braincube-connector 2.0.2

python client to the braincube web services

braincube_connector: a python client for Braincube

Description

The python package braincube_connector provides a tool for datascientists to access their data on Braincube directly from python.

Installation

Install with pip:

pip install braincube_connector

Usage

Client

A client can be inialized manually from a custom configuration file.

from braincube_connector import client

client.get_instance(config_file="pathto/config.json")

Note: If the client is not initialized manually, the package creates a client instance from one of these two files ./config.json or ~/.braincube/config.json (in this priority order) if they exist.

Braincube

To obtain a list of all the available Braincube entities with a client:

from braincube_connector import braincube

braincube.get_braincube_list()

Or to select a specific Braincube entity from its name:

bc = braincube.get_braincube("demo")

MemoryBase

The list of all the memory bases available within a Braincube is obtained with

mb_list = bc.get_memory_base_list()

Note: The number of memory bases in a braincube can be numerous, hence get_memory_base_list allows paginated requests bc.get_memory_base_list(page=0)

To select a unique memory base, go with its bcId:

mb_list = bc.get_memory_base(20)

VariableDescriptions

The variable description are linked to a memory base.

var_desc = mb.get_variable(bcid="2000034")

For multiple variable descriptions:

mb.get_variable_list(page=0)

Note: Similarly to memory bases, providing no argument to get_variable_list retrieves all the descriptions available in the memory base.

The type of variable is obtained with the function get_type

var_desc.get_type()

DataGroup

DataGroup are obtained from a memory base:

datagroup = mb.get_datagroup(bcid="10")

The list of the available datagroups can also be obtained with mb.get_datagroup_list().

A datagroup is a container that includes multiple variables. They are accessed with

datagroup.get_variable_ids() # Gets the variable bcIds
datagroup.get_variable_list() # Gets the list of VariableDescription objects.

Event

An event is a predifined set of conditions in braincube. It is accessed as follows:

event = mb.get_event(bcid="10")
event_list = mb.get_event_list()

The interest of events is that you can access the conditions they contain in order create new filters for a get_data function:

event.get_conditions()

JobDescription

The job desciption contains the settings used to build an analysis and gives a proxy to access these parameters easily. A JobDescription is obtained from a memory base as follows:

job_desc = mb.get_job(bcid="573")
job_list = mb.get_job_list(page=0)

The properties are acced with the following methods:

• get_conditions:
  Gets a list of the conditions used to select the job variables.

  job_desc.get_conditions()
  job_desc.get_conditions(combine=True) # Merge the conditions into one
  job_desc.get_conditions(include_events=True) # Includes the conditions from
                                               # the job's events
  

• get_variable_ids:
  Gets a list of the variables involved in the job, including the target variables and the influence variables.

  job_desc.get_variable_ids()
  

• get_events:
  Gets a list of the event objects used by the job.

  job_desc.get_events()
  

• get_categories:
  Gets a list of conditions used to categorise a job's data as good or bad. You may have a middle category, it's an old categorisation which will not be used anymore.

  job_desc.get_categories()
  

• get_data:
  When a job is created on braincube, a separate copy of the data is made. As for now this copy is not available from the webservices. However the get_data method collects the job's data from the memory base using the same filters as when the job was created. Be aware that these data might be different from the job's data if the memory base has been updated since the job creation.

  Similarly to other object get_data, a filters parameter is available to add additional filters to the job's conditions.

  job_desc.get_data()
  

Job rules

The job rule descriptions are obtained with the methods get_rule or get_rule_list either from a job or a memory base. The only difference being that in the case of a memory base get_rule_list gets all the rules existing in the memory base whereas for a job, it gets the rules specific to the job under consideration.

rule = job.get_rule(bcid="200")
rule_list = job.get_rule_list()

To access a RuleDescription object's metadata, you can calle the get_metadata function

rule.get_metadata()

Get variable data

A memory base can also request the data for a custom set of variable ids. Adding filters restricts the returned data to a desired subset of the data. The method is called as follows:

data = mb.get_data(["2000001", "2000034"], filters=my_filters)

The output format is a dictionary in which the keys are the variable bcIds and the value a list of data. This allows an easy conversion to a pandas dataframe:

import pandas as pd

df = pd.DataFrame(data)

Note: By default the dates are not parsed to datetime objects in order to speed up the get_data function but it is possible to enable the parsing:

from braincube_connector import parameters
parameters.set_parameter({"parse_date": True})

Data filters

The get_data methods have the option to restrict the data that are collected by using a set of filters. The filters parameter must be a list conditions (even for a single condition):

object.get_data(filters=[{"BETWEEN",["mb20/d2000002",0,10]},{"BETWEEN",["mb20/d2000003", -1, 1]}])

Here is a selection of the most common types of filters:

• Equals to
  Selects data when a variable is equal to

  {
    "EQUALS": [ "mb20/d2000002", 2.0]
  }
  

• Between
  Selects the data when a variable belongs to a range.

  {
    "BETWEEN": [ "mb20/d2000003", -1, 1]
  }
  

• Lower than
  Selects the data when a variable is lower than a certain value.

  {
    "LESS": [ "mb20/d2000003", 10]
  }
  

  Note: The LESS_EQUALS filter also exists.

• Greater than
  Selects the data when a variable is greater than a certain value.

  {
    "GREAT": [ "mb20/d2000003", 10]
  }
  

  Note: The GREAT_EQUALS filter also exists.

• Not:
  The NOT condition creates the opposite of an existing condition.

  {
    "Not": [{"filter":...}]
  }
  

• And gate
  It is possible to combine filters using a and gate.

  {
    "AND": [{"filter1":...}, {"filter2":...}]
  }
  

  Notes:

  • A AND filter can only host two conditions. In order to join more than two filters multiple AND conditions should be nested one into another.
  • When multiple filters are provided in the get_data's filters parmeters, they are joined together within the function using AND gates.

• Or gate:
  Similar to AND but uses a OR gate.

  {
    "OR": [{"filter1":...}, {"filter2":...}]
  }
  

Library parameters

The library parameters can be set to custom values:

from braincube_connector import parameters

# Change the request pagination size to 10
parameters.set_parameter({"page_size": 10})

# Parse dates to datetime objects
parameters.set_parameter({"parse_date": True})

Configuration

In order to connect to the web service, the client needs a Oauth2 token saved in a configuration file. For simplicity it is recommended to use a helper braincube-token-getter to get the token and to setup the configuration file as follows:

config.json

{
    "client_id": "app id",
    "client_secret": "app key",
    "domain": "mybraincube.com",
    "verify": true,
    "oauth2_token": "token value"
}

The token-getter script saves the configuration in the ~/.braincube/config.json file by default.