Replace arange by range for iteration

Author: ricardoV94

pymc/backends/mcbackend.py

@@ -181,7 +181,7 @@ def get_sampler_stats(
     def _slice(self, idx: slice) -> "IBaseTrace":
         # Get the integer indices
         start, stop, step = idx.indices(len(self))
-        indices = np.arange(start, stop, step)
+        indices = tuple(range(start, stop, step))
 
         # Create a NumPyChain for the sliced data
         nchain = mcb.backends.numpy.NumPyChain(

pymc/distributions/shape_utils.py

@@ -378,9 +378,7 @@ def get_support_shape(
             raise ValueError(
                 f"Number of shape dimensions is too small for ndim_supp of {ndim_supp}"
             )
-        inferred_support_shape = [
-            shape[i] - support_shape_offset[i] for i in np.arange(-ndim_supp, 0)
-        ]
+        inferred_support_shape = [shape[i] - support_shape_offset[i] for i in range(-ndim_supp, 0)]
 
     if inferred_support_shape is None and dims is not None:
         dims = convert_dims(dims)
@@ -389,7 +387,7 @@ def get_support_shape(
             raise ValueError(f"Number of dims is too small for ndim_supp of {ndim_supp}")
         model = modelcontext(None)
         inferred_support_shape = [
-            model.dim_lengths[dims[i]] - support_shape_offset[i] for i in np.arange(-ndim_supp, 0)
+            model.dim_lengths[dims[i]] - support_shape_offset[i] for i in range(-ndim_supp, 0)
         ]
 
     if inferred_support_shape is None and observed is not None:
@@ -399,7 +397,7 @@ def get_support_shape(
                 f"Number of observed dimensions is too small for ndim_supp of {ndim_supp}"
             )
         inferred_support_shape = [
-            observed.shape[i] - support_shape_offset[i] for i in np.arange(-ndim_supp, 0)
+            observed.shape[i] - support_shape_offset[i] for i in range(-ndim_supp, 0)
         ]
 
     if inferred_support_shape is None:

pymc/sampling/forward.py

@@ -876,7 +876,7 @@ def sample_posterior_predictive(
     try:
         with progress:
             task = progress.add_task("Sampling ...", completed=0, total=samples)
-            for idx in np.arange(samples):
+            for idx in range(samples):
                 if nchain > 1:
                     # the trace object will either be a MultiTrace (and have _straces)...
                     if hasattr(_trace, "_straces"):

tests/distributions/test_multivariate.py

@@ -1564,8 +1564,8 @@ def test_zsn_dims(self, dims, n_zerosum_axes):
         )
 
         ndim_supp = v.owner.op.ndim_supp
-        n_zerosum_axes = np.arange(-ndim_supp, 0)
-        nonzero_axes = np.arange(v.ndim - ndim_supp)
+        n_zerosum_axes = tuple(range(-ndim_supp, 0))
+        nonzero_axes = tuple(range(v.ndim - ndim_supp))
         for samples in [
             s.posterior.v,
             random_samples,
@@ -1595,8 +1595,8 @@ def test_zsn_shape(self, n_zerosum_axes):
         )
 
         ndim_supp = v.owner.op.ndim_supp
-        n_zerosum_axes = np.arange(-ndim_supp, 0)
-        nonzero_axes = np.arange(v.ndim - ndim_supp)
+        n_zerosum_axes = tuple(range(-ndim_supp, 0))
+        nonzero_axes = tuple(range(v.ndim - ndim_supp))
         for samples in [
             s.posterior.v,
             random_samples,