diff --git a/example.py b/example.py
index c08c2ce..52c88e3 100644
--- a/example.py
+++ b/example.py
@@ -8,6 +8,7 @@
 
 # load modules
 import os
+import json
 import numpy as np
 import pickle
 import warnings
@@ -31,14 +32,18 @@
 # can also be loaded from a .kml polygon
 # kml_polygon = os.path.join(os.getcwd(), 'examples', 'NARRA_polygon.kml')
 # polygon = SDS_tools.polygon_from_kml(kml_polygon)
+# or read from geojson polygon (create it from https://geojson.io/)
+# geojson_polygon = os.path.join(os.getcwd(), 'examples', 'NARRA_polygon.geojson')
+# polygon = SDS_tools.polygon_from_geojson(geojson_polygon)
 # convert polygon to a smallest rectangle (sides parallel to coordinate axes)
 polygon = SDS_tools.smallest_rectangle(polygon)
 
 # date range
-dates = ['1984-01-01', '2022-01-01']
-
+# dates = ['1984-01-01', '2022-01-01']
+dates = ['2023-12-01', '2024-01-31']
 # satellite missions
-sat_list = ['L5','L7','L8']
+# sat_list = ['L5','L7','L8']
+sat_list = ['L9']
 collection = 'C02' # choose Landsat collection 'C01' or 'C02'
 # name of the site
 sitename = 'NARRA'
@@ -88,11 +93,17 @@
     'cloud_mask_issue': False,  # switch this parameter to True if sand pixels are masked (in black) on many images
     'sand_color': 'default',    # 'default', 'latest', 'dark' (for grey/black sand beaches) or 'bright' (for white sand beaches)
     'pan_off': False,           # True to switch pansharpening off for Landsat 7/8/9 imagery
+    's2cloudless_prob': 40,      # threshold to identify cloud pixels in the s2cloudless probability mask
     # add the inputs defined previously
     'inputs': inputs,
     'create_plot': True,        #True create a matplotlib plot of the image with the datetime as the title. False save as a standard JPG
 }
 
+# fn_animation = os.path.join(inputs['filepath'],inputs['sitename'], '%s_animation_RGB.mp4'%inputs['sitename'])
+# fp_images = os.path.join(inputs['filepath'], inputs['sitename'], 'jpg_files', 'preprocessed')
+# fps = 4 # frames per second in animation
+# SDS_tools.make_animation_mp4(fp_images, fps, fn_animation)
+
 # [OPTIONAL] preprocess images (cloud masking, pansharpening/down-sampling)
 # SDS_preprocess.save_jpg(metadata, settings)
 
@@ -119,6 +130,13 @@
 gdf.to_file(os.path.join(inputs['filepath'], inputs['sitename'], '%s_output_%s.geojson'%(sitename,geomtype)),
                                 driver='GeoJSON', encoding='utf-8')
 
+# # create MP4 timelapse animation
+# fn_animation = os.path.join(inputs['filepath'],inputs['sitename'], '%s_animation_shorelines.mp4'%inputs['sitename'])
+# fp_images = os.path.join(inputs['filepath'], inputs['sitename'], 'jpg_files', 'detection')
+# fps = 4 # frames per second in animation
+# SDS_tools.make_animation_mp4(fp_images, fps, fn_animation)
+
+
 # plot the mapped shorelines
 plt.ion()
 fig = plt.figure(figsize=[15,8], tight_layout=True)
@@ -134,11 +152,54 @@
 fig.savefig(os.path.join(inputs['filepath'], inputs['sitename'], 'mapped_shorelines.jpg'),dpi=200)
 
 #%% 4. Shoreline analysis
+def load_data_from_json(filepath: str) -> dict:
+    """
+    Reads data from a JSON file and returns it as a dictionary.
+
+    The function reads the data from the specified JSON file using the provided filepath.
+    It applies a custom object hook, `DecodeDateTime`, to decode the datetime and shoreline
+    data if they exist in the dictionary.
+
+    Args:
+        filepath (str): Path to the JSON file.
+
+    Returns:
+        dict: Data read from the JSON file as a dictionary.
+
+    """
+
+    def DecodeDateTime(readDict):
+        """
+        Helper function to decode datetime and shoreline data in the dictionary.
+
+        Args:
+            readDict (dict): Dictionary to decode.
+
+        Returns:
+            dict: Decoded dictionary.
+
+        """
+        if "dates" in readDict:
+            tmp = [
+                datetime.fromisoformat(dates) for dates in readDict["dates"]
+            ]
+            readDict["dates"] = tmp
+        if "shorelines" in readDict:
+            tmp = [
+                np.array(shoreline) if len(shoreline) > 0 else np.empty((0, 2))
+                for shoreline in readDict["shorelines"]
+            ]
+            readDict["shorelines"] = tmp
+        return readDict
+
+    with open(filepath, "r") as fp:
+        data = json.load(fp, object_hook=DecodeDateTime)
+    return data
+
 
 # if you have already mapped the shorelines, load the output.pkl file
 filepath = os.path.join(inputs['filepath'], sitename)
-with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:
-    output = pickle.load(f)
+output = load_data_from_json(os.path.join(filepath, sitename + '_output' + '.json'))
 # remove duplicates (images taken on the same date by the same satellite)
 output = SDS_tools.remove_duplicates(output)
 # remove inaccurate georeferencing (set threshold to 10 m)
@@ -193,7 +254,7 @@
                       'max_range':           30,        # max range for points around transect
                       'min_chainage':        -100,      # largest negative value along transect (landwards of transect origin)
                       'multiple_inter':      'auto',    # mode for removing outliers ('auto', 'nan', 'max')
-                      'prc_multiple':         0.1,      # percentage to use in 'auto' mode to switch from 'nan' to 'max'
+                      'auto_prc':            0.1,      # percentage to use in 'auto' mode to switch from 'nan' to 'max'
                      }
 cross_distance = SDS_transects.compute_intersection_QC(output, transects, settings_transects)
 
@@ -239,7 +300,7 @@
 # load the measured tide data
 filepath = os.path.join(os.getcwd(),'examples','NARRA_tides.csv')
 tide_data = pd.read_csv(filepath, parse_dates=['dates'])
-dates_ts = [_.to_pydatetime() for _ in tide_data['dates']]
+dates_ts = [pd.to_datetime(_).to_pydatetime() for _ in tide_data['dates']]
 tides_ts = np.array(tide_data['tide'])
 
 # get tide levels corresponding to the time of image acquisition
@@ -358,7 +419,7 @@
 
     # plot seasonal averages
     fig,ax=plt.subplots(1,1,figsize=[14,4],tight_layout=True)
-    ax.grid(b=True,which='major', linestyle=':', color='0.5')
+    ax.grid(which='major', linestyle=':', color='0.5')
     ax.set_title('Time-series at %s'%key, x=0, ha='left')
     ax.set(ylabel='distance [m]')
     ax.plot(dates_nonan, chainage,'+', lw=1, color='k', mfc='w', ms=4, alpha=0.5,label='raw datapoints')
@@ -384,7 +445,7 @@
 
     # plot seasonal averages
     fig,ax=plt.subplots(1,1,figsize=[14,4],tight_layout=True)
-    ax.grid(b=True,which='major', linestyle=':', color='0.5')
+    ax.grid(which='major', linestyle=':', color='0.5')
     ax.set_title('Time-series at %s'%key, x=0, ha='left')
     ax.set(ylabel='distance [m]')
     ax.plot(dates_nonan, chainage,'+', lw=1, color='k', mfc='w', ms=4, alpha=0.5,label='raw datapoints')
@@ -627,4 +688,4 @@
     ax[1].text(j+1,median_data[j]+1, '%.1f' % median_data[j], horizontalalignment='center', fontsize=14)
     ax[1].text(j+1+0.35,median_data[j]+1, ('n=%.d' % int(n_data[j])), ha='center', va='center', fontsize=12, rotation='vertical')
 ax[1].set(ylabel='error [m]', ylim=sett['lims']);
-fig.savefig(os.path.join(os.getcwd(),'examples','comparison_all_transects.jpg'), dpi=150)
+fig.savefig(os.path.join(os.getcwd(),'examples','comparison_all_transects.jpg'), dpi=150)
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