picks, reject_by_annotation=True)
src = ica.get_sources(raw)
src = src.get_data()
mixing_mat = ica.get_components()

i_cmp = 8  # 8 or 11  # this component is selected previously manualy
src_i = src[i_cmp, :]
topo_i = mixing_mat[:, i_cmp]

n_samples = len(src_i)
times = np.arange(0, n_samples) / sfreq
# filter in the harmonic frequency ranges
src_10 = filtfilt(b10, a10, src_i)
src_20 = filtfilt(b20, a20, src_i)
src_30 = filtfilt(b30, a30, src_i)

src_10 = src_10 / np.std(src_10)
src_20 = src_20 / np.std(src_20)
src_20_res, c_opt, phi_opt = harmonic_removal_simple(src_10, src_20, sfreq, return_all=True)

scr10_h = hilbert_(src_10).ravel()
scr20_h = hilbert_(src_20).ravel()


phase_diff_all = np.mod(np.angle(scr20_h) - 2 * np.angle(scr10_h), 2*np.pi)
fig = plt.figure()
circular_hist(phase_diff_all, alpha=1, bins=100, plot_mean=True)
plt.title('histogram of the phase difference of the whole signals')

print('mean angle al==', stats.circmean(phase_diff_all))

win_len = int(10 * sfreq)
overlap_perc = 0.8
start_step = int((1 - overlap_perc) * win_len)
n_win = 10
cc = []
rr = []
win_no = -1
phase_mean = []
power_ratio = []
pow_alpha = []
pow_beta = []
c_opt_seg = []
phi_opt_seg = []
fig = plt.figure()
ax = plt.subplot(111)
while 1:
    # win_no = 3
    win_no += 1
    print(win_no)
    win_start = win_no * start_step
    win_end = win_start + win_len
    if win_end > len(scr10_h):
        break
    s1 = scr10_h[win_start:win_end]
    s2 = scr20_h[win_start:win_end]
    phase_diff = np.mod(np.angle(s2) - 2 * np.angle(s1), 2*np.pi)
    # cc1, rr1 = np.histogram(phase_diff)
    # cc.append(cc1)
    # rr.append(rr1)
    phase_mean.append(stats.circmean(phase_diff))
    # circular_hist(phase_diff, alpha=0.2, bins=100)

    # pow_ratio = np.mean(np.real(s1)**2) / np.mean(np.real(s2)**2)
    # pow_ratio = np.mean(np.abs(s2)) / np.mean(np.abs(s1))
    # plt.plot(np.abs(s1) / np.abs(s2))
    pow_ratio = np.mean(np.abs(s2) / np.abs(s1))
    power_ratio.append(pow_ratio)
    pow_alpha.append(np.mean(np.abs(s1)))
    pow_beta.append(np.mean(np.abs(s2)))
    # circular_hist(phase_diff, alpha=0.2)
    # _, c_opt_seg1, phi_opt_seg1 = harmonic_removal_simple(s1, s2, sfreq, return_all=True)
    # c_opt_seg.append(c_opt_seg1)
    # phi_opt_seg.append(phi_opt_seg1)

    psd(src_i[win_start:win_end], sfreq, f_max=45, fig=(fig, ax))


fig = plt.figure()
circular_hist(phase_mean, alpha=1, bins=100, plot_mean=True)
plt.title('histogram of the mean phase difference of the sliding windows')
print('mean angle al==', stats.circmean(phase_mean))

plt.figure()
plt.hist(c_opt_seg)
plt.vlines(c_opt, 0, 15)
plt.vlines(np.mean(c_opt_seg), 0, 15, colors='red')

fig = plt.figure()
circular_hist(phi_opt_seg, alpha=1, bins=10, plot_mean=False, plot_ref=None)


fig = plt.figure()

cc1, rr1 = np.histogram(phi_opt_seg, bins=bins)
cc_norm = cc1 / np.sum(cc1)
width = rr1[1] - rr1[0]
angles = rr1[:-1] + width / 2  # Compute the angle each bar is centered on

ax = plt.subplot(projection='polar')

# Draw bars
bars = plt.bar(
    x=angles,
    height=cc_norm,
    width=width,
    bottom=None,
    linewidth=.2,
    edgecolor="lightblue",
    alpha=alpha)



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International Classification of Diseases 11 by the World Health Organization
ICD-11
What is it about?
International Classification of Diseases 11 by the World Health Organization.
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App Store Description
International Classification of Diseases 11 by the World Health Organization.
The ICD serves as a foundation for identifying global health trends and statistics and is used by health insurers whose reimbursements depend on ICD coding.
This new edition carries about 55,000 codes for injuries, diseases and causes of death. By comparison, the 10th edition had 14,400 codes. This advance preview closely reflect progress in medicine, and the codes relating to antimicrobial resistance in the new ICD are more closely aligned with the Global Antimicrobial Resistance Surveillance System. It can also better capture data regarding healthcare safety, meaning it will help identify and reduce unnecessary events that may harm health, including unsafe workflows in hospitals.
There are new chapters, including ones on traditional medicine and sexual health. Traditional medicine has not been classified in this system until now. The chapter on sexual health brings together conditions that were previously categorized in other ways. The WHO also added gaming disorder to the section on addictive disorders.
App Features:
- Search and browse through all topics
- Full Offline Content (no internet connection necessary)
- Dynamic Search on ICD topics
- Copy and Paste allowed through content
Download ICD 11 Now!
Official ICD WHO.Unit Converter
Conversion formula
The conversion factor from ounces to pounds is 0.0625, which means that 1 ounce is equal to 0.0625 pounds:
1 oz = 0.0625 lb
To convert 147 ounces into pounds we have to multiply 147 by the conversion factor in order to get the mass amount from ounces to pounds. We can also form a simple proportion to calculate the result:
1 oz → 0.0625 lb
147 oz → M(lb)
Solve the above proportion to obtain the mass M in pounds:
M(lb) = 147 oz × 0.0625 lb
M(lb) = 9.1875 lb
The final result is:
147 oz → 9.1875 lb
We conclude that 147 ounces is equivalent to 9.1875 pounds:
147 ounces = 9.1875 pounds
Alternative conversion
We can also convert by utilizing the inverse value of the conversion factor. In this case 1 pound is equal to 0.10884353741497 × 147 ounces.
Another way is saying that 147 ounces is equal to 1 ÷ 0.10884353741497 pounds.
Approximate result
For practical purposes we can round our final result to an approximate numerical value. We can say that one hundred forty-seven ounces is approximately nine point one eight eight pounds:
147 oz ≅ 9.188 lb
An alternative is also that one pound is approximately zero point one zero nine times one hundred forty-seven ounces.
Conversion table
ounces to pounds chart
For quick reference purposes, below is the conversion table you can use to convert from