Support NumPy 2

Not too long ago, NumPy 2.0 got released.  cluster_utils didn't work
with it out of the box, so we restricted the dependency to numpy<2.

Looking into it now, it seems the only actual problem was in the
`Discrete` distribution when using booleans:  Using NumPy's `choice()`
converted the list of native bools to an array of np.bool.  Later one,
this clashed when passing the parameters to the job script, where they
are parsed with `ast.literal_eval()`.
Simply converting back to list with `.tolist()` seems to be enough to
fix this.

Author: luator

CHANGELOG.md

@@ -14,6 +14,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Moved documentation from GitHub Pages to Read the Docs.  This allows to more easily
   manage docs for different versions.
 
+### Added
+- Support for Numpy 2.
+
+
 ## [3.0.0] - 2024-08-19
 
 ### Changed

pyproject.toml

@@ -53,7 +53,7 @@ Issues = "https://github.com/martius-lab/cluster_utils/issues"
 runner = [
     "colorama",
     "gitpython>=3.0.5",
-    "numpy<2",
+    "numpy",
     "pandas[output_formatting]>=2.0.3",
     "scipy",
     "tqdm",

src/cluster_utils/server/distributions.py

@@ -246,7 +246,16 @@ def prepare_samples(self, howmany):
         howmany = min(
             10, howmany
         )  # HACK: for smart rounding a reasonable sample size is needed
-        self.samples = get_rng().choice(self.option_list, p=self.probs, size=howmany)
+
+        _samples = get_rng().choice(self.option_list, p=self.probs, size=howmany)
+        # choice() returns a numpy array, implicitly converting value types to numpy
+        # types (e.g. native bool becomes np.bool).  This causes trouble later on, as
+        # the parameters passed to the job script are parsed with ast.literal_eval,
+        # which can only handle native types.  Converting back to list here, should also
+        # get us back to native types for the elements (at least successfully tested
+        # with bool).
+        self.samples = _samples.tolist()
+
         super().prepare_samples(howmany)
 
     def plot(self):