brainstem_api_tutorial.py

from brainstem_api_client import BrainstemClient


# 0. Load the client. User email and password will be requested.
client = BrainstemClient()


# 1. Loading datasets.

## load_model can be used to load any model:
## We just need to pass our settings and the name of the model.
output1 = client.load_model('dataset').json()

### We can fetch a single dataset entry from the loaded models.
dataset = output1["datasets"][0]

## We can also filter the models by providing a dictionary where
## the keys are the fields and the values the possible contents.
## In this example, it will just load datasets whose name is "yeah".
output1 = client.load_model('dataset', filters={'name': 'yeah'}).json()

## Loaded models can be sorted by different criteria applying to
## their fields. In this example, datasets will be sorted in 
## descending ording according to their name.
output1 = client.load_model('dataset', sort=['-name']).json()

## In some cases models contain relations with other models, and
## they can be also loaded with the models if requested. In this
## example, all the projects, experiment data, behaviors and
## manipulations related to each dataset will be included. 
output1 = client.load_model('dataset', include=['projects', 'experimentdata', 'behaviors', 'manipulations']).json()

### The list of related experiment data can be retrived from the
### returned dictionary.
experiment_data = output1["datasets"][0]["experimentdata"]


## All these options can be combined to suit the requirements
## of the users. For example, we can get only the dataset that
## contain the word "Rat" in their name, sorted in descending
## order by their name and including the related projects.
output1 = client.load_model('dataset', filters={'name.icontains': 'Rat'}, sort=["-name"], include=['projects']).json()



# 2. Updating a dataset

## We can make changes to a model and update it in the database.
## In this case, we changed the description of one of the
## previously loaded datasets.
dataset = {}
dataset["description"] = 'new description'
output2 = client.save_model("dataset", id="0e39c1fd-f413-4142-95f7-f50185e81fa4", data=dataset).json()




# 3. Creating a new dataset

## We can submit a new entry by defining a dictionary with the
## required fields.
dataset = {}
dataset["name"] = "New Dataset"
dataset["projects"] = ['e7475834-7733-48cf-9e3b-f4f2d2d0305a']
dataset["description"] = 'description'

## Submitting dataset
output3 = client.save_model("dataset", data=dataset).json()




# 4. Load public projects

## Request the public data by defining the portal to be public
output4 = client.load_model("project", portal="public").json()