From ad62774e82bdf100a6856439177afafa7a57eb78 Mon Sep 17 00:00:00 2001
From: Simon Bjurek <simbj106@student.liu.se>
Date: Tue, 18 Mar 2025 15:45:56 +0100
Subject: [PATCH] added the numpy-rule for ruff and fixed issues
---
pyproject.toml | 2 +-
test/unit/test_list_schedulers.py | 4 ++--
test/unit/test_sfg_generators.py | 14 +++++++-------
3 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/pyproject.toml b/pyproject.toml
index c1ab97e2..3cfd42e4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -100,7 +100,7 @@ precision = 2
exclude = ["examples"]
[tool.ruff.lint]
-select = ["E4", "E7", "E9", "F", "SIM", "B"]
+select = ["E4", "E7", "E9", "F", "SIM", "B", "NPY"]
ignore = ["F403", "B008", "B021", "B006"]
[tool.typos]
diff --git a/test/unit/test_list_schedulers.py b/test/unit/test_list_schedulers.py
index 52d29a13..b67147d9 100644
--- a/test/unit/test_list_schedulers.py
+++ b/test/unit/test_list_schedulers.py
@@ -882,7 +882,7 @@ class TestHybridScheduler:
assert schedule.schedule_time == 16
# validate regenerated sfg with random 2x2 real s.p.d. matrix
- A = np.random.rand(2, 2)
+ A = np.random.default_rng().random((2, 2))
A = np.dot(A, A.T)
A_inv = np.linalg.inv(A)
input_signals = []
@@ -1985,7 +1985,7 @@ def _validate_recreated_sfg_ldlt_matrix_inverse(
delays = [0 for i in range(num_of_outputs)]
# random real s.p.d matrix
- A = np.random.rand(N, N)
+ A = np.random.default_rng().random((N, N))
A = np.dot(A, A.T)
# iterate through the upper diagonal and construct the input to the SFG
diff --git a/test/unit/test_sfg_generators.py b/test/unit/test_sfg_generators.py
index a51224bd..f427259e 100644
--- a/test/unit/test_sfg_generators.py
+++ b/test/unit/test_sfg_generators.py
@@ -310,7 +310,7 @@ class TestDirectFormIIRType1:
b, a = signal.butter(N, Wc, btype="lowpass", output="ba")
- input_signal = np.random.randn(100)
+ input_signal = np.random.default_rng().standard_normal(100)
reference_filter_output = signal.lfilter(b, a, input_signal)
sfg = direct_form_1_iir(b, a, name="test iir direct form 1")
@@ -326,7 +326,7 @@ class TestDirectFormIIRType1:
b, a = signal.butter(N, Wc, btype="lowpass", output="ba")
- input_signal = np.random.randn(100)
+ input_signal = np.random.default_rng().standard_normal(100)
reference_filter_output = signal.lfilter(b, a, input_signal)
sfg = direct_form_1_iir(b, a, name="test iir direct form 1")
@@ -343,7 +343,7 @@ class TestDirectFormIIRType1:
b, a = signal.ellip(N, 0.1, 60, Wc, btype="low", analog=False)
b, a = signal.butter(N, Wc, btype="lowpass", output="ba")
- input_signal = np.random.randn(100)
+ input_signal = np.random.default_rng().standard_normal(100)
reference_filter_output = signal.lfilter(b, a, input_signal)
sfg = direct_form_1_iir(b, a, name="test iir direct form 1")
@@ -430,7 +430,7 @@ class TestDirectFormIIRType2:
b, a = signal.butter(N, Wc, btype="lowpass", output="ba")
- input_signal = np.random.randn(100)
+ input_signal = np.random.default_rng().standard_normal(100)
reference_filter_output = signal.lfilter(b, a, input_signal)
sfg = direct_form_2_iir(b, a, name="test iir direct form 1")
@@ -446,7 +446,7 @@ class TestDirectFormIIRType2:
b, a = signal.butter(N, Wc, btype="lowpass", output="ba")
- input_signal = np.random.randn(100)
+ input_signal = np.random.default_rng().standard_normal(100)
reference_filter_output = signal.lfilter(b, a, input_signal)
sfg = direct_form_2_iir(b, a, name="test iir direct form 1")
@@ -463,7 +463,7 @@ class TestDirectFormIIRType2:
b, a = signal.ellip(N, 0.1, 60, Wc, btype="low", analog=False)
b, a = signal.butter(N, Wc, btype="lowpass", output="ba")
- input_signal = np.random.randn(100)
+ input_signal = np.random.default_rng().standard_normal(100)
reference_filter_output = signal.lfilter(b, a, input_signal)
sfg = direct_form_2_iir(b, a, name="test iir direct form 1")
@@ -804,7 +804,7 @@ class TestLdltMatrixInverse:
# assert np.isclose(actual_values[i], expected_values[i])
def _generate_random_spd_matrix(self, N: int) -> np.ndarray:
- A = np.random.rand(N, N)
+ A = np.random.default_rng().random((N, N))
A = (A + A.T) / 2 # ensure symmetric
min_eig = np.min(np.linalg.eigvals(A))
A += (np.abs(min_eig) + 0.1) * np.eye(N) # ensure positive definiteness
--
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