chore: fix imports

Signed-off-by: David Wallace <[email protected]>

src/raman_fitting/deconvolution_models/base_model.py

@@ -1,25 +1,20 @@
 """ The members of the validated collection of BasePeaks are assembled here into fitting Models"""
 import logging
-
+from typing import Optional, Dict
 from warnings import warn
-from typing import List, Optional, Dict
-
-from lmfit.models import Model, GaussianModel
-
-from raman_fitting.deconvolution_models.peak_validation import PeakModelValidator
-from .base_peak import BasePeak, get_default_peaks
 
+from lmfit.models import Model
 from pydantic import (
     BaseModel,
-    PositiveInt,
     Field,
     ConfigDict,
     model_validator,
-    ValidationInfo,
-    field_validator,
-    ValidationError,
 )
 
+from .base_peak import BasePeak, get_default_peaks
+from .lmfit import construct_lmfit_model_from_components
+from ..config.filepath_helper import load_default_peak_toml_files
+
 logger = logging.getLogger(__name__)
 
 SUBSTRATE_PEAK = "Si1_peak"
@@ -31,19 +26,7 @@ class BaseModelWarning(UserWarning):
     pass
 
 
-def construct_lmfit_model_from_base_peaks(base_peaks: List[BasePeak]) -> Model:
-    """
-    Construct the lmfit model from a collection of (known) peaks
-    """
-    if not base_peaks:
-        raise ValueError("No peaks given to construct lmfit model from.")
-    lmfit_composite_model = sum(
-        map(lambda x: x.lmfit_model, base_peaks), base_peaks.pop().lmfit_model
-    )
-    return lmfit_composite_model
-
-
-class BaseModelCollection(BaseModel):
+class BaseModel(BaseModel):
     """
     This Model class combines the collection of valid peaks from BasePeak into a regression model
     of type lmfit.model.CompositeModel
@@ -116,10 +99,31 @@ def check_lmfit_model(self) -> "BaseModelCollection":
         self.lmfit_model = lmfit_model
         return self
 
-    def construct_lmfit_model(self, peaks, default_peaks) -> "Model":
+    def construct_lmfit_model(self, peaks, default_peaks) -> Optional["Model"]:
         peak_names = peaks.split(SEP)
         base_peaks = [default_peaks[i] for i in peak_names if i in default_peaks]
         if not base_peaks:
-            return GaussianModel()
-        lmfit_model = construct_lmfit_model_from_base_peaks(base_peaks)
+            return None
+        base_peaks_lmfit = [i.lmfit_model for i in base_peaks]
+        lmfit_model = construct_lmfit_model_from_components(base_peaks_lmfit)
         return lmfit_model
+
+
+def get_default_models() -> Dict[str, BasePeak]:
+    settings = load_default_peak_toml_files()
+    default_peaks = get_default_peaks()
+    models_settings = {k: val.get("models") for k, val in settings.items()}
+    base_models = {}
+    for model_name, model_peaks in models_settings.items():
+        base_models[model_name] = BaseModel(
+            name=model_name, peaks=model_peaks, default_peaks=default_peaks
+        )
+    return base_models
+
+
+def main():
+    models = get_default_models()
+
+
+if __name__ == "__main__":
+    main()

src/raman_fitting/deconvolution_models/base_peak.py

@@ -1,31 +1,21 @@
-import inspect
-from functools import partialmethod
-
-from warnings import warn
 from enum import StrEnum
-
-from lmfit import Parameter, Parameters
-
-from lmfit.models import GaussianModel, LorentzianModel, Model, VoigtModel
-from .lmfit_models import LMFIT_MODEL_MAPPER, LMFitParameterHints, parmeter_to_dict
-from ..config.filepath_helper import load_default_peak_toml_files
-
-from typing import List, Literal, Optional, Dict, final
+from typing import List, Optional, Dict
 
 from pydantic import (
     BaseModel,
     ConfigDict,
     InstanceOf,
     Field,
-    ValidationError,
-    ValidationInfo,
     field_validator,
     model_validator,
 )
-from pytest import param
+from lmfit import Parameters
+from lmfit.models import Model
 
+from .lmfit import LMFIT_MODEL_MAPPER, LMFitParameterHints, parmeter_to_dict
+from ..config.filepath_helper import load_default_peak_toml_files
 
-param_hint_dict = Dict[str, Dict[str, Optional[float | bool | str]]]
+ParamHintDict = Dict[str, Dict[str, Optional[float | bool | str]]]
 
 
 class BasePeakWarning(UserWarning):  # pragma: no cover
@@ -35,13 +25,6 @@ class BasePeakWarning(UserWarning):  # pragma: no cover
 PEAK_TYPE_OPTIONS = StrEnum("PEAK_TYPE_OPTIONS", ["Lorentzian", "Gaussian", "Voigt"])
 
 
-LMFIT_MODEL_MAPPER = {
-    "Lorentzian": LorentzianModel,
-    "Gaussian": GaussianModel,
-    "Voigt": VoigtModel,
-}
-
-
 def get_lmfit_model_from_peak_type(peak_type: str, prefix: str = "") -> Optional[Model]:
     """returns the lmfit model instance according to the chosen peak type and sets the prefix from peak_name"""
     model = None
@@ -117,9 +100,7 @@ class New_peak(metaclass=BasePeak):
     model_config = ConfigDict(arbitrary_types_allowed=True, from_attributes=True)
 
     peak_name: str
-    param_hints: Optional[
-        Parameters | List[LMFitParameterHints] | param_hint_dict
-    ] = None
+    param_hints: Optional[Parameters | List[LMFitParameterHints] | ParamHintDict] = None
     peak_type: Optional[str] = None
     is_substrate: Optional[bool] = False
     is_for_normalization: Optional[bool] = False
@@ -148,7 +129,7 @@ def check_peak_type(cls, v: Optional[str]) -> Optional[str]:
     @field_validator("param_hints")
     @classmethod
     def check_param_hints(
-        cls, v: Optional[Parameters | List[LMFitParameterHints] | param_hint_dict]
+        cls, v: Optional[Parameters | List[LMFitParameterHints] | ParamHintDict]
     ) -> Optional[Parameters]:
         if v is None:
             return v

src/raman_fitting/deconvolution_models/fit_models.py

@@ -4,8 +4,8 @@
 
 import pandas as pd
 
-from ..processing.spectrum_constructor import SpectrumDataCollection, SpectrumDataLoader
-from .init_models import InitializeModels
+# from ..processing.spectrum_constructor import SpectrumDataCollection, SpectrumDataLoader
+# from .init_models import InitializeModels
 
 logger = logging.getLogger(__name__)
 
@@ -19,11 +19,11 @@ class Fitter:
 
     fit_windows = ["1st_order", "2nd_order"]
 
-    def __init__(self, spectra_arg, RamanModels=InitializeModels(), start_fit=True):
+    def __init__(self, spectra_arg, models=None, start_fit=True):
         self._qcnm = self.__class__.__qualname__
         logger.debug(f"{self._qcnm} is called with spectrum\n\t{spectra_arg}\n")
         self.start_fit = start_fit
-        self.models = RamanModels
+        self.models = models
 
         self.spectra_arg = spectra_arg
         self.spectra = spectra_arg

src/raman_fitting/deconvolution_models/init_models.py

@@ -1,12 +1,7 @@
 from dataclasses import dataclass
 import logging
-from warnings import warn
-
-from lmfit import Model
 
 from raman_fitting.config.filepath_helper import load_default_peak_toml_files
-
-
 from raman_fitting.deconvolution_models.base_model import SEP
 from raman_fitting.deconvolution_models.base_peak import BasePeak
 

src/raman_fitting/deconvolution_models/lmfit_models.py → src/raman_fitting/deconvolution_models/lmfit.py

@@ -1,33 +1,17 @@
 from ast import main
-import inspect
-from functools import partialmethod
 import math
-
-from os import name
-from pyexpat import model
-
-from unittest.mock import DEFAULT
-from warnings import warn
 from enum import StrEnum
+from typing import List, Optional, Dict
 
-from lmfit import Parameter, Parameters
-
+from lmfit import Parameter
 from lmfit.models import GaussianModel, LorentzianModel, Model, VoigtModel
 
-from typing import List, Literal, Optional, Dict, final
-import numpy
-
 from pydantic import (
     BaseModel,
     ConfigDict,
-    InstanceOf,
     Field,
-    ValidationError,
-    ValidationInfo,
-    field_validator,
     model_validator,
 )
-from pytest import param
 
 
 param_hint_dict = Dict[str, Dict[str, Optional[float | bool | str]]]
@@ -42,6 +26,13 @@ class BasePeakWarning(UserWarning):  # pragma: no cover
 LMFIT_PARAM_KWARGS = ("value", "vary", "min", "max", "expr")
 
 
+LMFIT_MODEL_MAPPER = {
+    "Lorentzian": LorentzianModel,
+    "Gaussian": GaussianModel,
+    "Voigt": VoigtModel,
+}
+
+
 class LMFitParameterHints(BaseModel):
     """
     https://github.com/lmfit/lmfit-py/blob/master/lmfit/model.py#L566
@@ -130,15 +121,14 @@ def check_construct_parameter(self) -> "LMFitParameterHints":
         return self
 
 
-DEFAULT_GAMMA_PARAM_HINT = LMFitParameterHints(
-    name="gamma", value=1, min=1e-05, max=70, vary=False
-)
-
-LMFIT_MODEL_MAPPER = {
-    "Lorentzian": LorentzianModel,
-    "Gaussian": GaussianModel,
-    "Voigt": VoigtModel,
-}
+def construct_lmfit_model_from_components(models: List[Model]) -> "Model":
+    """
+    Construct the lmfit model from a collection of (known) peaks
+    """
+    if not models:
+        raise ValueError("No peaks given to construct lmfit model from.")
+    lmfit_composite_model = sum(models, models.pop())
+    return lmfit_composite_model
 
 
 def parmeter_to_dict(parameter: Parameter) -> dict:
@@ -147,6 +137,11 @@ def parmeter_to_dict(parameter: Parameter) -> dict:
     return ret
 
 
+DEFAULT_GAMMA_PARAM_HINT = LMFitParameterHints(
+    name="gamma", value=1, min=1e-05, max=70, vary=False
+)
+
+
 def main():
     breakpoint()