zinating
diff --git a/‎boolean_algebra/quine_mc_cluskey.py‎
Lines changed: 24 additions & 24 deletions b/‎boolean_algebra/quine_mc_cluskey.py‎
Lines changed: 24 additions & 24 deletions
diff --git a/‎data_structures/queue/circular_queue.py‎
Lines changed: 3 additions & 2 deletions b/‎data_structures/queue/circular_queue.py‎
Lines changed: 3 additions & 2 deletions
diff --git a/‎digital_image_processing/filters/gaussian_filter.py‎
Lines changed: 4 additions & 4 deletions b/‎digital_image_processing/filters/gaussian_filter.py‎
Lines changed: 4 additions & 4 deletions
@@ -1,11 +1,11 @@
 def compare_string(string1, string2):
     """
-        >>> compare_string('0010','0110')
-        '0_10'
+    >>> compare_string('0010','0110')
+    '0_10'
 
-        >>> compare_string('0110','1101')
-        -1
-        """
+    >>> compare_string('0110','1101')
+    -1
+    """
     l1 = list(string1)
     l2 = list(string2)
     count = 0
@@ -21,9 +21,9 @@ def compare_string(string1, string2):
 
 def check(binary):
     """
-        >>> check(['0.00.01.5'])
-        ['0.00.01.5']
-        """
+    >>> check(['0.00.01.5'])
+    ['0.00.01.5']
+    """
     pi = []
     while 1:
         check1 = ["$"] * len(binary)
@@ -45,9 +45,9 @@ def check(binary):
 
 def decimal_to_binary(no_of_variable, minterms):
     """
-        >>> decimal_to_binary(3,[1.5])
-        ['0.00.01.5']
-        """
+    >>> decimal_to_binary(3,[1.5])
+    ['0.00.01.5']
+    """
     temp = []
     s = ""
     for m in minterms:
@@ -61,12 +61,12 @@ def decimal_to_binary(no_of_variable, minterms):
 
 def is_for_table(string1, string2, count):
     """
-        >>> is_for_table('__1','011',2)
-        True
+    >>> is_for_table('__1','011',2)
+    True
 
-        >>> is_for_table('01_','001',1)
-        False
-        """
+    >>> is_for_table('01_','001',1)
+    False
+    """
     l1 = list(string1)
     l2 = list(string2)
     count_n = 0
@@ -81,12 +81,12 @@ def is_for_table(string1, string2, count):
 
 def selection(chart, prime_implicants):
     """
-        >>> selection([[1]],['0.00.01.5'])
-        ['0.00.01.5']
+    >>> selection([[1]],['0.00.01.5'])
+    ['0.00.01.5']
 
-        >>> selection([[1]],['0.00.01.5'])
-        ['0.00.01.5']
-        """
+    >>> selection([[1]],['0.00.01.5'])
+    ['0.00.01.5']
+    """
     temp = []
     select = [0] * len(chart)
     for i in range(len(chart[0])):
@@ -128,9 +128,9 @@ def selection(chart, prime_implicants):
 
 def prime_implicant_chart(prime_implicants, binary):
     """
-        >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
-        [[1]]
-        """
+    >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5'])
+    [[1]]
+    """
     chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))]
     for i in range(len(prime_implicants)):
         count = prime_implicants[i].count("_")
 
@@ -1,5 +1,6 @@
 # Implementation of Circular Queue (using Python lists)
 
+
 class CircularQueue:
     """Circular FIFO queue with a fixed capacity"""
 
@@ -59,7 +60,7 @@ def enqueue(self, data):
             raise Exception("QUEUE IS FULL")
 
         self.array[self.rear] = data
-        self.rear = (self.rear+1)%self.n
+        self.rear = (self.rear + 1) % self.n
         self.size += 1
         return self
 
@@ -88,6 +89,6 @@ def dequeue(self):
 
         temp = self.array[self.front]
         self.array[self.front] = None
-        self.front = (self.front + 1)%self.n
+        self.front = (self.front + 1) % self.n
         self.size -= 1
         return temp
@@ -22,10 +22,10 @@ def gaussian_filter(image, k_size, sigma):
     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     image_array = zeros((dst_height * dst_width, k_size * k_size))
     row = 0
-    for i, j in product(range(dst_height), range(dst_width)): 
-            window = ravel(image[i : i + k_size, j : j + k_size])
-            image_array[row, :] = window
-            row += 1
+    for i, j in product(range(dst_height), range(dst_width)):
+        window = ravel(image[i : i + k_size, j : j + k_size])
+        image_array[row, :] = window
+        row += 1
 
     #  turn the kernel into shape(k*k, 1)
     gaussian_kernel = gen_gaussian_kernel(k_size, sigma)