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  • matplotlib.png
  • plotly.png

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	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	+
1	1	== Purpose ==
2	2
3		-It is a Jupyter widget intended for the visualization of brain signals represented as time series.
	9	+It is a Jupyter Widget intended for the visualization of brain signals represented as time series.
4	4
	11	+== Backends ==
5	5
	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, the 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	+
6	6	== Inputs ==
7	7
8		-It supports time series inputs of two types:
	25	+Time series can be given as inputs in two forms:
9	9
10	10	* TVB TimeSeries datatype
11	11	* Numpy arrays
12	12
13		-The widget supports 2-, 3-, and 4-dimensional arrays. In all three cases, there is a fixed shape that the TimeSeries widget expects:
	30	+This widget supports 2D, 3D, and 4D arrays. In all three cases, there is a fixed shape that the TimeSeries widget expects:
14	14
15	15	* for **2D**: (no_timepoints, no_channels)
16	16	* for **3D**: (no_timepoints, state_variable/mode, no_channels)
17	17	* for **4D**: (no_timepoints, state_variable, no_channels, mode)
18	18
19		-~* Note that the TVB TimeSeries datatype is always 4D and already has the expected shape.
	36	+~* Note that the TVB TimeSeries datatype is always 4D and already has the expected shape.
20	20
21	21	== Requirements and installation ==
22	22
23		-Before installing the tvb-widgets library containing the TimeSeries widget, the following python libraries and  Jupyter Notebook extensions should be installed:
24	24
	41	+Before installing the tvb-widgets library containing the TimeSeries widget, the following python libraries and Jupyter extensions should be installed:
	42	+
25	25	* **Libraries:**
26		-** [[ipympl>>https://github.com/matplotlib/ipympl#installation]]
	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
27	27	* (((
28	28	**Extensions:**
29	29
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32	32	jupyter labextension install @jupyter-widgets/jupyterlab-manager
33	33
34	34	jupyter labextension install jupyter-matplotlib
	57	+
	58	+jupyter labextension install jupyterlab-plotly
35	35	)))
36	36	)))
37	37
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42	42	pip install tvb-widgets
43	43	)))
44	44
45		-
46	46	== API usage ==
47	47
48	48	First, the correct matplotlib backend must be set, which enables the interaction with the TimeSeries widget, by running the following command:
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52	52	%matplotlib widget
53	53	)))
54	54
55		- Then, the TimeSeries widget (from the tvb-widgets API) and the display function should be imported:
	78	+Then, simply import the **plot_timeseries** method, which gives you access to the TS widget:
56	56
57	57	(% class="box" %)
58	58	(((
59		-from tvbwidgets.api import TimeSeriesWidget
60		-from IPython.core.display_functions import display
	82	+from tvbwidgets.api import plot_timeseries
61	61	)))
62	62
63		-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):
64	64
	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	+
65	65	(% class="box" %)
66	66	(((
67		-tsw = TimeSeriesWidget()
68		-tsw.add_datatype(tsr)
	90	+backend = 'plotly' # change to 'matplotlib' to see the other TS widget
	91	+
	92	+plot_timeseries(data=tsr, backend=backend)
69	69	)))
70	70
71		-Finally, to display and interact with the TimeSeries widget, the **//get_widget//**// //method is used inside the //**display **//function:
	95	+After running the code from above into a Jupyter cell, you should see the TS widget corresponding to the backend you selected.
72	72
73		-(% class="box" %)
74		-(((
75		-display(tsw.get_widget())
76		-)))
77	77
78	78	{{html}}
79		-<iframe src="https://drive.google.com/file/d/1g4ryY1VIFMUD14Mb6Dq_KVb0b2_XU4VX/preview" width="840" height="480" allow="autoplay"></iframe>
	99	+<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>
80	80	{{/html}}

matplotlib.png

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