Fixed RIDME Issue

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "deeranalysis"
-version = "2026.0.2"
+version = "2026.0.3"
 description = "A graphical fitting and data managment tool for DEER spectroscopy, using DeerLab and DeerNet as fitting engines."
 authors = [
     {name = "Hugo Karas",email = "hkaras@ethz.ch"}

src/deeranalysis/components/dataset_search_model.py

@@ -32,7 +32,8 @@ def create_dataset_AGgrid(id="datasets_grid"):
         ),style={"height": "50vh", "marginBottom": "10px"})
     return grid
 
-def create_dataset_modal(page_id):
+def create_dataset_modal(page_id, select_btn_id=None):
+    select_btn_id = select_btn_id or {'type': 'select-dataset-btn', 'page': page_id}
     return html.Div([
         dmc.Modal(
             title="Search Datasets",
@@ -47,7 +48,7 @@ def create_dataset_modal(page_id):
                 ),
                 create_dataset_AGgrid("dataset_table"),
                 dmc.Group(
-                    [dmc.Button("Select Dataset", id={'type': 'select-dataset-btn', 'page': page_id})],
+                    [dmc.Button("Select Dataset", id=select_btn_id)],
                     justify="flex-end",
                     className="mt-2"
                 ),

src/deeranalysis/components/fit_page_components.py

@@ -117,7 +117,7 @@ def distance_slider(page_id):
     return dmc.Stack([dmc.Text("Distance Axis (nm): ", size="sm", fw=500, mb=4),
         dcc.RangeSlider(
                 id= {"type": "distance-axis", "page": page_id},
-                min=1.5,
+                min=1.25,
                 max=12,
                 step=0.25,
                 value=[1.75, 6],
@@ -358,7 +358,7 @@ def pathway_input(page_id):
     return dmc.Tooltip(dmc.CheckboxGroup(
                 id={'type': 'pathways-options', 'page': page_id},
                 label="Pathways to include:",
-                description="These pathways will be applied to all datasets, if they are fesiable for the corresponding experiment.",
+                description="These pathways will be applied to all datasets, if they are feasible for the corresponding experiment.",
                 children=dmc.Group([
                     dmc.Checkbox(value='1', label='1'),
                     dmc.Checkbox(value='2', label='2'),

src/deeranalysis/components/model_edit_modal.py

@@ -12,8 +12,8 @@
     '3pDEER': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_3pdeer.html',
     '4pDEER': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_4pdeer.html',
     '5pDEER': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_fwd5pdeer.html',
-    'dqc': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_dqc.html',
-    'ridme': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_ridme.html',
+    'DQC': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_dqc.html',
+    'RIDME': 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.ex_ridme.html',
 }
 
 b_model_links = {
@@ -52,8 +52,8 @@ def make_btn(label, href):
 
     base_path = 'https://jeschkelab.github.io/DeerLab/_autosummary/deerlab.{model}.html'
     return [
-        make_btn("Experiment Model", base_path.format(model=exp_type) if (exp_type and exp_type != 'single') else None),
-        make_btn("Background Model", base_path.format(model=bg_model) if bg_model else None),
+        make_btn("Experiment Model", exp_model_links.get(exp_type, None) if (exp_type and exp_type != 'single') else None),
+        make_btn("Background Model", b_model_links.get(bg_model, None)),
         make_btn("Distance Distribution Model", p_model_links.get(p_model)),
     ]
 

src/deeranalysis/pages/comparison.py

@@ -10,7 +10,7 @@
 from deeranalysis.utils.deerlab_options import plotly_comparison, colour_scheme_dark, colour_scheme_light
 
 
-from deerlab import UQResult
+from deerlab import UQResult, noiselevel
 dash.register_page(__name__)
 
 PAGE_ID = 'comparison'
@@ -22,9 +22,10 @@
 
 layout = html.Div([
     dcc.Store(id='comp-n-slots', data=N_SLOTS_DEFAULT),
+    dcc.Store(id='comp-search-target-slot'),
 
     # ── Hidden: modals & drawer ────────────────────────────────────────────
-    create_dataset_modal(PAGE_ID),
+    create_dataset_modal(PAGE_ID, select_btn_id={'type': 'comp-select-dataset-btn', 'page': PAGE_ID}),
     search_fit_modal(),
 
     dmc.Drawer(
@@ -243,24 +244,30 @@ def get_options(dataset_id):
 @callback(
     Output('comp-plot', 'figure'),
     Output('comp-stats-table', 'children'),
+    Input({'type': 'dataset-dropdown', 'page': PAGE_ID, 'index': ALL}, 'value'),
     Input({'type': 'fit-dropdown', 'page': PAGE_ID, 'index': ALL}, 'value'),
     Input('comp-n-slots', 'data'),
     Input('comp-voffset-slider', 'value'),
     Input('comp-ci-select', 'value'),
     Input('comp-show-ci-toggle', 'checked'),
 )
-def compare_fits(fit_ids, n_slots, offset, ci_str, show_ci):
+def compare_fits(dataset_ids, fit_ids, n_slots, offset, ci_str, show_ci):
     ci = int(ci_str) if ci_str else 95
+    dataset_ids = dataset_ids[:n_slots]
     fit_ids = fit_ids[:n_slots]
 
     session = get_session()
     loaded = []
-    for fid in fit_ids:
-        if not fid:
-            continue
-        fit = session.query(Fit).filter_by(id=fid).first()
-        if fit:
-            loaded.append((fit.dataset, fit))
+    for did, fid in zip(dataset_ids, fit_ids):
+        if fid:
+            fit = session.query(Fit).filter_by(id=fid).first()
+            if fit:
+                loaded.append((fit.dataset, fit))
+                continue
+        if did:
+            dataset = session.query(Dataset).filter_by(id=did).first()
+            if dataset:
+                loaded.append((dataset, None))
     session.close()
 
     data_dicts = [_fit_to_dict(ds, fit) for ds, fit in loaded]
@@ -275,6 +282,47 @@ def compare_fits(fit_ids, n_slots, offset, ci_str, show_ci):
     return fig, stats
 
 
+@callback(
+    Output({'type': 'dataset-search-modal', 'page': PAGE_ID}, 'opened', allow_duplicate=True),
+    Output('comp-search-target-slot', 'data'),
+    Input({'type': 'open-dataset-search-btn', 'page': PAGE_ID, 'index': ALL}, 'n_clicks'),
+    prevent_initial_call=True,
+)
+def comp_open_dataset_search(n_clicks_list):
+    if not any(n_clicks_list):
+        return dash.no_update, dash.no_update
+    return True, ctx.triggered_id['index']
+
+
+@callback(
+    Output({'type': 'dataset-search-modal', 'page': PAGE_ID}, 'opened', allow_duplicate=True),
+    Output({'type': 'dataset-dropdown', 'page': PAGE_ID, 'index': ALL}, 'value'),
+    Input({'type': 'comp-select-dataset-btn', 'page': PAGE_ID}, 'n_clicks'),
+    State('dataset_table', 'selectedRows'),
+    State('comp-search-target-slot', 'data'),
+    State({'type': 'dataset-dropdown', 'page': PAGE_ID, 'index': ALL}, 'id'),
+    State({'type': 'dataset-dropdown', 'page': PAGE_ID, 'index': ALL}, 'value'),
+    prevent_initial_call=True,
+)
+def comp_select_dataset(n_clicks, selected_rows, target_slot, ids, current_values):
+    no_change = [dash.no_update] * len(current_values)
+    if not (n_clicks and selected_rows and target_slot):
+        return dash.no_update, no_change
+
+    dataset_title = selected_rows[0].get('Title')
+    session = get_session()
+    dataset = session.query(Dataset).filter_by(name=dataset_title).first()
+    session.close()
+    if dataset is None:
+        return dash.no_update, no_change
+
+    values = list(current_values)
+    for i, slot_id in enumerate(ids):
+        if str(slot_id['index']) == str(target_slot):
+            values[i] = str(dataset.id)
+    return False, values
+
+
 # ── Helpers ──────────────────────────────────────────────────────────────────
 
 def _convert_lists_in_dicts_to_arrays(d):
@@ -291,6 +339,21 @@ def _fit_to_dict(dataset, fit):
     out['t'] = np.array(dataset.t, dtype=float)
     out['V'] = np.array(dataset.V, dtype=float)
     out['V'] /= out['V'].max()
+
+    if fit is None:
+        out['model_t'] = out['t']
+        out['dist_stats'] = {}
+        try:
+            out['gof'] = {'SNR': float(1.0 / noiselevel(out['V']))}
+        except Exception:
+            out['gof'] = {}
+        out['model'] = None
+        out['r'] = None
+        out['P'] = None
+        out['PUncert'] = None
+        out['background'] = None
+        return out
+
     out['model_t'] = np.array(fit.t, dtype=float)
     out['dist_stats'] = fit.dist_stats or {}
     out['gof'] = fit.gof or {}
@@ -333,7 +396,8 @@ def _fit_to_dict(dataset, fit):
     'SNR': 'SNR',
     'MNR': 'MNR',
     'rmsd': 'RMSD',
-    'R2': 'R²',}
+    'R2': 'R²',
+    'lam': 'Mod. Depth.',}
 
 def _compute_stat(metric, dd):
     try:

src/deeranalysis/pages/global.py

@@ -67,7 +67,7 @@
             dmc.CheckboxGroup(
                 id={'type': 'pathways-options', 'page': page_id},
                 label="Pathways to include:",
-                description="These pathways will be applied to all datasets, if they are fesiable for the corresponding experiment.",
+                description="These pathways will be applied to all datasets, if they are feasible for the corresponding experiment.",
                 children=dmc.Group([
                     dmc.Checkbox(value='1', label='1'),
                     dmc.Checkbox(value='2', label='2'),

src/deeranalysis/pages/parametric.py

@@ -66,7 +66,7 @@
             dmc.CheckboxGroup(
                 id={'type': 'pathways-options', 'page': page_id},
                 label="Pathways to include:",
-                description="These pathways will be applied to all datasets, if they are fesiable for the corresponding experiment.",
+                description="These pathways will be applied to all datasets, if they are feasible for the corresponding experiment.",
                 children=dmc.Group([
                     dmc.Checkbox(value='1', label='1'),
                     dmc.Checkbox(value='2', label='2'),

src/deeranalysis/pages/population.py

@@ -77,7 +77,7 @@
             dmc.CheckboxGroup(
                 id={'type': 'pathways-options', 'page': page_id},
                 label="Pathways to include:",
-                description="These pathways will be applied to all datasets, if they are fesiable for the corresponding experiment.",
+                description="These pathways will be applied to all datasets, if they are feasible for the corresponding experiment.",
                 children=dmc.Group([
                     dmc.Checkbox(value='1', label='1'),
                     dmc.Checkbox(value='2', label='2'),

src/deeranalysis/utils/deerlab_normal.py

@@ -240,15 +240,15 @@ def deerlab_fitting(dataset, compactness=True, model=None, exp_type='5pDEER', ve
     elif exp_type == "3pDEER":
         experimentInfo = dl.ex_3pdeer(tau=tau1,pathways=pathways,pulselength=pulselength)
     elif exp_type == "RIDME":
-        experimentInfo = dl.ex_ridme(tau=tau1,tau2=tau2,pathways=pathways,pulselength=pulselength)
+        experimentInfo = dl.ex_ridme(tau1=tau1,tau2=tau2,pathways=pathways,pulselength=pulselength)
     elif exp_type == "SIFTER":
         experimentInfo = dl.ex_sifter(tau1=tau1,tau2=tau2,pathways=pathways,pulselength=pulselength)
     elif exp_type == "DQC":
         experimentInfo = dl.ex_dqc(tau1=tau1,tau2=tau2,tau3=tau3,pathways=pathways,pulselength=pulselength)
     elif exp_type == "single":
         experimentInfo = None
     else:
-        raise ValueError(f"Experiment type {exp_type} not recognized. Please specify a valid experiment type (e.g., '4pDEER', '5pDEER', '3pDEER').")
+        raise ValueError(f"Experiment type {exp_type} not recognized. Please specify a valid experiment type (e.g., '4pDEER', '5pDEER', '3pDEER', 'RIDME', 'SIFTER', 'DQC', 'single').")
 
     if 'r' in kwargs:
         r = kwargs.pop('r')

src/deeranalysis/utils/deerlab_options.py

@@ -14,7 +14,7 @@
 ]
 
 experiment_type_options = [
-     {"label": "Single Pathway", "value": "single", "max_pathways": 1, "delays": []},
+     {"label": "Single Pathway", "value": "single", "max_pathways": 1, "delays": [], 'link': None},
     {"label": "4-pulse DEER", "value": "4pDEER", "max_pathways": 4, "delays": ['tau1', 'tau2']},
     {"label": "3-pulse DEER", "value": "3pDEER", "max_pathways": 3, "delays": ['tau1']},
     {"label": "5-pulse DEER", "value": "5pDEER", "max_pathways": 5, "delays": ['tau1', 'tau2', 'tau3']},
@@ -593,7 +593,7 @@ def build_model_data(dataset, bg_model_name, pathways, r_range,
         Vmodel = bg_model
         exp_type='background'
     else:
-        if seq_name == '5pDEER' or ('tau3' in attrs and seq_name != '4pDEER'):
+        if seq_name == '5pDEER':
             exp_type = '5pDEER'
             tau1 = attrs['tau1'] / 1e3
             tau2 = attrs['tau2'] / 1e3
@@ -615,21 +615,21 @@ def build_model_data(dataset, bg_model_name, pathways, r_range,
         elif seq_name == 'single':
             exp_type = 'single'
             exp_info = None
-        elif seq_name == 'ridme':
-            exp_type = 'ridme'
+        elif seq_name == 'RIDME':
+            exp_type = 'RIDME'
             tau1 = attrs['tau1'] / 1e3
             tau2 = attrs['tau2'] / 1e3 
             pathways = [p for p in pathways if p <= 2]
-            exp_info = dl.ex_ridme(tau=tau1, tau2=tau2, pathways=pathways)
-        elif seq_name == 'dqc':
-            exp_type = 'dqc'
+            exp_info = dl.ex_ridme(tau1=tau1, tau2=tau2, pathways=pathways)
+        elif seq_name == 'DQC':
+            exp_type = 'DQC'
             tau1 = attrs['tau1'] / 1e3
             tau2 = attrs['tau2'] / 1e3 
             tau3 = attrs['tau3'] / 1e3 
             pathways = [p for p in pathways if p <= 2]
-            exp_info = dl.ex_dqc(tau=tau1, tau2=tau2, tau3=tau3, pathways=pathways)
-        elif seq_name == 'sifter':
-            exp_type = 'sifter'
+            exp_info = dl.ex_dqc(tau1=tau1, tau2=tau2, tau3=tau3, pathways=pathways)
+        elif seq_name == 'SIFTER':
+            exp_type = 'SIFTER'
             tau1 = attrs['tau1'] / 1e3
             tau2 = attrs['tau2'] / 1e3 
             pathways = [p for p in pathways if p <= 2]