Mascope Jupyter Widgets

This package provides interactive Jupyter widgets to construct data analysis notebooks for Mascope, utilizing Mascope SDK to fetch data from the server.

See also Mascope Tools for extended analysis capabilities.

🚀 Getting started

1. Install Python

• Install Python >= 3.12.

2. Set up a virtual environment and install dependencies

When developing in Python, it is best practice to use virtual environments to manage project dependencies. To set up a virtual environment for the project, in your terminal, run the following commands:

# initialize virtual environment
project-dir> python -m venv .venv
# activate the virtual environment
project-dir> .venv/Scripts/activate  # On Windows
project-dir> source .venv/bin/activate  # On Mac or Linux
# install dependencies
(.venv) project-dir> pip install mascope-jupyter-widgets jupyterlab

3. Configure Mascope access

To enable access from the notebook environment to your organization's Mascope instance, the following configuration steps need to be performed:

1. In the project directory, create an empty text file named .env.
2. In Mascope account settings, generate and copy a Jupyter Notebooks access token.
3. Edit the .env file with the URL of your Mascope instance and your personal access token, as follows:

MASCOPE_URL="https://org.mascope.app"
MASCOPE_ACCESS_TOKEN="p3R5oN4l4cC35s70Ken"

4. Launch JupyterLab

JupyterLab is a popular notebook authoring and editing environment.

To run Jupyter Lab, you need to:

1. Open a terminal.
2. Navigate to the directory where your virtual environment is located.
3. Activate the virtual environment.
4. Launch JupyterLab.

# navigate to the source code directory
user> cd /path/to/project-dir
# activate the virtual environment
project-dir> .venv/Scripts/activate  # On Windows
project-dir> source .venv/bin/activate  # On Mac or Linux
# run jupyter lab
(.venv) project-dir> jupyter-lab

A browser window opens, with JupyterLab open. Now browse and open a notebook you wish to run and edit, or create a new one.

If you are not familiar with Jupyter Lab, we recommend starting from their getting started tutorial.

📒 Examples

Below there are a few examples demonstrating how to load processed data from Mascope, and quick ways to visualize it. For more elaborate data visualization, you may have a look at for example Plotly for Python.

Load data

To load data from Mascope, start with MascopeDataBrowser:

import mascope_jupyter_widgets as mjw

db = mjw.MascopeDataBrowser()

After loading, in the next cell, initialize a MascopeDataWrapper instance:

dataset = mjw.MascopeDataWrapper(db)

These two cells you would typically place at the beginning of your notebook. Now, you have easy access to many useful data properties.

NOTE: When you load new data in the data browser, the dataset object gets automatically updated so you don't need to re-instantiate it.

Target timeseries

To access intensities of all targets matched in Mascope, indexed by datetime, you may use the following dataset properties:

dataset.target_compound_timeseries

dataset.target_ion_timeseries

It is very easy to resample the data into different time resolution, for example to compute 1 hour means:

1h_timeseries = dataset.target_compound_timeseries.resample("1H").mean()

To see matched compositions, yoy may use e.g. dataset.target_ion_timeseries.columns. To access the timeseries of a specific target ion or compound, just select the corresponding column from the dataframe, e.g.

dataset.target_ion_timeseries['HSO4-']

Peak data

If you have selected to Import Peaks when loading the data, you also have the property

dataset.peaks_matched

available, which contains all peaks of the loaded samples.

To get a quick glance of the peaks, you can for example visualize the, in a simple mass defect plot like this:

dataset.peaks_matched.plot(x="mz", y="mass_defect", kind="scatter")

Match data

All the "match data" computed by Mascope are available as dataframes in the dataset object:

dataset.match_samples
dataset.match_compounds
dataset.match_ions
dataset.match_isotopes

NOTE: The property dataset.match_samples contains all sample metadata from the loaded batches, including timestamps, filenames as well as other attributes.