Opti-HPLC-Handler 0.2.8

Simplified proxy API for interacting with the Waters Empower Web API.

OptiHPLCHandler

Simplified proxy API for interacting with the Waters Empower Web API.

Using the package

The package can be installed into a Python environment with the command

pip install Opti-HPLC-Handler

You can then import packge and start an EmpowerHandler. You need to select the Empower project to log in to. Note that the user logging in needs to have access to both that project, and the project Mobile.

from OptiHPLCHandler import EmpowerHandler
handler=EmpowerHandler(project="project", address="https://API_url.com:3076")

your username will be auto-detected. Add the input username to use another account.

You will be prompted you for your password. The password will only be used to get a token from the Empower Web API. When the token runs out, you will have to input your passwrod again.

You can now get a list of the methodset methods in the project:

handler.GetMethodList()

To create a new sampleset method, first create it as a list of dicitonaries. Each dictionary must have the keys Method, SamplePos, SampleName, and Injectionvolume. If you want to populate oher fields, also add a key called OtherFields, with a value that is a list of dicts, each dict having the keys name and value:

sample_list = [
    {
        "Method": "test_method_1",
        "SamplePos": "test_sample_pos_1",
        "SampleName": "test_sample_name_1",
        "InjectionVolume": 1,
    },
    {
        "Method": "test_method_2",
        "SamplePos": "test_sample_pos_2",
        "SampleName": "test_sample_name_2",
        "InjectionVolume": 2,
        "OtherFields": [
            {"name": "test_field_1", "value": "test_value"},
            {"name": "test_field_2", "value": 2.3},
        ],
    },
]

At the moment, only Injection Sampleset lines are supported, but the injection volume can be set to 0.

You can then use the handler to create the sampleset:

handler.PostExperiment(
    sample_set_method_name="test_sampleset_method_name",
    sample_list=sample_list,
    plate_list=[],
    audit_trail_message="test_audit_trail_message",
)

Note that plate_list should be filled out in order to run the sampleset.

You can run a sampleset method to create a sampleset:

handler.RunExperiment(
    sample_set_method="test_sampleset_method_name",
    hplc = "test_hplc",
)

Getting started with developing the package

You can get the repo by cloning it from github at the URL https://github.com/novonordisk-research/OptiHPLCHandler.git.

If you can't clone the repo on a Windows machine, you might need to set the SSL backend. Run the following command in a terminal: git config --global http.sslbackend schannel

It is recommended to make and activate a virtual environment by running the following commands

pip install venv
python -m venv .env
.\.env\Scripts\activate

You need to run the last command every time you restart the computer

When the virtual environment is activated, install the package locally as an editable installation

pip install -e .[dev]

If this doesn't work, you might need to upgrade pip and/or setuptools:

.\.env\scripts\python.exe -m pip install --upgrade pip
.\.env\scripts\python.exe -m pip install --upgrade setuptools

You should then be able to install the package locally as an editable installation.

Releasing

To release a new version, get all of the changes you want into the branch main. Then manually run the release GitHub action by clicking Run workflow. Select what type of release it is by typing in --patch, --minor, or --major in The type of release to perform, and then click Run workflow.

Fetch the new branch release. Run the commands

rm -r -fo dist
py -m build
py -m twine upload dist/*

you will be prompted for your pipy.org username and password.

In GitHub, merge the release branch into the main branch by creating a pull request and merging it.