kmeans

setup.py

@@ -29,7 +29,7 @@
 # Setup configuration
 setuptools.setup(
     name="Simba-UW-tf-dev",
-    version="2.3.5",
+    version="2.3.6",
     author="Simon Nilsson, Jia Jie Choong, Sophia Hwang",
     author_email="[email protected]",
     description="Toolkit for computer classification and analysis of behaviors in experimental animals",

simba/data_processors/agg_clf_calculator.py

@@ -235,9 +235,9 @@ def save(self) -> None:
         )
 
 
-# test = AggregateClfCalculator(config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini",
+# test = AggregateClfCalculator(config_path=r"C:\troubleshooting\mitra\project_folder\project_config.ini",
 #                               data_measures=["Bout count", "Total event duration (s)", "Mean event bout duration (s)", "Median event bout duration (s)", "First event occurrence (s)", "Mean event bout interval duration (s)", "Median event bout interval duration (s)"],
-#                               classifiers=['lay-on-belly'],
+#                               classifiers=['rearing'],
 #                               video_meta_data =['Frame count', "Video length (s)"],
 #                               transpose=True)
 # test.run()

simba/data_processors/circling_detector.py

@@ -27,8 +27,8 @@ class CirclingDetector(ConfigReader):
 
     .. important::
 
-        Circling is detected as :underline:`present` when **the circular range of the animal is above the ``circular_range_threshold`` within the preceding ``time_threshold``** AND
-        **the movement of the animal (defined as the sum of the center movement) is above the  ``movement_threshold`` within the preceding ``time_threshold``.**
+        Circling is detected as `present` when **the circular range of the animal is above the specied circular range threshold within the passed preceding time threshold** AND
+        **the movement of the animal (defined as the sum of the center movement) is above the specified movement threshold within the passed preceding time threshold.**
 
         Circling is detected as :underline:`absent` when not present.
 
@@ -120,8 +120,9 @@ def run(self):
         agg_results.to_csv(agg_results_path)
         stdout_success(msg=f'Results saved in {self.save_dir} directory.')
 
-
-
-# detector = CirclingDetector(data_dir=r'D:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location', config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini")
+#
+#
+# detector = CirclingDetector(data_dir=r'C:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location',
+#                             config_path=r"C:\troubleshooting\mitra\project_folder\project_config.ini")
 # detector.run()
 

simba/data_processors/freezing_detector.py

@@ -27,10 +27,10 @@ class FreezingDetector(ConfigReader):
 
     .. important::
 
-        Freezing is detected as :underline:`present` when ** the velocity (computed from the mean movement of the nape, nose, and tail-base body-parts) falls below
-        the movement threshold for the duration of the defined time-window or longer.
+        Freezing is detected as `present` when **the velocity (computed from the mean movement of the nape, nose, and tail-base body-parts) falls below
+        the movement threshold for the duration (and longer) of the specied time-window.
 
-        Freezing is detected as :underline:`absent` when not present.
+        Freezing is detected as `absent` when not present.
 
     :param Union[str, os.PathLike] data_dir: Path to directory containing pose-estimated body-part data in CSV format.
     :param Union[str, os.PathLike] config_path: Path to SimBA project config file.
@@ -90,7 +90,7 @@ def run(self):
         check_all_file_names_are_represented_in_video_log(video_info_df=self.video_info_df, data_paths=self.data_paths)
         for file_cnt, file_path in enumerate(self.data_paths):
             video_name = get_fn_ext(filepath=file_path)[1]
-            print(f'Analyzing {video_name}...')
+            print(f'Analyzing {video_name}...({file_cnt+1}/{len(self.data_paths)})')
             save_file_path = os.path.join(self.save_dir, f'{video_name}.csv')
             df = read_df(file_path=file_path, file_type='csv').reset_index(drop=True)
             _, px_per_mm, fps = read_video_info(vid_info_df=self.video_info_df, video_name=video_name)
@@ -131,4 +131,4 @@ def run(self):
 
 #
 # FreezingDetector(data_dir=r'C:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location',
-#                  config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini")
+#                  config_path=r"C:\troubleshooting\mitra\project_folder\project_config.ini")

simba/mixins/plotting_mixin.py

@@ -1909,7 +1909,7 @@ def plot_bar_chart(df: pd.DataFrame,
         check_valid_lst(data=list(df[y]), source=f"{PlottingMixin.plot_bar_chart.__name__} y", valid_dtypes=Formats.NUMERIC_DTYPES.value)
         fig, ax = plt.subplots(figsize=fig_size)
         sns.barplot(x=x, y=y, data=df, palette=palette, ax=ax)
-        ax.set_xticklabels(df[x], rotation=90, fontsize=8)
+        ax.set_xticklabels(df[x].unique(), rotation=90, fontsize=8)
         if error is not None:
             check_str(name=f"{PlottingMixin.plot_bar_chart.__name__} error", value=error, options=tuple(df.columns))
             check_valid_lst(data=list(df[error]), source=f"{PlottingMixin.plot_bar_chart.__name__} error",valid_dtypes=Formats.NUMERIC_DTYPES.value)

simba/model/inference_batch.py

@@ -92,6 +92,11 @@ def run(self):
         self.timer.stop_timer()
         stdout_success(msg=f"Machine predictions complete. Files saved in {self.save_dir} directory", elapsed_time=self.timer.elapsed_time_str, source=self.__class__.__name__)
 
+# test = InferenceBatch(config_path=r"C:\troubleshooting\mitra\project_folder\project_config.ini")
+# test.run()
+
+
+
 
 
 # test = InferenceBatch(config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini",