revo-client 0.9.0

Python client for Myotest smart coaching API

# Myotest client API

## Setup

- Python > 3.4
- pip install -r requirement.txt

## Install dev version in your notebook

This will install a python package named "revo-client" in your current environment

##### Local version

- ```pip install -e {PATH TO YOUR REPO}```

##### Repository version

- ```pip install revo-client```



## Create Client

By default a connect will connect to the production server

```python
from myotest import Client

# Connect to production server with your api token
client = Client("my-api-token")

# Connect to staging server
client = Client("my-api-token", server="staging")

# Connect to another server
client = Client("my-api-token", server="https://my-server")

# Create a client for another user ( you must be the user's coach to be able to get his information )
client = Client("my-api-token", user_id="other-user-uuid")

```

## Root queries

```python

# Fetch last workout that contains tags
workout = client.get_last_workout_with_tags(["vam_high", "ranges"])

# Fetch workout with id
workout = client.get_workout_with_id("workout-uuid")

# Fetch current profile
profile = client.get_profile()

```

## Objects

### Object :: Workout

#### Attributes
- id (ID)
- title (string)
- start (datetime)
- end (datetime)
- type (string) workout type
- target_duration (timedelta) workout requested duration
- effective_duration (timedelta)
- slots (list of Object::Slot)
- datasets (list of Object::Dataset)
- training_load (Object::TrainingLoad)

#### workout.get_dataset(name)
Return the dataset matching name, if the name is complete (i.e. "mil-1") the dataset is returned.
If the name is partial (i.e. "mil") the first dataset is returned (lower index)

#### workout.get_slots()
Return all workout slots in execution order

#### workout.get_slot_with_tags(tags)
Return the first slot (Object :: Slot) containing all tags, if any

```workout.get_slot_with_tags(["vm_high])```


### Object :: Dataset

#### Attributes
- id (ID)
- name (string)
- dataframe (Pandas dataframe)
- describe (dict) Full dataset describe
- avro_schema (dict)
- workout (Object::workout)

### Object :: Slot

#### Attributes
- id (ID)
- tags (list of string)
- type (string)
- value (Object::SlotValue)
- text (string)
- result (Object::SlotResult)
- end_time (datetime)
- start_time (datetime)
- power_type (string)
- analysis (list of dict)
- workout (Object::workout)
- training_load (Object::TrainingLoad)

### Object :: SlotResult

#### Attributes
- power (dict) Describe of power **("max","min","std","mean","count","median")**
- speed (dict) Describe of speed **("max","min","std","mean","count","median")**
- gps_power (dict) Describe of gps_power **("max","min","std","mean","count","median")**
- gps_speed (dict) Describe of gps_speed **("max","min","std","mean","count","median")**
- distance (float) distance in meters
- gps_distance (float) distance in meters
- regularity_90 (float) regularity 90% of the time
- step_count_ratio (float) step vs run ratio
- regularity_median (float) median on regularity

**gps_speed** and **gps_power** are only available if the mil contains those information, if
the user created a workout without gps, those values are null

### Object :: SlotValue

#### Attributes
- value (float) distance=meters, duration=seconds, repetition=count
- type (distance | duration | repetition )

### Object :: Profile

#### Attributes
- id (ID)
- full_name (string)
- gender (male|female)
- weight (float) in kilogram
- height (float) in meter
- leg_length (float) in meter
- waist (float) in meter
- vma (float)
- pma (float)
- birth_date (date)
- age (float) in years


### Object :: AvroSchema

#### Attributes
- name (string)
- type (string) record type
- fields (Array of dict)


### Object :: TrainingLoad

Training load is only available to a slot using PMA calculation.
For other slot types, all the values are 0

#### Attributes
- requested_min (integer) requested training load minimum
- requested_max (integer) requested training load maximum
- fields (Array of dict) effetctive training load


## Script Example

```python
from myotest import Client

client = Client("my-api-token")
workout = client.get_last_workout_with_tags(["vma_high"])
await workout.load_slots()
slot = workout.get_slot_with_tags(["vma_high"])
profile = client.get_profile()

print(
"Hello I'm {} and I am {} years old, my last"
"vma workout had a regularity of {}".format(
profile.full_name,
profile.age,
slot.result.regularity_median))
```