fix: fix progress bar for parallel objects that return generators

Tqdm imports are also simplified. We no longer handle `is_notebook` ourselves, but just import from `tqdm.auto`

src/erlab/analysis/gold.py

@@ -21,6 +21,7 @@
 import numpy as np
 import numpy.typing as npt
 import scipy.interpolate
+import tqdm.auto
 import uncertainties
 import xarray as xr
 
@@ -243,6 +244,11 @@ def edge(
             parallel_kw.setdefault("n_jobs", -1)
         else:
             parallel_kw.setdefault("n_jobs", 1)
+
+        parallel_kw.setdefault("max_nbytes", None)
+        parallel_kw.setdefault("return_as", "generator")
+        parallel_kw.setdefault("pre_dispatch", "n_jobs")
+
         parallel_obj = joblib.Parallel(**parallel_kw)
 
     if normalize:
@@ -260,23 +266,35 @@ def _fit(data, w):
             method=method,
             scale_covar=scale_covar,
             weights=w,
+            **kwargs,
         )
         return res
 
-    if progress:
-        with joblib_progress(desc="Fitting", total=n_fits) as _:
+    tqdm_kw = {"desc": "Fitting", "total": n_fits, "disable": not progress}
+
+    if parallel_obj.return_generator:
+        fitresults = tqdm.auto.tqdm(
+            parallel_obj(
+                joblib.delayed(_fit)(gold_sel.isel(alpha=i), weights[i])
+                for i in range(n_fits)
+            ),
+            **tqdm_kw,
+        )
+    else:
+        if progress:
+            with joblib_progress(**tqdm_kw) as _:
+                fitresults = parallel_obj(
+                    joblib.delayed(_fit)(gold_sel.isel(alpha=i), weights[i])
+                    for i in range(n_fits)
+                )
+        else:
             fitresults = parallel_obj(
                 joblib.delayed(_fit)(gold_sel.isel(alpha=i), weights[i])
                 for i in range(n_fits)
             )
-    else:
-        fitresults = parallel_obj(
-            joblib.delayed(_fit)(gold_sel.isel(alpha=i), weights[i])
-            for i in range(n_fits)
-        )
 
     if return_full:
-        return fitresults
+        return list(fitresults)
 
     xval = []
     res_vals = []

src/erlab/interactive/fermiedge.py

@@ -62,7 +62,10 @@ def set_params(self, data, x0, y0, x1, y1, params):
         self.y_range: tuple[float, float] = (y0, y1)
         self.params = params
         self.parallel_obj = joblib.Parallel(
-            n_jobs=self.params["# CPU"], max_nbytes=None
+            n_jobs=self.params["# CPU"],
+            max_nbytes=None,
+            return_as="list",
+            pre_dispatch="n_jobs",
         )
 
     @QtCore.Slot()

src/erlab/parallel.py

@@ -12,40 +12,19 @@
 
 import joblib
 import joblib._parallel_backends
-import tqdm
-import tqdm.notebook
+import tqdm.auto
 from qtpy import QtCore
 
 
-def is_notebook():
-    # http://stackoverflow.com/questions/34091701/determine-if-were-in-an-ipython-notebook-session
-    if "IPython" not in sys.modules:  # IPython hasn't been imported
-        return False
-    from IPython import get_ipython
-
-    # check for `kernel` attribute on the IPython instance
-    return getattr(get_ipython(), "kernel", None) is not None
-
-
 @contextlib.contextmanager
-def joblib_progress(file=None, notebook=None, dynamic_ncols=True, **kwargs):
+def joblib_progress(file=None, **kwargs):
     """Context manager to patch joblib to report into tqdm progress bar given as
     argument"""
 
     if file is None:
         file = sys.stdout
 
-    if notebook is None:
-        notebook = is_notebook()
-
-    if notebook:
-        tqdm_object = tqdm.notebook.tqdm(
-            iterable=None, dynamic_ncols=dynamic_ncols, file=file, **kwargs
-        )
-    else:
-        tqdm_object = tqdm.tqdm(
-            iterable=None, dynamic_ncols=dynamic_ncols, file=file, **kwargs
-        )
+    tqdm_object = tqdm.auto.tqdm(iterable=None, file=file, **kwargs)
 
     def tqdm_print_progress(self):
         if self.n_completed_tasks > tqdm_object.n: