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...	...	@@ -8,6 +8,18 @@
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	+
11	11	== Inputs ==
12	12
13	13	Time series can be given as inputs in two forms:
...	...	@@ -25,11 +25,15 @@
25	25
26	26	== Requirements and installation ==
27	27
	40	+
28	28	Before installing the tvb-widgets library containing the TimeSeries widget, the following python libraries and Jupyter extensions should be installed:
29	29
30	30	* **Libraries:**
31		-** [[mne>>https://mne.tools/0.24/install/index.html]] version 0.24
32		-** [[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
33	33	* (((
34	34	**Extensions:**
35	35
...	...	@@ -38,6 +38,8 @@
38	38	jupyter labextension install @jupyter-widgets/jupyterlab-manager
39	39
40	40	jupyter labextension install jupyter-matplotlib
	57	+
	58	+jupyter labextension install jupyterlab-plotly
41	41	)))
42	42	)))
43	43
...	...	@@ -57,28 +57,25 @@
57	57	%matplotlib widget
58	58	)))
59	59
60		- Then, the **TimeSeriesWidget** (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:
61	61
62	62	(% class="box" %)
63	63	(((
64		-from tvbwidgets.api import TimeSeriesWidget
65		-from IPython.core.display_functions import display
	82	+from tvbwidgets.api import plot_timeseries
66	66	)))
67	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	69
	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	+
70	70	(% class="box" %)
71	71	(((
72		-tsw = TimeSeriesWidget()
73		-tsw.add_datatype(tsr)
	90	+backend = 'plotly' # change to 'matplotlib' to see the other TS widget
	91	+
	92	+plot_timeseries(data=tsr, backend=backend)
74	74	)))
75	75
76		-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.
77	77
78		-(% class="box" %)
79		-(((
80		-display(tsw)
81		-)))
82	82
83	83	{{html}}
84	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>