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1		-XWiki.shailesh
	1	+XWiki.annedevismes

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1	1	== Learning objectives ==
2	2
3		-In this tutorial, you will learn how to install PyNN, together with the Brian 2, NEST and NEURON simulators, on Linux.
	3	+In this tutorial, you will learn how to install PyNN, together with the NEST, NEURON, and Brian 2 simulators, on Linux.
4	4
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37	37	**Slide** listing learning objectives
38	38	)))
39	39
40		-In this tutorial, I will guide you through setting up PyNN, together with the Brian 2, NEST, and NEURON simulators, on a Linux  environment. Note that we have a dedicated version of this tutorial for other environments, such as Mac OS, Windows, and EBRAINS Jupyter Lab.
	40	+In this tutorial, I will guide you through setting up PyNN, together with the NEST, NEURON, and Brian 2 simulators, on a Linux  environment. Note that we have a dedicated version of this tutorial for other environments, such as Mac OS, Windows, and EBRAINS Jupyter Lab.
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44	44	**Slide** listing prerequisites
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46	46
47		-I will demonstrate the installation on a computer with Ubuntu 20.04 OS installed. The steps are likely to remain very similar for other versions of Ubuntu OS and are also not expected to vary significantly for other Linux distributions. In the latter case, you can find information on the Internet about how to carry out the equivalent of the tasks demonstrated here. Also, this tutorial focuses only on Python 3, because Python 2 is no longer supported. It is recommended to use Python version 3.6 or higher. I will be using Python 3.8.10 in this tutorial, because it is the default version provided with Ubuntu 20.04.
	47	+I shall be demonstrating the installation on a computer with Ubuntu 20.04 OS installed. The steps are likely to remain very similar for other versions of Ubuntu OS and are also not expected to vary significantly for other Linux distributions. In the latter case, you will find on the Internet about how to carry out the equivalent of the tasks demonstrated here using Ubuntu OS. Also, the tutorial will focus only on Python 3, because Python 2 has now been deprecated. It is recommended to use Python version 3.6 or higher. I will be using Python 3.8.10 in this tutorial, because it is the default version provided with Ubuntu 20.04.
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57	57	**Screencast** - terminal
58	58	)))
59	59
60		-In this tutorial, we will make use of virtual environments. This allows multiple Python projects to coexist on the same computer, even when they might have different, and even conflicting, requirements. It helps isolate projects, thereby preventing unrequested changes in others, when any one of them is updated.
	60	+We shall make use of virtual environments in this tutorial. This allows multiple Python projects to coexist on the same computer, even when they might have different, and even conflicting, requirements. It helps isolate projects and thereby preventing unrequested changes in others, when any one of them is updated.
61	61
62		-Let's begin by creating a directory for our project.
	62	+We begin by creating a directory for our project.
63	63
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69	69	cd pynn_project
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71	71
72		-Next, we will create a virtual environment within this directory. Python 3 provides support for creating virtual environments. To use this, we first install the package named 'python3-venv':
	72	+Next, we shall create a virtual environment within this directory. Python 3 provides support for creating virtual environments. Since Python 3.6, the recommended method of creating a new virtual environment is as follows.
73	73
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76		-**Screencast** - terminal
	76	+**Note**
77	77
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79		-sudo apt-get install python3-venv
	78	+
	79	+For older versions of Python 3, you might be required to manually install `python3-venv` package before being able to run the below command. To install, run:
	80	+\\sudo apt-get install python3-venv
	81	+\\More recent versions of Python 3 (e.g., v3.9) already have this pre-installed.
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81	81
82		-And once this is installed, we can create a new virtual environment as follows. We name this
83		-
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86	86	**Screencast** - terminal
87		-
88		-(% style="color:#000000" %)
89		-python3 -m venv pynn_env
	87	+\\(% style="color:#000000" %)python3 -m venv pynn_env
90	90	)))
91	91
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94	94	**Note**
95	95
96	96
97		-Note that this command is 'python3' and not simply 'python'. This is because Ubuntu 20, by default, understands only the former. You can find on the Internet various ways to have 'python' also refer to 'python3', but for the purposes of this tutorial, we keep things simple and try to work with the bare minimum changes to the system.
	95	+Observe that this command is 'python3' and not simply 'python'. This is because Ubuntu 20, by default, understands only the former. You can find on the Internet various ways to have 'python' also refer to 'python3', but for the purposes of this tutorial, we shall keep things simple and try to work with the bare minimum changes to the system.
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99	99
100		-This will create a sub-directory named 'pynn_env' within our project directory, with several files and sub-directories. Let's take a look at the 'site-packages' directory.
	98	+This will create a sub-directory named 'pynn_env' within our project directory, with several files and sub-directories. Let us take a look at the 'site-packages' directory.
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105	105	\\(% style="color:#000000" %)<< show directory contents; especially lib/python3.8/site-packages >>
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107	107
108		-As you see here, only a limited number of basic packages have currently been installed in this virtual environment. In the steps ahead, we will install various other packages, which you will be able to see here.
	106	+As you see here, only a limited number of basic packages have currently been installed in this virtual environment. In the steps ahead, we shall install various other packages, and you shall see that these will be reflected here.
109	109
110		-To enter this virtual environment, and thereby use its resources in isolation from other projects on your computer, we have to "activate" it. This is achieved by running the command:
	108	+To enter into this virtual environment, and thereby use its resources in isolation from other projects on your computer, we have to "activate" it. This is achieved by running the command:
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122	122	**Note**
123	123
124	124
125		-You might be required to run the above command every time you open a new terminal window. Check that the terminal command prompt indicates the name of your virtual environment to confirm that you have indeed activated it.
	123	+You might be required to run the above command every time you open a new terminal window. Do verify that the terminal command prompt indicates the name of your virtual environment to confirm that you have indeed activated it.
126	126	)))
127	127
128		-Before we proceed, let us run the following commands to ensure that our environment is setup as required:
	126	+Now that we have our project's virtual environment setup, we are now ready to install PyNN and other simulators. In general, it is advisable to install the various simulators (especially NEURON and NEST) prior to installing PyNN, because PyNN will then auto compile NEURON's NMODL fles and NEST's extensions during installation. In this tutorial, we will adopt this approach and begin by installing the simulators. For the purposes of this tutorial, we shall demonstrate the installation of Brian2, NEURON and NEST simulators.
129	129
130		-
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133		-**Screencast** - terminal
134		-\\(% style="color:#000000" %)pip install ~-~-upgrade pip
135		-sudo apt install make
136		-)))
137		-
138		-Now that we have our project's virtual environment setup, we are ready to install PyNN and other simulators. In general, it is advisable to install the various simulators (especially NEURON and NEST) prior to installing PyNN, because PyNN will then auto compile NEURON's NMODL fles and NEST's extensions during installation. In this tutorial, we will adopt this approach and begin by installing the simulators. For the purposes of this tutorial, we will demonstrate the installation of Brian2, NEST, and NEURON simulators.
139		-
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142	142	**Note:**
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152	152	\\(% style="color:#000000" %)pip install brian2
153	153	)))
154	154
155		-This will install Brian 2, along with all its dependencies such as 'cython', 'numpy', and so on. We can now go back to our virtual environment's 'site-packages' directory to see how it is populated with all these packages.
	143	+This will install Brian 2, along with all its dependencies such as 'cython', 'numpy', and so on. We can now go back into our virtual environment's 'site-packages' directory to see how it is now populated with all these packages.
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175		-**Note**
176		-\\You might remember that, earlier in this tutorial, we had to use the term 'python3' to run Python on our system. But here, as in the rest of this tutorial, we will simply write 'python'. This is possible because, once we have activated our virtual environment, this environment understands that both 'python' and 'python3' are equivalent.
	163	+**Note:**
	164	+\\You might remember that, earlier in this tutorial, we had to use the term 'python3' to run Python on our system. But here, as in the rest of this tutorial, we shall simply write 'python'. This is possible because, once we have activated our virtual environment, this environment understands that both 'python' and 'python3' are equivalent.
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178	178
179	179	If there are no error messages here, and the import is successful, we have completed the installation of Brian 2.
180	180
181		-We will now move on to install the NEST simulator. Unlike Brian 2, NEST is not a Python package and therefore, it cannot be installed via the 'pip' command.
	169	+We shall now move on to install the NEST simulator. Unlike Brian 2, NEST is not a Python package and therefore, it cannot be installed via the 'pip' command.
182	182
183		-At the time of creating this tutorial, the latest version of NEST is v3.1. This is currently supported by PyNN v0.10, and it is likely that other versions of NEST are potentially incompatible with this version of PyNN. The installation is done by first adding the PPA repository for NEST and updating apt, followed by the installation of NEST itself.
	171	+At the time of creating this tutorial, the latest version of NEST is v3.1. This is currently supported by PyNN v0.10, and it is likely that other versions of NEST are potentially be incompatible with this version of PyNN. The installation is done by first adding the PPA repository for NEST and updating apt, followed by the installation of NEST itself.
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196	196	(((
197	197	**Screencast** - terminal
198	198	\\(% style="color:#000000" %)nest
199		-
200		-(% style="color:#000000" %)exit
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202	202
203		-This will display the NEST banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEST v3.1 on our system. Next, let's verify that this is indeed accessible via Python.
	189	+This will display the NEST banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEST v3.1 on our system. Let us next verify that this is indeed accessible via Python.
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215	215	**Note**
216		-\\I find that I receive a "no module named nest" error when trying this right after installing NEST. But it succeeds after a restart. So, if you do observe an error, close all programs, restart your computer, and try again. This time, it should execute as expected.
	202	+\\I find that I receive a "no module named nest" error when trying this right after installing NEST. But it succeeds after a restart. So, if you do observe an error, close all programs, restart your computer, and try again. This time it should execute as expected.
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218	218
219	219	If there are no error messages here, and the import is successful, we have completed installing NEST simulator and are able to load it via Python.
220	220
221		-We next move on to the third simulator, NEURON. Similar to Brian2, the installation for NEURON can be easily done via the 'pip' command. Do note that this method of installation does not auto-enable MPI support, which would be required for running simulations in parallel. Do visit the NEURON website if you wish to install on clusters or HPC machines.
	207	+We next move on to the third simulator, NEURON. The installation for NEURON used to be more complex but can now be easily completed by using the 'pip' command.
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226	226	\\(% style="color:#000000" %)pip install neuron
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228	228
229		-This installs the NEURON simulator on our system. To confirm that we have properly installed NEURON, we can test as follows:
	215	+This installs the NEURON simulator on your system. To confirm that we have properly installed NEURON, we can test as follows:
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235	235	\\quit()
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237	237
238		-This will display the NEURON banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEURON v8.0.0 on our system. Next, let's verify that this is indeed accessible via Python.
	224	+This will display the NEURON banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEURON v8.0.0 on our system. Let us next verify that this is indeed accessible via Python.
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248	248	If there are no error messages here, and the import is successful, we have completed installing NEURON simulator and are able to load it via Python.
249	249
250		-Now that we have installed all the simulators we intend to use, we move on to installing PyNN itself. Because PyNN is a Python package, we can install it easily using the 'pip' command:
	236	+Now that we have installed all the simulators we intend to use, we move on to installing PyNN itself. As PyNN is a Python package, we can install it easily using the 'pip' command:
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260	260	**Note**
261		-\\Note that PyNN project is spelt with a captial P, small y, and two captital N. The pip command is case-insensitive, so you may write it differently here. But the Python module, as we will see later, is case-sensitive and is spelt starting with a small P. This is in line with general Python convention, whereby package names start with small letters. Thus, while importing PyNN module via Python, it needs to be written as 'pyNN'.
	247	+\\Note that PyNN project is spelt with a captial P, small y, and two captital N. The pip command is case-insensitive, so you may write it differently here. But the Python module, as we shall see later, is case-sensitive and is spelt starting with a small P. This is in line with general Python convention, whereby package names start with small letters. Thus, while importing PyNN module via Python, it needs to be written as 'pyNN'.
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263	263
264	264	To verify that PyNN has been successfully installed on our system and that it is indeed able to communicate with the other simulators that we installed earlier, we can try running:
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275	275	(% style="color:#000000" %)sim.end()
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277	277
278		-This confirms that PyNN has been properly setup and also that it is able to employ Brian 2. To verify that PyNN is also able to communicate with NEST and NEURON simulators, we can do the following:
	264	+This confirms that PyNN has been properly setup and also that is able to employ Brian 2. To verify similary that PyNN is also able to communicate with NEURON and NEST simulators, we can do the following:
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299	299
300	300	This confirms that all the required software packages have been successfully completed and are functioning as expected.
301	301
302		-As a last step, we will install a Python package named 'matplotlib' that would come in handy in the tutorials ahead.
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306		-**Screencast** - terminal
307		-\\(% style="color:#000000" %)pip install matplotlib
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309		-
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312	312	**Slide** recap of learning objectives
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314	314
315		-That is the end of this tutorial, in which I've demonstrated how to install PyNN and other required simulators in a Linux system. You are now ready to start modeling! To learn about model development in PyNN, take a look at our next tutorial. Also, we will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce more advanced features of PyNN, so keep an eye on the EBRAINS website.
	293	+That is the end of this tutorial, in which I've demonstrated how to install PyNN and other required simulators in a Linux system. You are now ready to start modeling! To learn about model development in PyNN, do take a look at our next tutorial. Also, we will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce more advanced features of PyNN, so keep an eye on the EBRAINS website.
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