I was trying to optimize this function using Numba, but I am unable to do it. I think this has no part of the code which can be accelerated. If anyone can help me with an optimized version of this, My program would become blazing fast. Please tell if any dataset or other info is needed. When I apply direct @jit on this, It is not working.
def c_a(x, y, z, counter, p_l):
# start = time.time()
if counter == 1:
l = x
m = y
n = z
path = "c_r.pdb"
global r_a_t
p = Bio.PDB.PDBParser()
structure = p.get_structure('mSN1', path)
c_r = [a.get_coord() for a in structure.get_atoms()]
lengthnew = len(c_r)
m_d = np.array([-45, -45, -45])
a_s_r = np.zeros((128, 128, 128), np.complex)
for i in range(0, lengthnew):
x = int(math.floor((c_r[i][0] - m_d[0]) / 1.2))
y = int(math.floor((c_r[i][1] - m_d[1]) / 1.2))
z = int(math.floor((c_r[i][2] - m_d[2]) / 1.2))
with open("Ei.txt", 'r') as ei_values:
for row in ei_values:
s_v = row.split()
if s_v[0] == r_a_t[i] :
a_s_r[x, y, z] = np.complex(s_v[1])
n_n = lambda x, y, z : [(x2, y2, z2) for x2 in range(x - 5, x + 6)
for y2 in range(y - 5, y + 6)
for z2 in range(z - 5, z + 6)
if (-1 < x < X and
-1 < y < Y and
-1 < z < Z and
(x != x2 or y != y2 or z != z2) and
(0 <= x2 < X) and
(0 <= y2 < Y) and
(0 <= z2 < Z) and
((( abs(x - x2)) ** 2 + (abs(y - y2)) ** 2 + (abs(z - z2)) ** 2 ) <= 25))]
m = n_n(l, m, n)
result = 0
for i in range(0, len(m)):
a = m[i][0]
b = m[i][1]
c = m[i][2]
result = result + a_s_r[a][b][c]
return result
else:
l = x
m = y
n = z
path = p_l
global l_a_t
p = Bio.PDB.PDBParser()
structure = p.get_structure('mSN1', path)
c_l = [a.get_coord() for a in structure.get_atoms()]
lengthnew = len(c_l)
m_d = np.array([-45, -45, -45])
a_s_l = np.zeros((128, 128, 128), np.complex)
for i in range(0, lengthnew):
x = int(math.floor((c_l[i][0] - m_d[0]) / 1.2))
y = int(math.floor((c_l[i][1] - m_d[1]) / 1.2))
z = int(math.floor((c_l[i][2] - m_d[2]) / 1.2))
with open("E.txt", 'r') as e_v:
for row in e_v:
s_v = row.split()
if s_v[0] == l_a_t[i] :
a_s_l[x, y, z] = np.complex(s_v[1])
n_n = lambda x, y, z : [(x2, y2, z2) for x2 in range(x - 5, x + 6)
for y2 in range(y - 5, y + 6)
for z2 in range(z - 5, z + 6)
if (-1 < x < X and
-1 < y < Y and
-1 < z < Z and
(x != x2 or y != y2 or z != z2) and
(0 <= x2 < X) and
(0 <= y2 < Y) and
(0 <= z2 < Z) and
(((abs(x - x2)) ** 2 + (abs(y - y2)) ** 2 + (abs(z - z2)) ** 2 ) <= 25))]
m = n_n(l, m, n)
result = 0
for i in range(0, len(m)):
a = m[i][0]
b = m[i][1]
c = m[i][2]
result = result + a_s_l[a][b][c]
# print "c_a : ", time.time() - start
return result
Solved.
Brought out all the file reading steps outside the function, as they were being executed many times. It gave a 70x boost.
Just left the lambda functions in the function as they are dependent on x, y & z.