updated np.product to np.prod (#2249)

* updated np.product to np.prod

changed np.product to np.prod in 3 files

* added pull request changes in changelog

* fix pr link in CHANGELOG.md

---------

Co-authored-by: Oriol Abril-Pla <oriol.abril.pla@gmail.com>

CHANGELOG.md

@@ -8,6 +8,7 @@
 - InferenceData objects can now be appended to existing netCDF4 files and to specific groups within them ([2227](https://github.com/arviz-devs/arviz/pull/2227))
 
 ### Maintenance and fixes
+- Replace deprecated np.product with np.prod ([2249](https://github.com/arviz-devs/arviz/pull/2249))
 - Fix numba deprecation warning ([2246](https://github.com/arviz-devs/arviz/pull/2246))
 - Fixes for creating numpy object array ([2233](https://github.com/arviz-devs/arviz/pull/2233) and [2239](https://github.com/arviz-devs/arviz/pull/2239))
 - Adapt histograms generated by plot_dist to input dtype ([2247](https://github.com/arviz-devs/arviz/pull/2247))

arviz/data/io_pystan.py

@@ -669,8 +669,8 @@ def get_draws(fit, variables=None, ignore=None, warmup=False, dtypes=None):
         par_idx = min(
             (dim, i) for i, dim in enumerate(fit.sim["dims_oi"]) if (dim and np.prod(dim) != 0)
         )[1]
-        offset = int(sum(map(np.product, fit.sim["dims_oi"][:par_idx])))
-        par_offset = int(np.product(fit.sim["dims_oi"][par_idx]))
+        offset = int(sum(map(np.prod, fit.sim["dims_oi"][:par_idx])))
+        par_offset = int(np.prod(fit.sim["dims_oi"][par_idx]))
         par_keys = fit.sim["fnames_oi"][offset : offset + par_offset]
         shift = len(par_keys)
         for item in par_keys:

arviz/stats/density_utils.py

@@ -240,7 +240,7 @@ def _fixed_point(t, N, k_sq, a_sq):
 
     for j in np.arange(l - 1, 2 - 1, -1):
         c1 = (1 + 0.5 ** (j + 0.5)) / 3
-        c2 = np.product(np.arange(1.0, 2 * j + 1, 2, dtype=np.float64))
+        c2 = np.prod(np.arange(1.0, 2 * j + 1, 2, dtype=np.float64))
         c2 /= (np.pi / 2) ** 0.5
         t_j = np.power((c1 * (c2 / (N * f))), (2.0 / (3.0 + 2.0 * j)))
         f = np.sum(k_sq**j * a_sq * np.exp(-k_sq * np.pi**2.0 * t_j))

arviz/stats/stats.py

@@ -769,7 +769,7 @@ def loo(data, pointwise=None, var_name=None, reff=None, scale=None):
     log_likelihood = log_likelihood.stack(__sample__=("chain", "draw"))
     shape = log_likelihood.shape
     n_samples = shape[-1]
-    n_data_points = np.product(shape[:-1])
+    n_data_points = np.prod(shape[:-1])
     scale = rcParams["stats.ic_scale"] if scale is None else scale.lower()
 
     if scale == "deviance":
@@ -1626,7 +1626,7 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None):
     log_likelihood = log_likelihood.stack(__sample__=("chain", "draw"))
     shape = log_likelihood.shape
     n_samples = shape[-1]
-    n_data_points = np.product(shape[:-1])
+    n_data_points = np.prod(shape[:-1])
 
     ufunc_kwargs = {"n_dims": 1, "ravel": False}
     kwargs = {"input_core_dims": [["__sample__"]]}