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#Hyperlocal Bed Demand Forecasting (User Interface)
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## Welcome to [HYLODE](https://hylode.org)!
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This repository is for Plotly Dash apps within a hospital environment. A
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template application would be split into a frontend and a backend with
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The application code including the backend API in `./api`, and the frontend written in Dash is kept in `./web`.
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The documentation is available [here](https://hylode.github.io/HyUi/).
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Documentation is available [here](https://hylode.org/developer/setup.html).
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## Project Structure
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```
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## Development Environments
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### Local Machine
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Your own laptop etc. without access to personally identifiable information (PII) etc.
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You wish to be able to build and run applications with test data.
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Read the docs in `./docs/developer/setup.qmd` to get started.
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### Live Machine
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An NHS machine or similar within sensitive environment with access to PII.
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You wish to be able to deploy the actual application.
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## Development workflow
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### 1. Make synthetic or mock versions of the data
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We imagine that the developer has the appropriate permissions to view the raw data including patient identifiable information (either themselves, or in partnership with a colleague). A justification for this position is [here][http://hylode.github.io/hyui/notes/provisioning.html]. Practically, this means early interactive data exploration using the UCLH Datascience Desktop and similar tools, and in turn access to EMAP and Clarity/Caboodle.
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This should generate an initial data specification, and this can be used to generate synthetic data. The synthetic data can then be used to drive the rest of the pathway.
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### 2. Develop with synthetic data
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### 3. Write some tests and quality control
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### 4. Update the plot to run live
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**Where this documentation refers to the root folder we mean where this README.md is
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