Merge pull request #528 from jungmannlab/development

v0.8.1

Author: rafalkowalewski1

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.0
+current_version = 0.8.1
 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 +1,14 @@
 Changelog
 =========
 
-Last change: 06-MAY-2025 MTS
+Last change: 15-JUL-2025 MTS
+
+0.8.1
+-----
+- Added ``n_events`` to cluster centers, i.e., number of binding events per cluster
+- .yaml files contain Picasso version number for easier tracking
+- Improved fiducial picking
+- Bug fixes and other cosmetic changes
 
 0.8.0
 -----

distribution/picasso.iss

@@ -2,10 +2,10 @@
 AppName=Picasso
 AppPublisher=Jungmann Lab, Max Planck Institute of Biochemistry
 
-AppVersion=0.8.0
+AppVersion=0.8.1
 DefaultDirName={commonpf}\Picasso
 DefaultGroupName=Picasso
-OutputBaseFilename="Picasso-Windows-64bit-0.8.0"
+OutputBaseFilename="Picasso-Windows-64bit-0.8.1"
 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 = "0.8.0"
+release = "0.8.1"
 
 # -- General configuration ---------------------------------------------------
 

picasso/__init__.py

@@ -8,7 +8,7 @@
 import os.path as _ospath
 import yaml as _yaml
 
-__version__ = "0.8.0"
+__version__ = "0.8.1"
 
 _this_file = _ospath.abspath(__file__)
 _this_dir = _ospath.dirname(_this_file)

picasso/__main__.py

@@ -9,6 +9,7 @@
     :copyright: Copyright (c) 2016-2019 Jungmann Lab, MPI of Biochemistry
 """
 import os.path
+from . import __version__
 
 
 def picasso_logo():
@@ -137,7 +138,7 @@ def _csv2hdf(path, pixelsize):
                 locs.sort(kind="mergesort", order="frame")
 
                 img_info = {}
-                img_info["Generated by"] = "Picasso csv2hdf"
+                img_info["Generated by"] = f"Picasso v{__version__} csv2hdf"
                 img_info["Frames"] = int(_np.max(frames)) + 1
                 img_info["Height"] = int(_np.ceil(_np.max(y)))
                 img_info["Width"] = int(_np.ceil(_np.max(x)))
@@ -203,7 +204,7 @@ def _link(files, d_max, tolerance):
             link_info = {
                 "Maximum Distance": d_max,
                 "Maximum Transient Dark Time": tolerance,
-                "Generated by": "Picasso Link",
+                "Generated by": f"Picasso v{__version__} Link",
             }
             info.append(link_info)
             io.save_locs(base + "_link.hdf5", linked_locs, info)
@@ -268,7 +269,7 @@ def _cluster_combine(files):
                 continue
             combined_locs = postprocess.cluster_combine(locs)
             base, ext = os.path.splitext(path)
-            combined_info = {"Generated by": "Picasso Combine"}
+            combined_info = {"Generated by": f"Picasso v{__version__} Combine"}
             info.append(combined_info)
             io.save_locs(base + "_comb.hdf5", combined_locs, info)
 
@@ -287,7 +288,7 @@ def _cluster_combine_dist(files):
                 continue
             combinedist_locs = postprocess.cluster_combine_dist(locs)
             base, ext = os.path.splitext(path)
-            cluster_combine_dist_info = {"Generated by": "Picasso Combineidst"}
+            cluster_combine_dist_info = {"Generated by": f"Picasso v{__version__} Combineidst"}
             info.append(cluster_combine_dist_info)
             io.save_locs(base + "_cdist.hdf5", combinedist_locs, info)
 
@@ -329,7 +330,7 @@ def _clusterfilter(files, clusterfile, parameter, minval, maxval):
 
                     base, ext = os.path.splitext(path)
                     clusterfilter_info = {
-                        "Generated by": "Picasso Clusterfilter - in",
+                        "Generated by": f"Picasso v{__version__} Clusterfilter - in",
                         "Paramter": parameter,
                         "Minval": minval,
                         "Maxval": maxval,
@@ -353,7 +354,7 @@ def _clusterfilter(files, clusterfile, parameter, minval, maxval):
 
                     base, ext = os.path.splitext(path)
                     clusterfilter_info = {
-                        "Generated by": "Picasso Clusterfilter - out",
+                        "Generated by": f"Picasso v{__version__} Clusterfilter - out",
                         "Paramter": parameter,
                         "Minval": minval,
                         "Maxval": maxval,
@@ -375,7 +376,7 @@ def _undrift(files, segmentation, display=True, fromfile=None):
     from numpy import genfromtxt, savetxt
 
     paths = glob.glob(files)
-    undrift_info = {"Generated by": "Picasso Undrift"}
+    undrift_info = {"Generated by": f"Picasso v{__version__} Undrift"}
     if fromfile is not None:
         undrift_info["From File"] = fromfile
         drift = genfromtxt(fromfile)
@@ -457,7 +458,7 @@ def _density(files, radius):
             locs = postprocess.compute_local_density(locs, info, radius)
             base, ext = os.path.splitext(path)
             density_info = {
-                "Generated by": "Picasso Density",
+                "Generated by": f"Picasso v{__version__} Density",
                 "Radius": radius,
             }
             info.append(density_info)
@@ -478,7 +479,7 @@ def _dbscan(files, radius, min_density, pixelsize=None):
             clusters = clusterer.find_cluster_centers(locs, pixelsize)
             base, _ = os.path.splitext(path)
             dbscan_info = {
-                "Generated by": "Picasso DBSCAN",
+                "Generated by": f"Picasso v{__version__} DBSCAN",
                 "Radius": radius,
                 "Minimum local density": min_density,
             }
@@ -506,7 +507,7 @@ def _hdbscan(files, min_cluster, min_samples, pixelsize=None):
             clusters = clusterer.find_cluster_centers(locs, pixelsize)
             base, ext = os.path.splitext(path)
             hdbscan_info = {
-                "Generated by": "Picasso HDBSCAN",
+                "Generated by": f"Picasso v{__version__} HDBSCAN",
                 "Min. cluster": min_cluster,
                 "Min. samples": min_samples,
             }
@@ -540,7 +541,7 @@ def _smlm_clusterer(
             clusters = clusterer.find_cluster_centers(locs, pixelsize)
             base, ext = os.path.splitext(path)
             smlm_cluster_info = {
-                "Generated by": "Picasso SMLM clusterer",
+                "Generated by": f"Picasso v{__version__} SMLM clusterer",
                 "Radius_xy": radius,
                 "Radius_z": radius_z,
                 "Min locs": min_locs,
@@ -590,7 +591,7 @@ def _dark(files):
             locs, info = io.load_locs(path)
             locs = postprocess.compute_dark_times(locs)
             base, ext = os.path.splitext(path)
-            d_info = {"Generated by": "Picasso Dark"}
+            d_info = {"Generated by": f"Picasso v{__version__} Dark"}
             info.append(d_info)
             io.save_locs(base + "_dark.hdf5", locs, info)
 
@@ -610,7 +611,7 @@ def _align(files, display):
     locs = [_[0] for _ in locs_infos]
     infos = [_[1] for _ in locs_infos]
     aligned_locs = align(locs, infos, display=display)
-    align_info = {"Generated by": "Picasso Align", "Files": files}
+    align_info = {"Generated by": f"Picasso v{__version__} Align", "Files": files}
     for file, locs_, info in zip(files, aligned_locs, infos):
         info.append(align_info)
         base, ext = splitext(file)
@@ -625,7 +626,7 @@ def _join(files, keep_index=True):
 
     locs, info = load_locs(files[0])
     total_frames = info[0]["Frames"]
-    join_info = {"Generated by": "Picasso Join", "Files": [files[0]]}
+    join_info = {"Generated by": f"Picasso v{__version__} Join", "Files": [files[0]]}
     for path in files[1:]:
         locs_, info_ = load_locs(path)
         try:
@@ -977,7 +978,7 @@ def prompt_info():
                 px = None
 
             localize_info = {
-                "Generated by": "Picasso Localize",
+                "Generated by": f"Picasso v{__version__} Localize",
                 "ROI": None, #TODO: change if ROI is given
                 "Box Size": box,
                 "Min. Net Gradient": min_net_gradient,
@@ -1477,12 +1478,20 @@ def _spinna_batch_analysis(parameters_filename, asynch=True, bootstrap=False, ve
                 nn_counts[f"{t1}-{t2}"] = nn_plotted
         mixer.nn_counts = nn_counts
         n_total = sum(n_simulated.values())
-        dist_sim = spinna.get_NN_dist_simulated(
-            mixer.convert_props_to_counts(opt_props[0], n_total), 
-            sim_repeats, 
-            mixer, 
-            duplicate=True,
-        )
+        if isinstance(opt_props, tuple):
+            dist_sim = spinna.get_NN_dist_simulated(
+                mixer.convert_props_to_counts(opt_props[0], n_total), 
+                sim_repeats, 
+                mixer, 
+                duplicate=True,
+            )
+        else:
+            dist_sim = spinna.get_NN_dist_simulated(
+                mixer.convert_props_to_counts(opt_props, n_total), 
+                sim_repeats, 
+                mixer, 
+                duplicate=True,
+            )
         for i, (t1, t2, _) in enumerate(mixer.get_neighbor_idx(duplicate=True)):
             fig, ax = spinna.plot_NN(
                 dist=dist_sim[i], mode='plot', show_legend=False,

picasso/__version__.py

@@ -1 +1 @@
-VERSION_NO = "0.8.0"
+VERSION_NO = "0.8.1"

picasso/aim.py

@@ -18,6 +18,7 @@
 from scipy.interpolate import InterpolatedUnivariateSpline as \
     _InterpolatedUnivariateSpline
 from tqdm import tqdm as _tqdm
+from . import __version__
 
 
 def intersect1d(a, b):
@@ -739,7 +740,7 @@ def aim(
         locs["z"] = z_pdc
 
     new_info = {
-        "Generated by": "AIM undrift",
+        "Generated by": f"Picasso v{__version__} AIM",
         "Intersect distance (nm)": intersect_d * pixelsize,
         "Segmentation": segmentation,
         "Search regions radius (nm)": roi_r * pixelsize,

picasso/clusterer.py

@@ -34,6 +34,7 @@
     ("ellipticity", "f4"),
     ("net_gradient", "f4"),
     ("n", "u4"),
+    ("n_events", "i4"), 
     ("area", "f4"),
     ("convexhull", "f4"),
     ("group", "i4"),
@@ -56,6 +57,7 @@
     ("ellipticity", "f4"),
     ("net_gradient", "f4"),
     ("n", "u4"),
+    ("n_events", "u4"), 
     ("volume", "f4"),
     ("convexhull", "f4"),
     ("group", "i4"),
@@ -325,9 +327,10 @@ def cluster(
 
     Returns
     -------
-    np.array
-        Cluster labels for each localization (-1 means no cluster 
-        assigned)
+    locs : np.recarray
+        Clusterered localizations, with column 'group' added, which
+        specifies cluster label for each localization. Noise (label -1)
+        is removed.  
     """
 
     if hasattr(locs, "z"): # 3D
@@ -405,8 +408,9 @@ def dbscan(locs, radius, min_samples, pixelsize=None):
     Returns
     -------
     locs : np.recarray
-        Clustered localizations; cluster labels are assigned to the
-        "group" column    
+        Clusterered localizations, with column 'group' added, which
+        specifies cluster label for each localization. Noise (label -1)
+        is removed.  
     """
 
     if hasattr(locs, "z"):
@@ -479,8 +483,9 @@ def hdbscan(
     Returns
     -------
     locs : np.recarray
-        Clustered localizations; cluster labels are assigned to the
-        "group" column    
+        Clusterered localizations, with column 'group' added, which
+        specifies cluster label for each localization. Noise (label -1)
+        is removed.  
     """
 
     if hasattr(locs, "z"):
@@ -593,12 +598,13 @@ def find_cluster_centers(locs, pixelsize=None):
     ellipticity = _np.array([_[12] for _ in centers_])
     net_gradient = _np.array([_[13] for _ in centers_])
     n = _np.array([_[14] for _ in centers_])
+    n_events = _np.array([_[15] for _ in centers_]) # number of localizations in cluster
 
     if hasattr(locs, "z"):
-        z = _np.array([_[15] for _ in centers_])
-        std_z = _np.array([_[16] for _ in centers_])
-        volume = _np.array([_[17] for _ in centers_])
-        convexhull = _np.array([_[18] for _ in centers_])
+        z = _np.array([_[16] for _ in centers_])
+        std_z = _np.array([_[17] for _ in centers_])
+        volume = _np.array([_[18] for _ in centers_])
+        convexhull = _np.array([_[19] for _ in centers_])
         centers = _np.rec.array(
             (
                 frame,
@@ -618,15 +624,16 @@ def find_cluster_centers(locs, pixelsize=None):
                 ellipticity,
                 net_gradient,
                 n,
+                n_events,
                 volume,
                 convexhull,
                 res.index.values, # group id
             ),
             dtype=CLUSTER_CENTERS_DTYPE_3D,
         )
     else:
-        area = _np.array([_[15] for _ in centers_])
-        convexhull = _np.array([_[16] for _ in centers_])
+        area = _np.array([_[16] for _ in centers_])
+        convexhull = _np.array([_[17] for _ in centers_])
         centers = _np.rec.array(
             (
                 frame,
@@ -644,6 +651,7 @@ def find_cluster_centers(locs, pixelsize=None):
                 ellipticity,
                 net_gradient,
                 n,
+                n_events,
                 area,
                 convexhull,
                 res.index.values, # group id
@@ -713,6 +721,10 @@ def cluster_center(grouplocs, pixelsize=None, separate_lp=False):
     net_gradient = grouplocs.net_gradient.mean()
     # n_locs in cluster
     n = len(grouplocs)
+    # number of binding events
+    split_idx = _np.where(_np.diff(grouplocs.frame) > 3)[0] + 1 # split locs by consecutive frames
+    x_events = _np.split(grouplocs.x, split_idx)
+    n_events = len(x_events) # number of binding events
     if hasattr(grouplocs, "z"):
         if pixelsize is None:
             raise ValueError(
@@ -753,6 +765,7 @@ def cluster_center(grouplocs, pixelsize=None, separate_lp=False):
             ellipticity,
             net_gradient,
             n, 
+            n_events,
             z,
             std_z,
             # lpz,
@@ -783,6 +796,7 @@ def cluster_center(grouplocs, pixelsize=None, separate_lp=False):
             ellipticity,
             net_gradient,
             n,
+            n_events,
             area,
             convexhull,
         ]

picasso/gui/average.py

@@ -294,7 +294,7 @@ def save(self, path):
         cy = self.info[0]["Height"] / 2
         self.locs.x += cx
         self.locs.y += cy
-        info = self.info + [{"Generated by": "Picasso Average"}]
+        info = self.info + [{"Generated by": f"Picasso v{__version__} Average"}]
         out_locs = self.locs
         io.save_locs(path, out_locs, info)
         self.window.status_bar.showMessage("File saved to {}.".format(path))