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Merge pull request #1 from miriamkw/repo_cleanup
Repo cleanup
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‎.github/workflows/ci.yml‎

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name: CI Workflow
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# Trigger the workflow on push and pull request events
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on:
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push:
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pull_request:
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# Define the jobs to run in the workflow
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jobs:
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build-and-test:
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runs-on: macos-latest # Use macOS for Swift and iOS-specific dependencies
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steps:
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- name: Checkout code
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9' # Specify your required Python version
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- name: Set up Swift
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run: |
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sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer
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sudo xcodebuild -runFirstLaunch
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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- name: Run build script
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run: |
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chmod +x build.sh
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./build.sh
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- name: Run tests
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run: |
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pytest
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‎.gitignore‎

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.swiftpm/configuration/registries.json
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.swiftpm/xcode/package.xcworkspace/contents.xcworkspacedata
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.netrc
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.idea

‎Package.swift‎

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@@ -5,6 +5,7 @@ import PackageDescription
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let package = Package(
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name: "LoopAlgorithmToPython",
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defaultLocalization: "no",
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platforms: [
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.macOS(.v13),
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.iOS(.v15),
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),
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.testTarget(
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name: "LoopAlgorithmToPythonTests",
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dependencies: ["LoopAlgorithmToPython"]),
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dependencies: ["LoopAlgorithmToPython"],
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resources: [
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.process("TestData")
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])
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]
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)

‎README.md‎

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This Swift module uses LoopAlgorithm to create C functions for generating predictions and prediction dates from JSON data.
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How?
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I create a foreign function interface (FFI) in Swift by using the unofficial @_cdecl Swift function. This interfaces the Swift code with C. Then we can create a dynamic library, import it into a Python (or other) repositories, and use for example ctypes to compile the C code.
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This is achieved by creating a foreign function interface (FFI) in Swift by using the unofficial @_cdecl Swift function. This interfaces the Swift code with C. Then we can create a dynamic library, import it into a Python (or other) repositories, and use for example ctypes to compile the C code.
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## Installation
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1. Clone the repository
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2. Build the dynamic library:
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## Repository Overview
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```
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swift package clean
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swift build --configuration release
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```
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Check if the dynamic library got properly generated and print the path:
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```
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find .build -name "libLoopAlgorithmToPython.dylib"
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```
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Output should be something like: /release/libLoopAlgorithmToPython.dylib
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### Exposed functions
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Copy that file into your repository.
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You can find the C-exposed functions in the file `Sources/LoopAlgorithmToPython/LoopAlgorithmToPython.swift`.
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### Python API
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## Exposed functions
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Python API functions are located in `loop_to_python_api/api.py`.
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You can find the C-exposed functions in the file `LoopAlgorithmToPython.swift`.
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### Tests and test data
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`python_tests/` contains examples of executing all the functions as well as example files providing templates on how to structure the input files.
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## Usage in Python
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Here's how you can use the dynamic library (`libLoopAlgorithmToPython.dylib`) in Python to call the exposed functions:
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```
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import ctypes
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import json
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## Python API Functions
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json_file_path = 'some_file.json'
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-------------------------
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# Load the shared library
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swift_lib = ctypes.CDLL('./libLoopAlgorithmToPython.dylib')
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### Initialize Exception Handlers
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# Specify the argument types and return type of the Swift function
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swift_lib.generatePrediction.argtypes = [ctypes.c_char_p]
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swift_lib.generatePrediction.restype = ctypes.POINTER(ctypes.c_double)
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`initialize_exception_handlers()`
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swift_lib.getPredictionDates.argtypes = [ctypes.c_char_p]
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swift_lib.getPredictionDates.restype = ctypes.c_char_p
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Initializes the exception and signal handlers in the Swift library to provide more informative error messages.
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swift_lib.getActiveCarbs.argtypes = [ctypes.c_char_p]
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swift_lib.getActiveCarbs.restype = ctypes.c_double
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-------------------------
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swift_lib.getActiveInsulin.argtypes = [ctypes.c_char_p]
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swift_lib.getActiveInsulin.restype = ctypes.c_double
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### Generate Prediction
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# Read JSON file
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def read_json_file(file_path):
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with open(file_path, 'r') as f:
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data = json.load(f)
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return data
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`generate_prediction(json_file, len=72)`
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json_data = read_json_file(json_file_path) # Read JSON file
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json_str = json.dumps(json_data) # Convert JSON data to JSON string
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json_bytes = json_str.encode('utf-8') # Convert JSON string to bytes
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Generates a prediction based on the provided JSON input.
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# Prepare a variable to receive the length of the predicted values
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length = 82
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- `len` (optional): The number of prediction values to generate. Defaults to 72.
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- **Returns**: A list of prediction values.
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# Call the Swift functions
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result_prediction_values = swift_lib.generatePrediction(json_bytes)
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result_prediction_dates = swift_lib.getPredictionDates(json_bytes).decode('utf-8')
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result_active_carbs = swift_lib.getActiveCarbs(json_bytes)
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result_active_insulin = swift_lib.getActiveInsulin(json_bytes)
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-------------------------
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# Read the generated predictions
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array = [result_prediction_values[i] for i in range(length)]
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print(array[0])
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print(f"The result from generatePrediction is: {array}")
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### Get Prediction Dates
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# Read the dates
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date_list = result.split(',')[:-1]
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print(f"The result from getPredictionDates is: {date_list}")
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`get_prediction_dates(json_file)`
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# Read the active carbohydrates
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print(f"The result from getActiveCarbs is: {result_active_carbs}")
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Fetches prediction dates based on the provided JSON input.
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# Read the active insulin
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print(f"The result from getActiveInsulin is: {result_active_insulin}")
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```
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: A list of prediction dates as strings.
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Adjust the paths, function names, and details as per your specific project setup and requirements.
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-------------------------
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### Get Prediction Values and Dates
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`get_prediction_values_and_dates(json_file)`
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Combines the `generate_prediction` and `get_prediction_dates` functions to return both prediction values and dates.
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: A tuple containing a list of prediction values and a list of prediction dates.
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-------------------------
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### Get Glucose Effect Velocity
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`get_glucose_effect_velocity(json_file, len=72)`
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Fetches the glucose effect velocity, which is equivalent to Insulin Counteraction Effect (ICE).
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- `len` (optional): The number of values to fetch. Defaults to 72.
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- **Returns**: A list of glucose effect velocity values.
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-------------------------
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### Get Glucose Effect Velocity Dates
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`get_glucose_effect_velocity_dates(json_file)`
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Fetches the dates associated with the glucose effect velocity.
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: A list of dates as strings.
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-------------------------
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### Get Glucose Velocity Values and Dates
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`get_glucose_velocity_values_and_dates(json_file)`
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Combines the `get_glucose_effect_velocity` and `get_glucose_effect_velocity_dates` functions to return both glucose effect velocity values and dates.
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: A tuple containing a list of glucose effect velocity values and a list of dates.
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-------------------------
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### Get Active Carbs
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`get_active_carbs(json_file)`
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Fetches the active carbohydrates based on the provided JSON input.
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: The active carbohydrates as a double.
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-------------------------
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### Get Active Insulin
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`get_active_insulin(json_file)`
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Fetches the active insulin based on the provided JSON input.
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: The active insulin as a double.
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-------------------------
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### Percent Absorption at Percent Time
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`percent_absorption_at_percent_time(percent_time)`
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Calculates the percentage of carbohydrate absorption at a given percent time using a piecewise linear model.
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- **Parameters**:
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- `percent_time`: The time as a fraction (e.g., 0.2 for 20%).
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- **Returns**: The percentage of absorption as a double.
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-------------------------
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### Piecewise Linear Percent Rate at Percent Time
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`piecewise_linear_percent_rate_at_percent_time(percent_time)`
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Calculates the percentage rate of carbohydrate absorption at a given percent time using a piecewise linear model.
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- **Parameters**:
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- `percent_time`: The time as a fraction (e.g., 0.2 for 20%).
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- **Returns**: The percentage rate of absorption as a double.
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-------------------------
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### Linear Percent Rate at Percent Time
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`linear_percent_rate_at_percent_time(percent_time)`
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Calculates the percentage rate of carbohydrate absorption at a given percent time using a linear model.
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- **Parameters**:
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- `percent_time`: The time as a fraction (e.g., 0.2 for 20%).
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- **Returns**: The percentage rate of absorption as a double.
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-------------------------
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### Get Dynamic Carbs on Board
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`get_dynamic_carbs_on_board(json_file)`
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Fetches the dynamic carbohydrates on board based on the provided JSON input.
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- **Parameters**:
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- `json_file`: The JSON data input. See python tests and test files for example inputs.
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- **Returns**: The dynamic carbohydrates on board as a double.
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-------------------------
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## Build Dynamic Library
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The file `python_api/libLoopAlgorithmToPython.dylib` contains the dynamic library that is containing the C-embedded Swift functions.
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After making changes in the Swift code, rebuild the dynamic library by running `chmod +x build.sh` followed by `./build.sh`.
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## Run Tests
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Run command `pytest`.
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‎Sources/LoopAlgorithmToPython/BundleFinder.swift‎

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import class Foundation.Bundle
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import class Foundation.ProcessInfo
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import struct Foundation.URL
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private class BundleFinder {}
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extension Foundation.Bundle {
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/// Returns the resource bundle associated with the current Swift module.
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static let module: Bundle = {
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let bundleName = "LoopAlgorithmToPython_LoopAlgorithmToPythonTests"
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let overrides: [URL]
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#if DEBUG
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// The 'PACKAGE_RESOURCE_BUNDLE_PATH' name is preferred since the expected value is a path. The
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// check for 'PACKAGE_RESOURCE_BUNDLE_URL' will be removed when all clients have switched over.
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// This removal is tracked by rdar://107766372.
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if let override = ProcessInfo.processInfo.environment["PACKAGE_RESOURCE_BUNDLE_PATH"]
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?? ProcessInfo.processInfo.environment["PACKAGE_RESOURCE_BUNDLE_URL"] {
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overrides = [URL(fileURLWithPath: override)]
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} else {
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overrides = []
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}
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#else
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overrides = []
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#endif
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print("BUNDLEFILE")
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let candidates = overrides + [
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// Bundle should be present here when the package is linked into an App.
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Bundle.main.resourceURL,
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// Bundle should be present here when the package is linked into a framework.
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Bundle(for: BundleFinder.self).resourceURL,
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// For command-line tools.
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Bundle.main.bundleURL,
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]
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for candidate in candidates {
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let bundlePath = candidate?.appendingPathComponent(bundleName + ".bundle")
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print(bundlePath)
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if let bundle = bundlePath.flatMap(Bundle.init(url:)) {
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print("BUNDLE:", bundle)
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return bundle
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}
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}
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fatalError("unable to find bundle named LoopAlgorithmToPython_LoopAlgorithmToPythonTests")
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}()
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}

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