def _convert_to_onehot(labels):
# use the original numpy functions
from numpy import zeros as nzeros
from numpy import arange as narange
# to one-hot
new_labels = nzeros((labels.size, labels.max() + 1))
new_labels[narange(labels.size), labels] = 1.
return new_labels
def calculateTripleFrequency(remainder, strobe):
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)
return [frequency1, frequency2]
def checkValidity_MultiFrquency(X1, Y1, f1, NFFT1):
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)
return [g1, d1, d2]
def calculateFrequency(d11, z):
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)
return w
def _DelObject(self, _id):
"""Removes an object from the VBO"""
if self._vbo is None:
return
index = self._indices[_id]
num_values = type(self).__num_values
del self._indices[_id]
if not (index == self._max_index):
self._empty_indices.append(index)
else:
self._max_index -= 1
self._vbo[index * num_values : (index + 1) * num_values] = nzeros(num_values, "f")
def _DelObject(self, _id):
"""Removes an object from the VBO"""
if self._vbo is None:
return
index = self._indices[_id]
num_values = type(self).__num_values
del self._indices[_id]
if not (index == self._max_index):
self._empty_indices.append(index)
else:
self._max_index -= 1
self._vbo[index * num_values:(index + 1) * num_values] = nzeros(
num_values, "f")
def velocityPendCenter(pend_centers):
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
return VelocityMarker
def _BuildData(self, descriptor):
"""Builds data array from scratch and creates VBO object"""
if len(descriptor) == 0:
return
num_objects = len(descriptor)
values_per_object = len(self.__descToArray(descriptor[0]))
self._data_size = 2 ** ceil(log(num_objects, 2))
self._data = nzeros(self._data_size * values_per_object, "f")
self._indices = {}
for i, obj in enumerate(descriptor):
_id = obj["id"]
values = self.__descToArray(obj)
for j, v in enumerate(values):
self._data[i * values_per_object + j] = v
self._indices[_id] = i
self._empty_indices = []
self._max_index = num_objects - 1
self._vbo = vbo.VBO(
self._data, usage=GL_DYNAMIC_DRAW, target=GL_ARRAY_BUFFER, size=self._data_size * values_per_object * 4
)
self._dirty_objects = {}
def _BuildData(self, descriptor):
"""Builds data array from scratch and creates VBO object"""
if len(descriptor) == 0:
return
num_objects = len(descriptor)
values_per_object = len(self.__descToArray(descriptor[0]))
self._data_size = 2**ceil(log(num_objects, 2))
self._data = nzeros(self._data_size * values_per_object, "f")
self._indices = {}
for i, obj in enumerate(descriptor):
_id = obj["id"]
values = self.__descToArray(obj)
for j, v in enumerate(values):
self._data[i * values_per_object + j] = v
self._indices[_id] = i
self._empty_indices = []
self._max_index = num_objects - 1
self._vbo = vbo.VBO(self._data,
usage=GL_DYNAMIC_DRAW,
target=GL_ARRAY_BUFFER,
size=self._data_size * values_per_object * 4)
self._dirty_objects = {}
def zeros(N, dtype="float", bytes=16):
return pyfftw.n_byte_align(nzeros(N, dtype=dtype), bytes)
def mfreq_simulate2(frequency):
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()
return [frequencies, sampling]
def uncertainty(self):
return (np.asarray(self._uncertainty)
if self._uncertainty is not None else
np.nzeros(self.length))
def zeros(N, dtype="float", bytes=16):
return pyfftw.n_byte_align(nzeros(N, dtype=dtype), bytes)
