Fixes failure to compile with GCC 13.

This is a small PR to track updates in the BluePyOpt API (cf.
https://github.com/BlueBrain/BluePyOpt/pull/393) compared to the v0.8
release.

- Profiler output is now correctly aligned regardless of widths.
- Python (and C++) can now actually set a limit on the nodes printed.

After (with a >=4% filter):
```
REGION                                  CALLS     WALL    THREAD        %
root                                        -    9.401    75.205    100.0
  advance                                   -    9.401    75.205    100.0
    integrate                               -    9.400    75.201    100.0
      current                               -    2.001    16.007     21.3
        zero                         29120728    0.518     4.142      5.5
        Granule_98_3D__all           24000600    0.459     3.675      4.9
        Granule_98_3D__soma_group    24000600    0.456     3.649      4.9
        Gran_CaPool_98               24000600    0.410     3.282      4.4
      state                                 -    1.861    14.888     19.8
        Granule_98_3D__soma_group    24000600    0.778     6.223      8.3
        Gran_CaPool_98               24000600    0.498     3.981      5.3
        Granule_98_3D__all           24000600    0.410     3.283      4.4
      events                         58241456    1.017     8.138     10.8
      voltage                        29120728    0.954     7.629     10.1
      ionupdate                      29120728    0.526     4.205      5.6
      threshold                      29120728    0.514     4.116      5.5
      samples                        29120728    0.513     4.103      5.5
      stimuli                        29120728    0.512     4.092      5.4
      post                           29120728    0.509     4.072      5.4
      stepsupdate                    29120728    0.500     4.003      5.3
      diffusion                      29120728    0.494     3.950      5.3
```

Before:
```
_p_ REGION                     CALLS      THREAD        WALL       %
_p_ root                           -      56.635       7.079   100.0
_p_   advance                      -      56.635       7.079   100.0
_p_     integrate                  -      56.633       7.079   100.0
_p_       current                  -      11.836       1.480    20.9
_p_         zero            29120728       3.133       0.392     5.5
_p_         Granule_98_3D__soma_group    24000600       2.816       0.352     5
_p_         Granule_98_3D__all    24000600       2.650       0.331     4.7
_p_         Gran_CaPool_98    24000600       2.291       0.286     4.0
_p_         MossyFiber_all     3840096       0.501       0.063     0.9
_p_         Golgi_98_3D_all     1280032       0.123       0.015     0.2
_p_         Golgi_98_3D_soma_group     1280032       0.120       0.015     0.2
_p_         Golgi_CaPool_CML     1280032       0.103       0.013     0.2
_p_         nernst           1280032       0.100       0.013     0.2
_p_       state                    -      11.343       1.418    20.0
_p_         Granule_98_3D__soma_group    24000600       4.918       0.615     8
_p_         Gran_CaPool_98    24000600       3.086       0.386     5.4
_p_         Granule_98_3D__all    24000600       2.288       0.286     4.0
_p_         MossyFiber_all     3840096       0.479       0.060     0.8
_p_         Golgi_98_3D_all     1280032       0.252       0.031     0.4
_p_         Golgi_98_3D_soma_group     1280032       0.180       0.023     0.3
_p_         Golgi_CaPool_CML     1280032       0.139       0.017     0.2
_p_       voltage           29120728       7.124       0.891    12.6
_p_       events            58241456       6.003       0.750    10.6
_p_       threshold         29120728       3.015       0.377     5.3
_p_       samples           29120728       2.976       0.372     5.3
_p_       ionupdate         29120728       2.961       0.370     5.2
_p_       stimuli           29120728       2.890       0.361     5.1
_p_       stepsupdate       29120728       2.868       0.358     5.1
_p_       post              29120728       2.820       0.353     5.0
_p_       diffusion         29120728       2.794       0.349     4.9
_p_       setup                  728       0.001       0.000     0.0
_p_     samplesetup              728       0.001       0.000     0.0
_p_     spikes                   728       0.000       0.000     0.0
_p_     eventsetup               728       0.000       0.000     0.0
_p_     sampledeliver            728       0.000       0.000     0.0
_p_   communication                -       0.000       0.000     0.0
_p_     enqueue                    -       0.000       0.000     0.0
_p_       sort                   728       0.000       0.000     0.0
_p_       merge                  728       0.000       0.000     0.0
_p_       setup                  728       0.000       0.000     0.0
_p_     walkspikes                 1       0.000       0.000     0.0
_p_     spikeio                    1       0.000       0.000     0.0
_p_     exchange                   -       0.000       0.000     0.0
_p_       gather                   1       0.000       0.000     0.0
_p_       sort                     1       0.000       0.000     0.0
_p_       gatherlocal              1       0.000       0.000     0.0
```

I have been playing around a bit with getting Neural ODE's working (and
in general ANN's as black-box homeostatic mechanisms) and found arbor
lacking in ANN activation functions. Now not everybody might agree that
this is actually needed in Arbor, so I don't expect this PR to be merged
directly. But if people like the idea, here it is :)

The PR contains 3 common unary functions used in ANN's as nonlinear
activations: `sigmoid(x)`, `relu(x)` and `tanh(x)`

Here is a very minimal example of a 'neural ode':

```
NEURON {
  SUFFIX node
}

PARAMETER {
    A1 = -0.662 A2 =  0.282 A3 =  0.957
    A4 = -0.189 A5 = -1.794 A6 =  1.094
    A7 = -1.133 A8 =  0.625 A9 =  0.074
    b1 = -1.434 b2 = -0.358 b3 = -0.093
}

STATE {
    x
    y
    z
}

INITIAL {
    x = 0
    y = 1
    z = 1
}

DERIVATIVE dstate {
    x' = tanh(A1 * x + A2 * y + A3 * z + b1)
    y' = tanh(A4 * x + A5 * y + A6 * z + b2)
    z' = tanh(A7 * x + A8 * y + A9 * z + b3)
}

BREAKPOINT {
    SOLVE dstate METHOD sparse
}
```

Before
```
✦2 ❯ modcc -t cpu jnml-arbor/SKv3_1.mod
Warnings:
  * jnml-arbor/SKv3_1.mod:(line 1,col 1) Assignments to local variable containing state variables will not be integrated in time
```
After
```
✦2 ❯ modcc -t cpu jnml-arbor/SKv3_1.mod
Warnings:
  * jnml-arbor/SKv3_1.mod:(line 172,col 14) Assignments to local variable containing state variables will not be integrated in time: (rate_m_q = ((m_inf - m_q) / m_tau))
```

Co-authored-by: boeschf <48126478+boeschf@users.noreply.github.com>

`[[maybe_unused]]` in generated code to silence warnings for mechanisms
with `net_receive` methods that do not use the connection weight.

- $x^{-1} \Rightarrow 1/x\quad \forall x$
- $x^n \Rightarrow x\cdot \dots \cdot x \quad x\in N; |x| < 5$
- $x^n \Rightarrow 1/(x\cdot \dots \cdot x) \quad x\in N; |x| < 5; x <
0$
- $b^e \Rightarrow \exp(\log(b) e)\quad \forall  b, e$

The last point introduces potential errors when `pow(b, e)` is allowed,
but `log(b)` is
undefined. These occur exactly when all of the following is true
- $b < 0$
- $e\in N$
- $e$, $b$ not known at compile time (since we cover these cases before)

* Python Wheel workflow uses `ubuntu-20.04` to fix the build
  * In #2058 I forgot to update `ciwheel.yml`
* Why this is necessarily: still have not figured it out. `cibuildwheel`
builds and tests in it's own container...
  * [x] https://github.com/brenthuisman/arbor/actions/runs/3704419712
* Small change to make `musllinux` wheels build. They don't pass testing
yet, so I don't add them to the workflow but that can be solved/added
[later](https://github.com/pypa/cibuildwheel/issues/1376)
* The reason for using `ubuntu-20.04` in the Spack workflow should [no
longer exist](https://github.com/actions/runner-images/issues/6724).

Allow `__` in profiler names, now that we split on `:`.

`exprelr` was described all wrong, fixed.

Mechanism ids are assigned by iterating through an unordered map within `fvm_mechanism_data`. This changes the container in question to an ordered `std::map`.

See the same jobs fail, which turn out to execute using different Python
versions:
-
https://github.com/arbor-sim/arbor/actions/runs/3618204857/jobs/6097794385
-
https://github.com/arbor-sim/arbor/actions/runs/3630819640/jobs/6141257441

Implementation of stochastic calcium plasticity curves, originally
authored by Sebastian Schmitt @schmitts, and adapted here for Arbor
inclusion.
- python example plasticity_stdp.py
- stochastic mechanism calcium_based_synapse.mod
- tutorial

Closes #1987

* Actually set the compiler to the user provided `--cxx` or the scripts default.
* Have a fallback to `c++` in case `arbor.config()['CXX']` does not exist (typically true for pip installed wheels).
* Verbose mode for `a-b-c`

Switch XML parser to pugixml; one less runtime dependency and one less blocker to WASM via emscripten.

required for making an emscripten build of the arbor python library

* make ARB_MODCC functional again

* move add_subdir

* no modcc/CMakeLists.txt is needed also when external modcc is used.

* review

* Remove deprecated spike_detector.

added conditional variant for cuda builds to enable GPU-based random number generation

- unit tests and some examples build their own mechanism catalogue
- repeated logic for detecting GPUs, passing compile time flags
- leverage the cmake variables and targets which are already there since these catalogues are internal to arbor
- build a static library with mechanisms, which links to arbor-private-deps

Add the VOLTAGE_PROCESS mechanism kind to modcc, allowing for direct writing to the membrane voltage
Obviously these are extremely sharp tools and can break the cable model, so we add some constraints to their
use (see docs). Still, user discretion is required.

As a sneak peek for what this might be useful for

* implementing voltage clamps
* limiting membrane voltage (irritatingly also a kind of clamping)
* faking ABSTRACT_CELL like behaviour (although that might require a bit more work)

Closes #1343

Throw an informative error instead of `bad_optional_access` when we do not find a global or cell default for an ion's diffusivity.

Added common math functions which I came across in the wild quite frequently. Implementations may be more optimized than hand-rolled versions.

- sqrt: usual square root
- step_right: right-continuous heaviside step function
- step_left: left-continuous heaviside step function
- step: heaviside step function with half step at 0
- signum: sign bit function
- extended iexpr with the step_left, step_right variants

fix ornstein_uhlenbeck example on gpu:
- use make_catalogue instead of make_catalogue_standalone
- link to arbor-private-deps
- mark all files as c++ sources when using cuda-clang or hip-clang

- Correct `.gitmodules`
- Update all git submodules to latest released versions, except google-benchmark
- Have CHANGELOG
- Breaking changes since v0.7:
  - A change in API: `arbor.cable_cell` has the labels and decor arguments swapped. I.e.: `(tree, labels, decor)`
-> `(tree, decor, label)`. Labels are now optional.
  - Mechanism ABI version is bumped to 0.3.1. https://github.com/arbor-sim/arbor/pull/1884
  - Remove the `generate-catalogue` script.  `modcc` accepts now a list of NMODL files and is able to spit out a catalogue.cpp file
  - Rename spike detector -> threshold detector
  - Remove access to time `t` in NMODL.
- Major dependency version bumps:
  - GCC: 9 and up
  - CUDA: 11 and up
  - Clang: 10 and up

Addresses #1987

* reproducibility test for random number generator
* C++ example demonstrating Ornstein-Uhlenbeck process
* Python example featuring stochastic mechanism
* stochastic mechanism catalogue

The Python example is contributed by Jannik Luboeinski's (@jlubo): https://github.com/jlubo/arbor_ou_lif_example
and adapted to fit into Arbor.

* fix compiler warnings

* fix some more warnings

Co-authored-by: Brent Huisman <brenthuisman@users.noreply.github.com>

- Add (again) a reset voltage E_R that for V_m = E_R in the refractory period.
- Make V_m decay towards resting potential E_L instead of 0.
- Adjust docs and tests accordingly.
- Double check this against literature.
- Explain weight, add C++ API.

* Added fixture dev docs. Made fixtures more robust.

Co-authored-by: Brent Huisman <brenthuisman@users.noreply.github.com>

* Functionality
  * Add probes to LIF cells.
* Docs
  * Remove errorneous statement(s) about LIF cells (there never was an E_reset...)
  * Move probing chapter one level up (concepts/cable_cells -> concepts)
* Tests
  * Add tests for LIF probes

Closes #1861 
Closes #1783 

- arbor-build-catalogue (a-b-c) does no longer need to be in a fixed location
- nor do we statically fix the configuration for a-b-c
- instead, we rely on `arbor.config` to read the relevant default values
- each value can be overriden, if desired
- added many more values to the configuration
  - gpu type and arch
  - paths and prefix 
  - CXX
- report default settings for better diagnosis
- implement a fallback for prefix if that does not exist; in particular for the amazing skbuild.

In essence you can now use a-b-c as a standalone tool, as long as you have a properly 
configured py-arbor.

Example output after removing `_skbuild`
```
Warning: prefix '/Users/hater/src/arbor/_skbuild/macosx-11.0-x86_64-3.10/cmake-install' does not exist, falling back to '/Users/hater/src/arbor/.direnv/python-3.10.6'.
usage: arbor-build-catalogue catalogue_name mod_source_dir

Generate dynamic catalogue and build it into ...

* build for py311
* update dependencies.csv

- Use ninja instead of make.
- Use ccache with a per-config cache key.

* Support for exporting models with parameters optimized in BluePyOpt to a mixed JSON/ACC-format that can be loaded in Arbor (with faithful axon-replacement modelling)
* A tutorial and examples on how to run simulations with such models including a neocortical layer-5 pyramidal cell exported from BluePyOpt
* Support for using Arbor as a backend for single cell optimization in BluePyOpt