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FHIR Converter for Python

Is there is any function available in python to convert the given json input into HL7 FHIR format. By passing the Liquid Template (Shopify) and the input source data.

Not that I am aware of, but I stand corrected; You will most likely have to create your own.
See example of someone who had a near similar problem: https://forums.librehealth.io/t/project-rest-json-to-fhir-json-mapping-implementation/1765/14

Thinking that you are looking for something like fhir.resources library, where you can provide some dictionary with data to some kind of object that can be serialized.
from fhir.resources.organization import Organization
from fhir.resources.address import Address
data = {
"id": "f001",
"active": True,
"name": "Acme Corporation",
"address": [{"country": "Switzerland"}]
}
org = Organization(**data)
org.resource_type == "Organization"
P.S. But don't quite follow why you mentioned Liquid Templates.

DynamoDB Query by by nested array

I have a Companies table in DynamoDB that looks like this:
company: {
id: "11",
name: "test",
jobs: [
{
"name": "painter",
"id": 3
},
{
"name": "gardner"
"id": 2
}
]
}
And I want to make a scan query that get all the companies with the "painter" job inside their jobs array
I am using python and boto3
I tried something like this but it didn't work
jobs = ["painter"]
response = self.table.scan(
FilterExpression=Attr('jobs.name').is_in(jobs)
)
Please help.
Thanks.

It looks like this may not be doable in general, however it's possible that the method applied in that link may still be useful. If you know the maximum length of the jobs array over all of your data, you could create an expression for each index chained with ORs. Notably I could not find documentation for handling map and list scan expressions, so I can't really say whether you'd also need to check that you're not going out of bounds.

Best practice for collections in jsons: array vs dict/map

I need to pass data in a python back-end to a front end through an api call, using a json format. In the python back end, the data is in a dictionary structure, which I can easily and directly convert to a json. But should I?
My front-end developer believes the answer is no, for reasons related to best practice.
But I challenge that:
Is the best to structure a json as it is in python, or should it rather be converted to some other form, such as several arrays (as would be necessary in my example case below)?
Or, differently put:
What should be the governing principles related to collections/dicts/maps/arrays for interfacing information through jsons?
I've done some googling for an answer, but I've not come across much that addresses this directly. Links would be appreciated.
(Note about the example below: of course if the data is written to a database, it would probably make most sense for the front-end to access the database directly, but let's assume this is not the case)
Example:
In the back end there is a collection of objects called pets:
each item in the collection has a unique pet_id, some non-optional properties, e.g. name and date_of_birth, some optional properties registration_certificate_nr, adopted_from_kennel, some lists like siblings and children and some objects like medication.
Assuming that the front end needs all of this info at some point, it could be
{
"pets": {
"17-01-24-01": {
"name": "Buster",
"date_of_birth": "04/01/2017",
"registration_certificate_nr": "AAD-1123-1432"
},
"17-03-04-01": {
"name": "Hooch",
"date_of_birth": "05/02/2015",
"adopted_from_kennel": "Pretoria Shire",
"children": [
"17-05-01-01",
"17-05-01-02",
"17-05-01-03"
]
},
"17-05-01-01": {
"name": "Snappy",
"date_of_birth": "17-05-01",
"siblings": [
"17-05-01-02",
"17-05-01-03"
]
},
"17-05-01-02": {
"name": "Gizmo",
"date_of_birth": "17-05-01",
"siblings": [
"17-05-01-01",
"17-05-01-03"
]
},
"17-05-01-03": {
"name": "Toothless",
"date_of_birth": "17-05-01",
"siblings": [
"17-05-01-01",
"17-05-01-03"
],
"medication": [
{
"name": "anti-worm",
"code": "aw445",
"dosage": "1 pill per day"
},
{
"name": "disinfectant",
"code": "pdi-2",
"dosage": "as required"
}
]
}
}
}

JSON formatting is a somewhat subjective matter, and related disagreements are usually best settled between colleagues.
That being said, there are some potentially valid criticisms to be made against the JSON format in the question, especially if we are trying to create a consistent, RESTful API.
The 2 pain points that stand out:
A map collection is represented in JSON, which isn't really JSON standard compliant, or particularly RESTful.
None of the pet objects have an id defined. There is a pet_id mentioned in the question, but it seems to be maintained separately from the pet object itself. If a value is accessed in the pets map in the question, a user of the API would have to manually add the pet_id to the provided pet object in order to have the id available further down the line, when the full JSON may no longer be available.
The closest things we have to guiding standards in this situation is the REST architectural style and the JSON standard.
We can start by looking at the JSON standard. Here is a quote from the JSON wiki:
JavaScript syntax defines several native data types that are not included in the JSON standard: Map, Set, Date, Error, Regular Expression, Function, Promise, and undefined.
The key takeaway here is that JSON is not meant to represent the map data type. Python dictionaries are a map implementation, so directly serializing a dictionary to JSON with the intent to represent a map-like collection goes against the intended use of JSON.
For an individual object like a pet, the JSON object is appropriate, but for collections there is one option: the JSON array. There is a usage example with the JSON array further down in this answer.
There may be edge cases where deviating from the standard makes sense, but I don't see a reason in this scenario.
There are also some shortcomings in the JSON format from a RESTful design perspective. RESTful API design is nice because it encourages one to keep things simple and consistent. It also happens to be a de facto industry standard.
In a RESTful HTTP API, this is how fetching a single pet resource should look:
Request: GET /api/pets/17-01-24-01
Response: 200 {
"id": "17-01-24-01",
"name": "Buster",
"date_of_birth": "04/01/2017",
"registration_certificate_nr": "AAD-1123-1432"
}
The response is a completely defined resource with an explicitly defined id. It is also the simplest complete JSON representation of a pet.
Next, we define what fetching multiple pet resources looks like, assuming only 2 pets are defined:
Request: GET /api/pets
Response: 200 [
{
"id": "17-01-24-01",
"name": "Buster",
"date_of_birth": "04/01/2017",
"registration_certificate_nr": "AAD-1123-1432"
},
{
"id": "17-03-04-01",
"name": "Hooch",
"date_of_birth": "05/02/2015",
"adopted_from_kennel": "Pretoria Shire",
"children": [
"17-05-01-01",
"17-05-01-02",
"17-05-01-03"
]
}
]
The above response format is the most straight forward way to pluralize the single resource response format, thus keeping the API as simple and consistent as possible. (for the sake of brevity, I only used 2 of the sample resources from the question). Once again, the ids are explicitly defined, and belong to their respective pet objects.
Nothing is gained from adding map keys to the above format.
Proponents of the JSON format in the question may suggest to just add the id field into each pet object in order to work around pain point 2, but that would raise the question of repeating data within the response. Why does the id need to be both inside and outside the object? Surely it should only be on the inside? After eliminating the redundant data, the result will look like the response above.
That is the REST argument. There are use cases where REST doesn't really work, but this is far from that.
PS. Front ends should never access databases directly. The API is responsible for writing to and reading from whatever data persistence infrastructure is used. In a lot of bigger real world systems, there is even an additional BFF layer between the front end and the API(s), separating the front end and the DB even further.

Convert data between databases with different schemes and value codings

As part of my job I am meant to write a script to convert data from an available database here on-site to an external database for publication which is similar but far less detailed. I want to achieve this in Python.
Some columns can just be converted by copying their content changing the coding, i.e. 2212 in the original database becomes 2 in the external database in column X. To achieve this I wrote the codings in JSON, e.g.
{
"2212": 2,
"2213": 1,
"2214": 2,
...
}
This leads to some repetition as you can see but since lists cannot be keys in JSON I don't see a better way to it (which is simple and clean; sure I could have the right hand side as a key but then instead of jsonParsedDict["2212"] I would have to go through all the keys 1, 2, ... and find my original key on the right hand side.)
Where it gets ugly (in my opinion) is when information from multiple columns in the original database need to be combined to get the new column. Right now I just wrote a Python function doing a lot of if checks. It works and I will finish the job this way but it just seems aesthetically wrong and I want to learn more about Python's possibilities in that task.
Imagine for example I have two columns in the original database X and Y and then based on values in X I either do nothing (for values that are not coded in the external database), return a value directly or I return a result based on what value Y has in the same row. Right now this leads to quite some if statements. My other idea was to have stacked entries in the JSON file, e.g.
{
"X":
{
"2211": 1,
"2212": null,
"2213":
{
"Y":
{
"3112": 1
"3212": 2
}
}
"2214":
{
"Y":
{
"3112": 2
"3212": 1
}
}
"2215":
{
"Y":
{
"3112": 1
"3212": 2
}
}
}
}
But this approach really blows up the JSON file and repetitions get even more painful. Alas I cannot think of any other way to code these kind of conditions, apart from if in the code.
Is this a feasible way to do it or is there a better solutions? It would be great if I could specify the variables and associated variables, which are part of the decision process, only in the JSON file. I want to abstract the conversion process such that it is mostly steered by these JSON files and the Python code is quite general. If there is a better format for this than JSON then suggestions are very welcome.

structured query language for JSON (in Python)

I am working on a system to output a JSON file and I use Python to parse the data and display it in a UI (PySide). I now would like to add filtering to that system and I think instead of writing a query system, if there was one out there for JSON (in Python), that would save me a lot of development time. I found this thread:
Is there a query language for JSON?
but that's more for a Web-based system. Any ideas on a Python equivalent?
EDIT [for clarity]:
The format the data that I'll be generating is like this:
{
"Operations": [
{
"OpID": "0",
"type": "callback",
"stringTag1": "foo1",
"stringTag2": "FooMsg",
"Children": [...],
"value": "0.000694053"
},
{
"OpID": "1",
"type": "callback",
"stringTag1": "moo1",
"string2": "MooMsg",
"Children": [...],
"value": "0.000468427"
}
}
Where 'Children' could be nested arrays of the same thing (other operations). The system will be built to allow users to add their own tags as well to the data. My hope was to have a querying system that would allow users to define their own 'filters' as well, hence the question about the querying language. If there was something that would let me do something like "SELECT * WHERE "type" == "callback" and get the requisite operations back, that would be great.
The suggestion of Pync is interesting, I'll give that a look.

I notice this question was asked a few years ago but if someone else find this, here are some newer projects trying to address this same problem:
ObjectPath (for Python and Javascript): http://objectpath.org/
jsonpath (Python reimplementation of the Javascript equivalent): https://pypi.org/project/jsonpath/
yaql: https://yaql.readthedocs.io/en/latest/readme.html
pyjq (Python bindings for jq https://stedolan.github.io/jq/): https://pypi.org/project/pyjq/
JMESPath: https://github.com/jmespath/jmespath.py
I personally went with pyjq because I use jq all the time for data exploration but ObjectPath seems very attractive and not limited to json.

I thought about this a little bit, and I lean towards something less specific such as a "JSON Query Language" and considered something more generic. I remembered from working with C# a bit that they had a somewhat generic querying system called LINQ for handling these sort of querying issues.
It looks as though Python has something similar called Pynq which supports basic querying such as:
filtered_collection = From(some_collection).where("item.property > 10").select_many()
It even appears to have some basic aggregation functions. While not being specific to JSON, I think it's a least a good starting point for querying.

You can also check out PythonQL, a query language extension to Python that handles SQL and JSON queries: pythonql

pyjsonq
https://github.com/s1s1ty/py-jsonq
from pyjsonq import JsonQ
qe = JsonQ('myfile.json')
res = qe.at('products').where('cat', '=', 2).get()
print(res)
"""
[
{
id: 3,
city: 'dhk',
name: 'Redmi 3S Prime',
cat: 2,
price: 12000
},
...
]
I think it's important that the interaction with json is in-memory so that you can still do things manually for complex criteria