scibite-toolkit 1.0.0

scibite-toolkit - python library for calling SciBite applications: TERMite, TExpress, SciBite Search, CENtree and Workbench. The library also enables processing of the JSON results from such requests

Project Description

scibite-toolkit - python library for making calls to SciBite's TERMite, CENtree, Workbench and SciBite Search. The library also enables post-processing of the JSON returned from such requests.

Install

$ pip3 install scibite_toolkit

Versions listed on PyPi!

Example call to TERMite

In this example call to TERMite, we will annotate one zip file from MEDLINE and then process the output to a dataframe with the built in functions of the toolkit.

We will use the first zip file from PubMed's Annual Baseline files.

Two example scripts will be shown - one that authenticates with a SciBite hosted instance of TERMite and one that hosts with a local instance of TERMite (hosted by customer).

*Please note the following:

you can test with any file. If you would like to test with just text (and not a file), please use "t.set_text('your text') and don't use the t.set_binary_content command.

Example 1 - SciBite Hosted instance of TERMite

import pandas as pd
from scibite_toolkit import termite

# Initialize your TERMite Request
t = termite.TermiteRequestBuilder()

# Specify your TERMite API Endpoint and login URL
t.set_url('url_endpoint')
t.set_saas_login_url('login_url')

# Authenticate with the instance
username = 'username
password = 'password'
t.set_auth_saas(username, password)

# Set your runtime options
t.set_entities('INDICATION')  # comma separated list of VOCabs you want to run over your data
t.set_input_format('medline.xml')  # the input format of the data sent to TERMite
t.set_output_format('json')  # the output format of the response from TERMite
t.set_binary_content('path/to/file')  # the file path of the file you want to annotate
t.set_subsume(True)  # set subsume run time option (RTO) to true

# Execute the request and convert response to dataframe for easy analysis
termite_response = t.execute()
resp_df = termite.get_termite_dataframe(termite_response)
print(resp_df.head(3))

Example 2 - Local Instance of TERMite (Hosted by Customer)

import pandas as pd
from scibite_toolkit import termite

# Initialize your TERMite Request
t = termite.TermiteRequestBuilder()

# Specify your TERMite API Endpoint and login URL
t.set_url('url_endpoint')

# Authenticate with the instance
username = 'username'
password = 'password^'
t.set_basic_auth(username, password)

# Set your runtime options
t.set_entities('INDICATION')  # comma separated list of VOCabs you want to run over your data
t.set_input_format('medline.xml')  # the input format of the data sent to TERMite
t.set_output_format('json')  # the output format of the response from TERMite
t.set_binary_content('path/to/file')  # the file path of the file you want to annotate
t.set_subsume(True)  # set subsume run time option (RTO) to true

# Execute the request and convert response to dataframe for easy analysis
termite_response = t.execute()
resp_df = termite.get_termite_dataframe(termite_response)
print(resp_df.head(3))

Example call to TExpress

In this example call to TExpress, we will annotate one zip file from Medline and then process the output to a dataframe with the built in functions of the toolkit.

We will use the first zip file from PubMed's Annual Baseline files.

Two example scripts will be shown - one that authenticates with a SciBite hosted instance of TExpress and one that authenticates with a local instance of TExpress (hosted by the customer).

Please note the following:

you can test with any file. If you would like to test with just text (and not a file), please use "t.set_text('your text') and don't use the t.set_binary_content command.

Example 1 - SciBite Hosted Instance of TExpress

import pandas as pd
from scibite_toolkit import texpress

# Initialize your TERMite Request
t = texpress.TexpressRequestBuilder()

# Specify your TERMite API Endpoint and login URL
t.set_url('url_endpoint')
t.set_saas_login_url('login_url')

# Authenticate with the instance
username = 'username'
password = 'password'
t.set_auth_saas(username, password)

# Set your runtime options
t.set_entities('INDICATION')  # comma separated list of VOCabs you want to run over your data
t.set_input_format('medline.xml')  # the input format of the data sent to TERMite
t.set_output_format('json')  # the output format of the response from TERMite
t.set_binary_content('path/to/file')  # the file path of the file you want to annotate
t.set_subsume(True)  # set subsume run time option (RTO) to true
t.set_pattern(':(INDICATION):{0,5}:(INDICATION)')  # pattern to tell TExpress what to look for within data

# Execute the request and convert response to dataframe for easy analysis
texpress_resp = t.execute()
resp_df = texpress.get_texpress_dataframe(texpress_resp)
print(resp_df.head(3))

Example 2 - Local Instance of TExpress (Hosted by Customer)

import pandas as pd
from scibite_toolkit import texpress

# Initialize your TERMite Request
t = texpress.TexpressRequestBuilder()

# Specify your TERMite API Endpoint
t.set_url('url_endpoint')

# Authenticate with the instance
username = 'username'
password = 'password'
t.set_basic_auth(username, password)

# Set your runtime options
t.set_entities('INDICATION')  # comma separated list of VOCabs you want to run over your data
t.set_input_format('pdf')  # the input format of the data sent to TERMite
t.set_output_format('medline.xml')  # the output format of the response from TERMite
t.set_binary_content('/path/to/file')  # the file path of the file you want to annotate
t.set_subsume(True)  # set subsume run time option (RTO) to true
t.set_pattern(':(INDICATION):{0,5}:(INDICATION)')  # pattern to tell TExpress what to look for within data

# Execute the request and convert response to dataframe for easy analysis
texpress_resp = t.execute()
resp_df = texpress.get_texpress_dataframe(texpress_resp)
print(resp_df.head(3))

Example call to SciBite Search

from scibite_toolkit import scibite_search

# First authenticate - The examples provided are assuming our SaaS-hosted instances, adapt accordingly
ss_home = 'https://yourdomain-search.saas.scibite.com/'
sbs_auth_url = "https://yourdomain.saas.scibite.com/"
client_id = "yourclientid"
client_secret ="yourclientsecret"
s = scibite_search.SBSRequestBuilder()
s.set_url(ss_home)
s.set_auth_url(sbs_auth_url)
s.set_oauth2(client_id,client_secret) #Authentication will last according to what was was set up when generating the client

# Now you can use the request object

# Search over documents
sample_query = 'schema_id="clinical_trial" AND (title~INDICATION$D011565 AND DRUG$*)'

# Note that endpoint is capped at 100 results, but you can paginate using the offset parameter
response = s.get_docs(query=sample_query,markup=True,limit=100)

# Co-ocurrence search across sentences
# Get the top 50 co-ocurrence sentence aggregates for psoriasis indication and any gene
response = s.get_aggregates(query='INDICATION$D011565',vocabs=['HGNCGENE'],limit=50)

Example call to Workbench

from scibite_toolkit import workbench
#first authenticate with the instance
username = 'username'
password = 'password'
client_id = 'client_id'
wb = WorkbenchRequestBuilder()
url = 'https://workbench-url.com'
wb.set_oauth2(client_id, username, password)
#then set up your call - here we will be creating a WB dataset, uploading a file to it and annotating it
wb.set_dataset_name = 'My Test Dataset'
wb.set_dataset_desc = 'My Test Description'
wb.create_dataset()
wb.set_file_input('path/to/file.xlsx')
wb.upload_file_to_dataset()
#In this example, we will only annotate two columns with pre-selected VOCabs.
#If you would like to tell WB to annotate the dataset without setting a termite config, just call auto_annotate_dataset
vocabs = [[5,6],[8,9]]
attrs = [200,201]
wb.set_termite_config('',vocabs,attrs)
wb.auto_annotate_dataset()

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.