Wiki source code of Widget TimeSeries
Version 36.2 by rominabaila on 2023/05/15 11:31
Show last authors
| 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 JupyterLab Widget intended for the visualization of brain signals represented as time series. | ||
| 10 | |||
| 11 | == Backends == | ||
| 12 | |||
| 13 | Starting with //**tvb-widgets 1.5.0**, //the TS widget comes in 2 forms, corresponding to the 2 different libraries (we called them backends) used for plotting: **matplotlib **and **plotly**. The matplotlib backend, build on top of the **mne** library, offers more advanced scientifical features, while the plotly backend has a more appealing look and moves faster when talking about the basic interactions. | ||
| 14 | |||
| 15 | Below you can see the TS widget with each backend option (first one using the matplotlib backend, second one using the plotly backend). | ||
| 16 | |||
| 17 | (% style="text-align:center" %) | ||
| 18 | [[image:matplotlib.png]] | ||
| 19 | |||
| 20 | (% style="text-align:center" %) | ||
| 21 | [[image:plotly.png]] | ||
| 22 | |||
| 23 | == Inputs == | ||
| 24 | |||
| 25 | Time series can be given as inputs in two forms: | ||
| 26 | |||
| 27 | * TVB TimeSeries datatype | ||
| 28 | * Numpy arrays | ||
| 29 | |||
| 30 | This widget supports 2D, 3D, and 4D arrays. In all three cases, there is a fixed shape that the TimeSeries widget expects: | ||
| 31 | |||
| 32 | * for **2D**: (no_timepoints, no_channels) | ||
| 33 | * for **3D**: (no_timepoints, state_variable/mode, no_channels) | ||
| 34 | * for **4D**: (no_timepoints, state_variable, no_channels, mode) | ||
| 35 | |||
| 36 | ~* Note that the TVB TimeSeries datatype is always 4D and already has the expected shape. | ||
| 37 | |||
| 38 | == Requirements and installation == | ||
| 39 | |||
| 40 | |||
| 41 | Before installing the tvb-widgets library containing the TimeSeries widget, the following python libraries and Jupyter extensions should be installed: | ||
| 42 | |||
| 43 | * **Libraries:** | ||
| 44 | ** [[mne>>https://mne.tools/stable/index.html]] >= 1.0 | ||
| 45 | ** [[matplotlib>>https://matplotlib.org/3.5.0/index.html]] | ||
| 46 | ** [[plotly>>https://plotly.com/python/]] == 5.14.0 | ||
| 47 | ** [[ipywidgets>>https://ipywidgets.readthedocs.io/en/7.x/]] == 7.7.2 | ||
| 48 | ** [[ipympl>>https://github.com/matplotlib/ipympl#installation]] >= 0.8.5 | ||
| 49 | * ((( | ||
| 50 | **Extensions:** | ||
| 51 | |||
| 52 | (% class="box" %) | ||
| 53 | ((( | ||
| 54 | jupyter labextension install @jupyter-widgets/jupyterlab-manager | ||
| 55 | |||
| 56 | jupyter labextension install jupyter-matplotlib | ||
| 57 | |||
| 58 | jupyter labextension install jupyterlab-plotly | ||
| 59 | ))) | ||
| 60 | ))) | ||
| 61 | |||
| 62 | Then, to install the tvb-widgets library, just type: | ||
| 63 | |||
| 64 | (% class="box" %) | ||
| 65 | ((( | ||
| 66 | pip install tvb-widgets | ||
| 67 | ))) | ||
| 68 | |||
| 69 | == API usage == | ||
| 70 | |||
| 71 | First, the correct matplotlib backend must be set, which enables the interaction with the TimeSeries widget, by running the following command: | ||
| 72 | |||
| 73 | (% class="box" %) | ||
| 74 | ((( | ||
| 75 | %matplotlib widget | ||
| 76 | ))) | ||
| 77 | |||
| 78 | Then, simply import the **plot_timeseries** method, which gives you access to the TS widget: | ||
| 79 | |||
| 80 | (% class="box" %) | ||
| 81 | ((( | ||
| 82 | from tvbwidgets.api import plot_timeseries | ||
| 83 | ))) | ||
| 84 | |||
| 85 | |||
| 86 | Assuming that the user has already created or imported a valid input, this is how the widget can be initialized and displayed (example below assumes that **tsr **is a TVB TimeSeries datatype): | ||
| 87 | |||
| 88 | (% class="box" %) | ||
| 89 | ((( | ||
| 90 | backend = 'plotly' # change to 'matplotlib' to see the other TS widget | ||
| 91 | |||
| 92 | plot_timeseries(data=tsr, backend=backend) | ||
| 93 | ))) | ||
| 94 | |||
| 95 | After running the code from above into a Jupyter cell, you should see the TS widget corresponding to the backend you selected. | ||
| 96 | |||
| 97 | {{html}} | ||
| 98 | <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> | ||
| 99 | {{/html}} | ||
| 100 | |||
| 101 | == 1. TS widget with matplotlib and mne == | ||
| 102 | |||
| 103 | As it was already mentioned, the matplotlib widget offers more advanced scientifical fearures. In the video below, you can see some of the functionalities that this backend provides, like increasing/decreasing signal amplitude, selecting/deselecting certain signals, selecting different dimensions (state variable/mode) from the input data, navigating through the timeline, etc. |