From ca35d225aec7bb7ba8a848926dc429e0ee1c6659 Mon Sep 17 00:00:00 2001
From: Shah Tanzeel Ahmed <141497653+Tanzeel161@users.noreply.github.com>
Date: Thu, 31 Aug 2023 17:03:02 +0500
Subject: [PATCH] feat(frontends): added Numpy's `logseries`

Co-authored-by: ivy-branch <ivy.branch@lets-unify.ai>
Reviewed-by: KareemMAX <kreem.morsy@hotmail.com>
Refs: #22719
---
 .../frontends/numpy/random/functions.py       | 13 +++++++
 .../test_numpy/test_random/test_functions.py  | 36 +++++++++++++++++++
 2 files changed, 49 insertions(+)

diff --git a/ivy/functional/frontends/numpy/random/functions.py b/ivy/functional/frontends/numpy/random/functions.py
index 10db884f2be0c..8c794d902a945 100644
--- a/ivy/functional/frontends/numpy/random/functions.py
+++ b/ivy/functional/frontends/numpy/random/functions.py
@@ -101,6 +101,19 @@ def lognormal(mean=0.0, sigma=1.0, size=None):
     return ret
 
 
+@to_ivy_arrays_and_back
+@from_zero_dim_arrays_to_scalar
+def logseries(p=0, size=None):
+    if p < 0 or p >= 1:
+        raise ValueError("p value must be in the open interval (0, 1)")
+    r = ivy.log(1 - p)
+    u = ivy.random_uniform(low=0.0, high=1.0, shape=size)
+    v = ivy.random_uniform(low=0.0, high=1.0, shape=size)
+    q = 1 - ivy.exp(r * u)
+    ret = 1 + ivy.log(v) / ivy.log(q)
+    return ret
+
+
 @to_ivy_arrays_and_back
 @from_zero_dim_arrays_to_scalar
 def multinomial(n, pvals, size=None):
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_random/test_functions.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_random/test_functions.py
index b2cae02335ca7..240c61bd6063f 100644
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_random/test_functions.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_random/test_functions.py
@@ -439,6 +439,42 @@ def test_numpy_lognormal(
     )
 
 
+@handle_frontend_test(
+    fn_tree="numpy.random.logseries",
+    input_dtypes=helpers.get_dtypes("float", index=2),
+    p=st.floats(
+        allow_nan=False,
+        allow_infinity=False,
+        min_value=0,
+        max_value=1,
+        exclude_max=True,
+    ),
+    size=helpers.get_shape(allow_none=True),
+    test_with_out=st.just(False),
+)
+def test_numpy_logseries(
+    input_dtypes,
+    frontend,
+    test_flags,
+    fn_tree,
+    backend_fw,
+    on_device,
+    p,
+    size,
+):
+    helpers.test_frontend_function(
+        input_dtypes=input_dtypes,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        p=p,
+        size=size,
+    )
+
+
 # multinomial
 @handle_frontend_test(
     fn_tree="numpy.random.multinomial",