fix: fitall button and clicking per file should output identical results

CHANGELOG

@@ -1,4 +1,6 @@
 0.16.4
+ - fix: correctly handle params states when swapping to new model (#51)
+ - fix: ensure identical results when clicking per file and compute button (#47, #51)
  - setup: support latest python versions (#32, #46)
  - setup: support latest matplotlib versions (#32, #46)
  - fix: remember user's choice upon curve open (#44, #45)

README.rst

@@ -36,3 +36,8 @@ If you have Python installed, you may also install via
    :target: https://codecov.io/gh/AFM-analysis/PyJibe
 .. |Docs Status| image:: https://readthedocs.org/projects/pyjibe/badge/?version=latest
    :target: https://readthedocs.org/projects/pyjibe/builds/
+
+Use of AI
+---------
+
+The model Claude Sonnet 4.6 was used to fix bugs such as #47.

pyjibe/fd/main.py

@@ -512,6 +512,8 @@ def on_fit_all(self):
             try:
                 # preprocessing could fail for bad data
                 self.tab_preprocess.apply_preprocessing(fdist)
+                # update model key and ancillary parameters before fitting
+                self.tab_fit.fit_update_parameters(fdist)
                 # external fitting model could fail
                 self.tab_fit.fit_approach_retract(fdist, update_ui=False)
             except BaseException as e:

pyjibe/fd/tab_fit.py

@@ -115,10 +115,14 @@ def anc_update_parameters(self, fdist):
                 if (atab.item(row, 0).checkState() ==
                         QtCore.Qt.CheckState.Checked):
                     # update initial parameters
+                    # (block signals to prevent recursive on_params_init
+                    # while applying ancillary values to itab)
+                    itab.blockSignals(True)
                     for rr in range(itab.rowCount()):
                         if itab.verticalHeaderItem(rr).text() == label:
                             if value_text != "nan":
                                 itab.item(rr, 1).setText(value_text)
+                    itab.blockSignals(False)
                 row += 1
             atab.blockSignals(False)
         else:
@@ -345,14 +349,21 @@ def fit_update_parameters(self, fdist):
         # set the model
         # - resets params_initial if model changed
         # - important for computing ancillary parameters
+        prev_model_key = fdist.fit_properties.get("model_key")
         fdist.fit_properties["model_key"] = model_key
-        if fdist.fit_properties.get("params_initial", False):
-            # (cannot coerce this into one line, because "params_initial"
-            # can be None.)
+        if prev_model_key != model_key:
+            # Clear the ancillary cache so anc_update_parameters gets fresh
+            # ancillaries for the new model (the cache may contain stale
+            # values from the previous model's fit)
+            fdist._anc_cache = None
+        if (fdist.fit_properties.get("params_initial", False)
+                and prev_model_key == model_key):
             # set the parameters of the previous fit
             params = fdist.fit_properties["params_initial"]
         else:
-            # use the initial model parameters
+            # use the initial model parameters (also when model changed, to
+            # ensure itab is set up with the new model's parameter labels so
+            # that anc_update_parameters can correctly apply ancillary values)
             params = self.fit_parameters()
 
         # parameter table

tests/test_fd_fit.py

@@ -262,3 +262,74 @@ def test_set_indentation_depth_manually_infdoublespinbox(qtbot):
         qtbot.keyClicks(war.tab_fit.sp_range_1, text_entered)
         assert war.tab_fit.sp_range_1.value() == resulting_value
     main_window.close()
+
+
+def test_fit_all_matches_single_fit(qtbot):
+    """on_fit_all must produce the same TSV output as fitting one-by-one."""
+    files = make_directory_with_data(2)
+    tsv_path = files[0].parent / "pyjibe_fit_results_leaf.tsv"
+
+    main_window = pyjibe.head.PyJibe()
+    qtbot.addWidget(main_window)
+    main_window.load_data(files=files)
+    war = main_window.subwindows[0].widget()
+    war.cb_autosave.setChecked(True)
+    war._autosave_override = 1  # always overwrite, avoid dialog
+    war.tab_preprocess.set_preprocessing(["compute_tip_position"])
+    war.tab_fit.cb_weight_cp.setCheckState(QtCore.Qt.CheckState.Unchecked)
+
+    # Click through both files via the single-curve path
+    cl1 = war.list_curves.currentItem()
+    cl2 = war.list_curves.itemBelow(cl1)
+    war.list_curves.setCurrentItem(cl1)
+    war.list_curves.setCurrentItem(cl2)
+    assert tsv_path.exists(), "autosave TSV not created after single-fit"
+    single_tsv = tsv_path.read_text()
+
+    # Re-fit everything via fit-all; TSV must be byte-for-byte identical
+    war.on_fit_all()
+    fitall_tsv = tsv_path.read_text()
+
+    assert single_tsv == fitall_tsv
+    main_window.close()
+
+
+def test_fit_all_matches_single_fit_kvm(qtbot):
+    """fit-all must produce the same TSV as single-fit for the KVM model.
+
+    KVM has ancillary parameters (eta, time_ind) that are derived by fitting
+    a preliminary Hertz model to each curve. The KVM model is loaded via
+    PyJibe's extension system at startup.
+    """
+    files = make_directory_with_data(2)
+    tsv_path = files[0].parent / "pyjibe_fit_results_leaf.tsv"
+
+    main_window = pyjibe.head.PyJibe()
+    qtbot.addWidget(main_window)
+    main_window.load_data(files=files)
+    war = main_window.subwindows[0].widget()
+    war.cb_autosave.setChecked(True)
+    war._autosave_override = 1  # always overwrite, avoid dialog
+    war.tab_preprocess.set_preprocessing(["compute_tip_position"])
+    war.tab_fit.cb_weight_cp.setCheckState(QtCore.Qt.CheckState.Unchecked)
+
+    # Switch to the KVM model (loaded via PyJibe's extension system)
+    idx = war.tab_fit.cb_model.findData("hertz_corr_visco_KVM")
+    if idx < 0:
+        pytest.skip("KVM model extension not installed in this environment")
+    war.tab_fit.cb_model.setCurrentIndex(idx)
+
+    # Click through both files via the single-curve path
+    cl1 = war.list_curves.currentItem()
+    cl2 = war.list_curves.itemBelow(cl1)
+    war.list_curves.setCurrentItem(cl1)
+    war.list_curves.setCurrentItem(cl2)
+    assert tsv_path.exists(), "autosave TSV not created after single-fit"
+    single_tsv = tsv_path.read_text()
+
+    # Re-fit everything via fit-all; TSV must be byte-for-byte identical
+    war.on_fit_all()
+    fitall_tsv = tsv_path.read_text()
+
+    assert single_tsv == fitall_tsv
+    main_window.close()