🎨 functions in __init__ are not documented in api documentation

- put fit_model in it's own module
- import it at the top-level for compatibility

Author: enryH

src/growthcurves/__init__.py

@@ -1,13 +1,12 @@
 # The __init__.py file is loaded when the package is loaded.
 # It is used to indicate that the directory in which it resides is a Python package
-import warnings
-from importlib import metadata
 
-import numpy as np
+from importlib import metadata
 
 __version__ = metadata.version("growthcurves")
 
 from . import inference, models, non_parametric, parametric, plot, preprocessing
+from .fit import fit_model
 from .inference import compare_methods
 from .models import (
     MODEL_REGISTRY,
@@ -33,71 +32,5 @@
     "blank_subtraction",
     "path_correct",
     "compare_methods",
+    "fit_model",
 ]
-
-
-def fit_model(
-    t: np.ndarray,
-    N: np.ndarray,
-    model_name: str,
-    lag_threshold: float = 0.15,
-    exp_threshold: float = 0.15,
-    phase_boundary_method=None,
-    **kwargs,
-) -> tuple[dict, dict]:
-    if model_name in models.MODEL_REGISTRY["non_parametric"]:
-        fit_res = non_parametric.fit_non_parametric(t, N, method=model_name, **kwargs)
-    else:
-        fit_res = parametric.fit_parametric(t, N, method=model_name, **kwargs)
-    # return None if fit fails, along with bad fit stats
-    if fit_res is None:
-        warnings.warn(
-            f"Model fitting failed for model {model_name}. Returning None.",
-            stacklevel=2,
-        )
-        return None, inference.bad_fit_stats()
-
-    stats_res = inference.extract_stats(
-        fit_res,
-        t=t,
-        N=N,
-        lag_threshold=lag_threshold,
-        exp_threshold=exp_threshold,
-        phase_boundary_method=phase_boundary_method,
-        **kwargs,
-    )
-
-    stats_res["model_name"] = model_name
-    return fit_res, stats_res
-
-
-# ! not so good for dynamic inspection tools...
-fit_model.__doc__ = f"""Fit a growth model to the provided t and N.
-
-    Parameters
-    ----------
-    t : np.ndarray
-        Time points corresponding to N (in hours).
-    N : np.ndarray
-        Growth data points corresponding to t.
-    model_name : str
-        One of the models in {', '.join(get_all_models())}.
-    lag_threshold : float, optional
-        Fraction of μ_max to define end of lag phase (threshold method, default: 0.15).
-    exp_threshold : float, optional
-        Fraction of μ_max to define end of exponential phase (threshold method,
-        default: 0.15).
-    phase_boundary_method : str, optional
-        Method to determine phase boundaries ("tangent", "threshold",
-        or None for default for model class).
-    **kwargs
-        Additiona keyword arguments to be passed to fitting and inference functions.
-
-    Returns
-    -------
-    tuple[dict, dict]
-        Return tuple of two dictionaries: (fit_res, stats_res)
-        - fit_res: Dictionary containing fitted model parameters.
-        - stats_res: Dictionary containing goodness-of-fit statistics and growth metrics
-
-    """

src/growthcurves/fit.py

@@ -0,0 +1,77 @@
+"""Entry point of growthcurves package"""
+
+import warnings
+
+import numpy as np
+
+from . import inference, models, non_parametric, parametric
+from .models import get_all_models
+
+__all__ = ["fit_model",]
+
+
+def fit_model(
+    t: np.ndarray,
+    N: np.ndarray,
+    model_name: str,
+    lag_threshold: float = 0.15,
+    exp_threshold: float = 0.15,
+    phase_boundary_method=None,
+    **kwargs,
+) -> tuple[dict, dict]:
+    if model_name in models.MODEL_REGISTRY["non_parametric"]:
+        fit_res = non_parametric.fit_non_parametric(t, N, method=model_name, **kwargs)
+    else:
+        fit_res = parametric.fit_parametric(t, N, method=model_name, **kwargs)
+    # return None if fit fails, along with bad fit stats
+    if fit_res is None:
+        warnings.warn(
+            f"Model fitting failed for model {model_name}. Returning None.",
+            stacklevel=2,
+        )
+        return None, inference.bad_fit_stats()
+
+    stats_res = inference.extract_stats(
+        fit_res,
+        t=t,
+        N=N,
+        lag_threshold=lag_threshold,
+        exp_threshold=exp_threshold,
+        phase_boundary_method=phase_boundary_method,
+        **kwargs,
+    )
+
+    stats_res["model_name"] = model_name
+    return fit_res, stats_res
+
+
+# ! not so good for dynamic inspection tools...
+fit_model.__doc__ = f"""Fit a growth model to the provided t and N.
+
+    Parameters
+    ----------
+    t : np.ndarray
+        Time points corresponding to N (in hours).
+    N : np.ndarray
+        Growth data points corresponding to t.
+    model_name : str
+        One of the models in {', '.join(get_all_models())}.
+    lag_threshold : float, optional
+        Fraction of μ_max to define end of lag phase (threshold method, default: 0.15).
+    exp_threshold : float, optional
+        Fraction of μ_max to define end of exponential phase (threshold method,
+        default: 0.15).
+    phase_boundary_method : str, optional
+        Method to determine phase boundaries ("tangent", "threshold",
+        or None for default for model class).
+    **kwargs
+        Additiona keyword arguments to be passed to fitting and inference functions.
+
+    Returns
+    -------
+    tuple[dict, dict]
+        Return tuple of two dictionaries: (fit_res, stats_res)
+        - fit_res: Dictionary containing fitted model parameters.
+        - stats_res: Dictionary containing goodness-of-fit statistics and growth metrics
+
+    """