def _generate_all_r():
""" Generate all combinations of rotation matrix. """
inc = 0.1 # Can change the increment
result = []
for t in frange(-pi / 2, pi / 2 + inc, inc):
for s in frange(-pi, pi + inc, inc):
for p in frange(-pi, pi + inc, inc):
r = tsp_to_r((t, s, p))
result.append(r)
return result
def plot_hist(all_data):
"""Plot the histogram of each data."""
skip = ['Injection_Info', 'Errors']
bins = list(frange(-4, 4, 0.01))
for key in all_data:
if key not in skip:
data = map(lambda t: float(t), all_data[key])
fig = plt.figure()
ax = fig.add_subplot(111, title=key)
n, bins, patches = ax.hist(data,
bins,
normed=1,
histtype='bar',
rwidth=1)
print(n)
print(bins)
print(patches)
plt.show()
#Check to see that you have the right image
cv2.namedWindow('Image' ,cv2.WINDOW_NORMAL)
cv2.resizeWindow('Image', 600,600)
cv2.imshow('Image', imgblur)
cv2.waitKey(0)
#points defines the number of coordinates found by the contour. The array defines how many were found per threshold
points = []
#Shapes defines the shapes found by each iteration of the thresholding
shapes = []
#v is used to cound the shapes that pass the sanity check that are passed into the "shapes" array.
v=0
#check threshold value with a loop
for x in frange(1, 7, 0.1):
v=0
#Thresholds using the value given by the loop
simple = cv2.adaptiveThreshold(imgblur,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,11,x)
#Inverts image
inv = cv2.bitwise_not(simple)
#Detects contours using thresholded image
drawn, contours, hierarchy = cv2.findContours(inv,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
points.append(len(contours))
petriDishSize = 100
width, height = img.shape[:2]
N = 400000
#test_label = np.random.randint(M, size=N)
#test_data = []
test_data = []
for i in range(N):
arr = np.zeros(k + 1)
num = np.random.randint(0, M)
num = (np.binary_repr(num))
for j in range(0, len(num)):
arr[j] = num[j]
test_data.append(arr)
from franges import frange
test_data = np.asarray(test_data)
EbNodB_range = list(frange(-4, 8.5, 0.5))
print(EbNodB_range)
ber = [None] * len(EbNodB_range)
#EbNodB_range = list()
for n in range(0, len(EbNodB_range)):
EbNo = 10.0 ** (EbNodB_range[n] / 10.0)
# print('Test' , EbNo)
# print('R value', R)
alpha1 = (2 * R * EbNo) ** (-0.5)
noise_std = alpha1
noise_mean = 0
no_errors = 0
nn = N
noise = noise_std * np.random.randn(nn, k)
print('noise', noise)
def test_basic_usage():
expected = [0, 0.5]
assert (expected == list(frange(0, 1, 0.5)))
expected = [0, 0.5, 1]
assert (expected == list(frange(0, 1.5, 0.5)))
def test_partial_args():
expected = list(range(10))
assert (expected == list(frange(10)))
def test_basic_usage():
expected = [0, 0.5]
assert(expected == list(frange(0, 1, 0.5)))
expected = [0, 0.5, 1]
assert(expected == list(frange(0, 1.5, 0.5)))
def test_partial_args():
expected = list(range(10))
assert(expected == list(frange(10)))
