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adapting the code to new api #9

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138517f
adapted the program to use new api. nutrient_dict is now a class.
jithesh82 Mar 19, 2021
4a9d11e
removing noms directory
jithesh82 Mar 19, 2021
9b5c4cf
removing objects/v_1_food.py
jithesh82 Mar 19, 2021
402ef64
removing client/v_ files
jithesh82 Mar 19, 2021
17fd56b
adapting to new api
jithesh82 Mar 19, 2021
9e7d508
adapting to new api
jithesh82 Mar 19, 2021
72d9cb8
removing objects/__pycache__, client/__pycache__
jithesh82 Mar 19, 2021
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removing __pycache__
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209 changes: 100 additions & 109 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Nutrient Object Management System (noms)

Note: I'm not able to maintain this package right now, so there is a chance that the current API integration is broken/will break given the new FoodData Central API from the USDA.
Code adatped to accomodate the new FoodData Central API from the USDA.

noms is a fun and simple Python package that allows you to obtain and work with highly detailed nutrition data for nearly 8,000 entries from the USDA Standard Reference Food Composition Database. No mainstream nutrition tracker apps reflect the level of detail that the USDA has compiled. With noms you can track:
1. Proximates including macronutrients (protein, carbs, and fat), calories, fiber and water content
Expand All @@ -10,7 +10,7 @@ noms is a fun and simple Python package that allows you to obtain and work with

This amounts to 41 nutrients being tracked, but many more are available from the database such as amino acids and other lipids. These can be viewed in all_nutrient_ids.txt, and support for other nutrients will be added in the future as requested. You can add support for these yourself by editing noms/objects/nutrient_ids.json accordingly with entries from all_nutrient_ids.txt.

Note: The Standard Reference Database is used explicitly without the addition of the USDA's Branded Foods database, as only the former allows for highly detailed reports which track 168 different nutrients -- much more information than you would find on an item's nutrition facts! This is especially valuable for nutritionists or people interested in their own health to explore the nutritional content of whole foods.
Note: The Standard Reference Database is used explicitly without the addition of the USDA's Branded Foods database, as only the former allows for highly detailed reports which track 168 different nutrients -- much more information than you would find on an item's nutrition facts! This is especially valuable for nutritionists or people interested in their own health to explore the nutritional content of whole foods.

## Installation
The noms package is listed on PyPI and can be installed with pip. Simply do:
Expand Down Expand Up @@ -38,57 +38,48 @@ print(search_results)
================================================================================================================
Search results for 'Raw Broccoli' on USDA Standard Reference Database
================================================================================================================
Name Group ID
Broccoli, raw Vegetables and Vegetable Pro.. 11090
Broccoli raab, raw Vegetables and Vegetable Pro.. 11096
Broccoli, leaves, raw Vegetables and Vegetable Pro.. 11739
Broccoli, stalks, raw Vegetables and Vegetable Pro.. 11741
Broccoli, chinese, raw Vegetables and Vegetable Pro.. 11994
Broccoli, flower clusters, raw Vegetables and Vegetable Pro.. 11740
description dataType ID
Broccoli, raw Foundation 747447
Broccoli, raw SR Legacy 170379
Broccoli raab, raw SR Legacy 170381
Broccoli, chinese, raw SR Legacy 169404
Broccoli, leaves, raw SR Legacy 169329
Broccoli, stalks, raw SR Legacy 169331
Broccoli, flower clusters, raw SR Legacy 169330
Broccoli, raw Survey (FNDDS) 1103170
Broccoli raab, raw Survey (FNDDS) 1103084
Broccoli, chinese, raw Survey (FNDDS) 1103184
================================================================================================================
```
## Requesting Food Data From the Database
In this example, the ids correlate with Raw Broccoli (11090) and a Cola Beverage (14400). The numbers afterwards represent the mass of that food, in grams. More mass for a given food equals a greater amount of each nutrient in equal proportion (twice the broccoli has twice the vitamins).
```python
food_list = client.get_foods({'11090':100, '14400':100})
search_results = client.get_foods({'747447':100, '174826':100})
```
## Initializing a Meal With a List of Foods
The foods() method returned a list of two Food objects when given the arguments above, but if you would like to generate a report, analyze or sort a group of foods, they should be merged into a Meal object. This is done by simply constructing a Meal instance with a list of Food objects.
```python
m = noms.Meal(food_list)
```

## Generating and Displaying a Report
## Generating and Displaying a Report
The report is a dictionary which shows if RDAs (or Adequate Intakes) are being met or exceeded. These values are assigned by default in noms.objects.nutrient_dict, but support will be added to modify these settings in the future.
```python
r = noms.report(m)
for i in r:
print(i)
```
```
{'name': 'Protein', 'rda': 125.0, 'limit': None, 'value': 2.89, 'state': 'deficient', 'unit': 'g'}
{'name': 'Fat', 'rda': 55.56, 'limit': None, 'value': 0.39, 'state': 'deficient', 'unit': 'g'}
{'name': 'Carbs', 'rda': 250.0, 'limit': None, 'value': 16.2, 'state': 'deficient', 'unit': 'g'}
{'name': 'Calories', 'rda': 2000, 'limit': None, 'value': 71.0, 'state': 'deficient', 'unit': 'kcal'}
{'name': 'Water', 'rda': 2000, 'limit': None, 'value': 179.61, 'state': 'deficient', 'unit': 'g'}
{'name': 'Caffeine', 'rda': 0, 'limit': 400, 'value': 8.0, 'state': 'satisfactory', 'unit': 'mg'}
{'name': 'Protein', 'rda': 125.0, 'limit': None, 'value': 2.57, 'state': 'deficient', 'unit': 'g'}
{'name': 'Fat', 'rda': 55.56, 'limit': None, 'value': 0.34, 'state': 'deficient', 'unit': 'g'}
{'name': 'Carbs', 'rda': 250.0, 'limit': None, 'value': 7.3999999999999995, 'state': 'deficient', 'unit': 'g'}
{'name': 'Calories', 'rda': 2000, 'limit': None, 'value': 31.0, 'state': 'deficient', 'unit': 'kcal'}
{'name': 'Water', 'rda': 2000, 'limit': None, 'value': 188.87, 'state': 'deficient', 'unit': 'g'}
{'name': 'Caffeine', 'rda': 0, 'limit': 400, 'value': 0.0, 'state': 'satisfactory', 'unit': 'mg'}
{'name': 'Theobromine', 'rda': 0, 'limit': 300, 'value': 0.0, 'state': 'satisfactory', 'unit': 'mg'}
{'name': 'Sugar', 'rda': 0, 'limit': 50.0, 'value': 10.67, 'state': 'satisfactory', 'unit': 'g'}
{'name': 'Fiber', 'rda': 28.0, 'limit': None, 'value': 2.6, 'state': 'deficient', 'unit': 'g'}
{'name': 'Calcium', 'rda': 1000, 'limit': 2500, 'value': 49.0, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Iron', 'rda': 8, 'limit': 45, 'value': 0.84, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Magnesium', 'rda': 300, 'limit': 700, 'value': 21.0, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Phosphorus', 'rda': 700, 'limit': 4000, 'value': 76.0, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Potassium', 'rda': 1400, 'limit': 6000, 'value': 318.0, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Sodium', 'rda': 1000, 'limit': 2300, 'value': 37.0, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Zinc', 'rda': 12, 'limit': 100, 'value': 0.43, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Copper', 'rda': 0.9, 'limit': 10, 'value': 0.05, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Fluoride', 'rda': 400, 'limit': 10000, 'value': 57.0, 'state': 'deficient', 'unit': 'µg'}
{'name': 'Manganese', 'rda': 1.8, 'limit': None, 'value': 0.21, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Selenium', 'rda': 70, 'limit': 400, 'value': 2.6, 'state': 'deficient', 'unit': 'µg'}
{'name': 'Vitamin A', 'rda': 900, 'limit': 20000, 'value': 623.0, 'state': 'deficient', 'unit': 'IU'}
{'name': 'Vitamin E', 'rda': 15, 'limit': 1000, 'value': 0.78, 'state': 'deficient', 'unit': 'mg'}
{'name': 'Vitamin D', 'rda': 1000, 'limit': 8000, 'value': 0.0, 'state': 'deficient', 'unit': 'IU'}
{'name': 'Sugar', 'rda': 0, 'limit': 50.0, 'value': 0.0, 'state': 'satisfactory', 'unit': 'g'}
{'name': 'Fiber', 'rda': 28.0, 'limit': None, 'value': 2.4, 'state': 'deficient', 'unit': 'g'}
{'name': 'Calcium', 'rda': 1000, 'limit': 2500, 'value': 46.0, 'state': 'deficient', 'unit': 'mg'}
... continued
```
## Sorting Foods in a Meal By a Specific Nutrient
Expand All @@ -101,85 +92,85 @@ for food in m.foods:
print(food.nutrients[ni])
```
```
{'nutrient_id': 269, 'name': 'Sugar', 'group': 'Proximates', 'unit': 'g', 'value': 8.97}
{'nutrient_id': 269, 'name': 'Sugar', 'group': 'Proximates', 'unit': 'g', 'value': 1.7}
{'nutrient_id': 269, 'name': 'Sugar', 'group': 'Proximates', 'unit': 'g', 'value': 0.0}
{'value': 0.0, 'name': 'Sugar', 'unit': 'g', 'nutrient_id': 269.0}
```
Note that this sorts the foods in the Meal object from greatest to least in terms of how much sugar each food has.

## Generate Food Recommendations in Context of a Meal and Pantry
Because it would be computationally expensive to generate a food recommendation in the context of every food in the database, and it may be unrealistic to recommend any food from the database as it may be hard to access, you must define a list of foods that will serve as a pantry object. Here is an example pantry object containing many common whole foods.

```python
pantry = {
# DAIRY AND EGG
"01001":100, # butter, salted
"01145":100, # butter, without salt
"01079":100, # 2% milk
"01077":100, # milk, whole
"01086":100, # skim milk
"01132":100, # scrambled eggs
"01129":100, # hard boiled eggs
"01128":100, # fried egg
# MEAT
"15076":100, # atlantic salmon
"07935":100, # chicken breast oven-roasted
"13647":100, # steak
"05192":100, # turkey
# FRUIT
"09037":100, # avocado
"09316":100, # strawberries
"09050":100, # blueberry
"09302":100, # raspberry
"09500":100, # red delicious apple
"09040":100, # banana
"09150":100, # lemon
"09201":100, # oranges
"09132":100, # grapes
# PROCESSED
"21250":100, # hamburger
"21272":100, # pizza
"19088":100, # ice cream
"18249":100, # donut
# DRINK
"14400":100, # coke
"14429":100, # tap water
"14433":100, # bottled water
"09206":100, # orange juice
"14278":100, # brewed green tea
"14209":100, # coffee brewed with tap water
# (milk is included in dairy group)
# GRAIN
"12006":100, # chia
"12220":100, # flaxseed
"20137":100, # quinoa, cooked
"20006":100, # pearled barley
"20051":100, # white rice enriched cooked
"20041":100, # brown rice cooked
"12151":100, # pistachio
"19047":100, # pretzel
"12061":100, # almond
# LEGUME
"16057":100, # chickpeas
"16015":100, # black beans
"16043":100, # pinto beans
"16072":100, # lima beans
"16167":100, # peanut butter smooth
# VEGETABLE
"11124":100, # raw carrots
"11090":100, # broccoli
"11457":100, # spinach, raw
"11357":100, # baked potato
"11508":100, # baked sweet potato
"11530":100, # tomato, red, cooked
"11253":100, # lettuce
"11233":100, # kale
"11313":100, # peas
"11215":100, # garlic
# OTHER
"04053":100, # olive oil
"19904":100, # dark chocolate
"11238":100, # shiitake mushrooms
"19165":100, # cocoa powder
pantry = {
# DAIRY AND EGG
"173410":100, # butter, salted
"173430":100, # butter, without salt
"1097517":100, # 2% milk
"1097512":100, # milk, whole
"1097521":100, # skim milk
"1100335":100, # scrambled eggs
"173424":100, # hard boiled eggs
"173423":100, # fried egg
# MEAT
"175167":100, # atlantic salmon
"174608":100, # chicken breast oven-roasted
"1099608":100, # steak
"1099888":100, # turkey
# FRUIT
"1103883":100, # avocado
"1102710":100, # strawberries
"1102702":100, # blueberry
"1102708":100, # raspberry
"1105430":100, # red delicious apple
"173945":100, # banana
"1102594":100, # lemon
"1102597":100, # oranges
"1102665":100, # grapes
# PROCESSED
"1099796":100, # hamburger
"1101112":100, # pizza
"1100918":100, # ice cream
"174993":100, # donut
# DRINK
"1104331":100, # coke
"1104492":100, # tap water
"1104493":100, # bottled water
"171354":100, # orange juice
"1104292":100, # brewed green tea
"171890":100, # coffee brewed with tap water
# (milk is included in dairy group)
# GRAIN
"1100612":100, # chia
"1103860":100, # flaxseed
"168917":100, # quinoa, cooked
"170285":100, # pearled barley
"168880":100, # white rice enriched cooked
"1101628":100, # brown rice cooked
"1100549":100, # pistachio
"171370":100, # pretzel
"1100555":100, # almond
# LEGUME
"1100429":100, # chickpeas
"1100410":100, # black beans
"1100393":100, # pinto beans
"1100383":100, # lima beans
"324860":100 # peanut butter smooth
# VEGETABLE
"1103193":100, # raw carrots
"1103183":100, # broccoli
"1103136":100, # spinach, raw
"1102880":100, # baked potato
"1103261":100, # baked sweet potato
"170050":100, # tomato, red, cooked
"1103358":100, # lettuce
"1103116":100, # kale
"1103645":100, # peas
"1103845":100, # garlic
# OTHER
"1103861":100, # olive oil
"1104032":100, # dark chocolate
"168436":100, # shiitake mushrooms
"1097878":100, # cocoa powder
}
pantry_food = client.get_foods(pantry)
```
Expand All @@ -190,11 +181,11 @@ for rec in recommendations:
# a recommendation is a list containing the calculated loss after the recommendation
# is applied, the index of the pantry for the recommendation, and the amount of that
# food / 100g
print(str(round(rec[2] * 100, 2)) + "g", "of", pantry_food[rec[1]].desc["name"])
print(str(round(rec[2] * 100, 2)) + "g", "of", pantry_food[rec[1]].desc)
```
```
50.36g of Seeds, chia seeds, dried
53.3g of Nuts, almonds
56.76g of Peanut Butter, smooth (Includes foods for USDA's Food Distribution Program)
72.59g of Chia seeds
72.94g of Almond butter
77.01g of Peanut butter, smooth style, with salt
```
It is reasonable that the function returned these foods from the pantry as the current daily nutrition is low in protein and Omega-3s, which chia seeds satisfy the most.
36 changes: 18 additions & 18 deletions noms/__init__.py → __init__.py
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@@ -1,18 +1,18 @@
name = "noms"
__version__ = "0.1.7"
__version_info__ = tuple(int(i) for i in __version__.split('.'))

# client object
from noms.client.main import Client
# food and meal objects
from noms.objects.food import Food, Meal
# csv reports
from noms.report import export_report, report

# nutrient dict
from noms.objects.nutrient_dict import index_from_name
from noms.objects.nutrient_dict import nutrient_dict

# recommendation method
from noms.analyze import generate_recommendations
from noms.analyze import recommend_removal
name = "noms"
__version__ = "0.1.7"
__version_info__ = tuple(int(i) for i in __version__.split('.'))
# client object
from noms.client.main import Client
# food and meal objects
from noms.objects.food import Food, Meal
# csv reports
from noms.report import export_report, report
# nutrient dict
from noms.objects.nutrient_dict import index_from_name
from noms.objects.nutrient_dict import nutrient_dict
# recommendation method
from noms.analyze import generate_recommendations
from noms.analyze import recommend_removal
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