Python Myfitnesspal

https://python-myfitnesspal.readthedocs.io/en/latest/
Admin
Python Myfitnesspal is a library that allows programmatic access to MyFitnessPal data, overcoming...
Tokens:15,884
Snippets:123
Trust Score:8.3
Update:11 months ago
Show doc for...
Tokens:
Raw
### Fetch MyFitnessPal Report Data (Specific Start Date) - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/reports

This Python code snippet shows how to retrieve MyFitnessPal report data starting from a specific date. It utilizes the 'datetime' module to define the start date and the 'myfitnesspal' client's get_report method. The output is an OrderedDict containing date-value pairs from the specified start date onwards.

```python
import datetime

may = datetime.date(2015, 5, 1)

client.get_report("Net Calories", "Nutrition", may)
# >> OrderedDict([(datetime.date(2015, 5, 14), 172.8), (datetime.date(2015, 5, 13), 173.1), (datetime.date(2015, 5, 12), 127.7),
#                 (datetime.date(2015, 5, 11), 172.7), (datetime.date(2015, 5, 10), 172.8), (datetime.date(2015, 5, 9), 172.4),
#                 (datetime.date(2015, 5, 8), 172.6),  (datetime.date(2015, 5, 7), 172.7),  (datetime.date(2015, 5, 6), 172.6),
#                 (datetime.date(2015, 5, 5), 172.9),  (datetime.date(2015, 5, 4), 173.0),  (datetime.date(2015, 5, 3), 172.6),
#                 (datetime.date(2015, 5, 2), 172.6),  (datetime.date(2015, 5, 1), 172.7)])
```

--------------------------------

### Get Cardiovascular Exercises using Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/exercises

Retrieves a list of cardiovascular exercises recorded for a specific day using the myfitnesspal Python library. It demonstrates initializing the client, fetching the day's data, and then accessing the cardiovascular exercises. The output is a list of dictionaries, each containing exercise details and nutrition information.

```python
import myfitnesspal

client = myfitnesspal.Client()

day = client.get_date(2019, 3, 12)

day.exercises[0].get_as_list()
# >> [{'name': 'Walking, 12.5 mins per km, mod. pace, walking dog', 'nutrition_information': {'minutes': 60, 'calories burned': 209}}, {'name': 'Running (jogging), 8 kph (7.5 min per km)', 'nutrition_information': {'minutes': 25, 'calories burned': 211}}]
```

--------------------------------

### Get Strength Exercises using Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/exercises

Retrieves a list of strength exercises recorded for a specific day using the myfitnesspal Python library. This snippet shows how to access the strength exercises, which are typically the second item in the `day.exercises` array. The output is a list of dictionaries, each detailing the exercise and its associated sets, reps, and weight.

```python
import myfitnesspal

client = myfitnesspal.Client()

day = client.get_date(2019, 3, 12)

day.exercises[1].get_as_list()
# >> [{'name': 'Leg Press', 'nutrition_information': {'sets': 3, 'reps/set': 12, 'weight/set': 20}}, {'name': 'Seated Row, Floor, Machine', 'nutrition_information': {'sets': 3, 'reps/set': 12, 'weight/set': 20}}]
```

--------------------------------

### Get measurements from past 30 days - Python myfitnesspal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/measurements

Retrieves body measurements from the past 30 days using the myfitnesspal Client. Requires authentication via myfitnesspal.Client() and returns an OrderedDict with datetime.date objects as keys and measurement values as floats. Example retrieves weight measurements over a 30-day period.

```python
import myfitnesspal

client = myfitnesspal.Client()

weight = client.get_measurements('Weight')
weight
```

--------------------------------

### Get Food Item Details using MyFitnessPal API (Python)

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/food_search

This snippet shows how to retrieve detailed information for a specific food item using its MyFitnessPal ID. It requires the 'myfitnesspal' library and returns an object containing details such as serving sizes and nutritional information like saturated fat.

```python
import myfitnesspal

client = myfitnesspal.Client()

item = client.get_food_item_details("89755756637885")
item.servings
# > [<1.00 x Sandwich>]
item.saturated_fat
# > 10.0
```

--------------------------------

### Get measurements since specific date - Python myfitnesspal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/measurements

Retrieves body measurements from a specified start date onwards using the myfitnesspal Client. Takes a measurement type name and a datetime.date object as parameters. Returns an OrderedDict with dates and corresponding measurement values, useful for tracking changes over custom time periods.

```python
import datetime

may = datetime.date(2015, 5, 1)

body_fat = client.get_measurements('Body Fat', may)
body_fat
```

--------------------------------

### Get Daily and Meal Nutritional Totals with myfitnesspal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/diary

Accesses the `totals` property of Day or Meal objects to get a summary of nutritional information like calories, carbohydrates, fat, protein, sodium, and sugar. This provides a quick overview of consumption for the day or a specific meal.

```python
day.totals
# >> {'calories': 2001,
#     'carbohydrates': 369,
#     'fat': 22,
#     'protein': 110,
#     'sodium': 3326,
#     'sugar': 103}

dinner.totals
# >> {'calories': 945,
#     'carbohydrates': 170,
#     'fat': 11,
#     'protein': 53,
#     'sodium': 2190,
#     'sugar': 17}
```

--------------------------------

### Get Food Item Details by ID - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/food_search

This code snippet shows how to retrieve detailed information for a specific food item using its MyFitnessPal ID. It requires the `myfitnesspal` library and a valid `mfp_id`. The input is a food item ID string, and the output provides details like servings and nutritional information (e.g., saturated fat).

```python
import myfitnesspal

client = myfitnesspal.Client()

item = client.get_food_item_details("89755756637885")
item.servings
item.saturated_fat
```

--------------------------------

### Get MyFitnessPal Measurements (Python)

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/measurements

Retrieves user measurements from MyFitnessPal. This function can fetch data for the past 30 days, since a specific date, or within a date range. It requires the `myfitnesspal` library and returns measurements as an ordered dictionary.

```python
import myfitnesspal
import datetime

client = myfitnesspal.Client()

# Get measurements for the past 30 days
weight_last_30_days = client.get_measurements('Weight')
print(weight_last_30_days)

# Get measurements since a specific date
may_1_2015 = datetime.date(2015, 5, 1)
body_fat_since_may = client.get_measurements('Body Fat', may_1_2015)
print(body_fat_since_may)

# Get measurements within a date range
this_week = datetime.date(2015, 5, 11)
last_week = datetime.date(2015, 5, 4)
weight_this_week = client.get_measurements('Weight', this_week, last_week)
print(weight_this_week)
```

--------------------------------

### Access Entries within a Meal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Get a list of food entries within a specific meal using the 'entries' property of a Meal object. Each entry represents a food item consumed during that meal and includes its nutritional breakdown.

```python
dinner.entries
# >> [<Montebello - Spaghetti noodles, 6 oz. {'sodium': 0, 'carbohydrates': 132, 'calories': 630, 'fat': 3, 'sugar': 3, 'protein': 21}>,
#     <Fresh Market - Arrabiatta Organic Pasta Sauce, 0.5 container (3 cups ea.) {'sodium': 1410, 'carbohydrates': 24, 'calories': 135, 'fat': 5, 'sugar': 12, 'protein': 6}>,
#     <Quorn - Meatless and Soy-Free Meatballs, 6 -4 pieces (68g) {'sodium': 780, 'carbohydrates': 14, 'calories': 180, 'fat': 3, 'sugar': 2, 'protein': 26}>]
```

--------------------------------

### Get Report Data Within Date Range using Python MyFitnessPal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/reports

Demonstrates retrieving report data within a specific date range by passing two date parameters to client.get_report(). The method returns an OrderedDict containing only the data between the specified dates. The order of date arguments does not matter - the method handles them appropriately.

```python
thisweek = datetime.date(2015, 5, 11)
lastweek = datetime.date(2015, 5, 4)

client.get_report("Net Calories", "Nutrition", thisweek, lastweek)
# >> OrderedDict([(datetime.date(2015, 5, 11), 1721.6), (datetime.date(2015, 5, 10), 1722.4), (datetime.date(2015, 5,9), 1720.2),
#                 (datetime.date(2015, 5, 8), 1271.0),  (datetime.date(2015, 5, 7), 1721.2),  (datetime.date(2015, 5, 6), 1720.8),
#                 (datetime.date(2015, 5, 5), 1721.8),  (datetime.date(2015, 5, 4), 1274.2)])
```

--------------------------------

### Fetch MyFitnessPal Report Data (Date Range) - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/reports

This Python snippet retrieves MyFitnessPal report data within a specified date range. It uses the 'datetime' module to define the start and end dates for the report query. The 'get_report' method accepts these dates, and the function returns an OrderedDict with data points falling within that range.

```python
import datetime

thisweek = datetime.date(2015, 5, 11)
lastweek = datetime.date(2015, 5, 4)

client.get_report("Net Calories", "Nutrition", thisweek, lastweek)
# >> OrderedDict([(datetime.date(2015, 5, 11), 1721.6), (datetime.date(2015, 5, 10), 1722.4), (datetime.date(2015, 5,9), 1720.2),
#                 (datetime.date(2015, 5, 8), 1271.0),  (datetime.date(2015, 5, 7), 1721.2),  (datetime.date(2015, 5, 6), 1720.8),
#                 (datetime.date(2015, 5, 5), 1721.8),  (datetime.date(2015, 5, 4), 1274.2)])
```

--------------------------------

### Get measurements within date range - Python myfitnesspal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/measurements

Retrieves body measurements between two specified dates using the myfitnesspal Client. Accepts measurement type and two datetime.date objects representing the date range. Date argument order does not matter. Returns an OrderedDict with measurements for dates falling within the specified range.

```python
thisweek = datetime.date(2015, 5, 11)
lastweek = datetime.date(2015, 5, 4)

weight = client.get_measurements('Weight', thisweek, lastweek)
weight
```

--------------------------------

### Instantiate myfitnesspal Client (v1.x - Obsolete)

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/upgrading

Demonstrates how to instantiate the `myfitnesspal.Client` object in version 1.x of the library. This version required a username and relied on a system keyring for authentication. It is now considered obsolete.

```python
import myfitnesspal

client = myfitnesspal.Client('myusername')
```

--------------------------------

### Search for Foods by Name - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/food_search

This code snippet demonstrates how to search for food items using the myfitnesspal Python library. It initializes a client, calls the `get_food_search_results` method with a query, and then prints details of the first result. Dependencies include the `myfitnesspal` library. Input is a food name string; output is a list of food item objects.

```python
import myfitnesspal

client = myfitnesspal.Client()

food_items = client.get_food_search_results("bacon cheeseburger")
food_items

print("{} ({}), {}, cals={}, mfp_id={}".format(
    food_items[0].name,
    food_items[0].brand,
    food_items[0].serving,
    food_items[0].calories,
    food_items[0].mfp_id
))
```

--------------------------------

### Instantiate myfitnesspal Client (v2.x - Current)

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/upgrading

Shows the current method for instantiating the `myfitnesspal.Client` object in version 2.x. This version simplifies authentication by reading login details directly from the user's web browser, eliminating the need to pass a username during instantiation.

```python
import myfitnesspal

client = myfitnesspal.Client()
```

--------------------------------

### Entry Class Constructor

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/entry

Creates a new Entry instance to store information about a single entry. The Entry class initializes with a name and nutrition dictionary containing nutritional values.

```APIDOC
## Entry Class Constructor

### Description
Initializes an Entry object that stores information about a single entry with its associated nutrition data.

### Class Definition
```
class Entry(name: str, nutrition: Dict[str, float])
```

### Parameters
#### Constructor Parameters
- **name** (str) - Required - Name of the item
- **nutrition** (Dict[str, float]) - Required - Dictionary containing nutrition values for the item

### Properties
- **name** (str) - Name of the item
- **totals** (Dict[str, float]) - Totals for the item
- **short_name** (Optional[str]) - Short name of the item
- **unit** (Optional[str]) - Unit of measurement
- **quantity** (Optional[str]) - Quantity of the item

### Example Usage
```python
from myfitnesspal.entry import Entry

nutrition_data = {
  "calories": 150.0,
  "protein": 20.0,
  "carbs": 10.0,
  "fat": 5.0
}

entry = Entry(
  name="Chicken Breast",
  nutrition=nutrition_data
)
```
```

--------------------------------

### MyFitnessPal Client Initialization and Properties

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/client

Demonstrates how to initialize the MyFitnessPal Client, optionally with a cookie jar, and access user-specific properties like user ID, metadata, access token, and effective username. The 'unit_aware' parameter controls unit sensitivity.

```python
from myfitnesspal import Client
from http.cookiejar import CookieJar

# Initialize client with optional cookie jar and unit awareness
cookie_jar = CookieJar()
client = Client(cookie_jar=cookie_jar, unit_aware=True)

# Access user properties
user_id = client.user_id
user_metadata = client.user_metadata
access_token = client.access_token
effective_username = client.effective_username
```

--------------------------------

### Meal Class Initialization and Properties - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/meal

Demonstrates the initialization of the Meal class and access to its core properties: name, entries, and totals. This class is part of the myfitnesspal library and requires Entry objects for initialization.

```python
from typing import List, Dict
from .entry import Entry

class Meal(_name : str_, _entries : List[Entry]_):
    """Stores information about a particular meal."""
    
    @property
    def entries(self) -> List[Entry]:
        """Entries for this meal."""
        # Implementation details omitted for brevity
        pass
        
    @property
    def name(self) -> str:
        """Name of this meal."""
        # Implementation details omitted for brevity
        pass
        
    @property
    def totals(self) -> Dict[str, float]:
        """Nutrition totals for all entries for this meal."""
        # Implementation details omitted for brevity
        pass
```

--------------------------------

### Search for Foods using MyFitnessPal API (Python)

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/food_search

This snippet demonstrates how to search for food items using the myfitnesspal Python client. It requires the 'myfitnesspal' library and returns a list of food items matching the search query, including their name, brand, serving size, calories, and MyFitnessPal ID.

```python
import myfitnesspal

client = myfitnesspal.Client()

food_items = client.get_food_search_results("bacon cheeseburger")
food_items
# >> [<Bacon Cheeseburger -- Sodexo Campus>,
# <Junior Bacon Cheeseburger -- Wendy's>,
# <Bacon Cheeseburger -- Continental CafÃ©>,
# <Bacon Cheddar Cheeseburger -- Applebees>,
# <Bacon Cheeseburger - Plain -- Homemade>,
# <Jr. Bacon Cheeseburger -- Wendys>,
# ...

print("{} ({}), {}, cals={}, mfp_id={}".format(
    food_items[0].name,
    food_items[0].brand,
    food_items[0].serving,
    food_items[0].calories,
    food_items[0].mfp_id
))
# > Bacon Cheeseburger (Sodexo Campus), 1 Sandwich, cals = 420.0
```

--------------------------------

### Day Class Overview

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/day

The Day class stores meal entries for a particular day and provides access to various daily statistics and goals.

```APIDOC
## Class Day

### Description
Stores meal entries for a particular day.

### Properties
- **date** (date) - The date for which the day's entries are recorded.
- **meals** (List[Meal]) - Returns a list of meals.
- **entries** (Generator[Entry, None, None]) - Yields all entries from all meals.
- **goals** (Dict[str, float]) - Returns goals.
- **notes** (str) - Returns notes.
- **water** (float) - Returns water intake.
- **exercises** (List[Exercise]) - Returns list of exercises.

### Methods
- **__getitem__**(value: str) -> Meal
  Returns a meal matching the provided name.
- **keys()** -> List[str]
  Returns a list of meal names.
- **get_as_dict()** -> Dict[str, List[MealEntry]]
  Returns a mapping of meal names to the list of entries for that meal.
```

--------------------------------

### Access Individual Strength Exercise Properties using Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/exercises

Demonstrates accessing specific details of strength exercises obtained via the myfitnesspal API. This includes extracting the exercise name, and within the 'nutrition_information', accessing values for 'sets', 'reps/set', and 'weight/set'.

```python
day.exercises[1].get_as_list()[0]['name']
# >> 'Leg Press'

day.exercises[1].get_as_list()[0]['nutrition_information']['sets']
# >> 3

day.exercises[1].get_as_list()[0]['nutrition_information']['reps/set']
# >> 12

day.exercises[1].get_as_list()[0]['nutrition_information']['weight/set']
# >> 20
```

--------------------------------

### Accessing Exercise Data with Myfitnesspal Library

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/exercise

This snippet demonstrates how to interact with the Exercise class in the Python Myfitnesspal library. It shows how to access the exercise name, retrieve a list of exercise entries, and fetch a specific entry by its index. This class is useful for parsing and manipulating exercise data from Myfitnesspal.

```python
from myfitnesspal.exercise import Exercise
from typing import List

# Assuming 'exercise_data' is an instance of the Exercise class
# For example:
# exercise_data = Exercise(name='Running', entries=[...]) 

# Accessing the name of the exercise
exercise_name = exercise_data.name
print(f"Exercise Name: {exercise_name}")

# Accessing the list of entries
entries_list = exercise_data.entries
print(f"Number of entries: {len(entries_list)}")

# Accessing a specific entry by index
if entries_list:
    first_entry = exercise_data[0]
    print(f"First entry details: {first_entry}")

# Getting exercises as a list of dictionaries
exercises_as_list = exercise_data.get_as_list()
print(f"Exercises as list: {exercises_as_list}")
```

--------------------------------

### Day Object Dictionary-like Access

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Demonstrates accessing meals within a Day object using dictionary-like key access, where keys are meal names. This provides an alternative to index-based access for specific meals.

```python
day.keys()
# >> ['Breakfast', 'Lunch', 'Dinner', 'Snack']
lunch = day['Lunch']
print lunch
# >> [<Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': 508, 'carbohydrates': 76, 'calories': 346, 'fat': 2, 'sugar': 0, 'protein': 12}>,
#     <Injera - Ethiopian Flatbread, 18 " diameter {'sodium': 204, 'carbohydrates': 30, 'calories': 139, 'fat': 1, 'sugar': 0, 'protein': 5}>]
```

--------------------------------

### Access Individual Cardiovascular Exercise Properties

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/exercises

Shows how to access specific properties of individual cardiovascular exercises retrieved from myfitnesspal. The code demonstrates accessing exercise name and nested nutrition information properties like minutes and calories burned using dictionary key notation.

```python
day.exercises[0].get_as_list()[0]['name']
# >> 'Walking, 12.5 mins per km, mod. pace, walking dog'

day.exercises[0].get_as_list()[0]['nutrition_information']['minutes']
# >> 60

day.exercises[0].get_as_list()[0]['nutrition_information']['calories burned']
# >> 209
```

--------------------------------

### Treat Day, Meal, and Entry Objects as Collections with myfitnesspal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/diary

Demonstrates how Day, Meal, and Entry objects in the myfitnesspal library can be treated like Python collections. Day objects behave like dictionaries (allowing access by meal name), Meal objects like lists (allowing access by index), and Entry objects like dictionaries (allowing access by nutritional key).

```python
day.keys()
# >> ['Breakfast', 'Lunch', 'Dinner', 'Snack']
lunch = day['Lunch']
print lunch
# >> [<Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': 508, 'carbohydrates': 76, 'calories': 346, 'fat': 2, 'sugar': 0, 'protein': 12}>,
#     <Injera - Ethiopian Flatbread, 18 " diameter {'sodium': 204, 'carbohydrates': 30, 'calories': 139, 'fat': 1, 'sugar': 0, 'protein': 5}>]

len(lunch)
# >> 2
miser_wat = lunch[0]
print miser_wat
# >> <Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': 508, 'carbohydrates': 76, 'calories': 346, 'fat': 2, 'sugar': 0, 'protein': 12}>

print miser_wat['calories']
# >> 346
```

--------------------------------

### Entry Object Dictionary-like Access

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Illustrates accessing specific nutritional values within an Entry object using dictionary-like key access. This allows direct retrieval of values like 'calories' for a food item.

```python
print miser_wat['calories']
# >> 346
```

--------------------------------

### Entry Properties

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/entry

Provides access to individual properties of an Entry object including name, totals, short name, unit, and quantity. These properties allow retrieval of specific entry attributes.

```APIDOC
## Entry Properties

### Description
Properties provide access to individual attributes of an Entry object.

### Available Properties

#### name (str)
- **Type**: str
- **Description**: Name of the item
- **Access**: Read-only

#### totals (Dict[str, float])
- **Type**: Dict[str, float]
- **Description**: Totals for the item containing nutrition values
- **Access**: Read-only

#### short_name (Optional[str])
- **Type**: Optional[str]
- **Description**: Short name or abbreviation of the item
- **Access**: Read-only

#### unit (Optional[str])
- **Type**: Optional[str]
- **Description**: Unit of measurement for the item
- **Access**: Read-only

#### quantity (Optional[str])
- **Type**: Optional[str]
- **Description**: Quantity of the item
- **Access**: Read-only

### Usage Examples
```python
# Access entry properties
item_name = entry.name
nutrition_totals = entry.totals
short = entry.short_name
measurement_unit = entry.unit
item_quantity = entry.quantity
```
```

--------------------------------

### Retrieve Specific Recipe and Meal Details

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/client

Fetches detailed information for a specific recipe using its ID or for a specific meal using its ID and title. The recipe details adhere to the schema.org/Recipe format.

```python
from myfitnesspal import Client

client = Client()

# Get details for recipe with ID 54321
recipe_details = client.get_recipe(recipeid=54321)

# Get details for meal with ID 101 and title 'Breakfast'
meal_details = client.get_meal(meal_id=101, meal_title='Breakfast')
```

--------------------------------

### Submit New Food Item

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/client

Provides a function to add new food items to the MyFitnessPal database with detailed nutritional information, serving size, and public sharing options. Returns True upon successful submission.

```python
from myfitnesspal import Client

client = Client()

# Submit a new food item
success = client.set_new_food(
    brand='Generic Foods',
    description='Healthy Snack Bar',
    calories=150,
    fat=8.0,
    carbs=15.0,
    protein=5.0,
    fiber=3.0,
    sugar=7.0,
    serving_size='1 Bar',
    servingspercontainer=5.0
)
```

--------------------------------

### Entry.get_as_dict()

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/entry

Returns the entry's totals and information as a dictionary. This method provides a convenient way to access all entry data in dictionary format for serialization or further processing.

```APIDOC
## get_as_dict() → MealEntry

### Description
Returns totals per-item as a dictionary representation of the entry.

### Method
GET (retrieves data)

### Signature
```python
get_as_dict() → MealEntry
```

### Parameters
None

### Returns
- **MealEntry** (Dict) - Dictionary containing the entry's totals and information

### Response Example
```python
entry_dict = entry.get_as_dict()
# Returns a MealEntry dictionary with all entry information
{
  "name": "Chicken Breast",
  "totals": {
    "calories": 150.0,
    "protein": 20.0,
    "carbs": 10.0,
    "fat": 5.0
  },
  "short_name": "Chicken",
  "unit": "g",
  "quantity": "100"
}
```
```

--------------------------------

### Access Daily Water and Notes

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Retrieve the recorded water intake and any notes entered for a specific day using the 'water' and 'notes' properties of a Day object.

```python
day.water
# >> 1
day.notes
# >> "This is the note I entered for this day"
```

--------------------------------

### Access Individual Cardiovascular Exercise Properties using Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/exercises

Demonstrates how to access specific properties of a cardiovascular exercise entry obtained from the myfitnesspal API. After retrieving the exercise list, individual dictionary keys like 'name', 'minutes', and 'calories burned' can be accessed.

```python
day.exercises[0].get_as_list()[0]['name']

# >> 'Walking, 12.5 mins per km, mod. pace, walking dog'

day.exercises[0].get_as_list()[0]['nutrition_information']['minutes']
# >> 60

day.exercises[0].get_as_list()[0]['nutrition_information']['calories burned']
# >> 209
```

--------------------------------

### Access MyFitnessPal Daily Data

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Retrieve and display a specific day's nutritional summary from MyFitnessPal. This requires initializing the client and calling get_date with year, month, and day. The output is a Day object containing aggregated nutritional information.

```python
import myfitnesspal

client = myfitnesspal.Client()

day = client.get_date(2013, 3, 2)
day
# >> <03/02/13 {'sodium': 3326, 'carbohydrates': 369, 'calories': 2001, 'fat': 22, 'sugar': 103, 'protein': 110}>
```

--------------------------------

### Unit-Aware Data Retrieval

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Enables unit-aware retrieval of measurement data by initializing the client with `unit_aware=True`. This returns measurements with unit information, such as `Weight(g=76)` or `Energy(Calorie=346)`.

```python
client = myfitnesspal.Client(unit_aware=True)
day = client.get_date(2013, 3, 2)
lunch = day['lunch']
print lunch
# >> [<Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': Weight(mg=508), 'carbohydrates': Weight(g=76), 'calories': Energy(Calorie=346), 'fat': Weight(g=2), 'sugar': Weight(g=0), 'protein': Weight(g=12)}>
print miser_wat['calories']
# >> Energy(Calorie=346)
```

--------------------------------

### Access Additional Day Information and Unit Awareness with myfitnesspal

Source: https://python-myfitnesspal.readthedocs.io/en/latest/how_to/diary

Retrieves additional daily data such as water intake (`day.water`) and notes (`day.notes`). It also demonstrates how to enable unit awareness by initializing the client with `unit_aware=True`, which returns nutritional values as specific unit objects (e.g., Weight(mg=508)).

```python
day.water
# >> 1
day.notes
# >> "This is the note I entered for this day"

client = myfitnesspal.Client(unit_aware=True)
day = client.get_date(2013, 3, 2)
lunch = day['lunch']
print lunch
# >> [<Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': Weight(mg=508), 'carbohydrates': Weight(g=76), 'calories': Energy(Calorie=346), 'fat': Weight(g=2), 'sugar': Weight(g=0), 'protein': Weight(g=12)}>, 
miser_wat = lunch[0]
print miser_wat['calories']
# >> Energy(Calorie=346)
```

--------------------------------

### Fetch MyFitnessPal Report Data (Past 30 Days) - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/reports

This Python code snippet demonstrates how to fetch report data for the past 30 days using the myfitnesspal client. It requires the 'myfitnesspal' library and initializes a client object. The function get_report is called with report name and category, returning an OrderedDict of dates and values.

```python
import myfitnesspal

client = myfitnesspal.Client()

client.get_report(report_name="Net Calories", report_category="Nutrition")
# >> OrderedDict([(datetime.date(2015, 5, 14), 1701.0), (datetime.date(2015, 5, 13), 1732.8), (datetime.date(2015, 5,12), 1721.8),
#                 (datetime.date(2015, 5, 11), 1701.6), (datetime.date(2015, 5, 10), 1272.4), (datetime.date(2015, 5, 9), 1720.2),
#                 (datetime.date(2015, 5, 8), 1071.0),  (datetime.date(2015, 5, 7), 1721.2),  (datetime.date(2015, 5, 6), 1270.8),
#                 (datetime.date(2015, 5, 5), 1701.8),  (datetime.date(2015, 5, 4), 1724.2),  (datetime.date(2015, 5, 3), 1722.2),
#                 (datetime.date(2015, 5, 2), 1701.0),  (datetime.date(2015, 5, 1), 1721.2),  (datetime.date(2015, 4, 30), 1721.6),
#                 (datetime.date(2015, 4, 29), 1072.4), (datetime.date(2015, 4, 28), 1272.2), (datetime.date(2015, 4, 27), 1723.2),
#                 (datetime.date(2015, 4, 26), 1791.8), (datetime.date(2015, 4, 25), 1720.8), (datetime.date(2015, 4, 24), 1721.2),
#                 (datetime.date(2015, 4, 23), 1721.6), (datetime.date(2015, 4, 22), 1723.2), (datetime.date(2015, 4, 21), 1724.2),
#                 (datetime.date(2015, 4, 20), 1273.6), (datetime.date(2015, 4, 19), 1721.8), (datetime.date(2015, 4, 18), 1720.4),
#                 (datetime.date(2015, 4, 17), 1629.8), (datetime.date(2015, 4, 16), 1270.4), (datetime.date(2015, 4, 15), 1270.8),
#                 (datetime.date(2015, 4, 14), 1721.6)])
```

--------------------------------

### Search and Retrieve Food Item Details

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/client

Enables searching for foods based on a query string, returning a list of 'FoodItem' objects. It also allows fetching detailed information for a specific food item using its MyFitnessPal ID.

```python
from myfitnesspal import Client

client = Client()

# Search for 'Apple'
search_results = client.get_food_search_results(query='Apple')

# Get details for a food item with MFP ID 12345
food_details = client.get_food_item_details(mfp_id=12345)
```

--------------------------------

### FoodItemServing Class Definition - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/fooditemserving

Defines the FoodItemServing class used to represent a serving of a food item. It initializes with serving ID, nutrition multiplier, value, unit, and index, and provides properties to access these attributes.

```python
class FoodItemServing:
    def __init__(self, _serving_id: str, _nutrition_multiplier: float, _value: float, _unit: str, _index: int):
        self._serving_id = _serving_id
        self._nutrition_multiplier = _nutrition_multiplier
        self._value = _value
        self._unit = _unit
        self._index = _index

    @property
    def serving_id(self) -> str:
        """Serving ID"""
        return self._serving_id

    @property
    def nutrition_multiplier(self) -> float:
        """Nutrition Multiplier"""
        return self._nutrition_multiplier

    @property
    def value(self) -> float:
        """Value"""
        return self._value

    @property
    def unit(self) -> str:
        """Unit"""
        return self._unit

    @property
    def index(self) -> int:
        """Index"""
        return self._index
```

--------------------------------

### Meal Object List-like Access

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Shows how Meal objects behave like lists, allowing access to individual food entries by index. This is useful for iterating through or selecting specific items within a meal.

```python
len(lunch)
# >> 2
miser_wat = lunch[0]
print miser_wat
# >> <Generic - Ethiopian - Miser Wat (Red Lentils), 2 cup {'sodium': 508, 'carbohydrates': 76, 'calories': 346, 'fat': 2, 'sugar': 0, 'protein': 12}>
```

--------------------------------

### Retrieve Saved Recipes and Meals

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/client

Fetches lists of all saved recipes and meals. For recipes, it returns a dictionary mapping recipe IDs to titles. For meals, it returns a dictionary mapping meal IDs to meal names.

```python
from myfitnesspal import Client

client = Client()

# Get all saved recipes
recipes = client.get_recipes()

# Get all saved meals
meals = client.get_meals()
```

--------------------------------

### Note Class Representation in Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/note

This snippet shows the Python class definition for storing Myfitnesspal note data. It includes properties for the note's type and date, along with a method to return the data as a dictionary.

```python
class Note:
    """Stores information about a note"""
    def __init__(self, _note_data : NoteDataDict_):
        # ... initialization logic ...
        pass

    _type: Optional[str]  # Type
    _date: Optional[date]  # Date

    def as_dict() -> dict:
        """Returns data as a dictionary."""
        # ... method implementation ...
        pass
```

--------------------------------

### Retrieve and Set Measurements

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/client

Allows retrieval of historical measurement data (e.g., 'Weight') within a specified date range, and setting new measurement values for today's date. Returns a dictionary of dates to measurement values when retrieving.

```python
from datetime import date
from myfitnesspal import Client

client = Client()

# Get weight measurements from the last month
measurements = client.get_measurements(measurement='Weight', lower_bound=date(2023, 9, 26), upper_bound=date(2023, 10, 26))

# Set today's weight measurement
client.set_measurements(measurement='Weight', value=75.5)
```

--------------------------------

### Python MyFitnessPal Type Definitions

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/types

Defines various data structures for the MyFitnessPal API, such as user profiles, goals, preferences, and authentication tokens. These classes are used to represent the data exchanged with the MyFitnessPal service.

```python
from typing import List, Dict, Optional, Any
from typing_extensions import Literal

class CommandDefinition():
    """Represents a command definition in the MyFitnessPal API."""
    function: Callable_  # Placeholder for callable type
    description: str
    is_alias: bool
    aliases: List[str]

class GoalDisplayDict():
    """Represents a goal display configuration."""
    id: str
    display_type: str
    nutrients: List[str]

class UnitPreferenceDict():
    """Represents user preferences for units of measurement."""
    energy: str
    weight: str
    distance: str
    height: str
    water: str

class DiaryPreferencesDict():
    """Represents user preferences for diary settings."""
    default_foot_view: str
    meal_names: List[str]
    tracked_nutrients: List[str]

class UnitValueContainer():
    """A container for a value with an associated unit."""
    unit: str
    value: float

class GoalPreferencesDict():
    """Represents user's goal preferences."""
    workouts_per_week: int
    weekly_workout_duration: int
    weekly_exercise_energy: UnitValueContainer
    weight_change_goal: UnitValueContainer
    weight_goal: UnitValueContainer
    diary_goal_display: str
    home_goal_display: str
    macro_goal_format: str

class LocationPreferencesDict():
    """Represents user's location-based preferences."""
    time_zone: str
    country_code: str
    locale: str
    postal_code: str
    state: str
    city: str

class AdminFlagDict():
    """Contains administrative flags for a user account."""
    status: str
    has_changed_username: bool
    forgot_password_or_username: bool
    warnings: int
    strikes: int
    revoked_privileges: List

class AccountDict():
    """Represents user account information."""
    created_at: str
    updated_at: str
    last_login: str
    valid_email: bool
    registration_source: str
    roles: List[str]
    admin_flags: AdminFlagDict

class SystemDataDict():
    """Contains system-related data for a user."""
    login_streak: int
    unseen_notifications: int

class UserProfile():
    """Represents a user's public profile information."""
    type: str
    starting_weight_date: str
    starting_weight: UnitValueContainer
    main_image_url: str
    main_image_id: Optional[Any]
    birthdate: str
    height: UnitValueContainer
    first_name: Optional[str]
    last_name: Optional[str]
    sex: Literal['M', 'F']
    activity_factor: str
    headline: Optional[str]
    about: Optional[str]
    why: Optional[str]
    inspirations: List

class UserMetadata():
    """Aggregates all metadata related to a user."""
    id: str
    username: str
    email: str
    goal_displays: List[GoalDisplayDict]
    unit_preferences: UnitPreferenceDict
    diary_preferences: DiaryPreferencesDict
    goal_preferences: GoalPreferencesDict
    location_preferences: LocationPreferencesDict
    account: AccountDict
    system_data: SystemDataDict
    step_sources: List
    profiles: List[UserProfile]

class AuthData():
    """Contains authentication tokens and related information."""
    token_type: str
    access_token: str
    expires_in: int
    refresh_token: str
    user_id: str

class MealEntry():
    """Represents an entry within a meal, including nutrition details."""
    name: str
    nutrition_information: Dict[str, float]

class NoteDataDict():
    """Represents a note entry, typically for food or exercise."""
    body: str
    type: str
    date: str

class FoodItemNutritionDict():
    """Details the nutritional breakdown of a food item."""
    calcium: float
    carbohydrates: float
    cholesterol: float
    fat: float
    fiber: float
    iron: float
    monounsaturated_fat: float
    polyunsaturated_fat: float
    potassium: float
    protein: float
    saturated_fat: float
    sodium: float
    sugar: float
    trans_fat: float
    vitamin_a: float
    vitamin_c: float

class ServingSizeDict():
    """Defines a serving size for a food item."""
    id: str
    nutrition_multiplier: float
    value: float
    unit: str
    index: int

class FoodItemDetailsResponse():
    """Response object containing detailed information about a food item."""
    description: str
    brand_name: Optional[str]
    verified: bool
    nutrition: FoodItemNutritionDict
    calories: float
    confirmations: int
    serving_sizes: List[ServingSizeDict]

class NutritionInformation():
    """Provides a summary of nutritional content, often as strings."""
    calories: str
    carbohydrateContent: str
    fiberContent: str
    sugarContent: str
    sodiumContent: str
    proteinContent: str
    fatContent: str
    saturatedFatContent: str
    monunsaturatedFatContent: str

```

--------------------------------

### Accessing Meal Entries - Python

Source: https://python-myfitnesspal.readthedocs.io/en/latest/api/meal

Shows how to retrieve a specific entry from a Meal object using its index. This method utilizes Python's item access protocol (__getitem__).

```python
from typing import List, Dict
from .entry import Entry

class Meal(_name : str_, _entries : List[Entry]_):
    """Stores information about a particular meal."""

    def __getitem__(self, value : int) -> Entry:
        """Returns a particular entry for this meal."""
        # Implementation details omitted for brevity
        pass
```

--------------------------------

### Access MyFitnessPal Meals for a Day

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Access all meals recorded for a specific day using the 'meals' property of a Day object. This returns a list of Meal objects, each potentially containing nutritional information.

```python
day.meals
# >> [<Breakfast {}>,
#    <Lunch {'sodium': 712, 'carbohydrates': 106, 'calories': 485, 'fat': 3, 'sugar': 0, 'protein': 17}>,
#    <Dinner {'sodium': 2190, 'carbohydrates': 170, 'calories': 945, 'fat': 11, 'sugar': 17, 'protein': 53}>,
#    <Snacks {'sodium': 424, 'carbohydrates': 93, 'calories': 571, 'fat': 8, 'sugar': 86, 'protein': 40}>]
```

--------------------------------

### Access Specific Food Entry by Index

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Access a particular food entry from a meal's list of entries using its index. This allows retrieving details for a single consumed item.

```python
spaghetti = dinner.entries[0]
spaghetti.name
# >> Montebello - Spaghetti noodles, 6 oz.
```

--------------------------------

### Access Specific Meal by Index

Source: https://python-myfitnesspal.readthedocs.io/en/latest/_sources/how_to/diary

Retrieve a specific meal from a day's meals by its index in the 'meals' list. This allows targeted access to the nutritional data of a particular meal, such as dinner.

```python
dinner = day.meals[2]
dinner
# >> <Dinner {'sodium': 2190, 'carbohydrates': 170, 'calories': 945, 'fat': 11, 'sugar': 17, 'protein': 53}>
```
Tokens:
Raw