Challenge Overview
Challenge Objectives
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Update calculations in Python backend
Project Background
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Our client has developed a specific method of calculating and visualizing Klinkenberg permeability
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All the calculations are currently in an Excel workbook - our goal is to create an API to store and process the data, i.e. move the calculations from Excel into the codebase.
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In this challenge, we’ll make changes to the data endpoints and update the calculations
Technology Stack
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Python 3
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Flask
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Flask-Restplus
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MongoDB
Code access
The base code is available in the forums. The calculation specification document and the sample Excel file are available in the forums. We recommend reading these files before moving on to the next section of the challenge spec.
Individual requirements
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Update data inputs / outputs
In the current codebase Bair is considered an input, but it will be a calculated, output value instead. New inputs should be Air permeability and P mean. Outputs will be Kklink(c), Kklink(p), Bair, klinkenberg and air permeability plot. -
Update calculations for Bair and Kklink(p)
See the calculations spec for exact calculations - if Pmean is provided, the code should calculate Bair, then calculate Kklink(p) and repeat the same for max 100 iterations or less if the change in calculated values is less than the threshold.
General requirements
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Unit tests are required for all endpoints.
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All endpoints should be annotated with Swagger annotations and swagger UI should be served by the API.
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All endpoint routes should be prefixed with “API_NAME_”
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All database collection names should be prefixed with “API_NAME_”
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All configuration parameters should be extracted to a common settings module. Sensitive configuration parameters should be set from environment variables (DB URL, credentials, etc). All environment variables have to be prefixed with “API_NAME_”
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Please make sure your code is well-documented. Use the following style guides Google Python Style Guide, Python Style Guide, and Docstring Conventions. Code linter is required. Please make sure it is well-engineered but not over-engineered (YAGNI and KISS) solution. We're looking for well-structured and tested code. Well-structured code follows good design principles like the SOLID principles and well-tested code has comprehensive unit tests.
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The code should be implemented using Python3 only.
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Use matrix operations with numpy/pandas where possible
What To Submit
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All source code
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Deployment guide
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Postman collection containing sample calls for all endpoints (success/failure)
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Verification guide - how to set up the environment, start the API, and verification screenshots
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The unit tests coverage report
Scoring Methodology
Contest Specification Requirements(60% weightage)
· Have all major specification requirements been met?
Score: 0-9
Major requirements are:
· all endpoints are implemented correctly and return the correct data and codes, etc,
· all DB models are defined and contain correct attributes and data types
· Unit tests - minimum coverage 80%
Have all major specification requirements been met?
Score: 0-3
Minor requirements are:
· Postman collection
· Swagger annotations
Best Practices & Comments(30% weightage)
· Does the submission follow standard best practices?
Score: 0-3
This section includes the PEP-8 code style, linter, patterns usage, code comments
Please make sure your code is well-documented. Use the following style guides Google Python Style Guide, Python Style Guide, and Docstring Conventions. Code linter is required. Please make sure it is well-engineered but not over-engineered (YAGNI and KISS) solution. We're looking for well-structured and tested code. Well-structured code follows good design principles like the SOLID principles and well-tested code has comprehensive unit tests.
Deployment and verification guide (10% weightage)
· Does the deployment guide contain everything needed to successfully configure and deploy the API?
Score: 0-3