Merge pull request #634 from jungmannlab/development

v0.9.10

Author: rafalkowalewski1

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.9
+current_version = 0.9.10
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

changelog.rst

@@ -1,7 +1,25 @@
 Changelog
 =========
 
-Last change: 10-MAR-2026 CEST
+Last change: 24-MAR-2026 CEST
+
+0.9.10
+------
+Important updates:
+^^^^^^^^^^^^^^^^^^
+- Added support for loading BigTIFF in Picasso Localize (#631), big thanks to @boydcpeters
+
+Small improvements:
++++++++++++++++++++
+- ``picasso.aim.aim`` accepts progress as a ``lib.ProgressDialog``, ``"console"`` or ``None``
+- SPINNA GUI: Small adjustment to GUI when loading search space
+- Adjusted label in subcluster check plot
+- Subcluster check plot outputs p value and test statistic
+
+Bug fixes:
+++++++++++
+- Fixed AIM in Localize GUI
+- Fixed saving search space in SPINNA for multiple-target structures
 
 0.9.8-9
 -------

distribution/picasso.iss

@@ -2,10 +2,10 @@
 AppName=Picasso
 AppPublisher=Jungmann Lab, Max Planck Institute of Biochemistry
 
-AppVersion=0.9.9
+AppVersion=0.9.10
 DefaultDirName={commonpf}\Picasso
 DefaultGroupName=Picasso
-OutputBaseFilename="Picasso-Windows-64bit-0.9.9"
+OutputBaseFilename="Picasso-Windows-64bit-0.9.10"
 ArchitecturesAllowed=x64
 ArchitecturesInstallIn64BitMode=x64
 

docs/conf.py

@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ""
 # The full version, including alpha/beta/rc tags
-release = "0.9.9"
+release = "0.9.10"
 
 # -- General configuration ---------------------------------------------------
 

docs/localize.rst

@@ -11,7 +11,15 @@ Localize allows performing super-resolution reconstruction of image stacks. For
 - LQ, Gaussian (least squares)
 - Average of ROI (finds summed intensity of spots)
 
-**Please note:** Picasso Localize supports five file formats: ``.ome.tif``, ``NDTiffStack`` with extension ``.tif``, ``.raw``, ``.ims`` and ``.nd2``. If your file has the extension ``.tiff`` or ``.ome.tiff``, it cannot be read. Usually it is enough to change the extension to ``.ome.tif``, i.e., remove the last letter.
+**Please note:** Picasso Localize supports file formats:
+
+- ``.ome.tif``, including BigTiff (as of Picasso 0.9.10),
+- ``NDTiffStack`` with extension ``.tif``,
+- ``.raw``,
+- ``.ims``,
+- ``.nd2``.
+
+There is limited support for ``.tiff`` files.
 
 Identification and fitting of single-molecule spots
 ---------------------------------------------------

docs/render.rst

@@ -97,7 +97,7 @@ To account for fluorophore non-specific sticking, frame analysis is normally rec
 
 The final postprocessing step is log-likelihood filtering (using the column ``p_val``). The recommended threshold is ``> 0.0015``, however, it might need to be adjusted for your data, especially in 3D this can be too conservative.
 
-As a final check for overfitting (i.e., too many assigned molecules), G5M automatically saves a bar plot of the number of binding events per molecule (``n_events`` column) for sparse (with neighbors within 25 nm) and clustered (without neighbors within 80 nm) molecules. While this is only a qualitative verification, it is a simple method to spot potential overfitting issues. If the clustered molecules show fewer binding events that the sparse molecules, overfitting likely occurred. See Fig. S15 of the publication for an example of well-behaved data. As of v0.9.8, Picasso saves the plot showing relative σ values (i.e., the fitted Gaussian σ divided by the average loc. precision around the molecule). This can be used to estimate if the loc. precision values are accurate (if not, many molecules will have relative σ values close to the user-selected min./max. σ).
+As a final check for overfitting (i.e., too many assigned molecules), G5M automatically saves a bar plot of the number of binding events per molecule (``n_events`` column) for clustered (with neighbors within 25 nm) and sparse (without neighbors within 80 nm) molecules. If the clustered molecules show fewer binding events that the sparse molecules, overfitting likely occurred. See Fig. S15 of the publication for an example of well-behaved data. As of v0.9.8, Picasso saves the plot showing relative σ values (i.e., the fitted Gaussian σ divided by the average loc. precision around the molecule). This can be used to estimate if the loc. precision values are accurate (if not, many molecules will have relative σ values close to the user-selected min./max. σ). As of version 0.9.10, Picasso runs KS 2 sample test to compare the two distributions. The output test statistic and theoretical p value correspond to the KS test, while the permutation p value is calculated by randomly permuting the labels of clustered and sparse molecules 1,000 times and calculating the fraction of permutations that result in a KS test statistic as extreme as the one observed with the original labels.
 
 If the outcome of G5M seems unsatisfactory, please check the following:
 

picasso/__main__.py

@@ -514,6 +514,7 @@ def _undrift_aim(
             segmentation,
             intersectdist,
             roiradius,
+            progress="console",
         )
         base, ext = os.path.splitext(path)
         io.save_locs(base + "_aim.hdf5", locs, new_info)

picasso/aim.py

@@ -383,7 +383,9 @@ def intersection_max(
     roi_r: float,
     width: int,
     aim_round: int = 1,
-    progress: Callable[[int], None] | Literal["console"] | None = None,
+    progress: (
+        lib.ProgressDialog | lib.TqdmProgress | lib.MockProgress | None
+    ) = None,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """Maximize intersection (undrift) for 2D localizations.
 
@@ -410,9 +412,10 @@ def intersection_max(
         as reference, the second round uses the entire dataset as
         reference. The impact is that in the second round, the first
         interval is also undrifted.
-    progress : lib.ProgressDialog or "console" or None, optional
-        Progress dialog. If "console", progress is displayed with tqdm.
-        If None, progress is not displayed. Default is None.
+    progress : lib.ProgressDialog | lib.TqdmProgress | lib.MockProgress \
+        | None, optional
+        Progress dialog. If TqdmProgress, progress is displayed with tqdm.
+        If None or MockProgress, progress is not displayed. Default is None.
 
     Returns
     -------
@@ -458,14 +461,7 @@ def intersection_max(
 
     # initialize progress such that if GUI is used, tqdm is omitted
     start_idx = 1 if aim_round == 1 else 0
-    if progress != "console":
-        iterator = range(start_idx, n_segments)
-    else:
-        iterator = tqdm(
-            range(start_idx, n_segments),
-            desc=f"Undrifting ({aim_round}/2)",
-            unit="segment",
-        )
+    iterator = progress.get_iterator(start_idx, n_segments)
 
     # run across each segment
     for s in iterator:
@@ -509,10 +505,7 @@ def intersection_max(
         drift_y[s] = -rel_drift_y
 
         # update progress
-        if progress != "console":
-            progress.set_value(s)
-        else:
-            iterator.update(s - iterator.n)
+        progress.set_value(s)
 
     # interpolate the drifts (cubic spline) for all frames
     t = (seg_bounds[1:] + seg_bounds[:-1]) / 2
@@ -544,7 +537,9 @@ def intersection_max_z(
     height: int,
     pixelsize: float,
     aim_round: int = 1,
-    progress: Callable[[int], None] | None = None,
+    progress: (
+        lib.ProgressDialog | lib.TqdmProgress | lib.MockProgress | None
+    ) = None,
 ) -> tuple[np.ndarray, np.ndarray]:
     """Maximize intersection (undrift) for 3D localizations. Assumes
     that x and y coordinates were already undrifted. x and y are in
@@ -583,14 +578,7 @@ def intersection_max_z(
 
     # initialize progress such that if GUI is used, tqdm is omitted
     start_idx = 1 if aim_round == 1 else 0
-    if progress is not None:
-        iterator = range(start_idx, n_segments)
-    else:
-        iterator = tqdm(
-            range(start_idx, n_segments),
-            desc=f"Undrifting z ({aim_round}/2)",
-            unit="segment",
-        )
+    iterator = progress.get_iterator(start_idx, n_segments)
 
     # run across each segment
     for s in iterator:
@@ -632,10 +620,7 @@ def intersection_max_z(
         drift_z[s] = -rel_drift_z
 
         # update progress
-        if progress is not None:
-            progress.set_value(s)
-        else:
-            iterator.update(s - iterator.n)
+        progress.set_value(s)
 
     # interpolate the drifts (cubic spline) for all frames
     t = (seg_bounds[1:] + seg_bounds[:-1]) / 2
@@ -659,7 +644,7 @@ def aim(
     segmentation: int = 100,
     intersect_d: float = 20 / 130,
     roi_r: float = 60 / 130,
-    progress: Callable[[int], None] | None = None,
+    progress: lib.ProgressDialog | Literal["console"] | None = None,
 ) -> tuple[pd.DataFrame, list[dict], pd.DataFrame]:
     """Apply AIM undrifting to the localizations.
 
@@ -676,9 +661,9 @@ def aim(
     roi_r : float
         Radius of the local search region in camera pixels. Should be
         larger than the  maximum expected drift within segmentation.
-    progress : picasso.lib.ProgressDialog, optional
-        Progress dialog. If None, progress is displayed with into the
-        console. Default is None.
+    progress : picasso.lib.ProgressDialog or "console" or None, optional
+        Progress dialog. If "console", progress is displayed in the
+        console. If None, no progress is displayed. Default is None.
 
     Returns
     -------
@@ -689,6 +674,16 @@ def aim(
     drift : pd.DataFrame
         Drift in x and y directions (and z if applicable).
     """
+    assert (
+        progress is None
+        or progress == "console"
+        or isinstance(progress, lib.ProgressDialog)
+    ), "progress must be None, 'console', or a ProgressDialog instance."
+    if progress is None:
+        progress = lib.MockProgress()
+    elif progress == "console":
+        progress = lib.TqdmProgress(description="Undrifting by AIM (1/2)")
+
     locs = locs.copy()
     # extract metadata
     width = lib.get_from_metadata(info, "Width", raise_error=True)
@@ -724,8 +719,7 @@ def aim(
         progress=progress,
     )
     # the second run is with the entire dataset as reference
-    if progress is not None:
-        progress.zero_progress(description="Undrifting by AIM (2/2)")
+    progress.zero_progress(description="Undrifting by AIM (2/2)")
     x_pdc, y_pdc, drift_x2, drift_y2 = intersection_max(
         x_pdc,
         y_pdc,
@@ -754,8 +748,7 @@ def aim(
 
     # 3D undrifting
     if "z" in locs.columns:
-        if progress is not None:
-            progress.zero_progress(description="Undrifting z (1/2)")
+        progress.zero_progress(description="Undrifting z (1/2)")
         ref_x = x_pdc[frame <= segmentation]
         ref_y = y_pdc[frame <= segmentation]
         ref_z = locs["z"][frame <= segmentation]
@@ -776,8 +769,7 @@ def aim(
             aim_round=1,
             progress=progress,
         )
-        if progress is not None:
-            progress.zero_progress(description="Undrifting z (2/2)")
+        progress.zero_progress(description="Undrifting z (2/2)")
         z_pdc, drift_z2 = intersection_max_z(
             x_pdc,
             y_pdc,
@@ -812,16 +804,12 @@ def aim(
     locs["y"] = y_pdc
     if "z" in locs.columns:
         locs["z"] = z_pdc
-
     new_info = {
         "Generated by": f"Picasso v{__version__} AIM",
         "Intersect distance (nm)": intersect_d * pixelsize,
         "Segmentation": segmentation,
         "Search regions radius (nm)": roi_r * pixelsize,
     }
     new_info = info + [new_info]
-
-    if progress is not None:
-        progress.close()
-
+    progress.close()
     return locs, new_info, drift

picasso/clusterer.py

@@ -994,9 +994,7 @@ def test_subclustering(
     assert sparse_dist > clustering_dist, (
         "The sparse distance must be larger than the clustering " "distance."
     )
-    pixelsize = lib.get_from_metadata(info, "Pixelsize")
-    if pixelsize is None:
-        raise ValueError("Pixelsize not found in metadata.")
+    pixelsize = lib.get_from_metadata(info, "Pixelsize", raise_error=True)
 
     # get 1st nearest neighbor distances
     if "z" in mols.columns:

picasso/gui/filter.py

@@ -558,7 +558,11 @@ def plot(self) -> None:
             )
             df.to_csv(path, index=False)
         fig, ax = lib.plot_subclustering_check(
-            clustered_nevents, sparse_nevents, return_fig=True
+            clustered_nevents,
+            sparse_nevents,
+            return_fig=True,
+            clustering_dist=dist_clustered,
+            sparse_dist=dist_sparse,
         )
         plt.show()