I suggest installing sage through conda.
https://doc.sagemath.org/html/en/installation/conda.html
Conda is a package manager that runs on linux and osx and sagemath is
available as a binary package. There are lots of jupyter kernels
pre-packaged, so it's only a matter of typing,
conda install sage=9.1 r-irkernel pari_jupyter gap
to get sage, R, gp, gap kernels.
Isuru
On Tue, Aug 4, 2020 at 1:21 AM Nils Bruin <nbr...@sfu.ca> wrote:
> On Monday, August 3, 2020 at 9:26:38 PM UTC-7, Tim McLarnan wrote:
>>
>> I tried copying the kernel folders from CoCalc and editing them in what I
>> thought was the right way and putting them with the kernel folders on my
>> machine. This produced kernels that show up in Jupyter, but that don't
>> work. No doubt someone who knows the right things to put in the .json
>> files could make this work, but I can't. This has to be an absurdly
>> complicated approach, though. Surely there's just some trivial thing I
>> don't know about.
>>
>
> That got you the configuration of the kernels, but likely not the actual
> code that implements them. Jupyter kernels are basically stand-alone
> programs. Often, they are written in python (but they don't have to be!)
> and installing them consists of two steps: installing the software and
> informing the jupyter notebook installation of their presence.
>
> For kernels written in python, you end up needing to install python
> software, for which there are excellent tools.
>
> A very simple example, that I happen to have written myself, so that's why
> I'm familiar with it, but needs "magma" present on your machine to work.
>
> https://github.com/nbruin/magma_kernel
>
> The instructions indeed show two installation steps: one for installing
> the python package and the other for setting up the jupyter notebook
> registration.
>
> To make extra kernels known to the *sage* jupyter notebook server, you
> have to make sure that you run the configurations in the sage copy of
> python. You'd want to do something like "sage -sh" (on linux or OSX) to get
> a shell that is set up with the sage environment, and run the
> configurations in there.
>
> An alternative is to register the sage kernel with the system-wide jupyter
> notebook. This has the advantage that other kernels that come prepackaged
> with your OS distribution are easier to install (just use your OS tools).
> It has the big disadvantage that currently, rhis is still not a
> straightforward process; it requires some hackery, so I would not recomment
> it as your first experiment.
>
> A big disadvantage of the python ecosystem is that one often end up with
> multiple copies of python living side-by-side on your OS and it's often
> hard to tell which version gets run for what.
>
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