lserest 1.2.0
Provides access to Leadscope Web Services REST API via Python.
Requires Python: >=3.7

Leadscope Python Interface
==========================

Provides access to Leadscope Web Services REST API via Python.
This is an interface client which requires access to a Leadscope Enterprise server, v2023.0.1 or later.

Getting started
---------------
Install the package:

    pip install lserest

Server connection details should be set in `config.toml` (see below for format) and placed in working directory,
or specified: `LSEClient(config_file=<path>)`. Alternatively, some or all of the parameters `root_url`, `username`, `password` and `company`
may be passed directly to the constructor.

Config file
-----------
The `config.toml` file (if used) should contain the following section:

    [server]
    root_url = "http://server_host_name:8080/rs/resources"
    username = "<user>"
    password = "<password>"
    company  = "<company>"

Server sessions
---------------
Most server requests require an active session to be created via `LSEClient.start_session`. Such sessions must be closed
after use using `LSEClient.end_session`. A preferable option is to use  `LSEClient.with_session`, which creates a session
and then closes it at the end, handling any exceptions which may be thrown, all examples will use this method.
See the documentation for the classes `LSEClient`, `StructureSet`, `StructureSearch`, `ModelApplication` and `NitrosamineAssessment`
for a full list of which methods require a session to be open.

Examples
--------
Requesting a list of available statistical models from the Leadscope server:

    from lserest import LSEClient, StructureSet, ModelApplication
    client = LSEClient()

    def example1(c):
        c.list_models()
    
    client.with_session(example1)

Running a model application, specifically the ICH M7 Consensus, on two structures:

    from lserest import LSEClient, StructureSet, ModelApplication
    client = LSEClient()

    def example2(c, molfiles):
        structures = StructureSet(c, molfiles)
        model = ModelApplication(c, structures, consensus_names=['Genetox'])
        model.apply()
        return model
    
    model = client.with_session(example2, ['structure1.mol', 'structure2.mol'])
    
    print(model.consensus_results())
    
Searching the database for similar molecules to a given structure, downloading structural images of each search result
and saving them to a zip archive:

    from lserest import LSEClient, StructureSet, StructureSearch
    import numpy as np
    client = LSEClient()

    def example3(c):
        structures = StructureSet(c, molfile)
        search = StructureSearch(c, structures)
        search.similar(match_pct=70)
        c.save_structure_images(np.unique(search.results()['Structure ID']), 'search_results.zip')
    
    client.with_session(example3, 'structure.mol')

Running an N-nitrosamine potency assessment, on two structures:

    from lserest import LSEClient, StructureSet, NitrosamineAssessment
    client = LSEClient()

    def example4(c):
        structures = StructureSet(c, 'structure1.mol', 'structure2.mol')
        nitro = NitrosamineAssessment(c, structures)
        nitro.apply()
        return nitro
    
    nitro = client.with_session(example4)
    print(nitro.results())

Creating a StructureSet from mol strings:

    mol_string = '<mol string>'
    from lserest import LSEClient, StructureSet, NitrosamineAssessment
    client = LSEClient()
    structures = StructureSet(client)
    structures.mol_strings.append(mol_string)