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...	...	@@ -8,18 +8,6 @@
8	8
9	9	It is a Jupyter Widget intended for the visualization of brain signals represented as time series.
10	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, 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		-
23	23	== Inputs ==
24	24
25	25	Time series can be given as inputs in two forms:
...	...	@@ -37,15 +37,11 @@
37	37
38	38	== Requirements and installation ==
39	39
40		-
41	41	Before installing the tvb-widgets library containing the TimeSeries widget, the following python libraries and Jupyter extensions should be installed:
42	42
43	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
	31	+** [[mne>>https://mne.tools/0.24/install/index.html]] version 0.24
	32	+** [[ipympl>>https://github.com/matplotlib/ipympl#installation]]
49	49	* (((
50	50	**Extensions:**
51	51
...	...	@@ -54,8 +54,6 @@
54	54	jupyter labextension install @jupyter-widgets/jupyterlab-manager
55	55
56	56	jupyter labextension install jupyter-matplotlib
57		-
58		-jupyter labextension install jupyterlab-plotly
59	59	)))
60	60	)))
61	61
...	...	@@ -75,25 +75,28 @@
75	75	%matplotlib widget
76	76	)))
77	77
78		-Then, simply import the **plot_timeseries** method, which gives you access to the TS widget:
	60	+ Then, the **TimeSeriesWidget** (from the tvb-widgets API) and the **//display//** function should be imported:
79	79
80	80	(% class="box" %)
81	81	(((
82		-from tvbwidgets.api import plot_timeseries
	64	+from tvbwidgets.api import TimeSeriesWidget
	65	+from IPython.core.display_functions import display
83	83	)))
84	84
	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):
85	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	88	(% class="box" %)
89	89	(((
90		-backend = 'plotly' # change to 'matplotlib' to see the other TS widget
91		-
92		-plot_timeseries(data=tsr, backend=backend)
	72	+tsw = TimeSeriesWidget()
	73	+tsw.add_datatype(tsr)
93	93	)))
94	94
95		-After running the code from above into a Jupyter cell, you should see the TS widget corresponding to the backend you selected.
	76	+Finally, to display and interact with the TimeSeries widget, the **//get_widget//**// //method is used inside the //**display **//function:
96	96
	78	+(% class="box" %)
	79	+(((
	80	+display(tsw)
	81	+)))
97	97
98	98	{{html}}
99	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>