ADD: Adjust the resolution of the image and dates you are pulling from in a large directory

lars/preprocessing/radar_preprocessing.py

@@ -6,9 +6,10 @@
 import cmweather    # noqa 
 
 
-def preprocess_radar_data(file_path, output_path, date=None, 
+def preprocess_radar_data(file_path, output_path, date=None,
                           radar_field='corrected_reflectivity',
                           x_bounds=(-150000, 150000), y_bounds=(-150000, 150000),
+                          size_px=256, dpi=150,
                           **kwargs):
     """
     Preprocess cf/Radial radar data from a given file path. This module will load the radar data,
@@ -20,10 +21,12 @@ def preprocess_radar_data(file_path, output_path, date=None,
 
     output_path (str): Path to save the processed .png images.
     date (str or list): Optional date string to filter radar files, in the format 'YYYYMMDD'.
-    radar_field (str): The radar field to be processed, 
+    radar_field (str): The radar field to be processed,
         default is 'corrected_reflectivity'.
     x_bounds (tuple): The x-axis bounds for plotting in meters.
     y_bounds (tuple): The y-axis bounds for plotting in meters.
+    size_px (int): Width and height of the output PNG in pixels. Default is 256.
+    dpi (int): Dots per inch for the saved figure. Default is 150.
 
     **kwargs:
 
@@ -39,9 +42,10 @@ def preprocess_radar_data(file_path, output_path, date=None,
     if date is not None:
         if isinstance(date, str):
             date = [date]
+        file_list2 = []
         for date_str in date:
-
-            file_list = [f for f in file_list if date_str in f]
+            file_list2.extend([f for f in file_list if date_str in f])
+        file_list = file_list2
     out_df = pd.DataFrame(columns=['file_path', 'time', 'label', 'ref_min', 'ref_max'])
     if not "vmin" in kwargs:
         kwargs['vmin'] = -20
@@ -62,11 +66,12 @@ def preprocess_radar_data(file_path, output_path, date=None,
         if 'sweep_0' in radar:
             sweep = radar['sweep_0']
             if sweep["sweep_mode"] == 'ppi' or sweep["sweep_mode"] == 'sector':
-                fig = plt.figure(figsize=(256/150, 256/150))
+                fig = plt.figure(figsize=(size_px/dpi, size_px/dpi))
                 ax = plt.axes()
                 sweep["corrected_reflectivity"].where(
                         sweep["corrected_reflectivity"] > min_ref).plot(x="x", y="y",
                                                                         ax=ax,
+                                                                        add_colorbar=False,
                                                                         **kwargs)
                 min_ref = sweep["corrected_reflectivity"].where(
                             sweep["corrected_reflectivity"] > min_ref).values.min()
@@ -75,18 +80,15 @@ def preprocess_radar_data(file_path, output_path, date=None,
 
                 ax.set_xlim(x_bounds)
                 ax.set_ylim(y_bounds)
-                ax.set_xlabel('X [m]')
-                ax.set_ylabel('Y [m]')
-                ax.set_xticks([-100000, -50000, 0, 50000, 100000])
-                ax.set_yticks([-100000, -50000, 0, 50000, 100000])
+
                 fig.tight_layout()
                 file_name = os.path.join(output_path,
                                          os.path.basename(file).replace('.nc', '.png'))
                 time_str = pd.to_datetime(sweep["time"].values[0]).strftime('%Y-%m-%d %H:%M:%S')
                 label = "UNKNOWN"   # Placeholder for actual label extraction logic
                 fig.savefig(os.path.join(output_path,
                                          os.path.basename(file).replace('.nc', '.png')),
-                            dpi=150)
+                            dpi=dpi, bbox_inches='tight', pad_inches=0)
                 plt.close(fig)
                 out_df.loc[len(out_df)] = [file_name, time_str, label, min_ref, max_ref]
 

pyproject.toml

@@ -20,7 +20,7 @@ classifiers = [
     "License :: OSI Approved :: MIT License",
     "Programming Language :: Python :: 3",
 ]
-dependencies = ["xradar"]
+dependencies = ["xradar", "scikit-learn", "python-dotenv", "aiohttp", "asksageclient", "pip_system_certs", "requests"]
 
 [project.optional-dependencies]
 dev = ["pytest>=6.0", "pytest-asyncio>=0.21", "black", "flake8", "openai", "xradar", "python-dotenv",