btns

simba/mixins/train_model_mixin.py

@@ -25,7 +25,7 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.feature_selection import VarianceThreshold
 from sklearn.inspection import partial_dependence, permutation_importance
-from sklearn.metrics import precision_recall_curve
+from sklearn.metrics import precision_recall_curve, classification_report
 from sklearn.model_selection import ShuffleSplit, learning_curve
 from sklearn.preprocessing import (MinMaxScaler, QuantileTransformer,
                                    StandardScaler)
@@ -322,6 +322,7 @@ def smote_oversampler(
         else:
             return smt.fit_resample(x_train, y_train)
 
+
     def calc_permutation_importance(self,
                                     x_test: np.ndarray,
                                     y_test: np.ndarray,
@@ -1373,9 +1374,6 @@ def clf_define(self,
                 raise SimBAModuleNotFoundError(msg='SimBA could not find the cuml library for GPU machine learning algorithms.', source=self.__class__.__name__)
 
 
-
-
-
     def clf_fit(self,
                 clf: Union[RandomForestClassifier, cuRF],
                 x_df: pd.DataFrame,
@@ -1852,9 +1850,7 @@ def create_shap_log_mp(
                 save_file_no=save_file_no,
             )
 
-    def check_df_dataset_integrity(
-        self, df: pd.DataFrame, file_name: str, logs_path: Union[str, os.PathLike]
-    ) -> None:
+    def check_df_dataset_integrity(self, df: pd.DataFrame, file_name: str, logs_path: Union[str, os.PathLike]) -> None:
         """
         Helper to check for non-numerical np.inf, -np.inf, NaN, None in a single dataframe.
         :parameter pd.DataFrame x_df: Features
@@ -2686,6 +2682,8 @@ def find_low_variance_fields(
         return low_variance_fields
 
 
+
+
 # test = TrainModelMixin()
 # test.read_all_files_in_folder(file_paths=['/Users/simon/Desktop/envs/troubleshooting/jake/project_folder/csv/targets_inserted/22-437C_c3_2022-11-01_13-16-23_color.csv', '/Users/simon/Desktop/envs/troubleshooting/jake/project_folder/csv/targets_inserted/22-437D_c4_2022-11-01_13-16-39_color.csv'],
 #                               file_type='csv', classifier_names=['attack', 'non-agresive parallel swimming'])

simba/pose_importers/sleap_h5_importer.py

@@ -50,7 +50,7 @@ def __init__(self,
                  config_path: Union[str, os.PathLike],
                  data_folder: Union[str, os.PathLike],
                  id_lst: List[str],
-                 interpolation_settings: str,
+                 interpolation_settings: Union[str, None],
                  smoothing_settings: Union[Dict[str, Any], None]):
 
         ConfigReader.__init__(self, config_path=config_path, read_video_info=False)
@@ -102,11 +102,13 @@ def run(self):
 
             csv_rows = []
             n_frames, n_nodes, _, n_tracks = tracks.shape
+            print(tracks)
             for frame_ind in range(n_frames):
                 csv_row = []
                 for track_ind in range(n_tracks):
                     for node_ind in range(n_nodes):
                         for xyp in range(3):
+                            #print(tracks[frame_ind])
                             if xyp == 0 or xyp == 1:
                                 data = tracks[frame_ind, node_ind, xyp, track_ind]
                             else:
@@ -143,6 +145,19 @@ def run(self):
         self.timer.stop_timer()
         stdout_success(msg="All SLEAP H5 data files imported", elapsed_time=self.timer.elapsed_time_str, source=self.__class__.__name__)
 
+
+
+#
+#
+# test = SLEAPImporterH5(config_path=r"C:\troubleshooting\slp_single_rat\project_folder\project_config.ini",
+#                         data_folder=r"C:\troubleshooting\slp_single_rat\h5",
+#                         id_lst=['Jarryd'],
+#                         interpolation_settings=None,
+#                         smoothing_settings = None)
+# test.run()
+
+
+
 # test = SLEAPImporterH5(config_path="/Users/simon/Desktop/envs/simba/troubleshooting/sleap_two_animals/project_folder/project_config.ini",
 #                         data_folder=r'/Users/simon/Desktop/envs/simba/troubleshooting/sleap_two_animals/h5_import',
 #                         id_lst=['White', 'Black'],

simba/ui/pop_ups/make_path_plot_pop_up.py

@@ -19,8 +19,13 @@ def __init__(self):
         self.body_part = Entry_Box(settings_frm, "BODY PART: ", "30")
         self.data_path = FileSelect(settings_frm, "DATA PATH (e.g., H5 or CSV file): ", lblwidth="30")
         color_lst = list(get_color_dict().keys())
+
+
         self.background_color = DropDownMenu(settings_frm, "BACKGROUND COLOR: ", color_lst, "30")
         self.background_color.setChoices(choice="White")
+
+
+
         self.line_color = DropDownMenu(settings_frm, "LINE COLOR: ", color_lst, "30")
         self.line_color.setChoices(choice="Red")
         self.line_thickness = DropDownMenu(settings_frm, "LINE THICKNESS: ", list(range(1, 11)), "30")
@@ -82,4 +87,4 @@ def run(self):
         threading.Thread(target=plotter.run).start()
 
 
-# MakePathPlotPopUp()
+#MakePathPlotPopUp()

simba/ui/pop_ups/quick_path_plot_pop_up.py

@@ -25,7 +25,7 @@ def __init__(self, config_path: Union[str, os.PathLike]):
 
         check_file_exist_and_readable(file_path=config_path)
         PopUpMixin.__init__(self, title="SIMPLE LINE PLOT")
-        ConfigReader.__init__(self, config_path=config_path, read_video_info=False)
+        ConfigReader.__init__(self, config_path=config_path)
         if len(self.outlier_corrected_paths) == 0:
             raise NoFilesFoundError(
                 msg=f"No data found in the {self.outlier_corrected_paths} directory. Place files in this directory to create quick path plots."
@@ -81,9 +81,7 @@ def __init__(self, config_path: Union[str, os.PathLike]):
     def run(self):
         video_name = self.video_dropdown.getChoices()
         data_path = self.video_filepaths[video_name]
-        meta_data, _, fps = read_video_info(
-            vid_info_df=self.video_info_df, video_name=video_name
-        )
+        meta_data, _, fps = read_video_info(vid_info_df=self.video_info_df, video_name=video_name)
         size = (int(meta_data["Resolution_width"]), int(meta_data["Resolution_height"]))
         last_frm = self.last_frm_only_dropdown.getChoices()
         if last_frm == "TRUE":

simba/utils/enums.py

@@ -108,8 +108,8 @@ class Paths(Enum):
     SPLASH_PATH_LINUX = Path("assets/img/splash.PNG")
     SPLASH_PATH_MOVIE = Path("assets/img/splash_2024.mp4")
     BG_IMG_PATH = Path("assets/img/bg_2024.png")
-    LOGO_ICON_WINDOWS_PATH = Path("assets/icons/SimBA_logo.ico")
-    LOGO_ICON_DARWIN_PATH = Path("assets/icons/SimBA_logo.png")
+    LOGO_ICON_WINDOWS_PATH = Path("assets/icons/SimBA_logo_3.ico")
+    LOGO_ICON_DARWIN_PATH = Path("assets/icons/SimBA_logo_3.png")
     UNSUPERVISED_MODEL_NAMES = Path("assets/lookups/model_names.parquet")
     CRITICAL_VALUES = Path("simba/assets/lookups/critical_values_05.pickle")