A few updates to get the png plots saved...

seems it won't save the png + html as creating one seems to destry the plot

src/hrvmeg/main.py

@@ -27,7 +27,7 @@ warnings.filterwarnings("ignore")
 def extract_ic_from_meg(
     args_fp: str = "config/args_meg_preproc.json",
     experiment_name: str = experiment_name,
-    run_name: str = "Run1",
+    run_name: str = "extract_ica_from_meg",
     test_run: bool = False,
 ) -> None:
     """
@@ -96,7 +96,7 @@ def extract_ic_from_meg(
 def label_cardiac_ic(
     args_fp: str = "config/args_meg_preproc.json",
     experiment_name: str = experiment_name,
-    run_name: str = "sgd",
+    run_name: str = "label_cardiac_ic",
     test_run: bool = False,
 ) -> None:
     """
@@ -137,23 +137,23 @@ def label_cardiac_ic(
 
             output_png = Path(config.INTERMEDIATE_DIR, fp.parent.name, "Heartifact.png")
             plot_cardiac_ic = PlotCardiacIC(
-                output_file=output_png, x_values=df[time_col], y_values=df[IC_col]
+                output_file=output_png.absolute(), x_values=df[time_col], y_values=df[IC_col]
             )
-            html_plot_out = plot_cardiac_ic()
+            plot_out = plot_cardiac_ic()
             
             mlflow.set_experiment(experiment_name)
             with mlflow.start_run(run_name=run_name) as run:
                 run_id = mlflow.active_run().info.run_id
                 mlflow.log_params(args)
-                mlflow.log_artifact(html_plot_out)
-
+                mlflow.log_artifact(plot_out)
+                mlflow.log_artifact(output_png)
             open(Path(config.CONFIG_DIR, "run_id.txt"), "w", encoding="utf-8").write(run_id)
 
 
 @app.command()
 def preprocess_ecgs(
-    experiment_name: str = "Preprocess ECG Data",
-    run_name: str = "preprocess 1",
+    experiment_name: str = experiment_name,
+    run_name: str = "preprocess ECG",
     test_run: bool = True,
 ):
     ecg_scan_folders = get_data.get_folders(config.ECG_DATA_DIR, ".txt")
@@ -171,8 +171,7 @@ def preprocess_ecgs(
         scaled_ecg = scaler.fit_transform(ecg_dat)
 
         print(scaled_ecg)
-
-    # Add standard scaling and baseline correction
+        
 
 
 @app.command()