def autocorrelation_addition(code, num):
"""
Given a code composed of num subcodes (of the same length), computes the autocorrelation of each and adds them, outputting
the psl of that addition.
:param code: initial sequence
:type code: array
:param num: number of sub_codes
:type num: int
:return: psl of the sum of the codes (how near-alternating the codes are).
"""
if len(
code
) % num != 0: # throw an error if we cannot properly divide the code
raise ValueError
else:
length = len(code) / num # length of each code
current_count = 1
subcodes = []
for i in range(num): # compute the subcodes
new_code = code[length * i:length * (i + 1)]
subcodes.append(new_code)
autocorrelation_lsts = []
for subcode in subcodes: # compute their autocorrelations
autocorrelation_lsts.append(con_psl_matrix(subcode))
final_autocorrelation = []
for i in range(len(autocorrelation_lsts[0])): # add entry by entry
sum = 0
for j in range(len(autocorrelation_lsts)):
sum += autocorrelation_lsts[j][i]
final_autocorrelation.append(sum)
return second_max(final_autocorrelation)
def gen_alternating_codes(length):
"""
Function to generate pairs of alternating codes; uses the naive approach of looping over all codes.
:param length: length of the codes in question
:type length: int
:return: none, just prints the codes.
"""
all_codes = gen_binary_codes(length) # generate all binary codes
for code_1 in all_codes: # double for loop over all codes, sums their con_psl_matrixes and checks if they are alternating
for code_2 in all_codes:
x = sum_codes(con_psl_matrix(code_1), con_psl_matrix(code_2))
if is_alternating(x):
print(x)
print(code_1)
print(code_2)
def fourth_alternating_code():
code = [-1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1]
x_1 = np.linspace(0, math.pi, 200)
y_1 = np.sin(x_1)
sin_fns = []
for i in range(len(code)):
x = np.linspace(2 * math.pi * i + math.pi / 2,
2 * math.pi * (i + 1) + math.pi / 2, 200)
y = -np.sin(x) * code[i]
sin_fns.append((x, y))
fig = plt.figure()
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.add_patch(
mpatches.Rectangle((math.pi / 2, -1),
2 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 2 * math.pi, 0),
4 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 6 * math.pi, -1),
4 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 10 * math.pi, 0),
2 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 12 * math.pi, -1),
2 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 14 * math.pi, 0),
2 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 16 * math.pi, -1),
4 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 20 * math.pi, 0),
2 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 22 * math.pi, -1),
4 * math.pi,
1,
fill=True,
color='g',
alpha=0.5))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 2 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 6 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 10 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 12 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 14 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 16 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 20 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
ax.add_patch(
mpatches.Rectangle((math.pi / 2 + 22 * math.pi, -1),
0,
2,
fill=True,
color='#000000'))
for i in range(len(sin_fns)):
ax.plot(sin_fns[i][0], sin_fns[i][1], c='r')
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.axis([0, 100, -5, 5])
ax.axis('off')
plt.show()
plt.clf()
autocorrelation = con_psl_matrix(code)
x = []
for i in range(len(autocorrelation)):
x.append(i)
fig = plt.figure()
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.scatter(x, np.abs(autocorrelation))
ax.plot(x, np.abs(autocorrelation))
ax.axis([-1, 30, -1, 15])
ax.axis('off')
plt.show()
plt.clf()