caimira.calculator.models import models
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import numpy as np
|
|||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
from caimira.calculator.models import models
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
from caimira import data
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models import data
|
||||
|
||||
|
||||
def test_piecewiseconstantfunction_wrongarguments():
|
||||
|
|
|
|||
|
|
@ -3,10 +3,10 @@ import typing
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
import caimira.monte_carlo as mc_models
|
||||
from caimira.apps.calculator.model_generator import build_expiration
|
||||
from caimira.monte_carlo.data import short_range_expiration_distributions,\
|
||||
from caimira.calculator.models import models
|
||||
import caimira.calculator.models.monte_carlo as mc_models
|
||||
from caimira.calculator.validators.virus.virus_validator import build_expiration
|
||||
from caimira.calculator.models.monte_carlo.data import short_range_expiration_distributions,\
|
||||
expiration_distributions, short_range_distances, activity_distributions
|
||||
|
||||
SAMPLE_SIZE = 250_000
|
||||
|
|
|
|||
|
|
@ -1,25 +0,0 @@
|
|||
import numpy as np
|
||||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"inside_temp, humidity, expected_halflife, expected_decay_constant",
|
||||
[
|
||||
[293.15, 0.5, 0.5947447349860315, 1.1654532436949188],
|
||||
[272.15, 0.7, 1.6070844193207476, 0.4313072619127947],
|
||||
[300.15, 1., 0.17367078830147223, 3.9911558376571805],
|
||||
[300.15, 0., 6.43, 0.10779893943389507],
|
||||
[np.array([272.15, 300.15]), np.array([0.7, 0.]),
|
||||
np.array([1.60708442, 6.43]), np.array([0.43130726, 0.10779894])],
|
||||
[np.array([293.15, 300.15]), np.array([0.5, 1.]),
|
||||
np.array([0.59474473, 0.17367079]), np.array([1.16545324, 3.99115584])]
|
||||
],
|
||||
)
|
||||
def test_decay_constant(inside_temp, humidity, expected_halflife, expected_decay_constant):
|
||||
npt.assert_almost_equal(models.Virus.types['SARS_CoV_2'].halflife(humidity, inside_temp),
|
||||
expected_halflife)
|
||||
npt.assert_almost_equal(models.Virus.types['SARS_CoV_2'].decay_constant(humidity, inside_temp),
|
||||
expected_decay_constant)
|
||||
|
|
@ -3,8 +3,8 @@ High-level tests for the package.
|
|||
|
||||
"""
|
||||
|
||||
import caimira
|
||||
import caimira.calculator.models
|
||||
|
||||
|
||||
def test_version():
|
||||
assert caimira.__version__ is not None
|
||||
assert caimira.calculator.models.__version__ is not None
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@ import numpy as np
|
|||
import pytest
|
||||
from retry import retry
|
||||
|
||||
import caimira.monte_carlo as mc
|
||||
from caimira import models
|
||||
from caimira.dataclass_utils import nested_replace
|
||||
from caimira.apps.calculator import report_generator
|
||||
from caimira.monte_carlo.data import activity_distributions, virus_distributions, expiration_distributions
|
||||
import caimira.calculator.models.monte_carlo as mc
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models.dataclass_utils import nested_replace
|
||||
from caimira.calculator.report import report_generator
|
||||
from caimira.calculator.models.monte_carlo.data import activity_distributions, virus_distributions, expiration_distributions
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import unittest
|
||||
from unittest.mock import Mock, patch
|
||||
|
||||
from caimira.store.data_service import DataService
|
||||
from caimira.calculator.store.data_service import DataService
|
||||
|
||||
|
||||
class DataServiceTests(unittest.TestCase):
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import dataclasses
|
||||
|
||||
from caimira.dataclass_utils import nested_replace, walk_dataclass
|
||||
from caimira.calculator.models.dataclass_utils import nested_replace, walk_dataclass
|
||||
|
||||
|
||||
@dataclasses.dataclass(frozen=True)
|
||||
|
|
|
|||
|
|
@ -5,8 +5,8 @@ import numpy.testing as npt
|
|||
import pytest
|
||||
from retry import retry
|
||||
|
||||
from caimira import models
|
||||
from caimira.monte_carlo.data import expiration_distribution
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models.monte_carlo.data import expiration_distribution
|
||||
|
||||
|
||||
def test_multiple_wrong_weight_size():
|
||||
|
|
|
|||
|
|
@ -8,11 +8,11 @@ import numpy.testing as npt
|
|||
import pytest
|
||||
from retry import retry
|
||||
|
||||
import caimira.monte_carlo as mc
|
||||
from caimira import models
|
||||
from caimira.utils import method_cache
|
||||
from caimira.models import _VectorisedFloat,Interval,SpecificInterval
|
||||
from caimira.monte_carlo.data import (expiration_distributions,
|
||||
import caimira.calculator.models.monte_carlo as mc
|
||||
from caimira.calculator.models import models
|
||||
from caimira.calculator.models.utils import method_cache
|
||||
from caimira.calculator.models.models import _VectorisedFloat,Interval,SpecificInterval
|
||||
from caimira.calculator.models.monte_carlo.data import (expiration_distributions,
|
||||
expiration_BLO_factors,short_range_expiration_distributions,
|
||||
short_range_distances,virus_distributions,activity_distributions)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import caimira.models
|
||||
import caimira.calculator.models.models
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -17,26 +17,26 @@ def test_infected_population_vectorisation(override_params, data_registry):
|
|||
}
|
||||
defaults.update(override_params)
|
||||
|
||||
office_hours = caimira.models.SpecificInterval(present_times=[(8,17)])
|
||||
infected = caimira.models.InfectedPopulation(
|
||||
office_hours = caimira.calculator.models.models.SpecificInterval(present_times=[(8,17)])
|
||||
infected = caimira.calculator.models.models.InfectedPopulation(
|
||||
data_registry=data_registry,
|
||||
number=1,
|
||||
presence=office_hours,
|
||||
mask=caimira.models.Mask(
|
||||
mask=caimira.calculator.models.models.Mask(
|
||||
factor_exhale=0.95,
|
||||
η_inhale=0.3,
|
||||
),
|
||||
activity=caimira.models.Activity(
|
||||
activity=caimira.calculator.models.models.Activity(
|
||||
0.51,
|
||||
defaults['exhalation_rate'],
|
||||
),
|
||||
virus=caimira.models.SARSCoV2(
|
||||
virus=caimira.calculator.models.models.SARSCoV2(
|
||||
viral_load_in_sputum=defaults['viral_load_in_sputum'],
|
||||
infectious_dose=50.,
|
||||
viable_to_RNA_ratio = 0.5,
|
||||
transmissibility_factor=1.0,
|
||||
),
|
||||
expiration=caimira.models._ExpirationBase.types['Breathing'],
|
||||
expiration=caimira.calculator.models.models._ExpirationBase.types['Breathing'],
|
||||
host_immunity=0.,
|
||||
)
|
||||
emission_rate = infected.emission_rate(10)
|
||||
|
|
|
|||
|
|
@ -2,8 +2,8 @@ import numpy as np
|
|||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
import caimira.models as models
|
||||
import caimira.data as data
|
||||
import caimira.calculator.models.models as models
|
||||
import caimira.calculator.models.data as data
|
||||
|
||||
|
||||
def test_no_mask_superspeading_emission_rate(baseline_concentration_model):
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
import caimira.models
|
||||
from caimira.dataclass_utils import nested_replace
|
||||
from caimira.calculator.models.dataclass_utils import nested_replace
|
||||
|
||||
|
||||
def test_exposure_r0(baseline_exposure_model):
|
||||
|
|
|
|||
|
|
@ -3,12 +3,12 @@ import dataclasses
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import caimira.models
|
||||
import caimira.monte_carlo.models as mc_models
|
||||
import caimira.monte_carlo.sampleable
|
||||
import caimira.calculator.models
|
||||
import caimira.calculator.models.models
|
||||
import caimira.calculator.models.monte_carlo.sampleable
|
||||
|
||||
MODEL_CLASSES = [
|
||||
cls for cls in vars(caimira.models).values()
|
||||
cls for cls in vars(caimira.calculator.models).values()
|
||||
if dataclasses.is_dataclass(cls)
|
||||
]
|
||||
|
||||
|
|
@ -21,11 +21,11 @@ def test_type_annotations():
|
|||
# runtime execution.
|
||||
missing = []
|
||||
for cls in MODEL_CLASSES:
|
||||
if not hasattr(caimira.monte_carlo, cls.__name__):
|
||||
if not hasattr(caimira.calculator.models.monte_carlo, cls.__name__):
|
||||
missing.append(cls.__name__)
|
||||
continue
|
||||
mc_cls = getattr(caimira.monte_carlo, cls.__name__)
|
||||
assert issubclass(mc_cls, caimira.monte_carlo.MCModelBase)
|
||||
mc_cls = getattr(caimira.calculator.models.monte_carlo, cls.__name__)
|
||||
assert issubclass(mc_cls, caimira.calculator.models.monte_carlo.MCModelBase)
|
||||
|
||||
if missing:
|
||||
msg = (
|
||||
|
|
@ -37,25 +37,25 @@ def test_type_annotations():
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def baseline_mc_concentration_model(data_registry) -> caimira.monte_carlo.ConcentrationModel:
|
||||
mc_model = caimira.monte_carlo.ConcentrationModel(
|
||||
def baseline_mc_concentration_model(data_registry) -> caimira.calculator.models.monte_carlo.ConcentrationModel:
|
||||
mc_model = caimira.calculator.models.monte_carlo.ConcentrationModel(
|
||||
data_registry=data_registry,
|
||||
room=caimira.monte_carlo.Room(volume=caimira.monte_carlo.sampleable.Normal(75, 20),
|
||||
inside_temp=caimira.models.PiecewiseConstant((0., 24.), (293,))),
|
||||
ventilation=caimira.monte_carlo.SlidingWindow(
|
||||
room=caimira.calculator.models.monte_carlo.Room(volume=caimira.calculator.models.monte_carlo.sampleable.Normal(75, 20),
|
||||
inside_temp=caimira.calculator.models.models.PiecewiseConstant((0., 24.), (293,))),
|
||||
ventilation=caimira.calculator.models.monte_carlo.SlidingWindow(
|
||||
data_registry=data_registry,
|
||||
active=caimira.models.PeriodicInterval(period=120, duration=120),
|
||||
outside_temp=caimira.models.PiecewiseConstant((0., 24.), (283,)),
|
||||
active=caimira.calculator.models.models.PeriodicInterval(period=120, duration=120),
|
||||
outside_temp=caimira.calculator.models.models.PiecewiseConstant((0., 24.), (283,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
infected=caimira.models.InfectedPopulation(
|
||||
infected=caimira.calculator.models.models.InfectedPopulation(
|
||||
data_registry=data_registry,
|
||||
number=1,
|
||||
virus=caimira.models.Virus.types['SARS_CoV_2'],
|
||||
presence=caimira.models.SpecificInterval(((0., 4.), (5., 8.))),
|
||||
mask=caimira.models.Mask.types['No mask'],
|
||||
activity=caimira.models.Activity.types['Light activity'],
|
||||
expiration=caimira.models.Expiration.types['Breathing'],
|
||||
virus=caimira.calculator.models.models.Virus.types['SARS_CoV_2'],
|
||||
presence=caimira.calculator.models.models.SpecificInterval(((0., 4.), (5., 8.))),
|
||||
mask=caimira.calculator.models.models.Mask.types['No mask'],
|
||||
activity=caimira.calculator.models.models.Activity.types['Light activity'],
|
||||
expiration=caimira.calculator.models.models.Expiration.types['Breathing'],
|
||||
host_immunity=0.,
|
||||
),
|
||||
evaporation_factor=0.3,
|
||||
|
|
@ -64,39 +64,39 @@ def baseline_mc_concentration_model(data_registry) -> caimira.monte_carlo.Concen
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def baseline_mc_sr_model() -> caimira.monte_carlo.ShortRangeModel:
|
||||
def baseline_mc_sr_model() -> caimira.calculator.models.monte_carlo.ShortRangeModel:
|
||||
return ()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def baseline_mc_exposure_model(data_registry, baseline_mc_concentration_model, baseline_mc_sr_model) -> caimira.monte_carlo.ExposureModel:
|
||||
return caimira.monte_carlo.ExposureModel(
|
||||
def baseline_mc_exposure_model(data_registry, baseline_mc_concentration_model, baseline_mc_sr_model) -> caimira.calculator.models.monte_carlo.ExposureModel:
|
||||
return caimira.calculator.models.monte_carlo.ExposureModel(
|
||||
data_registry,
|
||||
baseline_mc_concentration_model,
|
||||
baseline_mc_sr_model,
|
||||
exposed=caimira.models.Population(
|
||||
exposed=caimira.calculator.models.models.Population(
|
||||
number=10,
|
||||
presence=baseline_mc_concentration_model.infected.presence,
|
||||
activity=baseline_mc_concentration_model.infected.activity,
|
||||
mask=baseline_mc_concentration_model.infected.mask,
|
||||
host_immunity=0.,
|
||||
),
|
||||
geographical_data=caimira.models.Cases(),
|
||||
geographical_data=caimira.calculator.models.models.Cases(),
|
||||
)
|
||||
|
||||
|
||||
def test_build_concentration_model(baseline_mc_concentration_model: caimira.monte_carlo.ConcentrationModel):
|
||||
def test_build_concentration_model(baseline_mc_concentration_model: caimira.calculator.models.monte_carlo.ConcentrationModel):
|
||||
model = baseline_mc_concentration_model.build_model(7)
|
||||
assert isinstance(model, caimira.models.ConcentrationModel)
|
||||
assert isinstance(model, caimira.calculator.models.models.ConcentrationModel)
|
||||
assert isinstance(model.concentration(time=0.), float)
|
||||
conc = model.concentration(time=1.)
|
||||
assert isinstance(conc, np.ndarray)
|
||||
assert conc.shape == (7, )
|
||||
|
||||
|
||||
def test_build_exposure_model(baseline_mc_exposure_model: caimira.monte_carlo.ExposureModel):
|
||||
def test_build_exposure_model(baseline_mc_exposure_model: caimira.calculator.models.monte_carlo.ExposureModel):
|
||||
model = baseline_mc_exposure_model.build_model(7)
|
||||
assert isinstance(model, caimira.models.ExposureModel)
|
||||
assert isinstance(model, caimira.calculator.models.models.ExposureModel)
|
||||
prob = model.deposited_exposure()
|
||||
assert isinstance(prob, np.ndarray)
|
||||
assert prob.shape == (7, )
|
||||
|
|
|
|||
|
|
@ -3,10 +3,10 @@ import numpy.testing as npt
|
|||
import pytest
|
||||
from retry import retry
|
||||
|
||||
import caimira.monte_carlo as mc
|
||||
from caimira import models,data
|
||||
from caimira.monte_carlo.data import activity_distributions, virus_distributions, expiration_distributions, infectious_dose_distribution, viable_to_RNA_ratio_distribution
|
||||
from caimira.apps.calculator.model_generator import build_expiration
|
||||
import caimira.calculator.models.monte_carlo as mc
|
||||
from caimira.calculator.models import models, data
|
||||
from caimira.calculator.models.monte_carlo.data import activity_distributions, virus_distributions, expiration_distributions, infectious_dose_distribution, viable_to_RNA_ratio_distribution
|
||||
from caimira.calculator.validators.virus.virus_validator import build_expiration
|
||||
|
||||
SAMPLE_SIZE = 500_000
|
||||
TOLERANCE = 0.05
|
||||
|
|
|
|||
|
|
@ -2,8 +2,7 @@ import numpy as np
|
|||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
from caimira.monte_carlo.data import activity_distributions, virus_distributions
|
||||
from caimira.store import data_registry
|
||||
from caimira.calculator.models.monte_carlo.data import activity_distributions, virus_distributions
|
||||
|
||||
|
||||
# Mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import numpy.testing as npt
|
|||
import pytest
|
||||
from retry import retry
|
||||
|
||||
from caimira.monte_carlo import sampleable
|
||||
from caimira.calculator.models.monte_carlo import sampleable
|
||||
|
||||
|
||||
@retry(tries=10)
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ import numpy as np
|
|||
import numpy.testing as npt
|
||||
import pytest
|
||||
|
||||
from caimira import models
|
||||
from caimira.calculator.models import models
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
Some files were not shown because too many files have changed in this diff Show more
Loading…
Reference in a new issue