Voigt model now functional, height and fwhm added

src/ramanchada2/fitting_functions/models.py

@@ -7,7 +7,6 @@
 from ramanchada2.fitting_functions.voigtareaparametrizationnu import (
     VoigtAreaParametrizationNu,
 )
-import numpy as np
 
 
 class PearsonIVParametrizationHPWModel(Model):
@@ -113,14 +112,55 @@ def __init__(self, independent_vars=["x"], prefix="", nan_policy="raise", **kwar
         self._set_paramhints_prefix()
 
     def _set_paramhints_prefix(self):
-        self.set_param_hint("sigma", min=np.tiny)
-        self.set_param_hint("nu", min=0, max=1)
-
-        fexpr = "2.0*{pre:s}sigma"
-        self.set_param_hint("fwhm", expr=fexpr.format(pre=self.prefix))
+        self.set_param_hint("sigma", min=1E-100)
+        self.set_param_hint("nu", min=0.0, max=1.0)
 
     def guess(self, data, x, negative=False, **kwargs):
         """Estimate initial model parameter values from data."""
         pars = guess_from_peak(self, data, x, negative)
         pars[f"{self.prefix}nu"].set(value=1.0)
         return update_param_vals(pars, self.prefix, **kwargs)
+
+    def fit(
+        self,
+        data,
+        params=None,
+        weights=None,
+        method="leastsq",
+        iter_cb=None,
+        scale_covar=True,
+        verbose=False,
+        fit_kws=None,
+        nan_policy=None,
+        calc_covar=True,
+        max_nfev=None,
+        **kwargs,
+    ):
+        """overwrite fit in order to amend area and fwhm to the parameters"""
+        result = super().fit(
+            data,
+            params=params,
+            weights=weights,
+            method=method,
+            iter_cb=iter_cb,
+            scale_covar=scale_covar,
+            verbose=verbose,
+            fit_kws=fit_kws,
+            nan_policy=nan_policy,
+            calc_covar=calc_covar,
+            max_nfev=max_nfev,
+            **kwargs,
+        )
+        pahf = VoigtAreaParametrizationNu.GetPositionAreaHeightFWHMFromPeakParameters(
+            result.params[f"{self.prefix}amplitude"],
+            result.params[f"{self.prefix}center"],
+            result.params[f"{self.prefix}sigma"],
+            result.params[f"{self.prefix}nu"],
+            result.covar
+        )
+        p1 = Parameter(f"{self.prefix}height", value=pahf.Height)
+        p1.stderr = pahf.HeightStdDev
+        p2 = Parameter(f"{self.prefix}fwhm", value=pahf.FWHM)
+        p2.stderr = pahf.FWHMStdDev
+        result.params.add_many(p1, p2)
+        return result

src/ramanchada2/fitting_functions/voigtareaparametrizationnu.py

@@ -388,14 +388,32 @@ def GetPositionAreaHeightFWHMFromSinglePeakParameters(
         pos = parameters[f"pos{indexOfPeak}"]
         w = parameters[f"w{indexOfPeak}"]
         nu = parameters[f"nu{indexOfPeak}"]
-        sigma = w * np.sqrt(nu) * OneBySqrtLog4
-        gamma = w * (1 - nu)
 
         if cv is not None:
             cv = cv[
                 indexOfPeak * 4: (indexOfPeak + 1) * 4,
                 indexOfPeak * 4: (indexOfPeak + 1) * 4,
             ]
+        return VoigtAreaParametrizationNu.GetPositionAreaHeightFWHMFromPeakParameters(area, pos, w, nu, cv)
+
+    def GetPositionAreaHeightFWHMFromPeakParameters(
+        area, pos, w, nu, cv=None
+    ):
+        """
+        Get position, area, height, and FWHM of one peak from the peak parameters.
+        If the covariance matrix is given, the corresponding errors are calculated.
+
+        Parameters
+        ----------
+        parameters: Parameters
+            Existing instance of Parameters, which contains the result of the fit.
+        indexOfPeak: int
+            The index of the peak into consideration.
+        cv: np.array
+            Covariance matrix of the fit.
+        """
+        sigma = w * np.sqrt(nu) * OneBySqrtLog4
+        gamma = w * (1 - nu)
 
         if cv is not None:
             areaStdDev = SafeSqrt(cv[0, 0])

tests/fitting_functions/test_pearsonivamplitudeparametrizationhpw.py

@@ -475,3 +475,5 @@ def test_fit_with_model_oneterm():
     np.testing.assert_almost_equal(result.params["sigma"], w, 7)
     np.testing.assert_almost_equal(result.params["expon"], m, 7)
     np.testing.assert_almost_equal(result.params["skew"], v, 7)
+    np.testing.assert_almost_equal(result.params["amplitude"], 92.774482626349894596, 7)
+    np.testing.assert_almost_equal(result.params["fwhm"], 10.02952431724565395, 7)

tests/fitting_functions/test_voigtareaparametrizationnu.py

@@ -1,4 +1,5 @@
 from ramanchada2.fitting_functions import voigtareaparametrizationnu
+from ramanchada2.fitting_functions.models import VoigtAreaParametrizationNuModel
 from ramanchada2.fitting_functions.voigtareaparametrizationnu import (
     VoigtAreaParametrizationNu,
 )
@@ -794,3 +795,23 @@ def test_parameter_boundaries():
     c.add(d)
     assert c["foo"] == 34
     assert c["foo"].max == 77
+
+
+def test_fit_with_model_oneterm():
+    x = np.linspace(0, 99, 100)
+    a = 7
+    pos = 50
+    w = 5
+    nu = 6 / 11.0
+    sigma = w * np.sqrt(nu) * voigtareaparametrizationnu.OneBySqrtLog4
+    gamma = w * (1 - nu)
+    data = a * voigt_profile(x - pos, sigma, gamma)
+    model = VoigtAreaParametrizationNuModel(x=x)
+    params = model.guess(data, x)
+    result = model.fit(data, x=x, params=params)
+    np.testing.assert_almost_equal(result.params["amplitude"], a, 3)
+    np.testing.assert_almost_equal(result.params["center"], pos, 3)
+    np.testing.assert_almost_equal(result.params["sigma"], w, 3)
+    np.testing.assert_almost_equal(result.params["nu"], nu, 3)
+    np.testing.assert_almost_equal(result.params["height"], 0.542599801404569082663570, 3)
+    np.testing.assert_almost_equal(result.params["fwhm"], 10.111072472153491175127, 3)