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Tools for predicting glucose trends
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README.md

predict

An openaps plugin for predicting glucose effects and trends from historical input

Build Status

Disclaimer

This tool is highly experimental and intended for education, not intended for therapy.

Getting started

Installing from pypi

$ sudo easy_install openapscontrib.predict

Installing from source for development

Clone the repository and link via setuptools:

$ python setup.py develop

Adding to your openaps project

$ openaps vendor add openapscontrib.predict
$ openaps device add predict predict

Usage

Use the device help menu to see available commands.

$ openaps use predict -h
usage: openaps-use predict [-h] USAGE ...

optional arguments:
  -h, --help            show this help message and exit

## Device predict:
  vendor openapscontrib.predict

  predict - tools for predicting glucose trends





  USAGE                 Usage Details
    glucose             Predict glucose. This is a convenience shortcut for
                        insulin and carb effect prediction.
    glucose_from_effects
                        Predict glucose from one or more effect schedules
    scheiner_carb_effect
                        Predict carb effect on glucose, using the Scheiner GI
                        curve
    walsh_insulin_effect
                        Predict insulin effect on glucose, using Walsh's IOB
                        algorithm
    walsh_iob           Predict IOB using Walsh's algorithm

Use the command help menu to see available arguments.

usage: openaps-use predict glucose [-h] [--settings [SETTINGS]]
                                   [--insulin-action-curve [{3,4,5,6}]]
                                   [--insulin-sensitivities INSULIN_SENSITIVITIES]
                                   [--carb-ratios CARB_RATIOS]
                                   [--basal-dosing-end [BASAL_DOSING_END]]
                                   pump-history glucose

Predict glucose. This is a convenience shortcut for insulin and carb effect prediction.

positional arguments:
  pump-history          JSON-encoded pump history data file, normalized by
                        openapscontrib.mmhistorytools
  glucose               JSON-encoded glucose data file in reverse-
                        chronological order

optional arguments:
  -h, --help            show this help message and exit
  --settings [SETTINGS]
                        JSON-encoded pump settings file, optional if
                        --insulin-action-curve is set
  --insulin-action-curve [{3,4,5,6}]
                        Insulin action curve, optional if --settings is set
  --insulin-sensitivities INSULIN_SENSITIVITIES
                        JSON-encoded insulin sensitivities schedule file
  --carb-ratios CARB_RATIOS
                        JSON-encoded carb ratio schedule file
  --basal-dosing-end [BASAL_DOSING_END]
                        The timestamp at which temp basal dosing should be
                        assumed to end, as a JSON-encoded pump clock file

Examples

Add a report flow to predict future glucose from pump history:

$ openaps report add insulin_effect_without_future_basal.json JSON predict walsh_insulin_effect \
        normalize_history.json \
		--settings read_settings.json \
		--insulin-sensitivities read_insulin_sensitivies.json \
		--basal-dosing-end read_clock.json

$ openaps report add carb_effect.json JSON predict scheiner_carb_effect \
        normalize_history.json \
		--carb-ratios read_carb_ratios.json \
		--insulin-sensitivities read_insulin_sensitivies.json \
		--absorption-time 180

$ openaps report add predict_glucose_without_future_basal JSON predict glucose_from_effects \
        insulin_effect_without_future_basal.json \
        carb_effect.json \
        --glucose clean_glucose.json

Contributing

Contributions are welcome and encouraged in the form of bugs and pull requests.

Testing

Unit tests can be run manually via setuptools. This is also handled by TravisCI after opening a pull request.

$ python setup.py test
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