This is meant as a demo implementation aimed to demonstrate some of the capabilities of fhirbug. IT IS NOT A PRODUCTION READY SERVER, please do not use it as one!
It uses models generated from the FHIR specification files so it covers pretty much every possible resource and can populate the database using FHIR’s official examples.
Running the example server locally¶
The only requirement besides the python packages is mongodb. If you are on Linux, installing it is probably as simple as:
$ sudo apt install mongodb-server
or equivalent. See detailed instructions for your platform here.
All python packages required are in requirements.txt, so, in a Python >= 3.6 environment, do:
$ pip install -r requirements.txt
and you should be set to go.
Starting the development server¶
In order to run the server locally for testing/debugging run:
$ python examples/pymodm_autogenerated/flask_app.py
from the project’s root directory.
Deploying using gunicorn¶
If you intend to put heavier load on the server,it is recommended to run it using a wsgi server. Fhirbug comes with gunicorn included in it’s requirements but you can use any alternative as long as it can serve a flak app (so all of them).
So, if you haven’t installed gunicorn from the server’s requirements.txt, do:
$ pip install gunicorn
$ cd examples/pymodm_autogenerated $ gunicorn --bind 0.0.0.0:5001 flask_app:app
It is recommended to serve the app behind a proxy server like nginx. Have a look at https://gunicorn.org/#deployment for more details.
Deploying via docker¶
Then, simply clone the git repository and start the instances:
$ git clone https://github.com/zensoup/fhirbug.git $ cd fhirbug/examples/pymodm_autogenerated $ sudo docker-compose up
By default, this will create one docker instance for mongodb and one for python. It will then download the example files from fhir.org and populate the database with the example resources. Finally, it will start serving the application, binding to port 5000.
If you do not want to generate example resources you can edit the DockerFile before
If you want to acces the mongo database from the host machine for debugging purposes
docker-compose.yml to contain the following:
version: '3' services: web: build: . ports: - "5000:5000" depends_on: - mongo mongo: image: "mongo:4" ports: - "27018:27017"
Then, you can connect to the db from the command line:
$ mongo mongodb://localhost:27018
Generating Example data¶
The demo server comes with a couple of scripts that download the FHIR specification files from http://hl7.org/fhir/ and populates the database with the examples included within it.
If you are developing on fhirbug please note that these files are also used to generate the fixtures for some of fhirbug’s tests. So you should be aware that you may see failing tests if you change these files.
Downloading the specification files¶
To download the specification files fron the hl7 archive can either use:
$ python examples/pymodm_autogenerated/tools/download_examples.py examples/pymodm_autogenerated/test_cache
$ cd examples/pymodm_autogenerated/ $ python tools/download_examples.py
This will create a folder called
test_cache inside the dem server’s root directory.
Using your own sample data¶
You can feed the fixture generation script any valid FHIR JSON formatted data and
it will seed the database using them, as long as you place inside the
folder. For the script to recogninse them as seed data, they must fit the glob pattern
*example*.json. So any set of files like
would be recognized and parsed for use as seed data.
Populating the database¶
Once you have a cache folder with the seed data you want to use, run:
$ python examples/pymodm_autogenerated/generate_examples.py
This script will read the database configuration in
and use that caonnection to write the documents.
This script drops the database before starting so you will loose any existing documents in that database.
Generating the Models¶
These models have already been generated and live in
examples/pymodm_autogenerated/mappings.py. This section only
applies if you want to customize the way models are generated.
The script in
goes through all of fhirbug’s FHIR resource classes and creates code for the
corresponding fhirbug Mappings.
You can call it simply by calling:
$ python examples/pymodm_autogenerated/tools/generate_pymodm_schema.py <output_path>
By default this will create a file in
mappings.py. You can pass a path to the script to override this behavior.
There is currently a bug in pymodm that does not allow cyclic references between models. This means that some resource attributes have not been included in the generated models.
Namely, the attributes that are missing from the generated models are:CodeSystemConcept.concept CompositionSection.section ConsentProvision.provision ContractTerm.group ExampleScenarioProcessStep.process ExampleScenarioProcessStepAlternative.step Extension.extension FHIRReference.identifier GraphDefinitionLinkTarget.link ImplementationGuideDefinitionPage.page MedicinalProductAuthorizationProcedure.application MedicinalProductPackagedPackageItem.packageItem OperationDefinitionParameter.part ParametersParameter.part QuestionnaireItem.item QuestionnaireResponseItem.item QuestionnaireResponseItemAnswer.item RequestGroupAction.action resource.action StructureMapGroupRule.rule SubstanceSpecificationName.synonym SubstanceSpecificationName.translation ValueSetExpansionContains.contains
plus all of the value attributes of the Extension resource:valueAddress, valueAge, valueAnnotation, valueAttachment, valueCodeableConcept, valueCoding, valueContactDetail, valueContactPoint, valueContributor, valueCount, valueDataRequirement, valueDistance, valueDosage, valueDuration, valueExpression, valueHumanName, valueIdentifier, valueMoney, valueParameterDefinition, valuePeriod, valueQuantity, valueRange, valueRatio, valueReference, valueRelatedArtifact, valueSampledData, valueSignature, valueTiming, valueTriggerDefinition, valueUsageContext,