Various fixes

environment_rtd.yml

@@ -25,10 +25,10 @@ dependencies:
     - isosplit5
     - mountainsort4>=1.0.0    
     - tridesclous>=1.6.4
-    - herdingspikes
+    - herdingspikes<=0.3.99
     - sphinx-gallery
     - numpydoc
-    - numpy<1.21
-    - git+https://github.com/scikit-learn-contrib/hdbscan.git
+    - numpy<1.22
+    - hdbscan
     - numba==0.54.1
     - git+https://github.com/SpikeInterface/spikeinterface.git

spikeinterface/core/base.py

@@ -8,6 +8,7 @@
 import random
 import string
 import warnings
+from packaging.version import parse
 
 import numpy as np
 
@@ -874,9 +875,12 @@ def _get_class_from_string(class_string):
 def _check_same_version(class_string, version):
     module = class_string.split('.')[0]
     imported_module = importlib.import_module(module)
+
+    current_version = parse(imported_module.__version__)
+    saved_version = parse(version)
 
     try:
-        return imported_module.__version__ == version
+        return current_version.major == saved_version.major and current_version.minor == saved_version.minor
     except AttributeError:
         return 'unknown'
 

spikeinterface/sorters/launcher.py

@@ -329,6 +329,8 @@ def iter_working_folder(working_folder):
                 sorter_name = job_dict["sorter_name"]
                 yield rec_name, sorter_name, output_folder
             else:
+                rec_name = rec_folder.name
+                sorter_name = output_folder.name
                 if not output_folder.is_dir():
                     continue
                 if not is_log_ok(output_folder):

spikeinterface/toolkit/postprocessing/template_metrics.py

@@ -90,7 +90,7 @@ def calculate_template_metrics(waveform_extractor, feature_names=None, peak_sign
         multi_index = pd.MultiIndex.from_tuples(list(zip(unit_ids, channel_ids)),
                                                 names=["unit_id", "channel_id"])
         template_metrics = pd.DataFrame(
-            index=multi_index, columns=[feature_names])
+            index=multi_index, columns=feature_names)
 
     for unit_id in unit_ids:
         template_all_chans = waveform_extractor.get_template(unit_id)