feat: ruff check

.github/workflows/ci.yaml

@@ -18,8 +18,13 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install the project
-        run: uv pip install -r pyproject.toml --all-extras
+      - name: Build the project
+        run: uv build
+
+      - name: Install the project and dependencies
+        run: uv sync --all-extras
+
+      # - uses: astral-sh/ruff-action@v3
 
       - name: Run tests with coverage
         run: uv run pytest tests

src/ConditionalGMM/UnivariateGMM.py

@@ -3,7 +3,7 @@
 import numpy as np
 import scipy as sp
 
-from .condGMM import *
+from ConditionalGMM.condGMM import *
 
 
 class UniGMM(object):

src/ConditionalGMM/__init__.py

@@ -1,6 +1,2 @@
-from .condGMM import *
-from .MNorm import *
-from .UnivariateGMM import *
-
 __author__ = "Tom McClintock <thmsmcclintock@gmail.com>"
 __version__ = "0.1.1"

src/ConditionalGMM/condGMM.py

@@ -3,7 +3,7 @@
 import numpy as np
 import scipy as sp
 
-from .MNorm import *
+from ConditionalGMM.MNorm import *
 
 
 class CondGMM(object):

tests/test_cGMM.py

@@ -4,7 +4,7 @@
 import numpy.testing as npt
 import pytest
 
-import ConditionalGMM as cgmm
+from ConditionalGMM.condGMM import CondGMM
 
 
 def test_cGMM_basic():
@@ -14,22 +14,22 @@ def test_cGMM_basic():
     means = [[0.5, -0.2]]
     covs = [[[2.0, 0.3], [0.3, 0.5]]]
     fixed_inds = [1]
-    cGMM = cgmm.CondGMM(weights, means, covs, fixed_inds)
+    cGMM = CondGMM(weights, means, covs, fixed_inds)
 
     # 2d - 2 component
     weights = [0.5, 0.5]
     means = [[0.5, -0.2], [0.2, -0.2]]
     covs = [[[2.0, 0.3], [0.3, 0.5]], [[2.0, 0.3], [0.3, 0.5]]]
     fixed_inds = [1]
-    cGMM = cgmm.CondGMM(weights, means, covs, fixed_inds)
+    CondGMM(weights, means, covs, fixed_inds)
 
 
 def test_conditionals_moments():
     weights = [0.5, 0.5]
     means = np.array([[0.5, -0.2], [0.2, -0.2]])
     covs = np.array([[[2.0, 0.3], [0.3, 0.5]], [[2.0, 0.3], [0.3, 0.5]]])
     fixed_inds = [1]
-    cGMM = cgmm.CondGMM(weights, means, covs, fixed_inds)
+    cGMM = CondGMM(weights, means, covs, fixed_inds)
     mu2 = cGMM.conditional_component_means()
     npt.assert_equal(np.squeeze(mu2), means[:, 0])
 
@@ -42,9 +42,8 @@ def test_cGMM_exceptions():
     weights = [1.0]
     means = [[0.5, -0.2]]
     covs = [[[2.0, 0.3], [0.3, 0.5]]]
-    fixed_inds = [1]
     with pytest.raises(AssertionError):
-        cGMM = cgmm.CondGMM(weights, means, covs, 1)
+        CondGMM(weights, means, covs, 1)
 
 
 def test_rvs():
@@ -53,7 +52,7 @@ def test_rvs():
     means = [[0.5, -0.2, 1.0]]
     cov = [[[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]]
     ind = [2]
-    cGMM = cgmm.CondGMM(weights, means, cov, ind)
+    cGMM = CondGMM(weights, means, cov, ind)
     N = 10000
     x1_realizations = cGMM.rvs([1], size=N, random_state=42)
     npt.assert_equal(x1_realizations.shape, [N, 2])
@@ -66,7 +65,7 @@ def test_rvs():
         [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]],
     ]
     ind = [2]
-    cGMM = cgmm.CondGMM(weights, means, cov, ind)
+    cGMM = CondGMM(weights, means, cov, ind)
     N = 10000
     x1_realizations, labels = cGMM.rvs(
         [1], size=N, random_state=42, component_labels=True
@@ -80,7 +79,3 @@ def test_rvs():
         [1], size=1, random_state=42, component_labels=True
     )
     npt.assert_equal([1, 2], x1_realizations.shape)
-
-
-if __name__ == "__main__":
-    test_rvs()

tests/test_cMVN.py

@@ -4,7 +4,7 @@
 import pytest
 import scipy.stats as ss
 
-import ConditionalGMM as cGMM
+from ConditionalGMM.MNorm import CondMNorm
 
 
 def test_cMN_basic():
@@ -13,13 +13,13 @@ def test_cMN_basic():
     means = [0.5, -0.2]
     cov = [[2.0, 0.3], [0.3, 0.5]]
     ind = [0]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
 
     # 3D
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]
     inds = [1, 2]
-    cMN = cGMM.CondMNorm(means, cov, inds)
+    cMN = CondMNorm(means, cov, inds)
 
 
 def test_cMN_exceptions():
@@ -29,33 +29,33 @@ def test_cMN_exceptions():
     inds = [1, 2]
 
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov, [-1])
+        CondMNorm(means, cov, [-1])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov, [3])
+        CondMNorm(means, cov, [3])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(123, cov, [1, 2])
+        CondMNorm(123, cov, [1, 2])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, 123, [1, 2])
+        CondMNorm(means, 123, [1, 2])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means[:2], cov, [1, 2])
+        CondMNorm(means[:2], cov, [1, 2])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov[:2], [1, 2])
+        CondMNorm(means, cov[:2], [1, 2])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov, 2)
+        CondMNorm(means, cov, 2)
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov, 2)
+        CondMNorm(means, cov, 2)
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov, [0, 1, 2, 2])
+        CondMNorm(means, cov, [0, 1, 2, 2])
     with pytest.raises(AssertionError):
-        cGMM.CondMNorm(means, cov, [2, 2])
+        CondMNorm(means, cov, [2, 2])
 
 
 def test_conditional_mean_and_cov():
     # 2D
     means = [0.5, -0.2]
     cov = [[2.0, 0.0], [0.0, 0.5]]
     ind = [1]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     Sigma11 = cMN.conditional_cov()
     npt.assert_array_equal(Sigma11, [2.0])
     mu1 = cMN.conditional_mean([1])
@@ -65,7 +65,7 @@ def test_conditional_mean_and_cov():
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]
     ind = [2]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     Sigma11 = cMN.conditional_cov()
     npt.assert_array_equal(Sigma11, [[2.0, 0.3], [0.3, 0.5]])
     mu1 = cMN.conditional_mean([1])
@@ -78,7 +78,7 @@ def test_conditional_probs():
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]
     ind = [2]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     mu1 = cMN.conditional_mean([1])
     Sigma11 = cMN.conditional_cov()
 
@@ -99,7 +99,7 @@ def test_rvs():
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]
     ind = [2]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     N = 10000
     x1_realizations = cMN.rvs([1], size=N, random_state=42)
 
@@ -110,7 +110,7 @@ def test_jointpdfs():
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]
     ind = [2]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     x = [0.2, 0.0, 0.0]
     x1 = x[:2]
     x2 = x[2:]
@@ -128,7 +128,7 @@ def test_pdf():
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.0], [0.3, 0.5, 0.0], [0.0, 0.0, 1.0]]
     ind = [2]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     x = [0.2, 0.0, 0.0]
     x1 = x[:2]
     x2 = x[2:]
@@ -144,11 +144,7 @@ def test_conditional_mean():
     means = [0.5, -0.2, 1.0]
     cov = [[2.0, 0.3, 0.1], [0.3, 0.5, 0.1], [0.1, 0.1, 1.0]]
     ind = [2]
-    cMN = cGMM.CondMNorm(means, cov, ind)
+    cMN = CondMNorm(means, cov, ind)
     x2 = [1.0]
     cmean = cMN.conditional_mean(x2)
     npt.assert_array_equal(means[:2], cmean)
-
-
-if __name__ == "__main__":
-    test_conditional_probs()