feat: Initial bulk of type checking + setup

.github/dependabot.yml

@@ -26,6 +26,13 @@ updates:
       - dependency-name: "pyansys-tools-report"
       - dependency-name: "pyyaml"
       - dependency-name: "h5py"
+      - dependency-name: "tensorflow"
+      - dependency-name: "matplotlib"
+      - dependency-name: "plotly"
+      - dependency-name: "scikit-learn"
+      - dependency-name: "seaborn"
+      - dependency-name: "xgboost"
+      - dependency-name: "imageio"
     groups:
       dependencies:
         patterns:

.github/workflows/ci.yml

@@ -85,9 +85,52 @@ jobs:
   code-style:
     name: Code style
     runs-on: ubuntu-latest
+    env:
+      FLUENT_IMAGE_TAG: v26.1.latest
+
     steps:
       - name: "Run PyAnsys code style checks"
         uses: ansys/actions/code-style@v10.2
+        with:
+          python-version: ${{ env.MAIN_PYTHON_VERSION }}
+          skip-install: false
+
+      - name: "Make venv"
+        run: uv venv --clear
+
+      - name: "Install extras"
+        run: uv pip install -e ".[ci_types]"
+
+      - name: Retrieve PyFluent version
+        run: |
+          echo "PYFLUENT_VERSION=$(uv run python -c "from ansys.fluent.core import __version__; print(); print(__version__)" | tail -1)" >> $GITHUB_OUTPUT
+          echo "PYFLUENT version is: $(uv run python -c "from ansys.fluent.core import __version__; print(); print(__version__)" | tail -1)"
+        id: version
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@5e57cd118135c172c3672efd75eb46360885c0ef # v3.6.0
+        with:
+          registry: ghcr.io
+          username: ansys-bot
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Cache API Code
+        uses: actions/cache@9255dc7a253b0ccc959486e2bca901246202afeb # v5.0.1
+        id: cache-api-code
+        with:
+          path: |
+            src/ansys/fluent/core/generated
+          key: API-Code-v${{ env.API_CODE_CACHE }}-${{ steps.version.outputs.PYFLUENT_VERSION }}-${{ env.FLUENT_IMAGE_TAG }}-${{ hashFiles('src/ansys/fluent/core/codegen/**') }}
+          lookup-only: false # zizmor: ignore[cache-poisoning]
+
+      - name:  "Generate the certificates"
+        run: bash .ci/generate_certs.sh
+
+      - name: "Regenerate API codegen"
+        run: uv run codegen/allapigen.py
+
+      - name: "Check type hints"
+        run: uv run basedpyright --baselinemode=auto
 
   commit-style:
     name: "Run commit style checks"

.pre-commit-config.yaml

@@ -32,7 +32,7 @@ repos:
       --statistics,
       --max-complexity, "10",
       --max-line-length, "88",
-      --extend-ignore, E203 E501 C901 B007 B009 B010 B011 B028,
+      --extend-ignore, E203 E501 C901 B007 B009 B010 B011 B028 F403 F405 F401 E402,
       src, doc, examples, tests
     ]
 

devel/undocumented_fields/search_undocumented_fields.py

@@ -6,7 +6,6 @@
 
 import os
 import sys
-from typing import Dict, Set
 
 import ast_comments as ast
 
@@ -121,7 +120,7 @@ def visit_FunctionDef(self, node):
                         self.classes[self.current_class].add(child.target.attr)
 
 
-def analyze_file(file_path: str) -> Dict[str, Set[str]]:
+def analyze_file(file_path: str) -> dict[str, set[str]]:
     """Analyze a Python file and return classes with their public fields."""
     with open(file_path, "r", encoding="utf-8") as file:
         try:
@@ -134,7 +133,7 @@ def analyze_file(file_path: str) -> Dict[str, Set[str]]:
             return {}
 
 
-def analyze_package(package_path: str) -> Dict[str, Dict[str, Set[str]]]:
+def analyze_package(package_path: str) -> dict[str, dict[str, set[str]]]:
     """Analyze all Python files in a package directory."""
     result = {}
 
@@ -160,7 +159,7 @@ def analyze_package(package_path: str) -> Dict[str, Dict[str, Set[str]]]:
     return result
 
 
-def write_results(results: Dict[str, Dict[str, Set[str]]], f) -> str:
+def write_results(results: dict[str, dict[str, set[str]]], f) -> None:
     """Format the analysis results."""
     for module_name, classes in sorted(results.items()):
 

doc/changelog.d/4761.added.md

@@ -0,0 +1 @@
+Initial bulk of type checking + setup

doc/modify_html.py

@@ -32,7 +32,7 @@ def modify_html(soup: BeautifulSoup) -> None:
         / "settings"
     )
     for html_file in html_dir.glob("*.html"):
-        with open(html_file, "r", encoding="utf-8") as f:
+        with open(html_file, encoding="utf-8") as f:
             soup = BeautifulSoup(f, "html.parser", from_encoding="utf-8")
 
         modify_html(soup)

examples/00-fluent/DOE_ML.py

@@ -63,10 +63,11 @@
 # flake8: noqa: E402
 
 import os
-from pathlib import Path
+from typing import TypeAlias, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
@@ -234,8 +235,10 @@
 
 train_set, test_set = train_test_split(df, test_size=0.2, random_state=42)
 
-X_train = x_ct.fit_transform(train_set)
-X_test = x_ct.fit_transform(test_set)
+# cast is a function that improves intellisense by saying we know the data returned by
+# fit_transform is an ndarray
+X_train = cast(npt.NDArray[np.float64], x_ct.fit_transform(train_set))
+X_test = cast(npt.NDArray[np.float64], x_ct.fit_transform(test_set))
 
 y_train = train_set["Result"]
 y_test = test_set["Result"]
@@ -264,8 +267,8 @@
 def display_scores(scores):
     """Display scores."""
     print("\nCross-Validation Scores:", scores)
-    print("Mean:%0.2f" % (scores.mean()))
-    print("Std. Dev.:%0.2f" % (scores.std()))
+    print(f"Mean:{scores.mean():0.2f}")
+    print(f"Std. Dev.:{scores.std():0.2f}")
 
 
 def fit_and_predict(model):
@@ -282,8 +285,8 @@ def fit_and_predict(model):
     test_predictions = model.predict(X_test)
     print(train_predictions.shape[0])
     print("\n\nCoefficient Of Determination")
-    print("Train Data R2 Score: %0.3f" % (r2_score(train_predictions, y_train)))
-    print("Test Data R2 Score: %0.3f" % (r2_score(test_predictions, y_test)))
+    print(f"Train Data R2 Score: {r2_score(train_predictions, y_train):0.3f}")
+    print(f"Test Data R2 Score: {r2_score(test_predictions, y_test):0.3f}")
     print(
         "\n\nPredictions - Ground Truth (Kelvin): ", (test_predictions - y_test), "\n"
     )
@@ -399,21 +402,31 @@ def fit_and_predict(model):
 np.random.seed(42)
 tf.random.set_seed(42)
 
-model = keras.models.Sequential(
-    [
-        keras.layers.Dense(
-            20,
-            activation="relu",
-            input_shape=X_train.shape[1:],
-            kernel_initializer="lecun_normal",
-        ),
-        keras.layers.BatchNormalization(),
-        keras.layers.Dense(20, activation="relu", kernel_initializer="lecun_normal"),
-        keras.layers.BatchNormalization(),
-        keras.layers.Dense(20, activation="relu", kernel_initializer="lecun_normal"),
-        keras.layers.BatchNormalization(),
-        keras.layers.Dense(1),
-    ]
+ModelType: TypeAlias = keras.models.Model[
+    npt.NDArray[np.float64], npt.NDArray[np.float64]
+]
+model = cast(
+    ModelType,
+    keras.models.Sequential(
+        [
+            keras.layers.Dense(
+                20,
+                activation="relu",
+                input_shape=X_train.shape[1:],
+                kernel_initializer="lecun_normal",
+            ),
+            keras.layers.BatchNormalization(),
+            keras.layers.Dense(
+                20, activation="relu", kernel_initializer="lecun_normal"
+            ),
+            keras.layers.BatchNormalization(),
+            keras.layers.Dense(
+                20, activation="relu", kernel_initializer="lecun_normal"
+            ),
+            keras.layers.BatchNormalization(),
+            keras.layers.Dense(1),
+        ]
+    ),
 )
 
 optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999)
@@ -437,7 +450,7 @@ def fit_and_predict(model):
     validation_split=0.2,
     callbacks=[checkpoint_cb, early_stopping_cb],
 )
-model = keras.models.load_model("my_keras_model.h5")
+model = cast(ModelType, keras.models.load_model("my_keras_model.h5"))
 
 print(history.params)
 
@@ -451,8 +464,8 @@ def fit_and_predict(model):
 test_predictions = np.ravel(test_predictions.T)
 print(test_predictions.shape)
 
-print("\n\nTrain R2: %0.3f" % (r2_score(train_predictions, y_train)))
-print("Test R2: %0.3f" % (r2_score(test_predictions, y_test)))
+print(f"\n\nTrain R2: {r2_score(train_predictions, y_train):0.3f}")
+print(f"Test R2: {r2_score(test_predictions, y_test):0.3f}")
 print("Predictions - Ground Truth (Kelvin): ", (test_predictions - y_test))
 
 fig = plt.figure(figsize=(12, 5))

pyproject.toml

@@ -32,7 +32,9 @@ dependencies = [
     "nltk>=3.9.1",
     "numpy>=1.14.0,<3.0.0",
     "pandas>=1.1.0,<3.0.0",
+    "pandas-stubs>=1.1.0,<3.0.0",
     "pyyaml>=6.0",
+    "typing-extensions>=4.12"
 ]
 dynamic = ["version"]
 
@@ -76,6 +78,23 @@ docs = [
     "pdf2image==1.17.0",
     "seaborn>=0.13.2",
 ]
+ci_types = [
+    "basedpyright==1.38.0",
+    "ansys-fluent-visualization",
+    "h5py",
+    "pytest",
+    "pytest-cov",
+    "pytest-mock",
+    "pytest-xdist",
+    "pyfakefs",
+    "tensorflow",
+    "matplotlib",
+    "plotly",
+    "scikit-learn",
+    "seaborn",
+    "xgboost",
+    "imageio",
+]
 
 [tool.flit.module]
 name = "ansys.fluent.core"
@@ -233,3 +252,13 @@ skips = [
     "B604",
     "B607",
     ]
+
+[tool.basedpyright]
+reportAny = false
+reportUnknownMemberType = false
+reportExplicitAny = false
+reportPrivateUsage = false
+reportUnusedCallResult = false
+reportUnannotatedClassAttribute = false
+ignore = ["doc", "src/ansys/fluent/core/generated"]
+baselineFile = ".ci/basedpyright.json"