Prepare package for pypi release

See merge request !91

Mention licenses in the classifiers in setup.cfg (a classifier for
CeCILL-C has just recently been added :)

[skip ci]

also fix minimum Python version which I believe must be 3.7 now

omits licensing information for now pending discussion on #91

Flora/documentation/overview network

See merge request !120

Improved interrupt handling for dnadna train

See merge request !127

Add a default of `seed: null` for the simulator config in the schema

See merge request !126

schema

fixes the issue raised at !123 (comment 551445)

The message displayed when pausing training is now on a separate line
from the progress bar(s).

Unfortunately tqdm does not have a public method to get all active
progress bars, so their displays can be cleared.  It would be better if
they were hidden outright or something, and then when resuming training
could be redrawn again in the same place.  Currently this is difficult
to do with tqdm.  I have ideas for a workaround but it's not worth
spending a lot of time on.

Also handle when the user hits Ctrl-D while suspended.

Previously, trying to interrupt `dnadna train` (e.g. with Ctrl-C)
could often take several tries, and would result in a bunch of messy
tracebacks (often overlapping each other due to tracebacks from
interrupted worker processes)

This now handles some interrupts more cleanly.

In particular, pressing Ctrl-C does two things:

1) Rather than immediately interrupting the training, it simply pauses
   it.  Pressing Enter resumes the training, and pressing Ctrl-C again
   cancels it.

2) When canceling the training, it attempts to shut down gracefully:
   If in a validation pass it interrupts the validation, and if in a
   training pass it interrupts the training loop and tries to exit
   cleanly.  This is not always 100% guarantee as not all code is
   interrupt-safe, but it will be more rare to get a non-clean
   interrupt.

   In a follow-up, we could also choose to save a checkpoint right
   when interrupted.

Likewise, trying to terminate the process will attempt a clean shutdown.

Improved Simulator documentation

See merge request !88

commands' implementations

Instead it prints/logs them.  For testing purposes it's better to be
able to catch and check the original exceptions, so a raise_exceptions
flag is added to Command.main

around this pandas/numpy bug:

https://github.com/pandas-dev/pandas/issues/39520

If I understand correctly the bug only occurs when initializing an
"empty" DataFrame (even if the index is not empty).

Instead we construct a dict of the columns first, and then initialize
the DataFrame from this dict.  That should avoid triggering this bug.

documentation, including the tutorial on writing a custom simulator

The code in this documentation has been hand-tested but is not
automatically tested.  That will be a task for the future.

documentation.

usable.

Since !74 the note in its docstring about providing default templates is
no longer valid either.  Its existence is only likely to confuse users,
since it cannot be run.

Start to address #84

See merge request !117

Slightly improved error formatting

See merge request !122

Fix the test_random_seed test on CUDA again

See merge request !124

when using --overwrite instead of --backup

The fact that the same method is being used both for --backup and
--overwrite is a bit messy, but there's enough overlap in their
functionality that I keep it as is for now.

[documentation] remove note about random seed in training docs

See merge request !125

This warning is no longer applicable since I fixed it in !123

[skip ci]

Since merging !72 this seems to fail randomly a lot for SPIDNA.  It
didn't fail on the MR for some reason, but due to its non-deterministic
nature (especially when the tests are run in parallel) there could be
some other minor influence causing it to fail more often now that it's
merged.

[bug] get rid of all default seeds in different configuration sources

See merge request !123

There are currently 3 "seed" options:

1) A "seed" for simulations
2) A "seed" for preprocessing (this mostly controls randomization of
dataset splits)
3) A "seed" for training

All of these had default values in the default config files.  I think
partly as an artifact of when I ported over some of Jean and Theophile's
old config files into the code.

In practice, users should expect stochasticity by default.  The schemas
have a default value of "null" for all these seeds, which is equivalent
to random seeding of the PNRG.  If users want to set a specific seed for
reproducibility they should do so manually.

(Possible future enhancement: Record the seed that was used so they can
reproduce the same run even if the seed was not set explicitly first.)

[doc] start work on training doc

See merge request !95