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- Add `--raw` to `modcc`'s catalogue processing.
- Check `gpu == none` in `arbor-build-catalogue`
- Fix some cheap signed/unsigned warnings

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fixed code numbers

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Inserted the corresponding lines for importing pandas and seaborn in the
documentation

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---------

Co-authored-by: Thorsten Hater <24411438+thorstenhater@users.noreply.github.com>

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We have numpy for 3.12, so we can test it.

Closes #2203

We found that this
```
ASSIGNED { qt celsius }

INITIAL { qt = 2.3^(celsius - T) }
```
results in undefined/NaN `qt`, since `modcc` generates a new, assignable
variable `celsius`
instead of accessing the built-in 'magic' variable.

This PR fixes this miscompilation by reporting an error and overhauls
the relevant catalogues.

Complete Python support for remote spikes.

small typesetting fixes.

fixed sample code that may return an error

arb --> A; made small changes to keep the consistency of the
documentation.

added a missing "}"

Closes #2253

Housekeeping before v0.10.0

## bump and deprecate dependencies

Go through all submodules, build docs, and other dependencies to adjust
version numbers.
Submodules go to Spack's latest

## `pip` now protects managed environments
MacOS `brew` has becomes finicky lately when it comes to installing
globally via `pip`,
as new versions of `pip` are no longer allowing the user to break global
environments.
Fix by forcing `pip` to disregard that rule, as we set up and tear down
VMs for precisely
this purpose.

Fixes #2273 

## spack develop started to misbehave

Massage code in package.py around handling optimization flags. Output is
a `version` object,
input expects a string. Adjust by converting to string

## fix dubious type punning

`network.cpp` uses `reinterpret_cast` to type pun. Use `memcpy` instead
as recommended.
Also got flagged by compiler warning with newer GCC

## `result_of` -> `invoke_result`

Flagged as deprecated.

## fix includes in `test_*`

Newer versions of GCC require including `algorithm` directly.

How to use (mini)conda to install Arbor.

Since NMODL documentation is scarce and outdated, add a lengthy tutorial
about it.

Expose the remote cross-simulator coupling to python.

Implement a high-level network specification as proposed in
#418. It does not include support for gap junctions to allow the use of
domain decomposition for some distributed network generation.
The general idea is a DSL based on set algebra, which operates on the
set of all possible connections, by selecting based on different
criteria, such as the distance between cells or lists of labels. By
operating on all possible connections, a separate definition of cell
populations becomes unnecessary. An example for selecting all inter-cell
connections with a certain source and destination label is:
`(intersect (inter-cell) (source-label \"detector\") (destination-label
\"syn\"))`

For parameters such as weight and delay, a value can be defined in the
DSL in a similar way with the usual mathematical operations available.
An example would be:
`(max 0.1 (exp (mul -0.5 (distance))))`

The position of each connection site is calculated by resolving the
local position on the cell and applying an isometry, which is provided
by a new optional function of the recipe. In contrast to the usage of
policies to select a member within a locset, each site is treated
individually and can be distinguished by its position.

Internally, some steps have been implemented in an attempt to reduce the
overhead of generating connections:
- Pre-select source and destination sites based on the selection to
reduce the sampling space when possible
- If selection is limited to a maximum distance, use an octree for
efficient spatial sampling
- When using MPI, only instantiate local cells and exchange source sites
in a ring communication pattern to overlap communication and sampling.
In addition, this reduces memory usage, since only the current and next
source sites have to be stored in memory during the exchange process.

Custom selection and value functions can still be provided by storing
the wrapped function in a dictionary with an associated label, which can
then be used in the DSL.

Some challenges remain. In particular, how to handle combined explicit
connections returned by `connections_on` and the new way to describe a
network. Also, the use of non-blocking MPI is not easily integrated into
the current context types, and the dry-run context is not supported so
far.


# Example
A (trimmed) example in Python, where a ring connection combined with
random connections based on the distance:
```py
class recipe(arbor.recipe):
    def cell_isometry(self, gid):
        # place cells with equal distance on a circle
        radius = 500.0 # μm
        angle = 2.0 * math.pi * gid / self.ncells
        return arbor.isometry.translate(radius * math.cos(angle), radius * math.sin(angle), 0)

    def network_description(self):
        seed = 42

        # create a chain
        ring = f"(chain (gid-range 0 {self.ncells}))"
        # connect front and back of chain to form ring
        ring = f"(join {ring} (intersect (source-cell {self.ncells - 1}) (destination-cell 0)))"

        # Create random connections with probability inversely proportional to the distance within a
        # radius
        max_dist = 400.0 # μm
        probability = f"(div (sub {max_dist} (distance)) {max_dist})"
        rand = f"(intersect (random {seed} {probability}) (distance-lt {max_dist}))"

        # combine ring with random selection
        s = f"(join {ring} {rand})"
        # restrict to inter-cell connections and certain source / destination labels
        s = f"(intersect {s} (inter-cell) (source-label \"detector\") (destination-label \"syn\"))"

        # normal distributed weight with mean 0.02 μS, standard deviation 0.01 μS
        # and truncated to [0.005, 0.035]
        w = f"(truncated-normal-distribution {seed} 0.02 0.01 0.005 0.035)"
        # fixed delay
        d = "(scalar 5.0)"  # ms delay

        return arbor.network_description(s, w, d, {})
```
Co-authored-by: Thorsten Hater <24411438+thorstenhater@users.noreply.github.com>

This removes `simulation::inject_events` which is made obsolete by
`recipe::event_generators`.
The method exposed internals by giving access to the `lid` of synapses
(over labels) and broke
abstractions (`gid` must be present on this MPI rank and all events must
be deliverable during
the current time (as per documentation)). It was used only in a few
tests and examples and these
uses were replaced by `event_generator`s.