[FIX] Peak fit: support lmfit 1.3.1

conda/meta.yaml

@@ -38,7 +38,7 @@ requirements:
     - extranormal3 >=0.0.3
     - renishawWiRE >=0.1.8
     - pillow
-    - lmfit >=1.0.2
+    - lmfit >=1.3.1
     - bottleneck
     - pebble
     - agilent-format>=0.4.5

orangecontrib/spectroscopy/widgets/owpeakfit.py

@@ -33,7 +33,7 @@
     ThermalDistributionModelEditor, DoniachModelEditor, ConstantModelEditor, \
     LinearModelEditor, QuadraticModelEditor, PolynomialModelEditor, set_default_vary
 from orangecontrib.spectroscopy.widgets.peakfit_compute import n_best_fit_parameters, \
-    best_fit_results, LMFIT_LOADS_KWARGS, pool_initializer, pool_fit, pool_fit2
+    best_fit_results, pool_initializer, pool_fit, pool_fit2
 
 # number of processes used for computation
 N_PROCESSES = None
@@ -264,8 +264,7 @@ def progress_interrupt(_: float):
                         raise
                     concurrent.futures.wait([res], 0.05)
                 fits = res.result()
-                model_result[row.id] = ModelResult(model, parameters).loads(fits,
-                                                                            **LMFIT_LOADS_KWARGS)
+                model_result[row.id] = ModelResult(model, parameters).loads(fits)
 
         progress_interrupt(0)
         return orig_data, data, model_result
@@ -393,7 +392,7 @@ def done():
             progress_interrupt(99)
 
             for fit, bpar, fitted, resid in fitsr:
-                out = ModelResult(model, parameters).loads(fit, **LMFIT_LOADS_KWARGS)
+                out = ModelResult(model, parameters).loads(fit)
                 output.append(bpar)
                 fits.append(fitted)
                 residuals.append(resid)

orangecontrib/spectroscopy/widgets/peakfit_compute.py

@@ -6,15 +6,6 @@
 import numpy as np
 
 
-def constant(x, c=0.0):
-    return c
-
-
-# WORKAROUND  lmfit's inability to load constant models
-# Add this as kwargs to .loads
-LMFIT_LOADS_KWARGS = {"funcdefs": {"constant": constant}}
-
-
 def n_best_fit_parameters(model, params):
     """Number of output parameters for best fit results"""
     number_of_peaks = len(model.components)
@@ -54,7 +45,7 @@ def pool_initializer(model, parameters, x):
     # Therefore we need to use loads() and dumps() to transfer it between processes.
     global lmfit_model
     global lmfit_x
-    lmfit_model = Model(None).loads(model, **LMFIT_LOADS_KWARGS), parameters
+    lmfit_model = Model(None).loads(model), parameters
     lmfit_x = x
 
 
@@ -73,6 +64,6 @@ def pool_fit(v):
 
 
 def pool_fit2(v, model, parameters, x):
-    model = Model(None).loads(model, **LMFIT_LOADS_KWARGS)
+    model = Model(None).loads(model)
     model_result = model.fit(v, params=parameters, x=x)
     return model_result.dumps()

setup.py

@@ -142,7 +142,7 @@ def include_documentation(local_dir, install_dir):
             'extranormal3 >=0.0.3',
             'renishawWiRE>=0.1.8',
             'pillow',
-            'lmfit>=1.0.2',
+            'lmfit>=1.3.1',
             'bottleneck',
             'pebble',
             'agilent-format>=0.4.5'

tox.ini

@@ -30,7 +30,7 @@ deps =
     oldest: scipy~=1.9.0
     oldest: pandas~=1.3.0
     oldest: spectral~=0.22.3
-    oldest: lmfit~=1.0.2
+    oldest: lmfit==1.3.1
     latest: https://github.com/biolab/orange3/archive/refs/heads/master.zip#egg=orange3
     latest: https://github.com/biolab/orange-canvas-core/archive/refs/heads/master.zip#egg=orange-canvas-core
     latest: https://github.com/biolab/orange-widget-base/archive/refs/heads/master.zip#egg=orange-widget-base