Improve code

Improve code for plotting images
Solve typo

Thresholding/thresholding_functions.py

@@ -25,7 +25,7 @@ def thresholding(array):
     threshold = (cluster_center[0] + cluster_center[1]) / 2 
     threshold = threshold[0] 
     
-    # crate binary flood map 
+    # create binary flood map 
     # all pixels below the threshold (1) are flooded, all pixels above (0) are non-flooded
     output = copy.copy(array)
     output[np.where(output < threshold)] = 0.0001
@@ -41,10 +41,10 @@ def apply_thresholding(dest_name):
     for i in os.listdir(dest_name):
         dates.append(i[0:10])
         data = os.path.join(dest_name, i) # open and read tif files
-        data2 = rio.open(data)
-        data2 = data2.read(1) # get only first band (vv)
-        images.append(data2)
-        output.append(thresholding(data2)) # call thresholding function on all files
+        sar = rio.open(data)
+        array = sar.read(1) # get only first band (vv)
+        images.append(array)
+        output.append(thresholding(array)) # call thresholding function on all files
 
     return output, images, dates
         
@@ -52,14 +52,14 @@ def apply_thresholding(dest_name):
 # STEP 2: VISUALISE 
 # =============================================================================
 
-def show_stats(image):
+def show_stats(image, bins, title):
     """ Function that computes statistics and shows histogram for one image """
     stats = []
     stats.append({'min': np.nanmin(image), 
                   'mean': np.nanmean(image), 
                   'median': np.nanmedian(image),
                   'max': np.nanmax(image)})
-    hist = show_hist(image, bins = 50, lw = 0.0, stacked = False, alpha = 0.3, histtype = 'stepfilled', title = "Histogram")
+    hist = show_hist(image, bins = bins, lw = 0.0, stacked = True, histtype = 'stepfilled', title = title)
     return stats, hist 
     
 def visualise(images, nrows, ncols, dates, title='Title'):
@@ -67,8 +67,7 @@ def visualise(images, nrows, ncols, dates, title='Title'):
     plt.figure()
     plt.suptitle(title, fontsize = 18, x = 0.51, y = 0.98)
     for n, image in enumerate(images):
-        ax = plt.subplot(nrows, ncols, n + 1)
+        ax = plt.subplot(2, 2, n + 1)
         ax.imshow(image)
         ax.set_title(dates[n])
-        ax.set_xticks([])
-        ax.set_yticks([])
+        plt.axis('off')
\ No newline at end of file

Thresholding/thresholding_main.py

@@ -8,15 +8,13 @@ an impression of the situation in a certain area.
 # IMPORTS
 # =============================================================================
 
-from thresholding_functions import apply_thresholding
-from thresholding_functions import show_stats
-from thresholding_functions import visualise 
+from Thresholding.thresholding_functions import apply_thresholding, show_stats, visualise
 
 # =============================================================================
 # FILE SETUP
 # =============================================================================
 
-folder_name = 'data/SentinelTimeSeriesStacked' # folder with unzipped, stacked and filtered SAR images
+folder_name = 'data/CapHaitien_Flood2021' # folder with unzipped, stacked and filtered SAR images
 
 # =============================================================================
 # STEP 1: APPLY THRESHOLDING
@@ -28,6 +26,6 @@ thresholding, images, dates = apply_thresholding(folder_name)
 # STEP 2: VISUALISE
 # =============================================================================
 
-show_stats(images[1])
+show_stats(image = images[0], bins=50, title='Histogram')
 
 visualise(thresholding, 2, 3, dates, 'Thresholding method') # 2 = nrows, 3 = ncols