Typing

.github/workflows/mypy.yml

@@ -0,0 +1,24 @@
+name: mypy
+
+on: [push, pull_request]
+
+jobs:
+  type-check:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install package + dev deps
+        run: |
+          pip install .[dev]
+
+      - name: Run MyPy
+        run: |
+          mypy src/ecoff_fitter --pretty

README.md

@@ -21,7 +21,7 @@ Demo input files are provided in `demo_files/` to illustrate basic use.
 
 ---
 
-## 📦 Installation
+## 🛠 Installation
 
 ### Install from PyPI
 
@@ -39,25 +39,6 @@ pip install -e .
 
 ---
 
-## 🛠 Creating the Environment
-
-### Conda environment (env.yml)
-
-```bash
-conda env create -f env.yml
-conda activate ECOFFitter
-```
-
-### Pip environment (requirements.txt)
-
-```bash
-python -m venv ecoff-env
-source ecoff-env/bin/activate
-pip install -r requirements.txt
-```
-
----
-
 ## 📥 Input
 
 ### 1. MIC Data Input File

pyproject.toml

@@ -50,3 +50,20 @@ omit = [
     "*/ecoff_fitter/wts.py",
     "*/gui.py"
 ]
+
+[tool.mypy]
+python_version = "3.10"
+warn_return_any = true
+warn_unused_ignores = true
+warn_redundant_casts = true
+warn_unused_configs = true
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+ignore_missing_imports = true
+allow_redefinition = true
+no_implicit_optional = true
+implicit_reexport = true
+exclude = "src/ecoff_fitter/wts.py"
+
+[project.optional-dependencies]
+dev = ["mypy", "pandas-stubs", "types-PyYAML",  "scipy-stubs"]

src/ecoff_fitter/__init__.py

@@ -1,3 +1,6 @@
+from typing import Callable
+
+
 """
 ECOFF Fitter — Estimate epidemiological cutoff values (ECOFFs)
 using interval regression on MIC (Minimum Inhibitory Concentration) data.
@@ -16,7 +19,7 @@
 # --- Public API imports ---
 from .core import ECOFFitter
 
-__all__ = ["ECOFFitter"]
+__all__: list[str] = ["ECOFFitter"]
 
 # --- Optional: version handling ---
 try:
@@ -25,7 +28,7 @@
     __version__ = "0.0.0"
 
 # --- Optional: CLI hook for `python -m ecoff_fitter` ---
-def main():
+def main() -> None:
     """Entry point for running ecoff_fitter as a module (CLI)."""
     from .cli import main as cli_main
     cli_main()

src/ecoff_fitter/cli.py

@@ -7,6 +7,7 @@
 """
 
 import argparse
+from typing import Any, List, Optional
 from ecoff_fitter import ECOFFitter
 from ecoff_fitter.report import GenerateReport
 from ecoff_fitter.defence import validate_output_path
@@ -74,7 +75,7 @@ def build_parser() -> argparse.ArgumentParser:
     return parser
 
 
-def main(argv=None):
+def main(argv: Optional[List[str]] = None) -> None:
     """Main entry point for the ECOFFitter CLI."""
     parser = build_parser()
     args = parser.parse_args(argv)

src/ecoff_fitter/core.py

@@ -1,4 +1,7 @@
 import numpy as np
+from typing import Any, Optional, Tuple
+import pandas as pd
+from numpy.typing import NDArray
 from scipy.stats import norm
 from intreg.intreg import IntReg
 from ecoff_fitter.utils import read_input, read_params
@@ -24,14 +27,28 @@ class ECOFFitter:
     (wild-type) component at the given percentile.
     """
 
+    model_: IntReg | MixtureModel | None
+    x: NDArray[np.floating]
+    mus_: NDArray[np.floating]
+    sigmas_: NDArray[np.floating]
+    pis_: NDArray[np.floating]
+    loglike_: float
+    converged_: bool
+    n_iter_: int | None
+    ecoff_: float
+    z_percentile_: float
+    y_low_: NDArray[np.floating]
+    y_high_: NDArray[np.floating]
+    weights_: NDArray[np.floating]
+
     def __init__(
         self,
-        input,
-        params: dict | str | None = None,
+        input: pd.DataFrame | str,
+        params: dict[str, Any] | str | None = None,
         dilution_factor: int = 2,
         distributions: int = 1,
         boundary_support: int | None = 1,
-    ):
+    ) -> None:
         """
         Initialize the ECOFFitter.
 
@@ -75,7 +92,7 @@ def __init__(
         self.distributions = distributions
         self.boundary_support = boundary_support
 
-    def fit(self, options={}):
+    def fit(self, options: dict[str, Any] | None = None) -> "ECOFFitter":
         """
         Define MIC intervals and fit either a single censored-normal model
         or a finite mixture model.
@@ -96,7 +113,7 @@ def fit(self, options={}):
             # multiple gaussians
             return self.fit_mixture(options)
 
-    def fit_single(self, options=None):
+    def fit_single(self, options: dict[str, Any] | None = None) -> "ECOFFitter":
         """
         Fit a single-component censored normal distribution using interval
         regression.
@@ -122,7 +139,9 @@ def fit_single(self, options=None):
         self.converged_ = result.success
         self.n_iter_ = result.nit if hasattr(result, "nit") else None
 
-    def fit_mixture(self, options=None):
+        return self
+
+    def fit_mixture(self, options: dict[str, Any] | None = None) -> "ECOFFitter":
         """
         Fit a K-component finite mixture of censored normals using the EM
         algorithm followed by optional refinement.
@@ -156,7 +175,9 @@ def fit_mixture(self, options=None):
 
         return self
 
-    def define_intervals(self, df=None):
+    def define_intervals(
+        self, df: Optional[pd.DataFrame] = None
+    ) -> Tuple[NDArray[np.floating], NDArray[np.floating], NDArray[np.floating]]:
         """
         Construct MIC interval bounds and apply left-, right-, and interval-
         censoring rules, then transform to log dilution space.
@@ -210,7 +231,9 @@ def define_intervals(self, df=None):
 
         return y_low_log, y_high_log, weights
 
-    def log_transf_intervals(self, y_low, y_high):
+    def log_transf_intervals(
+        self, y_low: NDArray[np.floating], y_high: NDArray[np.floating]
+    ) -> Tuple[NDArray[np.floating], NDArray[np.floating]]:
         """
         Transform interval bounds into log base–dilution_factor space.
 
@@ -231,7 +254,9 @@ def log_transf_intervals(self, y_low, y_high):
 
         return y_low_log, y_high_log
 
-    def generate(self, percentile: int | float = 99, options={}):
+    def generate(
+        self, percentile: int | float = 99, options: dict[str, Any] | None = None
+    ) -> Tuple[Any, ...]:
         """
         Fit the model and compute the ECOFF at a specified percentile.
 
@@ -253,7 +278,7 @@ def generate(self, percentile: int | float = 99, options={}):
 
         return results
 
-    def compute_ecoff(self, percentile: float):
+    def compute_ecoff(self, percentile: float) -> Tuple[Any, ...]:
         """
         Compute the ECOFF and percentile location from the fitted model.
 

src/ecoff_fitter/defence.py

@@ -1,9 +1,11 @@
 import pandas as pd
+from typing import Any
+from pandas import DataFrame
 import os
 import re
 
 
-def validate_input_source(input):
+def validate_input_source(input: str | DataFrame | dict[str, Any]) -> None:
     """
     Validate the input source for ECOFFitter.
 
@@ -32,7 +34,9 @@ def validate_input_source(input):
         raise ValueError("Input must be a pandas DataFrame or a valid file path.")
 
 
-def validate_params_source(params):
+def validate_params_source(
+    params: dict[str, Any] | str | list[Any] | tuple[Any, ...] | DataFrame | Any | None,
+) -> None:
     """
     Pre-validate the params argument before attempting to read it.
 
@@ -69,8 +73,7 @@ def validate_params_source(params):
     )
 
 
-
-def validate_mic_data(df):
+def validate_mic_data(df: DataFrame) -> None:
     """
     Validate MIC and observations columns.
 
@@ -97,7 +100,9 @@ def validate_mic_data(df):
         raise ValueError(f"Invalid MIC format found in rows: {bad_rows.index.tolist()}")
 
 
-def validate_params(dilution_factor, distributions, boundary_support):
+def validate_params(
+    dilution_factor: int, distributions: int, boundary_support: int | None
+) -> None:
     """
     Validate ECOFFitter configuration values.
 
@@ -112,29 +117,30 @@ def validate_params(dilution_factor, distributions, boundary_support):
 
     if not isinstance(dilution_factor, int) or dilution_factor <= 1:
         raise ValueError("dilution_factor must be an integer > 1.")
-    
+
     if not isinstance(distributions, int):
-        raise NotImplementedError("The number of mixture components must be an integer.")
-
+        raise NotImplementedError(
+            "The number of mixture components must be an integer."
+        )
+
     if boundary_support is not None and (
         not isinstance(boundary_support, int) or boundary_support < 0
     ):
         raise ValueError("boundary_support must be a non-negative integer or None.")
 
 
-
 def validate_output_path(path: str) -> bool:
     """
     Checks if the given path is safe and writable, and that the file extension is .txt or .pdf.
 
     Returns True if valid, otherwise raises ValueError.
     """
     # Check extension
-    allowed_exts = ('.txt', '.pdf')
+    allowed_exts = (".txt", ".pdf")
     if not path.lower().endswith(allowed_exts):
         raise ValueError(f"File must end with {allowed_exts}, got '{path}'")
 
-    directory = os.path.dirname(path) or '.'
+    directory = os.path.dirname(path) or "."
 
     if not os.path.exists(directory):
         raise ValueError(f"Directory does not exist: {directory}")
@@ -143,4 +149,3 @@ def validate_output_path(path: str) -> bool:
         raise PermissionError(f"No write permission in directory: {directory}")
 
     return True
-

src/ecoff_fitter/graphs.py

@@ -1,21 +1,24 @@
 import numpy as np
+from typing import Optional
+from numpy.typing import NDArray
+import matplotlib.axes
 import matplotlib.pyplot as plt
 from scipy.stats import norm
 
 
 def plot_mic_distribution(
-    low_log,
-    high_log,
-    weights,
-    dilution_factor,
-    mus,
-    sigmas,
-    pis=None,
-    log2_ecoff=None,
-    global_x_min=None,
-    global_x_max=None,
-    ax=None,
-):
+    low_log: NDArray[np.floating],
+    high_log: NDArray[np.floating],
+    weights: NDArray[np.floating],
+    dilution_factor: float | int,
+    mus: NDArray[np.floating] | list[float],
+    sigmas: NDArray[np.floating] | list[float],
+    pis: Optional[NDArray[np.floating] | list[float]] = None,
+    log2_ecoff: Optional[float] = None,
+    global_x_min: Optional[float] = None,
+    global_x_max: Optional[float] = None,
+    ax: Optional[matplotlib.axes.Axes] = None,
+) -> matplotlib.axes.Axes:
     """
     Plot MIC intervals with a K-component Gaussian mixture fit.
     Supports left- and right-censoring with visual tail extensions.