python to c converter tool online

1import math
2x,y=input().split(),input().split()
3x,y=[int(i) for i in x],[int(i) for i in y]
4t,t1=abs(x[0]-y[0]),abs(x[1]-y[1])
5
6print("%.2f" %math.sqrt(t*t+t1*t1))

1class Buffer:
2
3    def __init__(self,size_max):
4        self.max = size_max
5        self.data = []
6
7    class __Full:
8
9        def append(self, x):
10
11            self.data[self.cur] = x
12            self.cur = (self.cur+1) % self.max
13        def get(self):
14
15            return self.data[self.cur:]+self.data[:self.cur]
16
17    def append(self,x):
18
19        self.data.append(x)
20        if len(self.data) == self.max:
21            self.cur = 0
22
23            self.__class__ = self.__Full
24
25    def get(self):
26
27        return self.data
28
29
30if __name__=='__main__':
31    n=input('Enter the occupide size of the buffer : ')
32    x=Buffer(int(n))
33
34    x.append(1); x.append(2); x.append(3); x.append(4)
35
36    print (x.__class__, x.get(  ))
37    l=len(x.data)
38    print(f"The free buffer is :{l}")
39    print(f"The left size buffer is :{int(n)-int(l)}")
40
41    x.append(5)
42
43    print (x.__class__, x.get(  ))
44    x.append(6)
45    print (x.data, x.get(  ))
46    x.append(7); x.append(8); x.append(9); x.append(10)
47    print (x.data, x.get(  ))

1def main():
2  # 4 x 4 csr matrix
3  #    [1, 0, 0, 0],
4  #    [2, 0, 3, 0],
5  #    [0, 0, 0, 0],
6  #    [0, 4, 0, 0],
7  csr_values = [2, 3, 1, 4,5]
8  col_idx    = [1, 2, 0, 1,1]
9  row_ptr    = [0, 2, 4,5]
10  csr_matrix = [
11      csr_values,
12      col_idx,
13      row_ptr
14      ]
15
16  dense_matrix = [
17      [0, 3, 0],
18      [1, 4, 5],
19      [2, 0, 0],
20      ]
21
22  res = [
23      [0, 0, 0],
24      [0, 0, 0],
25      [0, 0, 0],
26      ]
27
28  # matrix order, assumes both matrices are square
29  n = len(dense_matrix)
30
31  # res = dense X csr
32  csr_row = 0 # Current row in CSR matrix
33  for i in range(n):
34    start, end = row_ptr[i], row_ptr[i + 1]
35    for j in range(start, end):
36      col, csr_value = col_idx[j], csr_values[j]
37      for k in range(n):
38        dense_value = dense_matrix[k][csr_row]
39        res[k][col] += csr_value * dense_value
40    csr_row += 1
41
42  print(res) 
43
44
45if __name__ == '__main__':
46  main()
47

1np.linspace(0,1,11)