https://github.com/tomwhite/ingreedy-data
Code for preparing data for Ingreedy
https://github.com/tomwhite/ingreedy-data
carb-counter diabetes food food-nutrient-data t1d
Last synced: 7 months ago
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Code for preparing data for Ingreedy
- Host: GitHub
- URL: https://github.com/tomwhite/ingreedy-data
- Owner: tomwhite
- License: apache-2.0
- Created: 2020-04-22T11:26:38.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-24T14:49:30.000Z (about 5 years ago)
- Last Synced: 2025-01-17T21:43:52.594Z (9 months ago)
- Topics: carb-counter, diabetes, food, food-nutrient-data, t1d
- Language: Python
- Size: 20.3 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Ingreedy Data
Code for preparing data for [Ingreedy](https://github.com/tomwhite/ingreedy-js).
Installation:
```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
## 1. Turn food databases into CSV and JSON files
### McCance and Widdowson's 'composition of foods integrated dataset' (CoFID)
Download from https://www.gov.uk/government/publications/composition-of-foods-integrated-dataset-cofid.
Open in Libre Office then save the "Proximates" sheet as a CSV file in _data/raw_.
Then run this to generate a CSV and JSON with just the food nutrient data we need:
```bash
python build_db_mccance.py
echo -n "const mccance = " | cat - data/processed/mccance.json > data/processed/mccance_def.js
printf "\nmodule.exports = mccance\n" >> data/processed/mccance_def.js
```
### Australian Food Composition Database
Download from https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/default.aspx
Open in Libre Office then save the "All solids & liquids per 100g" sheet as a CSV file in _data/raw_.
Then run this to generate a CSV and JSON with just the food nutrient data we need:
```bash
python build_db_afcd.py
echo -n "const afcd = " | cat - data/processed/afcd.json > data/processed/afcd_def.js
printf "\nmodule.exports = afcd\n" >> data/processed/afcd_def.js
```
### US FoodData Central
Download CSVs for SR Legacy from https://fdc.nal.usda.gov/download-datasets.html and save in _data/raw_.
Then run this to generate a CSV and JSON with just the food nutrient data we need:
```bash
python build_db_usfdc.py
echo -n "const usfdc = " | cat - data/processed/usfdc.json > data/processed/usfdc_def.js
printf "\nmodule.exports = usfdc\n" >> data/processed/usfdc_def.js
```
## 2. Normalize NYT ingredient food names
(Note: it would be better to do this in Javascript, so we are sure the normalization is identical.)
```bash
python normalize_nyt_food_names.py
```
## 3. Manually map food names to database food entries
This is done by going through each food name and assigning it to a food in the database.
See https://docs.google.com/spreadsheets/d/1wEZqp9394Bobxl4W8__99i2OcAxBt2pARnoG-3j0u8E/edit#gid=582435857
## 4. Build the foodmap file
Download the "foodmap" sheet as a CSV file and save in _data/processed_
```bash
mv ~/Downloads/"foodmap - foodmap.csv" data/processed/
python build_foodmap.py
echo -n "const foods = " | cat - data/processed/foodmap.json > data/processed/foodmap_def.js
printf "\nmodule.exports = foods\n" >> data/processed/foodmap_def.js
```
## 5. Build the measures file
Download the "measures" sheet as a CSV file and save in _data/processed_
```bash
mv ~/Downloads/"foodmap - measures.csv" data/processed/
python build_measures.py
echo -n "const foodMeasures = " | cat - data/processed/measures.json > data/processed/measures_def.js
printf "\nmodule.exports = foodMeasures\n" >> data/processed/measures_def.js
```
## 6. Copy everything for use in Ingreedy
```bash
cp data/processed/*_def.js ../ingreedy-js/src
```