bump version, make it compatible with numpy 2 and change to pyproject

Author: scarlehoff

examples/example_pineappl.py

@@ -61,7 +61,7 @@ def hadronic_pspgen(xarr, mmin, mmax):
     x2 = tau / y
     s = tau * smax
 
-    jacobian = np.math.log(tau0) * np.math.log(tau0) * tau * r1
+    jacobian = np.log(tau0) * np.log(tau0) * tau * r1
 
     # theta integration (in the CMS)
     cos_theta = 2.0 * r3 - 1.0
@@ -71,7 +71,7 @@ def hadronic_pspgen(xarr, mmin, mmax):
     u = -0.5 * s * (1.0 + cos_theta)
 
     # phi integration
-    jacobian *= 2.0 * np.math.acos(-1.0)
+    jacobian *= 2.0 * np.acos(-1.0)
 
     return s, t, u, x1, x2, jacobian
 

pyproject.toml

@@ -0,0 +1,45 @@
+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "vegasflow"
+version = "1.4.0"
+description="Hardware agnostic Monte Carlo integration"
+authors = [
+	{name = "S. Carrazza", email = "stefano.carraza@cern.ch"},
+	{name = "J.Cruz-Martinez", email = "juacrumar@lairen.eu"}
+]
+classifiers=[
+	'Operating System :: Unix',
+	'Programming Language :: Python',
+	'Programming Language :: Python :: 3',
+	'Topic :: Scientific/Engineering',
+	'Topic :: Scientific/Engineering :: Physics',
+]
+dependencies = [
+	"joblib",
+	"numpy",
+	"tensorflow>2.2"
+]
+
+[project.optional-dependencies] 
+docs = [
+'sphinx_rtd_theme',
+'recommonmark',
+'sphinxcontrib-bibtex',
+]
+examples = [
+'cffi',
+'pineappl',
+'pdfflow',
+'scipy'
+]
+benchmark = [
+'vegas', # Lepage's Vegas for benchmarking
+]
+distribute = [
+'dask',
+'distributed',
+'dask-jobqueue',
+]

setup.py

@@ -7,4 +7,4 @@
 from vegasflow.plain import PlainFlow, plain_wrapper, plain_sampler
 from vegasflow.vflowplus import VegasFlowPlus, vegasflowplus_wrapper, vegasflowplus_sampler
 
-__version__ = "1.3.0"
+__version__ = "1.4.0"

src/vegasflow/__init__.py

@@ -606,7 +606,7 @@ def batch_events(**kwargs):
                 )
             test_array = tf.random.uniform((event_size, self.n_dim), dtype=DTYPE)
             wgt = tf.random.uniform((event_size,), dtype=DTYPE)
-            res_tmp = new_integrand(test_array, weight=wgt).numpy()
+            res_tmp = new_integrand(test_array, weight=wgt)#.numpy()
             res_shape = res_tmp.shape
 
             expected_shape = (event_size,)