Merge pull request #449 from reneeotten/small_fixes_after_merge

Small fixes after merge

examples/doc_model_loadmodel.py

@@ -1,9 +1,12 @@
 #!/usr/bin/env python
-#<examples/doc_model_loadmodel.py>
-import numpy as np
+
+# <examples/doc_model_loadmodel.py>
 import matplotlib.pyplot as plt
+import numpy as np
+
 from lmfit.model import load_model
 
+
 def mysine(x, amp, freq, shift):
     return amp * np.sin(x*freq + shift)
 
@@ -24,5 +27,4 @@ def mysine(x, amp, freq, shift):
 plt.plot(x, y, 'bo')
 plt.plot(x, result.best_fit, 'r-')
 plt.show()
-
-#<end examples/doc_model_loadmodel.py>
+# <end examples/doc_model_loadmodel.py>

examples/doc_model_loadmodelresult.py

@@ -1,7 +1,9 @@
 #!/usr/bin/env python
-#<examples/doc_model_loadmodelresult.py>
-import numpy as np
+
+# <examples/doc_model_loadmodelresult.py>
 import matplotlib.pyplot as plt
+import numpy as np
+
 from lmfit.model import load_modelresult
 
 data = np.loadtxt('model1d_gauss.dat')
@@ -14,5 +16,4 @@
 plt.plot(x, y, 'bo')
 plt.plot(x, result.best_fit, 'r-')
 plt.show()
-
-#<end examples/doc_model_loadmodelresult.py>
+# <end examples/doc_model_loadmodelresult.py>

examples/doc_model_loadmodelresult2.py

@@ -1,7 +1,9 @@
 #!/usr/bin/env python
-#<examples/doc_model_loadmodelresult2.py>
-import numpy as np
+
+# <examples/doc_model_loadmodelresult2.py>
 import matplotlib.pyplot as plt
+import numpy as np
+
 from lmfit.model import load_modelresult
 
 dat = np.loadtxt('NIST_Gauss2.dat')
@@ -14,4 +16,4 @@
 plt.plot(x, y, 'bo')
 plt.plot(x, result.best_fit, 'r-')
 plt.show()
-#<end examples/doc_model_loadmodelresult2.py>
+# <end examples/doc_model_loadmodelresult2.py>

examples/doc_model_savemodel.py

@@ -1,8 +1,11 @@
 #!/usr/bin/env python
-#<examples/doc_mode_savemodel.py>
+
+# <examples/doc_mode_savemodel.py>
 import numpy as np
+
 from lmfit.model import Model, save_model
 
+
 def mysine(x, amp, freq, shift):
     return amp * np.sin(x*freq + shift)
 
@@ -11,4 +14,4 @@ def mysine(x, amp, freq, shift):
 pars = sinemodel.make_params(amp=1, freq=0.25, shift=0)
 
 save_model(sinemodel, 'sinemodel.sav')
-#<end examples/doc_model_savemodel.py>
+# <end examples/doc_model_savemodel.py>

examples/doc_model_savemodelresult.py

@@ -1,9 +1,11 @@
 #!/usr/bin/env python
-#<examples/doc_model_savemodelresult.py>
-import numpy as np
+
+# <examples/doc_model_savemodelresult.py>
 import matplotlib.pyplot as plt
-from lmfit.models import GaussianModel
+import numpy as np
+
 from lmfit.model import save_modelresult
+from lmfit.models import GaussianModel
 
 data = np.loadtxt('model1d_gauss.dat')
 x = data[:, 0]
@@ -20,4 +22,4 @@
 plt.plot(x, result.init_fit, 'k--')
 plt.plot(x, result.best_fit, 'r-')
 plt.show()
-#<end examples/doc_model_savemodelresult.py>
+# <end examples/doc_model_savemodelresult.py>

examples/doc_model_savemodelresult2.py

@@ -1,6 +1,8 @@
 #!/usr/bin/env python
-#<examples/doc_model_savemodelresult2.py>
+
+# <examples/doc_model_savemodelresult2.py>
 import numpy as np
+
 from lmfit.model import save_modelresult
 from lmfit.models import ExponentialModel, GaussianModel
 
@@ -13,15 +15,12 @@
 
 gauss1 = GaussianModel(prefix='g1_')
 pars.update(gauss1.make_params())
-
 pars['g1_center'].set(value=105, min=75, max=125)
 pars['g1_sigma'].set(value=15, min=3)
 pars['g1_amplitude'].set(value=2000, min=10)
 
 gauss2 = GaussianModel(prefix='g2_')
-
 pars.update(gauss2.make_params())
-
 pars['g2_center'].set(value=155, min=125, max=175)
 pars['g2_sigma'].set(value=15, min=3)
 pars['g2_amplitude'].set(value=2000, min=10)
@@ -35,5 +34,4 @@
 save_modelresult(result, 'nistgauss_modelresult.sav')
 
 print(result.fit_report())
-
-#<end examples/doc_model_savemodelresult2.py>
+# <end examples/doc_model_savemodelresult2.py>

lmfit/model.py

@@ -1649,11 +1649,6 @@ def plot_fit(self, ax=None, datafmt='o', fitfmt='-', initfmt='--',
                                 **{independent_var: x_array_dense}),
                 initfmt, label='init', **init_kws)
 
-        ax.plot(
-            x_array_dense,
-            self.model.eval(self.params, **{independent_var: x_array_dense}),
-            fitfmt, label='best-fit', **fit_kws)
-
         if yerr is None and self.weights is not None:
             yerr = 1.0/self.weights
         if yerr is not None:
@@ -1662,6 +1657,11 @@ def plot_fit(self, ax=None, datafmt='o', fitfmt='-', initfmt='--',
         else:
             ax.plot(x_array, self.data, datafmt, label='data', **data_kws)
 
+        ax.plot(
+            x_array_dense,
+            self.model.eval(self.params, **{independent_var: x_array_dense}),
+            fitfmt, label='best-fit', **fit_kws)
+
         ax.set_title(self.model.name)
         if xlabel is None:
             ax.set_xlabel(independent_var)

lmfit/models.py

@@ -308,7 +308,7 @@ class GaussianModel(Model):
 
     """
 
-    fwhm_factor = 2.354820
+    fwhm_factor = 2*np.sqrt(2*np.log(2))
     height_factor = 1./np.sqrt(2*np.pi)
 
     def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',
@@ -526,8 +526,6 @@ class Pearson7Model(Model):
 
     """
 
-    fwhm_factor = 1.0
-
     def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',
                  **kwargs):
         kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
@@ -808,7 +806,7 @@ class SkewedGaussianModel(Model):
 
     """
 
-    fwhm_factor = 2.354820
+    fwhm_factor = 2*np.sqrt(2*np.log(2))
     height_factor = 1./np.sqrt(2*np.pi)
 
     def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',