time = 1. / 30.

[r, c] = np.shape(pend_centers)

VelocityMarker = nzeros([r,c],dtype=float)

for i in narange(2., (r) + 1):

VelocityMarker[int(i) - 1, int((c - 1.)) - 1] = (

pend_centers[int(i) - 1, int((c - 1.)) - 1] - pend_centers[int((i - 1.)) - 1, int((c - 1.)) - 1])/ time

VelocityMarker[int(i) - 1, int(c) - 1] = (

pend_centers[int(i) - 1, int(c) - 1] - pend_centers[int((i - 1.)) - 1, int(c) - 1])/ time

colSize = d11.shape[1]

diff = nzeros([3,colSize],dtype=float)

diff[0,:] = nabs(nround((d11[int((z-3.))-1]-d11[int((z-2.))-1])))

diff[1,:] = nabs(nround((d11[int((z-2.))-1]-d11[int((z-1.))-1])))

diff[2,:] = nabs(nround((d11[int((z-1.))-1]-d11[int(z)-1])))

w = scp.stats.mode(diff)

a = np.array([1., 2., 3.])

remainder1 = remainder.copy()

remainder2 = remainder1.copy() - 0.6

strobeLen = strobe.shape[1]

aLen = a.shape[1]

fre_1 = nzeros([strobeLen,aLen],dtype=float)

for i in narange(1., (strobeLen) + 1):

for j in narange(1., (aLen) + 1):

fre_1[int(i) - 1, int(j) - 1] = ndot(strobe[int(i) - 1], a[int(j) - 1])

t = np.shape(fre_1)

iter = 1.

remainder2Len = remainder2.shape[1]

freq_plus = nzeros([t[0,0]*t[0,1],remainder2Len])

freq_minus = nzeros([t[0,0]*t[0,1],remainder2Len])

freq_plus_shift_negative1 = nzeros([t[0,0]*t[0,1],remainder2Len])

freq_plus_shift_negative2 = nzeros([t[0,0]*t[0,1],remainder2Len])

for i in narange(1., (t[0, 0]) + 1):

for j in narange(1., (t[0, 1]) + 1):

fre_11 = fre_1[int(i) - 1, int(j) - 1].copy()

remainder_1 = remainder2[int(i) - 1, :].copy()

for k in narange(1., remainder2Len + 1):

if remainder_1[0, int(k) - 1] > 0.:

freq_plus[int(iter) - 1, int(k) - 1] = fre_11 + remainder_1[0, int(k) - 1]

freq_minus[int(iter) - 1, int(k) - 1] = fre_11 - remainder_1[0, int(k) - 1]

freq_plus_shift_negative1[int(iter) - 1, int(k) - 1] = fre_11 + 30. - remainder_1[0, int(k) - 1]

freq_plus_shift_negative2[int(iter) - 1, int(k) - 1] = fre_11 - 30. - remainder_1[0, int(k) - 1]

iter = iter + 1.

est_fre = np.vstack((freq_plus, freq_minus, freq_plus_shift_negative1, freq_plus_shift_negative2))

frequency1 = nround(est_fre)

frequency2 = nfloor(est_fre)

peakPoints = []

for i in narange(1.,dataSample.size()):

if((dataSample[0,int(i-1)] < dataSample[0,int(i)]) and (dataSample[0,int(i)] < dataSample[0,int(i+1)])):

peakPoints.append(dataSample[int(i)])

d1_list = []

d2_list = []

g2_list = []

peak1 = np.sort(findPeakPyVersion((2. * X1[0:int(NFFT1 / 2. + 1.)])), 'descend')

peak1Len = peak1.shape[1]

chk_val1 = nzeros([peak1Len,1])

index1 = nzeros([peak1Len,1])

for i in narange(1., (peak1Len + 1)):

chk_val1[int(i) - 1, :] = peak1[int(i) - 1]/ (nmean(peak1[int(i):peak1Len]))

if chk_val1[int(i) - 1, :] > 2.:

index1[int(i) - 1, :] = np.nonzero(X1[0:NFFT1 / 2. + 1.] == peak1[:, int(i) - 1] / 2.)

d1_list.append(f1[index1[int(i) - 1, :]])

mean1 = nmean(peak1[0:peak1Len])

k = 1.

peak2 = np.sort(findPeakPyVersion((2. * Y1[0:NFFT1 / 2. + 1.])), 'descend')

peak2Len = peak2.shape[1]

chk_val = nzeros([peak2Len, 1])

index2 = nzeros([peak2Len, 1])

for i in narange(1., peak2Len):

chk_val[int(i) - 1, :] = peak2[0, int(i) - 1]/nmean(peak2[int(i):peak2Len])

if chk_val[int(i) - 1, :] > 3.:

g2_list.append(chk_val[int(i) - 1, :])

index2[int(i) - 1, :] = np.nonzero((Y1[0:NFFT1 / 2. + 1.] == peak2[:, int(i) - 1] / 2.))

d2_list.append(f1[index2[int(i) - 1, :]])

d2 = np.array([d2_list])

d1 = np.array([d1_list])

g2 = np.array([g2_list])

if g2 >= 0.:

g1 = nmean(g2)

else:

g1 = nmean(g2)

Fs = np.array([[31.]])

#% Sampling frequency

T = 1./Fs

#% Sample time

L = 512.

#% Length of signal

t = ndot(narange(0., L), T)

NFFT1 = float(pow(2,pyNextPow2(L)))

#% Next power of 2 from length of y

s1[0,:] = s1[0,:]-nmean(s1[0,:])

s2[0,:] = s2[0,:]-nmean(s2[0,:])

X1 = nabs(np.fft(s1, NFFT1)/ L)

Y1 = nabs(np.fft(s2, NFFT1)/ L)

f1 = ndot(Fs/2., np.linspace(0., 1., (NFFT1/2.+1.)))

[g1, d1, d2] = checkValidity_MultiFrquency(X1, Y1, f1, NFFT1)

nfreq = 4.

nsampl = 33.

sampling_option = 0.

cam_fps = 15.

prime1 = np.array([61.,67., 71., 73., 79., 83., 89., 97., 101., 103., 107.,

109., 113., 127., 131., 137., 139., 149., 151., 157., 163.,

167., 173., 179., 181., 191., 193., 197., 199., 211., 223., 227., 229.])

sFile = open('strobe_file.txt','w+')

freq = np.random.rand(1., nfreq)

frequencies = np.array([70., 100., 170., 230.])

if sampling_option == 1.:

sampling = nzeros([nsampl])

for i in narange(1., (nsampl) + 1):

stri = "give"+str(i)+"th frequency of strobe"

print stri

sampling[int(i) - 1] = float(raw_input())

else:

sampling = prime1.copy()

print sampling

remainder = nzeros([int(nfreq),int(nsampl)])

remainder2 = remainder.copy()

for j in narange(1., (nsampl) + 1):

for i in narange(1., (nfreq) + 1):

if nmod(nfloor((np.minimum(nmod(frequencies[int(i) - 1], sampling[int(j) - 1]), (

sampling[int(j) - 1] - nmod(frequencies[int(i) - 1], sampling[int(j) - 1])))/cam_fps)), 2.) == 0.:

remainder[int(i) - 1, int(j) - 1] = nmod(

np.minimum(nmod(frequencies[int(i) - 1], sampling[int(j) - 1]),

(sampling[int(j) - 1] - np.mod(frequencies[int(i) - 1], sampling[int(j) - 1]))),

cam_fps)

else:

remainder[int(i) - 1, int(j) - 1] = 15. - nmod(

np.minimum(nmod(frequencies[int(i) - 1], sampling[int(j) - 1]),

(sampling[int(j) - 1] - np.mod(frequencies[int(i) - 1], sampling[int(j) - 1]))),

cam_fps)

remainder2[:, int(j) - 1] = np.sort(remainder[:, int(j) - 1])

sFile.write("%f\n" % nfreq)

sFile.write("%f\n" % nsampl)

for j in narange(1., (nsampl) + 1):

sFile.write("%f " % sampling[int(j) - 1])

sFile.write("\n")

for i in narange(1., (nfreq) + 1):

for j in np.arange(1., (nsampl) + 1):

sFile.write('%8.2f '% remainder2[int(i) - 1, int(j) - 1])

sFile.write('\n')

sFile.close()

s = open('strobe_file.txt', 'r')

nfreq = fscanf(s, '%f', 1.)

nsampl = fscanf(s, '%f', 1.)

for j in np.arange(1., (nsampl) + 1):

frequencies[int(j) - 1] = fscanf(s, '%f', 1.)

for i in np.arange(1., (nfreq) + 1):

for j in np.arange(1., (nsampl) + 1):

sampling[int(i) - 1, int(j) - 1] = fscanf(s, '%f', 1.)

fclose(s)

nfreq_orig = nfreq

nsampl_orig = nsampl

for i1 in np.arange(1., (nfreq_orig) + 1):

il_all = 1.

one_frequency = -1.

while one_frequency < 0.:

if nfreq < 8.:

nsampl = matcompat.max((nfreq * 3.), nsampl)

margin = nsampl_orig - nsampl

if margin > 0.:

rand_start = np.floor(np.dot(np.random.rand(1., 1.), margin - 1.)) + 1.

else:

rand_start = 1.

[one_frequency, probable_mod1] = find_frequency(nfreq, nsampl, nsampl_orig, frequencies, sampling,

sampling[0:nfreq,

int(rand_start) - 1:rand_start - 1. + nsampl])

num_freq = numel(one_frequency)

for i_num in np.arange(1., (num_freq) + 1):

if one_frequency[int(i_num) - 1] > 0.:

freq_all[int(i1) - 1] = one_frequency[int(i_num) - 1]

freq_all_all1[int(il_all) - 1] = one_frequency[int(i_num) - 1]

il_all = il_all + 1.

np.sort(freq_all)

# %pause;

# %probable_mod;

for j in np.arange(1., (nsampl_orig) + 1):

count = 0.

for k in np.arange(1., (nfreq - 1.) + 1):

if count == 1. or sampling[int(k) - 1, int(j) - 1] == probable_mod1[int(j) - 1]:

count = 1.

sampling[int(k) - 1, int(j) - 1] = sampling[int((k + 1.)) - 1, int(j) - 1]

# %sampling;

nfreq = nfreq - 1.

# %sampling

# %pause;

# %freq_all;

# %nfreq;

# % for j=1:nsampl_orig

# % for i=1:nfreq_orig

# % remainder(i,j)=mod(freq_all(i),frequencies(j));

# % end

# % remainder2(:,j)=sort(remainder(:,j));

# % end

np.sort(freq_all)

freq_all_all = np.unique(np.sort(freq_all_all1))

# Local Variables: i_nom, pmr, mm1, sampling, nsampl, one_frequency, num_of_mod, pmc, nfreq, nsampl_orig, cardinality, test_set, i_sampln, probable_mod, tempfkm, field_common1, field, fields_to_check, fps, field_common, frequencies, probable_mod1, sampling_all, mm2, num_fps, i, k, j, m, eta, search_limit, common, k_fps

# Function calls: size, rand, intersect, floor, min, find_frequency, zeros, numel, unique, mod

fps = 15.

eta = np.floor(matdiv(nsampl, nfreq))

for j in np.arange(1., (nsampl) + 1):

test_set[int(j) - 1] = sampling[0, int(j) - 1]

if eta == 1.:

eta = 2.

fields_to_check = np.zeros(eta)

# % fields_to_check=[1 2];

# % end;

# % fields_to_check=fields_to_check-1;

while numel(np.unique(fields_to_check)) < eta:

fields_to_check = np.floor(np.dot(np.random.rand(1., eta), nsampl)) + 1.

# %eta

# %fields_to_check

# %pause;

search_limit = 1.

for k in np.arange(1., (eta) + 1):

search_limit = np.dot(search_limit, frequencies[int(fields_to_check[int(k) - 1]) - 1])

if search_limit > 2000.:

search_limit = 2000.

for k in np.arange(1., (eta) + 1):

m = 2.

field[int(k) - 1, 1] = test_set[int(fields_to_check[int(k) - 1]) - 1]

# %pause

num_fps[int(k) - 1] = np.floor(matdiv(frequencies[int(fields_to_check[int(k) - 1]) - 1] / 2., fps))

# %frequencies(fields_to_check(k))

# %num_fps(k)

# %pause;

for k_fps in np.arange(1., (num_fps[int(k) - 1] + 3.) + 1):

if np.mod(k_fps, 2.) == 0.:

tempfkm = np.dot(k_fps - 1., fps) + fps - field[int(k) - 1, 1]

if tempfkm < frequencies[int(fields_to_check[int(k) - 1]) - 1] / 2.:

field[int(k) - 1, int(m) - 1] = tempfkm

else:

tempfkm = np.dot(k_fps - 1., fps) + field[int(k) - 1, 1]

if tempfkm < frequencies[int(fields_to_check[int(k) - 1]) - 1] / 2.:

field[int(k) - 1, int(m) - 1] = tempfkm

# %tempfkm

# %pause

mm1 = 1.

mm2 = 0.

while -tempfkm + np.dot(mm1, frequencies[int(fields_to_check[int(k) - 1]) - 1]) < search_limit:

field[int(k) - 1, int((m + 1.)) - 1] = -tempfkm + np.dot(mm1, frequencies[

int(fields_to_check[int(k) - 1]) - 1])

mm1 = mm1 + 1.

m = m + 1.

while tempfkm + np.dot(mm2, frequencies[int(fields_to_check[int(k) - 1]) - 1]) < search_limit:

field[int(k) - 1, int((m + 1.)) - 1] = tempfkm + np.dot(mm2, frequencies[

int(fields_to_check[int(k) - 1]) - 1])

mm2 = mm2 + 1.

m = m + 1.

field[int(k) - 1, 0] = m - 2.

# %field

# %search_limit

# %pause;

field_common1[0, 0] = 0.

for i in np.arange(1., (eta - 1.) + 1):

cardinality = numel(intersect(field[int(i) - 1, 1:field[int(i) - 1, 0] + 1.],

field[int((i + 1.)) - 1, 1:field[int((i + 1.)) - 1, 0] + 1.]))

field_common1[0:cardinality] = intersect(field[int(i) - 1, 1:field[int(i) - 1, 0] + 1.],

field[int((i + 1.)) - 1, 1:field[int((i + 1.)) - 1, 0] + 1.])

field_common1.flatten(1)

if numel(field_common1) > 0.:

field[int((i + 1.)) - 1, 0] = numel(field_common1.flatten(1))

field[int((i + 1.)) - 1, 1:numel[field_common1[:]] + 1.] = field_common1.flatten(1)

else:

break

if numel(field_common1) < 1.:

field_common = 0.

else:

field_common = field_common1.flatten(1)

# %field_common

# %pause;

# %frequencies

fps

# %pause

num_of_mod = numel(field_common)

for i_nom in np.arange(1., (num_of_mod) + 1):

for i_sampln in np.arange(1., (nsampl_orig) + 1):

if np.mod(np.floor(matdiv(

matcompat.max(np.mod(field_common[int(i_nom) - 1], frequencies[int(i_sampln) - 1]), (

frequencies[int(i_sampln) - 1] - np.mod(field_common[int(i_nom) - 1],

frequencies[int(i_sampln) - 1]))), fps)), 2.) == 0.:

probable_mod[int(i_nom) - 1, int(i_sampln) - 1] = np.mod(

matcompat.max(np.mod(field_common[int(i_nom) - 1], frequencies[int(i_sampln) - 1]), (

frequencies[int(i_sampln) - 1] - np.mod(field_common[int(i_nom) - 1],

frequencies[int(i_sampln) - 1]))), fps)

else:

probable_mod[int(i_nom) - 1, int(i_sampln) - 1] = 15. - np.mod(

matcompat.max(np.mod(field_common[int(i_nom) - 1], frequencies[int(i_sampln) - 1]), (

frequencies[int(i_sampln) - 1] - np.mod(field_common[int(i_nom) - 1],

frequencies[int(i_sampln) - 1]))), fps)

# %probable_mod

# %pause;

[pmr, pmc] = matcompat.size(probable_mod)

probable_mod1 = np.zeros(pmc)

# %frequencies

common = np.zeros(num_of_mod)

# %fps

field_common

# %frequencies

# %probable_mod

for i_nom in np.arange(1., (num_of_mod) + 1):

for j in np.arange(1., (nsampl_orig) + 1):

common[int(i_nom) - 1] = common[int(i_nom) - 1] + numel(

intersect(probable_mod[int(i_nom) - 1, int(j) - 1], sampling_all[:, int(j) - 1]))

common[int(i_nom) - 1]

if common[int(i_nom) - 1] >= 1. * nsampl_orig:

one_frequency[int(i_nom) - 1] = field_common[int(i_nom) - 1]

field_common[int(i_nom) - 1]

probable_mod1[0:pmc] = probable_mod[int(i_nom) - 1, 0:pmc]

# % disp('The Vibration Frequency of the machine:::::::::::::::');

# %one_frequency

# %pause;

else:

# % [pmr,pmc]=size(probable_mod);

one_frequency[int(i_nom) - 1] = -1.

# %disp('hi')

# % probable_mod1=zeros(pmc);

return [one_frequency, probable_mod1]