Cargo and Ruff linting improvements

.github/workflows/docs.yml

@@ -61,7 +61,7 @@ jobs:
   deploy:
     runs-on: ubuntu-latest
     needs: build
-    if: (github.event_name == 'push' && startsWith(github.ref, 'refs/tags/')) || github.event_name == 'workflow_dispatch'
+    if: (github.event_name == 'push' && startsWith(github.ref, 'refs/tags/'))
     permissions:
       pages: write
       id-token: write

Cargo.toml

@@ -10,4 +10,40 @@ license = "BSD-3-Clause"
 
 [workspace.dependencies]
 nalgebra = "0.34.1"
-pyo3 = { version = "0.27.1", default-features = false, features = ["macros"] }
+pyo3 = { version = "0.27.1", default-features = false, features = ["macros"] }
+
+[workspace.lints.clippy]
+# Enable pedantic lints for higher code quality standards
+pedantic = { level = "deny", priority = -1 }
+
+# Specific allows to reduce noise from pedantic
+module_name_repetitions = "allow"      # Common in large codebases
+similar_names = "allow"                # Scientific code often has x, y, z, etc.
+must_use_candidate = "allow"           # Too noisy for mathematical functions
+cast_precision_loss = "allow"          # Acceptable in numerical code (usize to f64)
+cast_possible_truncation = "allow"     # Acceptable when converting calculated values
+cast_sign_loss = "allow"               # Acceptable in constrained numerical contexts
+cast_possible_wrap = "allow"           # Acceptable in controlled numerical contexts
+
+# Additional strict lints
+missing_errors_doc = "warn"
+missing_panics_doc = "warn"
+missing_safety_doc = "warn"
+undocumented_unsafe_blocks = "warn"
+
+# Performance and correctness
+inefficient_to_string = "warn"
+manual_ok_or = "warn"
+redundant_closure_for_method_calls = "warn"
+
+# Complexity and style
+cognitive_complexity = "warn"
+too_many_lines = "warn"
+
+# Selected nursery lints
+option_if_let_else = "warn"
+suboptimal_flops = "allow"             # Suggested optimisations often reduce readability in tests
+
+# Test-specific allows
+float_cmp = "allow"                    # Exact float equality is standard in tests
+cast_lossless = "allow"                # i32 to f64 conversions in tests/benchmarks

docs/tutorials/notebooks/ode_fitting_diffsol.ipynb

@@ -35,7 +35,13 @@
     }
    },
    "outputs": [],
-   "source": "# Import plotting utilities\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom diffid.plotting import ode_fit"
+   "source": [
+    "# Import plotting utilities\n",
+    "import diffid\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from diffid.plotting import ode_fit"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -114,7 +120,34 @@
     }
    },
    "outputs": [],
-   "source": "# Time points\nt_span = np.linspace(0, 4, 100)\n\n# True logistic growth solution with known parameters\n# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\ny0 = 0.1\nr_true = 1.0\nk_true = 1.0\n\n# Generate clean data\nexp_rt = np.exp(r_true * t_span)\ny_data = y0 * exp_rt / (1 + y0 * (exp_rt - 1) / k_true)\n\n# Add some noise\nnp.random.seed(42)\nnoise_level = 0.01\ny_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n\n# Diffid expects data as [time, observation] columns\ndata = np.column_stack((t_span, y_noisy))\n\nprint(f\"Generated {len(t_span)} data points\")\nprint(f\"Time range: [{t_span[0]:.2f}, {t_span[-1]:.2f}]\")\nprint(f\"Value range: [{y_noisy.min():.3f}, {y_noisy.max():.3f}]\")\nprint(f\"Noise level: {noise_level}\")\nprint(f\"\\nTrue parameters: r = {r_true}, k = {k_true}\")"
+   "source": [
+    "# Time points\n",
+    "t_span = np.linspace(0, 4, 100)\n",
+    "\n",
+    "# True logistic growth solution with known parameters\n",
+    "# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\n",
+    "y0 = 0.1\n",
+    "r_true = 1.0\n",
+    "k_true = 1.0\n",
+    "\n",
+    "# Generate clean data\n",
+    "exp_rt = np.exp(r_true * t_span)\n",
+    "y_data = y0 * exp_rt / (1 + y0 * (exp_rt - 1) / k_true)\n",
+    "\n",
+    "# Add some noise\n",
+    "np.random.seed(42)\n",
+    "noise_level = 0.01\n",
+    "y_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n",
+    "\n",
+    "# Diffid expects data as [time, observation] columns\n",
+    "data = np.column_stack((t_span, y_noisy))\n",
+    "\n",
+    "print(f\"Generated {len(t_span)} data points\")\n",
+    "print(f\"Time range: [{t_span[0]:.2f}, {t_span[-1]:.2f}]\")\n",
+    "print(f\"Value range: [{y_noisy.min():.3f}, {y_noisy.max():.3f}]\")\n",
+    "print(f\"Noise level: {noise_level}\")\n",
+    "print(f\"\\nTrue parameters: r = {r_true}, k = {k_true}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -184,7 +217,24 @@
     }
    },
    "outputs": [],
-   "source": "# Create builder\nbuilder = (\n    diffid.DiffsolBuilder()\n    .with_diffsl(dsl_model)\n    .with_data(data)\n    .with_tolerances(1e-6, 1e-8)  # Relative and absolute tolerances\n    .with_parameter(\"r\", 10.0)  # Initial guess (deliberately wrong)\n    .with_parameter(\"k\", 10.0)  # Initial guess (deliberately wrong)\n    .with_parallel(True)  # Enable parallel evaluation\n)\n\nproblem = builder.build()\n\nprint(\"Problem built successfully!\")\nprint(\"\\nInitial parameter guesses: r = 50.0, k = 50.0\")\nprint(f\"(Far from true values: r = {r_true}, k = {k_true})\")"
+   "source": [
+    "# Create builder\n",
+    "builder = (\n",
+    "    diffid.DiffsolBuilder()\n",
+    "    .with_diffsl(dsl_model)\n",
+    "    .with_data(data)\n",
+    "    .with_tolerances(1e-6, 1e-8)  # Relative and absolute tolerances\n",
+    "    .with_parameter(\"r\", 10.0)  # Initial guess\n",
+    "    .with_parameter(\"k\", 10.0)  # Initial guess\n",
+    "    .with_parallel(True)  # Enable parallel evaluation\n",
+    ")\n",
+    "\n",
+    "problem = builder.build()\n",
+    "\n",
+    "print(\"Problem built successfully!\")\n",
+    "print(\"\\nInitial parameter guesses: r = 50.0, k = 50.0\")\n",
+    "print(f\"(Far from true values: r = {r_true}, k = {k_true})\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -513,4 +563,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/tutorials/notebooks/optimisation_basics.ipynb

@@ -249,7 +249,7 @@
     "\n",
     "for name, result in results.items():\n",
     "    print(\n",
-    "        f\"{name:<15} {str(result.success):<10} {result.value:<15.3e} \"\n",
+    "        f\"{name:<15} {result.success!s:<10} {result.value:<15.3e} \"\n",
     "        f\"{result.iterations:<12} {result.evaluations}\"\n",
     "    )\n",
     "\n",