jungmannlab · rafalkowalewski1 · Feb 23, 2026 · Feb 17, 2026 · Feb 17, 2026 · Feb 17, 2026
diff --git a/.bumpversion.cfg b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.6
+current_version = 0.9.7
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
@@ -15,10 +15,16 @@ serialize =
 
 [bumpversion:file:./picasso/version.py]
 
+[bumpversion:file:./release/pyinstaller/picasso.spec]
+
+[bumpversion:file:./release/pyinstaller/picassow.spec]
+
 [bumpversion:file:./release/one_click_windows_gui/picasso_innoinstaller.iss]
 
 [bumpversion:file:./release/one_click_windows_gui/create_installer_windows.bat]
 
+[bumpversion:file:./release/one_click_macos_gui/create_macos_dmg.sh]
+
 [bumpversion:file:./docs/conf.py]
 
 [bumpversion:file:pyproject.toml]

diff --git a/.gitignore b/.gitignore
@@ -36,6 +36,7 @@ picasso/config.yaml
 
 # One-click-installer output
 release/one_click_windows_gui/Output/
+release/one_click_macos_gui/*.dmg
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

diff --git a/changelog.rst b/changelog.rst
@@ -1,7 +1,37 @@
 Changelog
 =========
 
-Last change: 16-FEB-2026 CEST
+Last change: 23-FEB-2026 CEST
+
+0.9.7
+-----
+Important updates:
+^^^^^^^^^^^^^^^^^^
+- Windows one-click-installer allows for selecting only a subset of Picasso modules to install
+- Added ToRaw and Nanotron to one-click-installer
+- *Experimental* One-click-installer for macOS (only for Apple Silicon), see `here <https://github.com/jungmannlab/picasso/tree/master/release/one_click_macos_gui>`__
+
+Small improvements:
++++++++++++++++++++
+- Adjusted the ``config.yaml`` and plugins instructions for the one-click-installer Picasso release (new Pyinstaller stores everything in the ``_internal`` folder)
+- G5M output can save more columns (if present in the input localizations)
+- Further enhancement of G5M documentation
+- Render GUI: implemented filter by number of localizations for multichannel data
+- Render GUI: allow removal of any column from localizations, not only ``group``
+- Filter GUI: allow removal of any column from localizations
+- ``REQUIRED_COLUMNS`` moved from ``picasso.localize`` to ``picasso.lib``
+
+Bug fixes:
+++++++++++
+- Fixed basic frame analysis in SMLM clusterer
+- Fixed labels of the vertical lines in the subcluster test plot
+- Fixed automatic Localize loading/unloading z-calibration paths when changing cameras
+- Fixed ``rel_sigma_z`` in G5M (previously incorrectly divided by pixel size)
+- Fixed G5M molmap ``lpz`` output
+- Fixed loading square picks in Render
+- Fixed appearance of the Apply expression dialog in Render for files with many columns
+- Fixed initial x, y and N in LQ Gaussian fitting (might results in faster convergence and slightly different (<< NeNA) results) (#616)
+- Fixed picking circular regions around left and top edges of the FOV
 
 0.9.6
 -----

diff --git a/distribution/README.md b/distribution/README.md
@@ -1,4 +1,7 @@
 # Packaging Picasso and Creating an Installer
+
+**This folder will be removed in Picasso v0.11 as it is mostly a duplicate of picasso/release.**
+
 This document describes the procedure to generate a Windows installer for Picasso end-users. The result is that Picasso (and Python) is installed in a single folder, the command line interface is exposed and start menu shortcuts are created. The first step is to package Picasso and its underlying Python distribution into a single folder.
 
 ## Requirements

diff --git a/distribution/picasso.iss b/distribution/picasso.iss
@@ -2,10 +2,10 @@
 AppName=Picasso
 AppPublisher=Jungmann Lab, Max Planck Institute of Biochemistry
 
-AppVersion=0.9.6
+AppVersion=0.9.7
 DefaultDirName={commonpf}\Picasso
 DefaultGroupName=Picasso
-OutputBaseFilename="Picasso-Windows-64bit-0.9.6"
+OutputBaseFilename="Picasso-Windows-64bit-0.9.7"
 ArchitecturesAllowed=x64
 ArchitecturesInstallIn64BitMode=x64
 

diff --git a/docs/conf.py b/docs/conf.py
@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ""
 # The full version, including alpha/beta/rc tags
-release = "0.9.6"
+release = "0.9.7"
 
 # -- General configuration ---------------------------------------------------
 

diff --git a/docs/localize.rst b/docs/localize.rst
@@ -27,7 +27,43 @@ Identification and fitting of single-molecule spots
 Camera Config
 -------------
 
-Picasso can remember default cameras and will use saved camera parameters. In order to use camera configs, create a file named ``config.yaml`` in the picasso folder. To start with a template, modify ``config_template.yaml`` that can be found in the folder by default. Picasso will compare the entries with Micro-Manager-Metadata and match the sensitivity values. If no matching entries can be found (e.g., if the file was not created with Micro-Manager) the config file will still be used to create a dropdown menu to select the different categories. The camera config can also be used to define a default camera that will always be used. Indentions are used for definitions.
+Picasso can remember default cameras and will use saved camera parameters. In order to use camera configs, create a file named ``config.yaml`` in the ``picasso`` folder. See below on how to locate it.
+
+To start with a template, modify ``config_template.yaml`` that can be found in the folder by default. Picasso will compare the entries with Micro-Manager-Metadata and match the sensitivity values. If no matching entries can be found (e.g., if the file was not created with Micro-Manager) the config file will still be used to create a dropdown menu to select the different categories. The camera config can also be used to define a default camera that will always be used. Indentions are used for definitions.
+
+One click installer (Windows)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you downloaded an .exe Picasso file from the `release page <https://github.com/jungmannlab/picasso/releases>`_:
+
+- Navigate to the installation folder, by default, it's ``C:/Picasso``. *Before version 0.8.3, the default location was* ``C:/Program Files/Picasso``.
+- Go to the folder ``_internal/picasso``. *Before version 0.9.6, the folder was simply* ``picasso``.
+- Add your config file there.
+
+One click installer (macOS)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you downloaded a .dmg Picasso file from the `release page <https://github.com/jungmannlab/picasso/releases>`_:
+
+- Navigate to your Applications folder and right-click on the picasso app, then select "Show Package Contents".
+- Go to the folder ``Contents/Frameworks/picasso``.
+- Add your config file there.
+
+PyPI
+~~~~
+If you installed Picasso using ``pip install picassosr``:
+
+- Activate your conda environment where ``picassosr`` is installed by typing ``conda activate YOUR_ENVIRONMENT``.
+- To find the location of the package, type ``pip show picassosr`` and look for the line starting with ``Location:``.
+- Navigate to this location and go to ``picasso``.
+- Add your config file there.
+
+GitHub
+~~~~~~
+If you cloned the GitHub repository, you can add plugins by following these steps:
+- Find the directory where you cloned the GitHub repository with Picasso.
+- Go to ``picasso/picasso/``.
+- Copy the config file to this folder.
 
 Example: Default Camera
 ~~~~~~~~~~~~~~~~~~~~~~~

diff --git a/docs/others.rst b/docs/others.rst
@@ -28,10 +28,18 @@ If you installed Picasso using ``pip install picassosr``, you can add sound noti
 - Copy the sound files to this folder.
 
 
-One click installer
--------------------
+One click installer (Windows)
+-----------------------------
 If you installed Picasso using the one click installer from `the Picasso release page <https://github.com/jungmannlab/picasso/releases/>`__ , you can add sound notifications by following these steps:
 
 - Find the location where you installed Picasso. By default, it is ``C:/Picasso``. *Before version 0.8.3, the default location was* ``C:/Program Files/Picasso``.
 - Go to the following subfolder: ``picasso/gui/notification_sounds``.
-- Copy the sound files to this folder.
+- Copy the sound files to this folder.
+
+
+One click installer (macOS)
+---------------------------
+If you installed Picasso using the one click installer from `the Picasso release page <https://github.com/jungmannlab/picasso/releases/>`__ , you can add sound notifications by following these steps:
+
+- Navigate to your Applications folder and right-click on the picasso app, then select "Show Package Contents".
+- Add your sound files to ``Contents/Frameworks/picasso/gui/notification_sounds``.
diff --git a/docs/plugins.rst b/docs/plugins.rst
@@ -8,16 +8,24 @@ Starting in version 0.5.0, Picasso supports plugins. Below are the instructions
 
 *Keep in mind that the* ``__init__.py`` *file in the* ``picasso/picasso/gui/plugins`` *folder must not be modified or deleted.*
 
-One click installer
-~~~~~~~~~~~~~~~~~~~
-**NOTE**: After uninstalling Picasso, ``Picasso`` folder needs to be deleted manually, as the uninstaller currently does not remove the plugins automatically.
+One click installer (Windows)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+**NOTE**: After uninstalling Picasso, ``Picasso`` folder may need to be deleted manually, as the uninstaller currently does not remove the plugins automatically.
 
 - Find the location where you installed Picasso. By default, it is ``C:/Picasso``. *Before version 0.8.3, the default location was* ``C:/Program Files/Picasso``.
-- Then go to the folder ``picasso/gui/plugins``.
+- Then go to the folder ``_internal/picasso/gui/plugins``. *Before version 0.9.6, the folder was* ``/picasso/gui/plugins``
 - Copy the plugin(s) to this folder.
 
 **NOTE**: Plugins added in this distribution will not be able to use packages that are not installed automatically (dependencies in the file ``pyproject.toml``).
 
+One click installer (macOS)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- Navigate to your Applications folder and right-click on the picasso app, then select "Show Package Contents".
+- Add your plugin(s) to ``Contents/Frameworks/picasso/gui/plugins``.
+
+**NOTE**: Plugins added in this distribution will not be able to use packages that are not installed automatically (dependencies in the file ``pyproject.toml``).
+
 PyPI
 ~~~~
 If you installed Picasso using ``pip install picassosr``, you can add plugins by following these steps:

diff --git a/docs/render.rst b/docs/render.rst
@@ -106,7 +106,7 @@ If the outcome of G5M seems unsatisfactory, please check the following:
 - Another reason why the loc. precision values can be off is due to the small box size in the localization step; especially in 3D astigmatic imaging, single-emitter images can be quite large, potentially exceeding the user-defined box size; in such cases, we recommend increasing the box size in the localization step and rerunning the analysis;
 - Inspect if the localizations were preprocessed as described above;
 - Rerun the analysis without postprocessing (filtering) and redo it manually, since the step may be too stringent, especially for short acquisition times;
-- Adjust min./max. σ, especially too low max. σ may lead to high false positive error rates (i.e., overfitting);
+- Adjust min./max. σ, especially too low max. σ may lead to high false positive error rates (i.e., overfitting); We suggest inspecting ``rel_sigma`` values of the assigned molecules, which are calculated as the fitted σ divided by the mean localization precision of the surrounding localizations. If the values are close to the user-selected min./max. σ, min./max. σ might need to be adjusted. Alternatively, this might be a sign of inaccurate/inprecise loc. precision values, see above;
 - Adjust min. locs;
 - Adjust DBSCAN (or other clustering algorithm) parameters. For example, if G5M takes too long to run, the DBSCAN clusters most likely contain too many molecules. In such a case, we recommend splitting such clusters further;
 

diff --git a/picasso/clusterer.py b/picasso/clusterer.py
@@ -73,9 +73,7 @@ def _frame_analysis(frame: pd.SeriesGroupBy, n_frames: int) -> int:
     return passed
 
 
-def frame_analysis(
-    labels: np.ndarray, frame: pd.Series | np.ndarray
-) -> np.ndarray:
+def frame_analysis(labels: np.ndarray, frame: np.ndarray) -> np.ndarray:
     """Perform basic frame analysis on clustered localizations. Reject
     clusters whose mean frame is outside of the [20, 80] % (max frame)
     range or any 1/20th of measurement's time contains more than 80 % of
@@ -87,7 +85,7 @@ def frame_analysis(
     ----------
     labels : np.ndarray
         Cluster labels (-1 means no cluster assigned).
-    frame : pd.Series or np.ndarray
+    frame : np.ndarray
         Frame number for each localization.
 
     Returns
@@ -195,7 +193,8 @@ def _cluster(
     labels[np.isin(labels, to_discard)] = -1
 
     if frame is not None:
-        labels = frame_analysis(labels, frame)
+        # must convert frames to an array, do not change!
+        labels = frame_analysis(labels, frame.to_numpy())
 
     return labels
 
@@ -956,7 +955,7 @@ def test_subclustering(
     sparse_dist: float = 80,
 ) -> tuple[np.ndarray, np.ndarray]:
     """Extract number of events from molecular maps based on their
-    numbers of binding events assinged.
+    numbers of binding events assigned.
 
     The reasoning is that 'subclustered' molecules will tend to have
     fewer binding events assigned to them since multiple molecules

diff --git a/picasso/g5m.py b/picasso/g5m.py
@@ -1768,7 +1768,7 @@ def convert_G5M_results(
         sigma_x = np.sqrt(covariances[:, 0]) * pixelsize
         sigma_y = np.sqrt(covariances[:, 1]) * pixelsize
         sigma_z = np.sqrt(covariances[:, 2]) * pixelsize
-        lpz = sem[:, 2]
+        lpz = sem[:, 2] * pixelsize
         weighted_lpx = (
             (resp * locs_group["lpx"].to_numpy().reshape(-1, 1)).sum(0) / rsum
         ).reshape(-1)
@@ -1780,7 +1780,7 @@ def convert_G5M_results(
         ).reshape(-1)
         rel_sigma_x = sigma_x / weighted_lpx / pixelsize
         rel_sigma_y = sigma_y / weighted_lpy / pixelsize
-        rel_sigma_z = sigma_z / weighted_lpz / pixelsize
+        rel_sigma_z = sigma_z / weighted_lpz
     else:
         sigma = np.sqrt(covariances) * pixelsize
         # relative sigma
@@ -1810,20 +1810,6 @@ def convert_G5M_results(
     log_likelihood = g5m.score_samples(X)
     locs_group["log_likelihood"] = log_likelihood
 
-    # photons, PSF size and background (weighted average)
-    photons = (
-        (resp * locs_group["photons"].to_numpy().reshape(-1, 1)).sum(0) / rsum
-    ).reshape(-1)
-    sx = (
-        (resp * locs_group["sx"].to_numpy().reshape(-1, 1)).sum(0) / rsum
-    ).reshape(-1)
-    sy = (
-        (resp * locs_group["sy"].to_numpy().reshape(-1, 1)).sum(0) / rsum
-    ).reshape(-1)
-    bg = (
-        (resp * locs_group["bg"].to_numpy().reshape(-1, 1)).sum(0) / rsum
-    ).reshape(-1)
-
     # extract the number of binding events, i.e., link localizations
     # and assign them to molecules - sticky events will likely have only
     # one or two such events associated
@@ -1861,10 +1847,6 @@ def convert_G5M_results(
                 "x": x.astype(np.float32),
                 "y": y.astype(np.float32),
                 "z": z.astype(np.float32),
-                "photons": photons.astype(np.float32),
-                "sx": sx.astype(np.float32),
-                "sy": sy.astype(np.float32),
-                "bg": bg.astype(np.float32),
                 "lpx": lpx.astype(np.float32),
                 "lpy": lpy.astype(np.float32),
                 "lpz": lpz.astype(np.float32),
@@ -1889,10 +1871,6 @@ def convert_G5M_results(
                 "std_frame": std_frame.astype(np.float32),
                 "x": x.astype(np.float32),
                 "y": y.astype(np.float32),
-                "photons": photons.astype(np.float32),
-                "sx": sx.astype(np.float32),
-                "sy": sy.astype(np.float32),
-                "bg": bg.astype(np.float32),
                 "lpx": lpx.astype(np.float32),
                 "lpy": lpy.astype(np.float32),
                 "fitted_sigma": sigma.astype(np.float32),
@@ -1905,6 +1883,25 @@ def convert_G5M_results(
                 "group_input": group_input.astype(np.int32),
             }
         )
+    # add mean values of extra columns from locs_group so that
+    # the info is not lost, e.g., mean photons
+    ignore_columns = [
+        "frame",
+        "x",
+        "y",
+        "z",
+        "lpx",
+        "lpy",
+        "lpz",
+        "group",
+        "group_input",
+    ]
+    for col in locs_group.columns:
+        if col not in ignore_columns:
+            centers[f"{col}_mean"] = (
+                (resp * locs_group[col].to_numpy().reshape(-1, 1)).sum(0)
+                / rsum
+            ).reshape(-1)
     return centers, locs_group
 
 

diff --git a/picasso/gausslq.py b/picasso/gausslq.py
@@ -50,16 +50,16 @@ def _sum_and_center_of_mass(
     size: int,
 ) -> tuple[float, float, float]:
     """Calculate the sum and center of mass of a 2D spot."""
-    x = 0.0
     y = 0.0
+    x = 0.0
     _sum_ = 0.0
     for i in range(size):
         for j in range(size):
-            x += spot[i, j] * i
-            y += spot[i, j] * j
+            y += spot[i, j] * i
+            x += spot[i, j] * j
             _sum_ += spot[i, j]
-    x /= _sum_
     y /= _sum_
+    x /= _sum_
     return _sum_, y, x
 
 

diff --git a/picasso/gaussmle.py b/picasso/gaussmle.py
@@ -30,16 +30,16 @@ def _sum_and_center_of_mass(
     size: int,
 ) -> tuple[float, float, float]:
     """Calculate the sum and center of mass of a 2D spot."""
-    x = 0.0
     y = 0.0
+    x = 0.0
     _sum_ = 0.0
     for i in range(size):
         for j in range(size):
-            x += spot[i, j] * i
-            y += spot[i, j] * j
+            y += spot[i, j] * i
+            x += spot[i, j] * j
             _sum_ += spot[i, j]
-    x /= _sum_
     y /= _sum_
+    x /= _sum_
     return _sum_, y, x