Wiki source code of Widget TimeSeries
Version 31.1 by rominabaila on 2023/05/15 10:50
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| author | version | line-number | content |
|---|---|---|---|
| 1 | Source code: [[https:~~/~~/github.com/the-virtual-brain/tvb-widgets>>url:https://github.com/the-virtual-brain/tvb-widgets]] | ||
| 2 | |||
| 3 | This is part of a Pypi release: [[https:~~/~~/pypi.org/project/tvb-widgets/>>url:https://pypi.org/project/tvb-widgets/]] | ||
| 4 | |||
| 5 | //**tvb-widgets**// is also already installed in the official image released for EBRAINS lab, where you can test it directly. | ||
| 6 | |||
| 7 | == Purpose == | ||
| 8 | |||
| 9 | It is a Jupyter Widget intended for the visualization of brain signals represented as time series. | ||
| 10 | |||
| 11 | == Inputs == | ||
| 12 | |||
| 13 | Time series can be given as inputs in two forms: | ||
| 14 | |||
| 15 | * TVB TimeSeries datatype | ||
| 16 | * Numpy arrays | ||
| 17 | |||
| 18 | This widget supports 2D, 3D, and 4D arrays. In all three cases, there is a fixed shape that the TimeSeries widget expects: | ||
| 19 | |||
| 20 | * for **2D**: (no_timepoints, no_channels) | ||
| 21 | * for **3D**: (no_timepoints, state_variable/mode, no_channels) | ||
| 22 | * for **4D**: (no_timepoints, state_variable, no_channels, mode) | ||
| 23 | |||
| 24 | ~* Note that the TVB TimeSeries datatype is always 4D and already has the expected shape. | ||
| 25 | |||
| 26 | == Requirements and installation == | ||
| 27 | |||
| 28 | Before installing the tvb-widgets library containing the TimeSeries widget, the following python libraries and Jupyter extensions should be installed: | ||
| 29 | |||
| 30 | * **Libraries:** | ||
| 31 | ** [[mne>>https://mne.tools/0.24/install/index.html]] version 0.24 | ||
| 32 | ** [[ipympl>>https://github.com/matplotlib/ipympl#installation]] | ||
| 33 | * ((( | ||
| 34 | **Extensions:** | ||
| 35 | |||
| 36 | (% class="box" %) | ||
| 37 | ((( | ||
| 38 | jupyter labextension install @jupyter-widgets/jupyterlab-manager | ||
| 39 | |||
| 40 | jupyter labextension install jupyter-matplotlib | ||
| 41 | ))) | ||
| 42 | ))) | ||
| 43 | |||
| 44 | Then, to install the tvb-widgets library, just type: | ||
| 45 | |||
| 46 | (% class="box" %) | ||
| 47 | ((( | ||
| 48 | pip install tvb-widgets | ||
| 49 | ))) | ||
| 50 | |||
| 51 | == API usage == | ||
| 52 | |||
| 53 | First, the correct matplotlib backend must be set, which enables the interaction with the TimeSeries widget, by running the following command: | ||
| 54 | |||
| 55 | (% class="box" %) | ||
| 56 | ((( | ||
| 57 | %matplotlib widget | ||
| 58 | ))) | ||
| 59 | |||
| 60 | Then, the **TimeSeriesWidget** (from the tvb-widgets API) and the **//display//** function should be imported: | ||
| 61 | |||
| 62 | (% class="box" %) | ||
| 63 | ((( | ||
| 64 | from tvbwidgets.api import TimeSeriesWidget | ||
| 65 | from IPython.core.display_functions import display | ||
| 66 | ))) | ||
| 67 | |||
| 68 | Assuming that the user has already created or imported a valid input, this is how the widget can be initialized and how an input can be assigned to it, using the //**add_datatype** //method (example below assumes that **//tsr// **is a TVB TimeSeries datatype): | ||
| 69 | |||
| 70 | (% class="box" %) | ||
| 71 | ((( | ||
| 72 | tsw = TimeSeriesWidget() | ||
| 73 | tsw.add_datatype(tsr) | ||
| 74 | ))) | ||
| 75 | |||
| 76 | Finally, to display and interact with the TimeSeries widget, the **//get_widget//**// //method is used inside the //**display **//function: | ||
| 77 | |||
| 78 | (% class="box" %) | ||
| 79 | ((( | ||
| 80 | display(tsw) | ||
| 81 | ))) | ||
| 82 | |||
| 83 | {{html}} | ||
| 84 | <iframe width="840" height="480" src="https://www.youtube.com/embed/VmueiXMxbVk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> | ||
| 85 | {{/html}} |