Overview

SciQLop (SCIentific Qt application for Learning from Observations of Plasmas) is a powerful and user-friendly tool designed for the visualization and analysis of in-situ space plasma data. The software has been developed to address the technical challenges that arise from the high time resolution required by modern plasma measurements. It is capable of plotting millions of data points without compromising on interactivity, ensuring that users can scroll, zoom, move, and export plots with ease.

One of the key features of SciQLop is its ability to abstract the manipulation of physics data, making it accessible to users with different levels of expertise. The software also provides contextual features such as coordinate transforms and physical quantity extraction from data.

Keeping SciQLop lightweight and intuitive has been a top priority during the software's development, making it both usable a nd competitive. By balancing advanced graphical features with a simple and streamlined GUI, SciQLop delivers an exceptional user experience that sets it apart from other software tools in the field.

If you are looking for a reliable and powerful tool for analyzing space plasma data, SciQLop is an excellent choice. With its intuitive interface and advanced features, it offers a seamless workflow that can save you time and effort. Download SciQLop today and experience the benefits for yourself!

Features

• Interactive and responsive: SciQLop can handle millions of data points without compromising on interactivity. Users can scroll, zoom, move, and export plots with ease.

  

• User-friendly: SciQLop abstracts the manipulation of physics data, making it accessible to users with different levels of expertise.

  

• Jupyter notebook integration: SciQLop can be used as a backend for Jupyter notebooks, allowing users to create and manipulate plots from within their notebooks.

  

• Catalogs: SciQLop provides a catalog system that allows users to easily label events in their data or visualize existing catalogs of events.

  

• Evolving and growing list of examples: SciQLop comes with a growing list of examples that demonstrate how to perform common tasks such as loading data, creating plots, and using the catalog system.

  

Upcoming features

• community-driven plugins repository: SciQLop will soon have a plugin system that will allow users to extend the software's capabilities by installing community-driven plugins.
• catalogs coediting: SciQLop will soon allow users to coedit catalogs, making it easier to collaborate on event labeling and visualization.

How to install SciQLop

Warning: Due to this issue you should not use any * Python* version higher than 3.10.x.

Since SciQLop depends on specific versions of PySide6 you should use a dedicated virtualenv for SciQLop to avoid any conflict with any other Python package already installed in your system.

• Using releases from PyPi

python -m pip install sciqlop

• Using the latest code from GitHub

python -m pip install git+https://github.com/SciQLop/SciQLop

Once installed the sciqlop launcher should be in your PATH and you should be able to start SciQLop from your terminal.

sciqlop

or

python -m SciQLop.app

Linux Users

If you are using a Linux distribution, you may not need to install anything, you can just download the AppImage from the latest release and run it (after making it executable).

Experimental Python API Examples:

The following API examples are for early adopters and will likely change a bit in the future!

• Creating plot panels:

from SciQLop.backend import TimeRange
from datetime import datetime

# all plots are stacked
p = main_window.new_plot_panel()
p.time_range = TimeRange(datetime(2015, 10, 22, 6, 4, 30).timestamp(), datetime(2015, 10, 22, 6, 6, 0).timestamp())
p.plot("speasy/cda/MMS/MMS1/FGM/MMS1_FGM_BRST_L2/mms1_fgm_b_gsm_brst_l2")
p.plot("speasy/cda/MMS/MMS1/DIS/MMS1_FPI_BRST_L2_DIS_MOMS/mms1_dis_bulkv_gse_brst")
p.plot("speasy/cda/MMS/MMS1/DIS/MMS1_FPI_BRST_L2_DIS_MOMS/mms1_dis_energyspectr_omni_brst")

# tha_peif_sc_pot and tha_peif_en_eflux will share the same plot 
p2 = main_window.new_plot_panel()
p2.plot("speasy/cda/THEMIS/THA/L2/THA_L2_ESA/tha_peif_en_eflux")
p2.plots[0].plot("speasy/cda/THEMIS/THA/L2/THA_L2_ESA/tha_peif_sc_pot")
p2.plot("speasy/cda/THEMIS/THA/L2/THA_L2_ESA/tha_peif_velocity_dsl")

NOTE: An easy way to get product paths, is to drag a product from Products Tree to any text zone or even your Python terminal.

• Custom products:

from datetime import datetime

import numpy as np

from SciQLop.backend.pipelines_model.easy_provider import EasyVector, EasyScalar


# The following functions can do anything from loading data from a file to any complex computation, they should not 
# block the GUI since they will be run in background.

def my_vect(start: datetime, stop: datetime) -> (np.ndarray, np.ndarray):
    x = np.arange(start.timestamp(), stop.timestamp(), 0.1) * 1.
    y = np.empty((len(x), 3))
    y[:, 0] = np.cos(x / 100.) * 10.
    y[:, 1] = np.cos((x + 100) / 100.) * 10.
    y[:, 2] = np.cos((x + 200) / 100.) * 10.
    return x, y


def my_scalar(start: datetime, stop: datetime) -> (np.ndarray, np.ndarray):
    x = np.arange(start.timestamp(), stop.timestamp(), 0.1) * 1.
    y = np.empty((len(x), 1))
    y[:, 0] = np.cos(x / 100.) * 10.
    return x, y


my_vector_provider = EasyVector(path='some_root_folder/my_hello_world_vector', get_data_callback=my_vect,
                                components_names=["x", "y", "z"], metadata={})
my_scalar_provider = EasyScalar(path='some_other_root_folder/my_hello_world_scalar', get_data_callback=my_scalar,
                                component_name="x", metadata={})

More examples can be found in the examples folder, they are also available from the welcome screen.

How to contribute

Just fork the repository, make your changes and submit a pull request. We will be happy to review and merge your changes. Reports of bugs and feature requests are also welcome.

Credits

The development of SciQLop is supported by the CDPP.