def distance(n1, n2):
"""Calculate distance in km between two nodes"""
lat1, lon1 = n1[0], n1[1]
lat2, lon2 = n2[0], n2[1]
delta_lat = lat2 - lat1
delta_lon = lon2 - lon1
d = math.sin(math.radians(delta_lat) * 0.5) ** 2 + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) \
* math.sin(math.radians(delta_lon) * 0.5) ** 2
return math.asin(math.sqrt(d)) * 12742
def distance_on_unit_sphere(self, coord1, coord2, lat_lon=None):
# todo: need to make this work with ellipsoid earth models for more accurate distance calculations
# todo:
"""
Calculate the distance on a spherical earth model with radius hard-coded.
:param coord1: start coordinates
:param coord2: end coordinates
:param lat_lon: Optional: give either 'lat' or 'lon' to get just the lateral or longitudinal distances by
themselves
:return: return the distance in km
"""
if lat_lon is not None:
if lat_lon is 'lat':
lat1, lon1 = coord1.lat, coord1.lon
lat2, lon2 = coord1.lat, coord2.lon
elif lat_lon is 'lon':
lat1, lon1 = coord1.lat, coord1.lon
lat2, lon2 = coord2.lat, coord1.lon
else:
raise Exception("lat or lon not specified")
else:
lat1, lon1 = coord1.lat, coord1.lon
lat2, lon2 = coord2.lat, coord2.lon
# Convert latitude and longitude to
# spherical coordinates in radians
degrees_to_radians = math.pi / 180.0
# phi = 90 - latitude
phi1 = (90.0 - lat1) * degrees_to_radians
phi2 = (90.0 - lat2) * degrees_to_radians
# theta = longitude
theta1 = lon1 * degrees_to_radians
theta2 = lon2 * degrees_to_radians
# Compute spherical distance from spherical coordinates.
# For two locations in spherical coordinates
# (1, theta, phi) and (1, theta', phi')
# cosine( arc length ) =
# sin phi sin phi' cos(theta-theta') + cos phi cos phi'
# distance = rho * arc length
cos = (math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) +
math.cos(phi1) * math.cos(phi2))
""" @todo: need to implement domain check"""
arc = math.acos(cos)
return arc * self.semimajor # scale to return in km
def affine_registration(source,
target,
pyramidDepth,
minLevel=0,
downscale=2,
debug=False):
'''
Find affine transformation that registers source image to the target image.
This function computes the image pyramid for source and target and calculates the transformation
that minimizes the least-square error between images (starting at the lowest resolution).
Args:
source (np.ndarray): source image, the one that will be transformed.
target (np.ndarray): target image, the one that will not move.
pyramidDepth (int): number of pyramid levels, in addition to the original.
minLevel (int): 0 for original level, >0 for coarser resolution.
Return:
tfrm (np.ndarray): (3,3) best transformation
'''
sourcePyramid = tuple(
skimage.transform.pyramid_gaussian(source,
max_layer=pyramidDepth,
downscale=downscale))
targetPyramid = tuple(
skimage.transform.pyramid_gaussian(target,
max_layer=pyramidDepth,
downscale=downscale))
# -- compute small scale rigid body transformation to provide the initial guess for the affine transformation --
rtfrm = imreg.rigid_body_registration(sourcePyramid[minLevel],
targetPyramid[minLevel],
pyramidDepth - minLevel)
rotmatrix = np.array([[math.cos(rtfrm[0]), -math.sin(rtfrm[0])],
[math.sin(rtfrm[0]),
math.cos(rtfrm[0])]])
tfrm = np.append(rotmatrix, [[rtfrm[1]], [rtfrm[2]]], 1)
tfrm[:, -1] /= pow(downscale, pyramidDepth - minLevel)
tfrm = np.vstack((tfrm, [0, 0, 1]))
#tfrm = np.array([[1,0,0],[0,1,0],[0,0,1]])
for layer in range(pyramidDepth, minLevel - 1, -1):
tfrm[:2,
-1] *= downscale # Scale translation for next level in pyramid
tfrm = affine_least_squares(sourcePyramid[layer], targetPyramid[layer],
tfrm, 10 * 2**(layer - 1))
toptfrm = np.concatenate(
(tfrm[:2, 0:2], tfrm[:2, -1:] * pow(downscale, layer)), axis=1)
toptfrm = np.vstack((toptfrm, np.array([0, 0, 1])))
if debug:
pass #some debugging stuff Santiago was using
#print 'Layer {0}: {1}x{2}'.format(layer, *targetPyramid[layer].shape)
#print 'th={0:0.4}, x={1:0.1f} , y={2:0.1f}'.format(*toptfrm) ### DEBUG
return toptfrm
def _plotLayout(self):
# Plot the cell with the nucleus, and the limits of the simulation
xc = list(); yc = list()
xe = list(); ye = list()
for ii in range(0, 365):
rangle = to_radian(ii)
xc.append(self.nucleus.x + self.r * math.cos(rangle))
yc.append(self.nucleus.y + self.r * math.sin(rangle))
xe.append(self.nucleus.x + self.R * math.cos(rangle))
ye.append(self.nucleus.y + self.R * math.sin(rangle))
plot(xc, yc, "-k")
plot(xe, ye, "--k")
def _plotLayout(self):
# Plot the cell with the nucleus, and the limits of the simulation
xc = list()
yc = list()
xe = list()
ye = list()
for ii in range(0, 365):
rangle = to_radian(ii)
xc.append(self.nucleus.x + self.r * math.cos(rangle))
yc.append(self.nucleus.y + self.r * math.sin(rangle))
xe.append(self.nucleus.x + self.R * math.cos(rangle))
ye.append(self.nucleus.y + self.R * math.sin(rangle))
plot(xc, yc, "-k")
plot(xe, ye, "--k")
def waveform(frequency, rate, duration=1, amplitude=1):
count = math.ceil(rate * duration)
return np.fromiter(
(math.sin(2.0 * math.pi * r * frequency / rate) * amplitude
for r in range(count)),
dtype=np.float32,
count=count)
def calcTvalue(p, df):
t = None
if (df == 1):
t = math.cos(p*math.pi/2.)/math.sin(p*math.pi/2.)
elif (df == 2):
t = math.sqrt(2./(p*(2. - p)) - 2.)
else:
a = 1./(df - 0.5)
b = 48./(a*a)
c = ((20700.*a/b - 98.)*a - 16.)*a + 96.36
d = ((94.5/(b + c) - 3.)/b + 1.)*math.sqrt(a*math.pi*0.5)*df
x = d*p
y = pow(x, 2./df)
if (y > 0.05 + a):
x = Norm_z(0.5*(1. - p))
y = x*x
if (df < 5.):
c = c + 0.3*(df - 4.5)*(x + 0.6)
c = (((0.05*d*x - 5.)*x - 7.)*x - 2.)*x + b + c
y = (((((0.4*y + 6.3)*y + 36.)*y + 94.5)/c - y - 3.)/b + 1.)*x
y = a*y*y
if (y > 0.002):
y = math.exp(y) - 1.
else:
y = 0.5*y*y + y
t = sqrt(df*y)
else:
y = ((1./(((df + 6.)/(df*y) - 0.089*d - 0.822)*(df + 2.)*3.) + 0.5/(df + 4.))*y - 1.)*(df + 1.)/(df + 2.) + 1./y;
t = math.sqrt(df*y)
return t
def combo(A, strX, strY, ang, shiftX, shiftY):
c, s = math.cos(ang), math.sin(ang)
strec = np.array([[strX, 0.], [0., strY]])
rotat = np.array([[c, -s], [s, c]])
shift = np.array([[shiftX], [shiftY]])
newA = rotat.dot(strec).dot(A)+shift
return newA
def distance_on_unit_sphere(coord1, coord2, units='km'):
# todo: need to make this work with ellipsoid earth models for more accurate distance calculations
# todo:
"""
Calculate the distance on a spherical earth model with radius hard-coded.
:param coord1: start coordinates
:param coord2: end coordinates
:param units: choose whether to return in km or feet
:return: return the distance in km
"""
lat1, lon1 = coord1.lat, coord1.lon
lat2, lon2 = coord2.lat, coord2.lon
# Convert latitude and longitude to
# spherical coordinates in radians
degrees_to_radians = math.pi / 180.0
# phi = 90 - latitude
phi1 = (90.0 - lat1) * degrees_to_radians
phi2 = (90.0 - lat2) * degrees_to_radians
# theta = longitude
theta1 = lon1 * degrees_to_radians
theta2 = lon2 * degrees_to_radians
# Compute spherical distance from spherical coordinates.
# For two locations in spherical coordinates
# (1, theta, phi) and (1, theta', phi')
# cosine( arc length ) =
# sin phi sin phi' cos(theta-theta') + cos phi cos phi'
# distance = rho * arc length
cos = (math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) +
math.cos(phi1) * math.cos(phi2))
""" @todo: need to implement domain check"""
arc = math.acos(cos)
if units is 'km':
return arc * 6373
else:
return arc * 3960
def reset_heading(sensor, freq, delay, Mat11, Mat21, Mat31, Mat12, Mat22,
Mat32, Mat13, Mat23, Mat33):
if sensor == 'Xsens':
rotmat = [[Mat11[0], Mat12[0],
Mat13[0]], [Mat21[0], Mat22[0], Mat23[0]],
[Mat31[0], Mat32[0], Mat33[0]]]
if sensor == 'Vicon':
rotmat = [
[Mat11[delay * freq], Mat12[delay * freq], Mat13[delay * freq]],
[Mat21[delay * freq], Mat22[delay * freq], Mat23[delay * freq]],
[Mat31[delay * freq], Mat32[delay * freq], Mat33[delay * freq]]
]
# decomposition
yaw = math.atan2(-rotmat[0][1], rotmat[0][0])
print(' yaw correction = {} [rad]'.format(-yaw))
Ryaw_reset = [[math.cos(-yaw), -math.sin(-yaw), 0],
[math.sin(-yaw), math.cos(-yaw), 0], [0, 0, 1]]
length = len(Mat11)
Mat11new, Mat21new, Mat31new, Mat12new, Mat22new, Mat32new, Mat13new, Mat23new, Mat33new = [
0
] * length, [0] * length, [0] * length, [0] * length, [0] * length, [
0
] * length, [0] * length, [0] * length, [0] * length
count = 0
for x in Mat11:
rotmat = [[Mat11[count], Mat12[count], Mat13[count]],
[Mat21[count], Mat22[count], Mat23[count]],
[Mat31[count], Mat32[count], Mat33[count]]]
rotmat_new = np.dot(Ryaw_reset, rotmat)
Mat11new[count] = rotmat_new[0][0]
Mat12new[count] = rotmat_new[0][1]
Mat13new[count] = rotmat_new[0][2]
Mat21new[count] = rotmat_new[1][0]
Mat22new[count] = rotmat_new[1][1]
Mat23new[count] = rotmat_new[1][2]
Mat31new[count] = rotmat_new[2][0]
Mat32new[count] = rotmat_new[2][1]
Mat33new[count] = rotmat_new[2][2]
count += 1
return (Mat11new, Mat21new, Mat31new, Mat12new, Mat22new, Mat32new,
Mat13new, Mat23new, Mat33new)
def mk_lookup_table(self, freq=440):
freq = float(freq)
period = int(self.sr / freq)
lookup_table = [
self.amplitude * math.sin(2.0 * math.pi * float(freq) *
(float(i % period) / float(self.sr)))
for i in range(period)
]
return lookup_table
def lat_lon_distance_on_unit_sphere(coord1, coord2, lat_lon, units='km'):
"""
Compute either the lateral or longitudinal distance from on point to another;
This corresponds to finding the length of one of the legs of the right triangle between
the two points.
:param coord1: start coordinates
:param coord2: end coordinates
:param units: choose whether to return in km or miles
:return: distance in units specified by 'units' param
"""
if lat_lon is 'lat':
lat1, lon1 = coord1.lat, coord1.lon
lat2, lon2 = coord1.lat, coord2.lon
elif lat_lon is 'lon':
lat1, lon1 = coord1.lat, coord1.lon
lat2, lon2 = coord2.lat, coord1.lon
else:
raise Exception("lat or lon not specified")
# Convert latitude and longitude to
# spherical coordinates in radians
degrees_to_radians = math.pi / 180.0
# phi = 90 - latitude
phi1 = (90.0 - lat1) * degrees_to_radians
phi2 = (90.0 - lat2) * degrees_to_radians
# theta = longitude
theta1 = lon1 * degrees_to_radians
theta2 = lon2 * degrees_to_radians
cos = (math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) +
math.cos(phi1) * math.cos(phi2))
arc = math.acos(cos)
# Remember to multiply arc by the radius of the earth
# in your favorite set of units to get length.
if units is 'km':
return arc * 6373
else:
return arc * 3960
def _get_alpha(self):
if abs(self.dec) + self.radius > 89.9:
return 180
return math.degrees(
abs(
math.atan(
math.sin(math.radians(self.radius)) / np.sqrt(
abs(
math.cos(math.radians(self.dec - self.radius)) *
math.cos(math.radians(self.dec + self.radius)))))))
def get_alpha(radius, dec):
if abs(dec) + radius > 89.9:
return 180
return math.degrees(
abs(
math.atan(
math.sin(math.radians(radius)) / np.sqrt(
abs(
math.cos(math.radians(dec - radius)) *
math.cos(math.radians(dec + radius)))))))
def draw_line(self, pos):
if self.viewer._ocr and self.x_clicked or self.y_clicked:
img_line = cv.line(self.image.copy(),
(self.x_clicked, self.y_clicked),
(pos.x(), pos.y()), (0, 0, 0), 2)
image_height, image_width, image_depth = img_line.shape
QIm = cv.cvtColor(img_line, cv.COLOR_BGR2RGB)
QIm = QImage(QIm.data, image_width, image_height,
image_width * image_depth, QImage.Format_RGB888)
self.viewer.setPhoto(QPixmap.fromImage(QIm))
# 终止画线
if self.x_released or self.y_released:
self.choosePoints = [] # 初始化存储点组
inf = float("inf")
if self.x_released or self.y_released:
if (self.x_released - self.x_clicked) == 0:
slope = inf
else:
slope = (self.y_released - self.y_clicked) / (
self.x_released - self.x_clicked)
siteLenth = 0.5 * math.sqrt(
square(self.y_released - self.y_clicked) +
square(self.x_released - self.x_clicked))
mySiteLenth = 2 * siteLenth
self.siteLenth = ("%.2f" % mySiteLenth)
self.editSiteLenth.setText(self.siteLenth)
radian = math.atan(slope)
self.siteSlope = ("%.2f" % radian)
self.editSiteSlope.setText(self.siteSlope)
x_bas = math.ceil(math.fabs(0.5 * siteLenth *
math.sin(radian)))
y_bas = math.ceil(math.fabs(0.5 * siteLenth *
math.cos(radian)))
if slope <= 0:
self.choosePoints.append([(self.x_clicked - x_bas),
(self.y_clicked - y_bas)])
self.choosePoints.append([(self.x_clicked + x_bas),
(self.y_clicked + y_bas)])
self.choosePoints.append([(self.x_released + x_bas),
(self.y_released + y_bas)])
self.choosePoints.append([(self.x_released - x_bas),
(self.y_released - y_bas)])
elif slope > 0:
self.choosePoints.append([(self.x_clicked + x_bas),
(self.y_clicked - y_bas)])
self.choosePoints.append([(self.x_clicked - x_bas),
(self.y_clicked + y_bas)])
self.choosePoints.append([(self.x_released - x_bas),
(self.y_released + y_bas)])
self.choosePoints.append([(self.x_released + x_bas),
(self.y_released - y_bas)])
self.viewer._ocr = False
def BL2XYZ(lat, lon):
R = 6371000.0
#6371km
eps = 1.0E-10
xyz = zeros(3)
if math.fabs(lat - 90) < eps:
xyz[0] = 0
xyz[1] = 0
xyz[2] = R
elif math.fabs(lat + 90) < eps:
xyz[0] = 0
xyz[1] = 0
xyz[2] = -R
else:
xyz[0] = math.cos(lat * math.pi / 180.0) * math.cos(
lon * math.pi / 180.0) * R
xyz[1] = math.cos(lat * math.pi / 180.0) * math.sin(
lon * math.pi / 180.0) * R
xyz[2] = math.sin(lat * math.pi / 180.0) * R
return xyz
def FindBusinessBasedOnLocation(categoriesToSearch, myLocation, maxDistance, saveLocation2, collection):
temp = []
for i in categoriesToSearch:
temp.append(i.title());
abc = collection.find({"categories": {"$all": temp}})
latit = float (myLocation[0])
longi = float (myLocation[1])
source_lat = radians(latit)
source_long = radians(longi)
R=3959
file=open(saveLocation2,"w")
for i in abc:
dest_lat = radians(i['latitude'])
dest_long = radians(i['longitude'])
dist_lat = (dest_lat - source_lat)
dist_long = (dest_long - source_long)
a = math.sin(dist_lat / 2) * math.sin(dist_lat / 2) + math.cos(source_lat) * math.cos(dest_lat) * math.sin(dist_long / 2) * math.sin(dist_long / 2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
distance = R * c
if (distance <= maxDistance):
file.write(i['name'].encode("utf-8").upper() + "\n")
file.close()
def imgR(img, ang):
m, n = img.shape[:2]
c, s = math.cos(ang), math.sin(ang)
q = int(max(m, n) * 1.5)
newImg = np.ones((q, q, 3))
for i in xrange(m):
for j in xrange(n):
k = int(round((i - m / 2) * c + (j - n / 2) * -s + q / 2))
l = int(round((i - m / 2) * s + (j - n / 2) * c + q / 2))
newImg[k, l, :] = img[i, j, :]
plt.imshow(newImg)
plt.show()
def imgR(img, ang):
m, n = img.shape[:2]
c, s = math.cos(ang), math.sin(ang)
q = int(max(m, n)*1.5)
newImg = np.ones((q, q, 3))
for i in xrange(m):
for j in xrange(n):
k = int(round((i-m/2)*c+(j-n/2)*-s+q/2))
l = int(round((i-m/2)*s+(j-n/2)*c+q/2))
newImg[k, l,:] = img[i, j,:]
plt.imshow(newImg)
plt.show()
def rotate(A, ang):
c, s = math.cos(ang), math.sin(ang)
rotat = np.array([[c, -s], [s, c]])
newA = np.dot(rotat, A)
plt.subplot(2, 1, 1)
plt.plot(A[0], A[1], '.')
plt.xlim(-5, 5)
plt.ylim(-5, 5)
plt.subplot(2, 1, 2)
plt.plot(newA[0], newA[1], '.')
plt.xlim(-5, 5)
plt.ylim(-5, 5)
plt.show()
def rotate(A, ang):
c, s = math.cos(ang), math.sin(ang)
rotat = np.array([[c, -s], [s, c]])
newA = np.dot(rotat, A)
plt.subplot(2, 1, 1)
plt.plot(A[0], A[1], '.')
plt.xlim(-5, 5)
plt.ylim(-5, 5)
plt.subplot(2, 1, 2)
plt.plot(newA[0], newA[1], '.')
plt.xlim(-5, 5)
plt.ylim(-5, 5)
plt.show()
def combo(A, strX, strY, ang, shiftX, shiftY):
c, s = math.cos(ang), math.sin(ang)
strec = np.array([[strX, 0.], [0., strY]])
rotat = np.array([[c, -s], [s, c]])
shift = np.array([[shiftX], [shiftY]])
newA = rotat.dot(strec).dot(A)+shift
plt.subplot(2, 1, 1)
plt.plot(A[0], A[1], '.')
plt.xlim(-8, 8)
plt.ylim(-8, 8)
plt.subplot(2, 1, 2)
plt.plot(newA[0], newA[1], '.')
plt.xlim(-8, 8)
plt.ylim(-8, 8)
plt.show()
def move(self, motion):
v = motion[0]
omega = motion[1]
self._currentV = v
self._currentOmega = omega
# translational and rotational speed is limited:
if omega > self._maxOmega:
omega = self._maxOmega
if omega < -self._maxOmega:
omega = -self._maxOmega
if v > self._maxSpeed:
v = self._maxSpeed
if v < -self._maxSpeed:
v = -self._maxSpeed
# print ("motion ", v, omega * 180 / math.pi)
# Odometry pose update (based on the motion command):
d = v * self._T
dTheta = omega * self._T
self._odoX += d * math.cos(self._odoTheta + 0.5 * dTheta)
self._odoY += d * math.sin(self._odoTheta + 0.5 * dTheta)
self._odoTheta = (self._odoTheta + dTheta) % (2 * math.pi)
# Add noise to v:
if not self._motionNoise:
return self._world.moveRobot(d, dTheta, self._T)
sigma_v_2 = (self._k_d / self._T) * abs(v)
v_noisy = v + random.gauss(0.0, math.sqrt(sigma_v_2))
# Add noise to omega:
sigma_omega_tr_2 = (self._k_theta / self._T) * abs(omega) # turning rate noise
sigma_omega_drift_2 = (self._k_drift / self._T) * abs(v) # drift noise
omega_noisy = omega + random.gauss(0.0, math.sqrt(sigma_omega_tr_2))
omega_noisy += random.gauss(0.0, math.sqrt(sigma_omega_drift_2))
# Move robot in the world (with noise):
d_noisy = v_noisy * self._T
dTheta_noisy = omega_noisy * self._T
# call the move listeners
self._moveListener.emit()
return self._world.moveRobot(d_noisy, dTheta_noisy, self._T)
def _sampleMotionModel(self, noiseFaktor=30, noiseReposition=0.05):
for i in range(self._numberOfParticles):
v = max(self._robot._currentV, 0.01)
omega = self._robot._currentOmega
sigma_v_2 = (self._robot._k_d * noiseFaktor / self._robot._T) * abs(v)
v_noisy = v + random.normal(0.05, math.sqrt(sigma_v_2))
# Add noise to omega:
sigma_omega_tr_2 = (self._robot._k_theta / self._robot._T) * abs(omega + 0.0001) # turning rate noise
sigma_omega_drift_2 = (self._robot._k_drift / self._robot._T) * abs(v) # drift noise
omega_noisy = omega + random.normal(0.0, math.sqrt(sigma_omega_tr_2))
omega_noisy += random.normal(0.0, math.sqrt(sigma_omega_drift_2))
omega = omega_noisy
v = v_noisy
# translational and rotational speed is limited:
if omega > self._robot._maxOmega:
omega = self._robot._maxOmega
if omega < -self._robot._maxOmega:
omega = -self._robot._maxOmega
if v > self._robot._maxSpeed:
v = self._robot._maxSpeed
if v < -self._robot._maxSpeed:
v = -self._robot._maxSpeed
d = v * self._robot._T
dTheta = omega * self._robot._T
x = self._particles[i][self.X]
y = self._particles[i][self.Y]
theta = self._particles[i][self.THETA]
x = (x + d * math.cos(theta + 0.5 * dTheta))
y = (y + d * math.sin(theta + 0.5 * dTheta))
theta = (theta + dTheta) % (2 * math.pi)
if self.drawWayOfParticle:
self.drawWayOfParticle(i, x, y)
self._particles[i][self.X] = x + random.normal(0.0, noiseReposition)
self._particles[i][self.Y] = y + random.normal(0.0, noiseReposition)
self._particles[i][self.THETA] = theta
self._particles[i][self.WEIGHT] = 0
self._particles[i][self.SUM] = 0
def combo(A, strX, strY, ang, shiftX, shiftY):
c, s = math.cos(ang), math.sin(ang)
strec = np.array([[strX, 0.], [0., strY]])
rotat = np.array([[c, -s], [s, c]])
shift = np.array([[shiftX], [shiftY]])
newA = rotat.dot(strec).dot(A) + shift
plt.subplot(2, 1, 1)
plt.plot(A[0], A[1], '.')
plt.xlim(-8, 8)
plt.ylim(-8, 8)
plt.subplot(2, 1, 2)
plt.plot(newA[0], newA[1], '.')
plt.xlim(-8, 8)
plt.ylim(-8, 8)
plt.show()
def get_sim_data(freq, exact=True):
"""
Make a simulated snapshot of data
"""
if exact:
offs = [0,0,0,0]
gains = [1,1,1,1]
else:
offs = [.2, .3, -.2, -.1]
gains = [1.001, .9984, .999, 1.002]
del_phi = 2 * math.pi * freq / samp_freq
data = np.empty((numpoints), dtype='int32')
phase = 2*math.pi * np.random.uniform()
for n in range(numpoints):
core = n&3
data[n] = (math.floor(0.5 + 119.0 * math.sin(del_phi * n + phase) + \
offs[core]))*gains[core]
return data
def get_sim_data(freq, exact=True):
"""
Make a simulated snapshot of data
"""
if exact:
offs = [0, 0, 0, 0]
gains = [1, 1, 1, 1]
else:
offs = [.2, .3, -.2, -.1]
gains = [1.001, .9984, .999, 1.002]
del_phi = 2 * math.pi * freq / samp_freq
data = np.empty((numpoints), dtype='int32')
phase = 2 * math.pi * np.random.uniform()
for n in range(numpoints):
core = n & 3
data[n] = (math.floor(0.5 + 119.0 * math.sin(del_phi * n + phase) + \
offs[core]))*gains[core]
return data
def rotate(pitch, roll, yaw):
p = np.array([[ 1, 0, 0],
[ 0, math.cos(pitch), -math.sin(pitch)],
[ 0, math.sin(pitch), math.cos(pitch)]])
r = np.array([[ math.cos(roll), 0, math.sin(roll)],
[ 0, 1, 0 ],
[-math.sin(roll), 0, math.cos(roll)]])
y = np.array([[ math.cos(yaw), -math.sin(yaw), 0],
[ math.sin(yaw), math.cos(yaw), 0],
[ 0, 0, 1]])
ro = np.dot(p, np.dot(r, y))
return ro
def visualResultAndSampleAndTarget(W, X, T):
"""
适用于单曲线的简单可视化，功能不如visualPoly强大
:param W:
:param X:
:param T:
:return:
"""
x = numpy.arange(0, 1, 0.001)
s = list(W)
s.reverse()
y = polyval(s, x)
plt.plot(x, y, 'r--', linewidth=1, label="W")
plt.plot(X, T, 'g*', linewidth=10, label="Sample")
plt.plot(x, [math.sin(2 * math.pi * x) for x in x],
'b--',
linewidth=1,
label="sin2πx")
plt.legend()
plt.show()
def cylindricalWarpImage(img1, K, savefig=False):
f = K[0,0]
im_h,im_w = img1.shape
# go inverse from cylindrical coord to the image
# (this way there are no gaps)
cyl = np.zeros_like(img1)
cyl_mask = np.zeros_like(img1)
cyl_h,cyl_w = cyl.shape
x_c = float(cyl_w) / 2.0
y_c = float(cyl_h) / 2.0
for x_cyl in np.arange(0,cyl_w):
for y_cyl in np.arange(0,cyl_h):
theta = (x_cyl - x_c) / f
h = (y_cyl - y_c) / f
X = np.array([math.sin(theta), h, math.cos(theta)])
X = np.dot(K,X)
x_im = X[0] / X[2]
if x_im < 0 or x_im >= im_w:
continue
y_im = X[1] / X[2]
if y_im < 0 or y_im >= im_h:
continue
cyl[int(y_cyl),int(x_cyl)] = img1[int(y_im),int(x_im)]
cyl_mask[int(y_cyl),int(x_cyl)] = 255
if savefig:
plt.imshow(cyl, cmap='gray')
plt.savefig("cyl.png",bbox_inches='tight')
return (cyl,cyl_mask)
def rotate(A, ang):
c, s = math.cos(ang), math.sin(ang)
L = np.array([[c, -s], [s, c]])
newA = np.dot(L, A)
return newA
def get_line_data(pixels, x1, y1, x2, y2, line_w=2, the_z=0, the_c=0, the_t=0):
"""
Grabs pixel data covering the specified line, and rotates it horizontally
so that x1,y1 is to the left,
Returning a numpy 2d array. Used by Kymograph.py script.
Uses PIL to handle rotating and interpolating the data. Converts to numpy
to PIL and back (may change dtype.)
@param pixels: PixelsWrapper object
@param x1, y1, x2, y2: Coordinates of line
@param line_w: Width of the line we want
@param the_z: Z index within pixels
@param the_c: Channel index
@param the_t: Time index
"""
size_x = pixels.getSizeX()
size_y = pixels.getSizeY()
line_x = x2 - x1
line_y = y2 - y1
rads = math.atan(float(line_x) / line_y)
# How much extra Height do we need, top and bottom?
extra_h = abs(math.sin(rads) * line_w)
bottom = int(max(y1, y2) + extra_h / 2)
top = int(min(y1, y2) - extra_h / 2)
# How much extra width do we need, left and right?
extra_w = abs(math.cos(rads) * line_w)
left = int(min(x1, x2) - extra_w)
right = int(max(x1, x2) + extra_w)
# What's the larger area we need? - Are we outside the image?
pad_left, pad_right, pad_top, pad_bottom = 0, 0, 0, 0
if left < 0:
pad_left = abs(left)
left = 0
x = left
if top < 0:
pad_top = abs(top)
top = 0
y = top
if right > size_x:
pad_right = right - size_x
right = size_x
w = int(right - left)
if bottom > size_y:
pad_bottom = bottom - size_y
bottom = size_y
h = int(bottom - top)
tile = (x, y, w, h)
# get the Tile
plane = pixels.getTile(the_z, the_c, the_t, tile)
# pad if we wanted a bigger region
if pad_left > 0:
data_h, data_w = plane.shape
pad_data = zeros((data_h, pad_left), dtype=plane.dtype)
plane = hstack((pad_data, plane))
if pad_right > 0:
data_h, data_w = plane.shape
pad_data = zeros((data_h, pad_right), dtype=plane.dtype)
plane = hstack((plane, pad_data))
if pad_top > 0:
data_h, data_w = plane.shape
pad_data = zeros((pad_top, data_w), dtype=plane.dtype)
plane = vstack((pad_data, plane))
if pad_bottom > 0:
data_h, data_w = plane.shape
pad_data = zeros((pad_bottom, data_w), dtype=plane.dtype)
plane = vstack((plane, pad_data))
pil = script_utils.numpy_to_image(plane, (plane.min(), plane.max()), int32)
# Now need to rotate so that x1,y1 is horizontally to the left of x2,y2
to_rotate = 90 - math.degrees(rads)
if x1 > x2:
to_rotate += 180
# filter=Image.BICUBIC see
# http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2172449/
rotated = pil.rotate(to_rotate, expand=True)
# rotated.show()
# finally we need to crop to the length of the line
length = int(math.sqrt(math.pow(line_x, 2) + math.pow(line_y, 2)))
rot_w, rot_h = rotated.size
crop_x = (rot_w - length) / 2
crop_x2 = crop_x + length
crop_y = (rot_h - line_w) / 2
crop_y2 = crop_y + line_w
cropped = rotated.crop((crop_x, crop_y, crop_x2, crop_y2))
return asarray(cropped)
def get_line_data(image, x1, y1, x2, y2, line_w=2, the_z=0, the_c=0, the_t=0):
"""
Grab pixel data covering the specified line, and rotates it horizontally.
Uses current rendering settings and returns 8-bit data.
Rotates it so that x1,y1 is to the left,
Returning a numpy 2d array. Used by Kymograph.py script.
Uses PIL to handle rotating and interpolating the data. Converts to numpy
to PIL and back (may change dtype.)
@param pixels: PixelsWrapper object
@param x1, y1, x2, y2: Coordinates of line
@param line_w: Width of the line we want
@param the_z: Z index within pixels
@param the_c: Channel index
@param the_t: Time index
"""
size_x = image.getSizeX()
size_y = image.getSizeY()
line_x = x2 - x1
line_y = y2 - y1
rads = math.atan2(line_y, line_x)
# How much extra Height do we need, top and bottom?
extra_h = abs(math.sin(rads) * line_w)
bottom = int(max(y1, y2) + extra_h / 2)
top = int(min(y1, y2) - extra_h / 2)
# How much extra width do we need, left and right?
extra_w = abs(math.cos(rads) * line_w)
left = int(min(x1, x2) - extra_w)
right = int(max(x1, x2) + extra_w)
# What's the larger area we need? - Are we outside the image?
pad_left, pad_right, pad_top, pad_bottom = 0, 0, 0, 0
if left < 0:
pad_left = abs(left)
left = 0
x = left
if top < 0:
pad_top = abs(top)
top = 0
y = top
if right > size_x:
pad_right = right - size_x
right = size_x
w = int(right - left)
if bottom > size_y:
pad_bottom = bottom - size_y
bottom = size_y
h = int(bottom - top)
# get the Tile - render single channel white
image.set_active_channels([the_c + 1], None, ['FFFFFF'])
jpeg_data = image.renderJpegRegion(the_z, the_t, x, y, w, h)
pil = Image.open(StringIO(jpeg_data))
# pad if we wanted a bigger region
if pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0:
img_w, img_h = pil.size
new_w = img_w + pad_left + pad_right
new_h = img_h + pad_top + pad_bottom
canvas = Image.new('RGB', (new_w, new_h), '#ff0000')
canvas.paste(pil, (pad_left, pad_top))
pil = canvas
# Now need to rotate so that x1,y1 is horizontally to the left of x2,y2
to_rotate = math.degrees(rads)
# filter=Image.BICUBIC see
# http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2172449/
rotated = pil.rotate(to_rotate, expand=True)
# finally we need to crop to the length of the line
length = int(math.sqrt(math.pow(line_x, 2) + math.pow(line_y, 2)))
rot_w, rot_h = rotated.size
crop_x = (rot_w - length) / 2
crop_x2 = crop_x + length
crop_y = (rot_h - line_w) / 2
crop_y2 = crop_y + line_w
cropped = rotated.crop((crop_x, crop_y, crop_x2, crop_y2))
# return numpy array
rgb_plane = asarray(cropped)
# greyscale image. r, g, b all same. Just use first
return rgb_plane[::, ::, 0]
def getLineData(pixels, x1, y1, x2, y2, lineW=2, theZ=0, theC=0, theT=0):
"""
Grabs pixel data covering the specified line, and rotates it horizontally
so that x1,y1 is to the left,
Returning a numpy 2d array. Used by Kymograph.py script.
Uses PIL to handle rotating and interpolating the data. Converts to numpy
to PIL and back (may change dtype.)
@param pixels: PixelsWrapper object
@param x1, y1, x2, y2: Coordinates of line
@param lineW: Width of the line we want
@param theZ: Z index within pixels
@param theC: Channel index
@param theT: Time index
"""
from numpy import asarray
sizeX = pixels.getSizeX()
sizeY = pixels.getSizeY()
lineX = x2 - x1
lineY = y2 - y1
rads = math.atan(float(lineX) / lineY)
# How much extra Height do we need, top and bottom?
extraH = abs(math.sin(rads) * lineW)
bottom = int(max(y1, y2) + extraH / 2)
top = int(min(y1, y2) - extraH / 2)
# How much extra width do we need, left and right?
extraW = abs(math.cos(rads) * lineW)
left = int(min(x1, x2) - extraW)
right = int(max(x1, x2) + extraW)
# What's the larger area we need? - Are we outside the image?
pad_left, pad_right, pad_top, pad_bottom = 0, 0, 0, 0
if left < 0:
pad_left = abs(left)
left = 0
x = left
if top < 0:
pad_top = abs(top)
top = 0
y = top
if right > sizeX:
pad_right = right - sizeX
right = sizeX
w = int(right - left)
if bottom > sizeY:
pad_bottom = bottom - sizeY
bottom = sizeY
h = int(bottom - top)
tile = (x, y, w, h)
# get the Tile
plane = pixels.getTile(theZ, theC, theT, tile)
# pad if we wanted a bigger region
if pad_left > 0:
data_h, data_w = plane.shape
pad_data = zeros((data_h, pad_left), dtype=plane.dtype)
plane = hstack((pad_data, plane))
if pad_right > 0:
data_h, data_w = plane.shape
pad_data = zeros((data_h, pad_right), dtype=plane.dtype)
plane = hstack((plane, pad_data))
if pad_top > 0:
data_h, data_w = plane.shape
pad_data = zeros((pad_top, data_w), dtype=plane.dtype)
plane = vstack((pad_data, plane))
if pad_bottom > 0:
data_h, data_w = plane.shape
pad_data = zeros((pad_bottom, data_w), dtype=plane.dtype)
plane = vstack((plane, pad_data))
pil = numpyToImage(plane)
#pil.show()
# Now need to rotate so that x1,y1 is horizontally to the left of x2,y2
toRotate = 90 - math.degrees(rads)
if x1 > x2:
toRotate += 180
# filter=Image.BICUBIC see
# http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2172449/
rotated = pil.rotate(toRotate, expand=True)
#rotated.show()
# finally we need to crop to the length of the line
length = int(math.sqrt(math.pow(lineX, 2) + math.pow(lineY, 2)))
rotW, rotH = rotated.size
cropX = (rotW - length) / 2
cropX2 = cropX + length
cropY = (rotH - lineW) / 2
cropY2 = cropY + lineW
cropped = rotated.crop((cropX, cropY, cropX2, cropY2))
#cropped.show()
return asarray(cropped)
def vinc_inv(f, a, coordinate_a, coordinate_b):
"""
Returns the distance between two geographic points on the ellipsoid
and the forward and reverse azimuths between these points.
lats, longs and azimuths are in radians, distance in metres
:param f: flattening of the geodesic
:param a: the semimajor axis of the geodesic
:param coordinate_a: decimal coordinate given as named tuple coordinate
:param coordinate_b: decimal coordinate given as named tuple coordinate
Note: The problem calculates forward and reverse azimuths as: coordinate_a -> coordinate_b
"""
phi1 = math.radians(coordinate_a.lat)
lembda1 = math.radians(coordinate_a.lon)
phi2 = math.radians(coordinate_b.lat)
lembda2 = math.radians(coordinate_b.lon)
if (abs(phi2 - phi1) < 1e-8) and (abs(lembda2 - lembda1) < 1e-8):
return {'distance': 0.0, 'forward_azimuth': 0.0, 'reverse_azimuth': 0.0}
two_pi = 2.0 * math.pi
b = a * (1 - f)
TanU1 = (1 - f) * math.tan(phi1)
TanU2 = (1 - f) * math.tan(phi2)
U1 = math.atan(TanU1)
U2 = math.atan(TanU2)
lembda = lembda2 - lembda1
last_lembda = -4000000.0 # an impossibe value
omega = lembda
# Iterate the following equations,
# until there is no significant change in lembda
while (last_lembda < -3000000.0 or lembda != 0 and abs((last_lembda - lembda) / lembda) > 1.0e-9):
sqr_sin_sigma = pow(math.cos(U2) * math.sin(lembda), 2) + \
pow((math.cos(U1) * math.sin(U2) -
math.sin(U1) * math.cos(U2) * math.cos(lembda)), 2)
Sin_sigma = math.sqrt(sqr_sin_sigma)
Cos_sigma = math.sin(U1) * math.sin(U2) + math.cos(U1) * math.cos(U2) * math.cos(lembda)
sigma = math.atan2(Sin_sigma, Cos_sigma)
Sin_alpha = math.cos(U1) * math.cos(U2) * math.sin(lembda) / math.sin(sigma)
alpha = math.asin(Sin_alpha)
Cos2sigma_m = math.cos(sigma) - (2 * math.sin(U1) * math.sin(U2) / pow(math.cos(alpha), 2))
C = (f / 16) * pow(math.cos(alpha), 2) * (4 + f * (4 - 3 * pow(math.cos(alpha), 2)))
last_lembda = lembda
lembda = omega + (1 - C) * f * math.sin(alpha) * (sigma + C * math.sin(sigma) * \
(Cos2sigma_m + C * math.cos(sigma) * (
-1 + 2 * pow(Cos2sigma_m, 2))))
u2 = pow(math.cos(alpha), 2) * (a * a - b * b) / (b * b)
A = 1 + (u2 / 16384) * (4096 + u2 * (-768 + u2 * (320 - 175 * u2)))
B = (u2 / 1024) * (256 + u2 * (-128 + u2 * (74 - 47 * u2)))
delta_sigma = B * Sin_sigma * (Cos2sigma_m + (B / 4) * \
(Cos_sigma * (-1 + 2 * pow(Cos2sigma_m, 2)) - \
(B / 6) * Cos2sigma_m * (-3 + 4 * sqr_sin_sigma) * \
(-3 + 4 * pow(Cos2sigma_m, 2))))
s = b * A * (sigma - delta_sigma)
alpha12 = math.atan2((math.cos(U2) * math.sin(lembda)), \
(math.cos(U1) * math.sin(U2) - math.sin(U1) * math.cos(U2) * math.cos(lembda)))
alpha21 = math.atan2((math.cos(U1) * math.sin(lembda)), \
(-math.sin(U1) * math.cos(U2) + math.cos(U1) * math.sin(U2) * math.cos(lembda)))
if (alpha12 < 0.0):
alpha12 += two_pi
if (alpha12 > two_pi):
alpha12 -= two_pi
alpha21 += two_pi / 2.0
if alpha21 < 0.0:
alpha21 += alpha21 + two_pi
if alpha21 > two_pi:
alpha21 -= two_pi
return {"distance": s, "forward_azimuth": alpha12, "reverse_azimuth": alpha21}
def getLineData(pixels, x1, y1, x2, y2, lineW=2, theZ=0, theC=0, theT=0):
"""
Grabs pixel data covering the specified line, and rotates it horizontally
so that x1,y1 is to the left,
Returning a numpy 2d array. Used by Kymograph.py script.
Uses PIL to handle rotating and interpolating the data. Converts to numpy
to PIL and back (may change dtype.)
@param pixels: PixelsWrapper object
@param x1, y1, x2, y2: Coordinates of line
@param lineW: Width of the line we want
@param theZ: Z index within pixels
@param theC: Channel index
@param theT: Time index
"""
from numpy import asarray
sizeX = pixels.getSizeX()
sizeY = pixels.getSizeY()
lineX = x2-x1
lineY = 1 if y2-y1 == 0 else y2-y1
rads = math.atan(float(lineX) / lineY)
# How much extra Height do we need, top and bottom?
extraH = abs(math.sin(rads) * lineW)
bottom = int(max(y1, y2) + extraH/2)
top = int(min(y1, y2) - extraH/2)
# How much extra width do we need, left and right?
extraW = abs(math.cos(rads) * lineW)
left = int(min(x1, x2) - extraW)
right = int(max(x1, x2) + extraW)
# What's the larger area we need? - Are we outside the image?
pad_left, pad_right, pad_top, pad_bottom = 0, 0, 0, 0
if left < 0:
pad_left = abs(left)
left = 0
x = left
if top < 0:
pad_top = abs(top)
top = 0
y = top
if right > sizeX:
pad_right = right - sizeX
right = sizeX
w = int(right - left)
if bottom > sizeY:
pad_bottom = bottom - sizeY
bottom = sizeY
h = int(bottom - top)
tile = (x, y, w, h)
# get the Tile
plane = pixels.getTile(theZ, theC, theT, tile)
# pad if we wanted a bigger region
if pad_left > 0:
data_h, data_w = plane.shape
pad_data = zeros((data_h, pad_left), dtype=plane.dtype)
plane = hstack((pad_data, plane))
if pad_right > 0:
data_h, data_w = plane.shape
pad_data = zeros((data_h, pad_right), dtype=plane.dtype)
plane = hstack((plane, pad_data))
if pad_top > 0:
data_h, data_w = plane.shape
pad_data = zeros((pad_top, data_w), dtype=plane.dtype)
plane = vstack((pad_data, plane))
if pad_bottom > 0:
data_h, data_w = plane.shape
pad_data = zeros((pad_bottom, data_w), dtype=plane.dtype)
plane = vstack((plane, pad_data))
pil = numpyToImage(plane)
# pil.show()
# Now need to rotate so that x1,y1 is horizontally to the left of x2,y2
toRotate = 90 - math.degrees(rads)
if x1 > x2:
toRotate += 180
# filter=Image.BICUBIC see
# http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2172449/
rotated = pil.rotate(toRotate, expand=True)
# rotated.show()
# finally we need to crop to the length of the line
length = int(math.sqrt(math.pow(lineX, 2) + math.pow(lineY, 2)))
rotW, rotH = rotated.size
cropX = (rotW - length)/2
cropX2 = cropX + length
cropY = (rotH - lineW)/2
cropY2 = cropY + lineW
cropped = rotated.crop((cropX, cropY, cropX2, cropY2))
# cropped.show()
return asarray(cropped)
@author: Benjamín Castro Pohl
"""
from numpy import math
from matplotlib import pyplot
import numpy as np
n = 4 #Cantidad de triángulos que parten circunferencia de radio 1
listarea = [
] #Lista de las áreas desde la partición n hasta una partición de 2 triángulos
listaerror = [] #Lista de errores entre área n y área n-1
listaerror1 = [] #Lista de errores entre área n y área n-1
a = []
while n > 2:
theta = math.pi * 2 / n #Ángulo según partición
area = n * (math.sin(theta / 2) * math.cos(theta / 2)
) #Suma de las áreas de todos los triángulos
listarea.append(area)
n -= 1
la32 = np.asarray(
listarea,
dtype=np.float32) #La lista de áreas se convierte en un arreglo float32
la64 = np.asarray(
listarea,
dtype=np.float64) #La lista de áreas se convierte en un arreglo float64
for i in range(len(la32) - 1):
listaerror.append(
-la32[i + 1] +
la32[i]) #Se agrega la diferencia de áreas consecutivas a una lista
# f2.write(temp_write)
# temp_write=" 4 .1455 2.2670 -.6395 1.1558 0. 0. 0. \n"
# f2.write(temp_write)
# temp_write=" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n"
# f2.write(temp_write)
## temp_write="#20|4|20"+" "+"Kick"+"\n"
# temp_write=" 1. cm"+"\n"
# f2.write(temp_write)
# temp_write=" 1 0.0 0.0 0.0 "+"\n"
# f2.write(temp_write)
elif type == "SBEND":
temp_write = "'BEND'" + " " + "SBEN" + " " + name + "\n"
f2.write(temp_write)
temp_write = "0" + " " + ".Bend" + "\n"
f2.write(temp_write)
temp_write = " " + str(2.0 * float(l) / float(angle) * math.sin(
float(angle) / 2.0) * 100.0) + " " + tilt + " " + str(
float(angle) / float(l) * 10.0) + "\n"
f2.write(temp_write)
temp_write = " 0.0 0.0 0.0" + "\n"
f2.write(temp_write)
temp_write = " 4 .2401 1.8639 -.5572 .3904 0. 0. 0. \n"
f2.write(temp_write)
temp_write = " 0.0 0.0 0.0" + "\n"
f2.write(temp_write)
temp_write = " 4 .2401 1.8639 -.5572 .3904 0. 0. 0. \n"
f2.write(temp_write)
# temp_write=" #200|"+str(int(float(l)*100.0))+"|200"+" "+"Bend"+" "+name+"\n"
temp_write = " 1. cm" + "\n"
f2.write(temp_write)
temp_write = " 3 0.0 0.0 " + str(-0.5 * float(angle)) + "\n"
# temp_write=" 3 0.0 0.0 0.0"+"\n"
def vinc_dir(f, a, coordinate_a, alpha12, s):
"""
Returns the lat and long of projected point and reverse azimuth
given a reference point and a distance and azimuth to project.
lats, longs and azimuths are passed in decimal degrees
Returns ( phi2, lambda2, alpha21 ) as a tuple
"""
phi1, lambda1 = coordinate_a.lat, coordinate_a.lon
piD4 = math.atan(1.0)
two_pi = piD4 * 8.0
phi1 = phi1 * piD4 / 45.0
lambda1 = lambda1 * piD4 / 45.0
alpha12 = alpha12 * piD4 / 45.0
if alpha12 < 0.0:
alpha12 += two_pi
if alpha12 > two_pi:
alpha12 -= two_pi
b = a * (1.0 - f)
tan_u1 = (1 - f) * math.tan(phi1)
u1 = math.atan(tan_u1)
sigma1 = math.atan2(tan_u1, math.cos(alpha12))
sin_alpha = math.cos(u1) * math.sin(alpha12)
cos_alpha_sq = 1.0 - sin_alpha * sin_alpha
u2 = cos_alpha_sq * (a * a - b * b) / (b * b)
# @todo: look into replacing A and B with vincenty's amendment, see if speed/accuracy is good
A = 1.0 + (u2 / 16384) * (4096 + u2 * (-768 + u2 * (320 - 175 * u2)))
B = (u2 / 1024) * (256 + u2 * (-128 + u2 * (74 - 47 * u2)))
# Starting with the approx
sigma = (s / (b * A))
last_sigma = 2.0 * sigma + 2.0 # something impossible
# Iterate the following 3 eqs unitl no sig change in sigma
# two_sigma_m , delta_sigma
while abs((last_sigma - sigma) / sigma) > 1.0e-9:
two_sigma_m = 2 * sigma1 + sigma
delta_sigma = B * math.sin(sigma) * (math.cos(two_sigma_m) + (B / 4) * (math.cos(sigma) *
(-1 + 2 * math.pow(
math.cos(two_sigma_m), 2) -
(B / 6) * math.cos(two_sigma_m) *
(-3 + 4 * math.pow(math.sin(sigma),
2)) *
(-3 + 4 * math.pow(
math.cos(two_sigma_m), 2)))))
last_sigma = sigma
sigma = (s / (b * A)) + delta_sigma
phi2 = math.atan2((math.sin(u1) * math.cos(sigma) + math.cos(u1) * math.sin(sigma) * math.cos(alpha12)),
((1 - f) * math.sqrt(math.pow(sin_alpha, 2) +
pow(math.sin(u1) * math.sin(sigma) - math.cos(u1) * math.cos(
sigma) * math.cos(alpha12), 2))))
lmbda = math.atan2((math.sin(sigma) * math.sin(alpha12)), (math.cos(u1) * math.cos(sigma) -
math.sin(u1) * math.sin(sigma) * math.cos(alpha12)))
C = (f / 16) * cos_alpha_sq * (4 + f * (4 - 3 * cos_alpha_sq))
omega = lmbda - (1 - C) * f * sin_alpha * (sigma + C * math.sin(sigma) *
(math.cos(two_sigma_m) + C * math.cos(sigma) *
(-1 + 2 * math.pow(math.cos(two_sigma_m), 2))))
lambda2 = lambda1 + omega
alpha21 = math.atan2(sin_alpha, (-math.sin(u1) * math.sin(sigma) +
math.cos(u1) * math.cos(sigma) * math.cos(alpha12)))
alpha21 += two_pi / 2.0
if alpha21 < 0.0:
alpha21 += two_pi
if alpha21 > two_pi:
alpha21 -= two_pi
phi2 = phi2 * 45.0 / piD4
lambda2 = lambda2 * 45.0 / piD4
alpha21 = alpha21 * 45.0 / piD4
return Coordinate(lat=phi2, lon=lambda2), alpha21
def __init__(self,
ra,
dec,
radius,
time_start,
time_end,
flag,
aws_profile,
aws_region,
s3_output_location,
local_output_location,
athena_database,
athena_workgroup,
query_life=10,
wait_time=0.1,
single_query=True):
self.ra = ra
self.dec = dec
self.radius = radius
self.time_start = time_start
self.time_end = time_end
self.flag = flag
self.s3_output_location = s3_output_location
self.local_output_location = local_output_location
self.athena_database = athena_database
self.athena_workgroup = athena_workgroup
self.query_life = query_life
self.wait_time = wait_time
self.single_query = single_query
sess = boto3.Session(profile_name=aws_profile, region_name=aws_region)
global athena_client, s3_client
athena_client = sess.client('athena')
s3_client = sess.client('s3')
self.s3_output_path = s3_output_location.replace('s3://',
'').split('/')
self.bucket = self.s3_output_path[0]
self.additional_s3_path = s3_output_location.replace(
's3://{}/'.format(self.bucket), '')
self.query = '''
SELECT *
FROM gPhoton_partitioned
WHERE zoneID = {}
AND dec BETWEEN {} AND {}
AND ra BETWEEN {} AND {}
AND ({}*cx + {}*cy + {}*cz) > {}
AND time >= {} AND time < {}
AND flag = {};
'''
# calculate_query_parameters
self.cx = math.cos(math.radians(self.dec)) * math.cos(
math.radians(self.ra))
self.cy = math.cos(math.radians(self.dec)) * math.sin(
math.radians(self.ra))
self.cz = math.sin(math.radians(self.dec))
self.alpha = self._get_alpha()
if (self.ra - self.alpha) < 0:
self.ra = self.ra + 360
self.zoneHeight = 30.0 / 3600.0
self.min_zoneid = int(
np.floor((self.dec - self.radius + 90.0) / self.zoneHeight))
self.max_zoneid = int(
np.floor((self.dec + self.radius + 90.0) / self.zoneHeight))
self.query_args_collection = {
'non-conditional': [
self.dec - self.radius, self.dec + self.radius,
self.ra - self.alpha, self.ra + self.alpha, self.cx, self.cy,
self.cz,
math.cos(math.radians(self.radius)), self.time_start,
self.time_end, self.flag
],
'conditional': [
self.dec - self.radius, self.dec + self.radius, 0,
self.ra - 360 + self.alpha, self.cx, self.cy, self.cz,
math.cos(math.radians(self.radius)), self.time_start,
self.time_end, self.flag
]
}
plt.title("Error function for session")
plt.xlabel("Epoch")
plt.ylabel("Error percentage")
plt.show()
#
######################################################
#
# Visualise activations
activation_range = arange(-10, 10,
0.1) # interval of [-6,6) with step size 0.1
coordinates = [(x, y) for x in activation_range for y in activation_range]
if span_of_x == 4:
coordinates = [(x, y, math.sin(x), math.sin(y)) for x in activation_range
for y in activation_range]
classifications = round(sess.run(layer_2, feed_dict={x_: coordinates}))
meshx, meshy = meshgrid(activation_range, activation_range)
plt.scatter(meshx, meshy, c=['b' if x > 0 else 'y' for x in classifications])
plt.title("Classification of Two Spiral Task")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()
##################################################
classifications = round(sess.run(layer_2, feed_dict={x_: x}))
plt.title("Predictions, red is spiral 1 and blue is spiral 2")
def Evolvent(self):
'''Расчет эвольвенты зуба
'''
#Расчет эвольветы зуба
# Читаем данные из полей формы
# z = Количество зубьев
z = self.doubleSpinBox_Z.value()
# m = Модуль зуба
m = self.doubleSpinBox_m.value()
# a = Угол главного профиля
a = self.doubleSpinBox_a.value()
#b = Угол наклона зубьев
b = self.doubleSpinBox_b.value()
#ha = Коэффициент высоты головки
ha = self.doubleSpinBox_ha.value()
#pf = К-т радиуса кривизны переходной кривой
pf = self.doubleSpinBox_pf.value()
#c = Коэффициент радиального зазора
c = self.doubleSpinBox_c.value()
#x = К-т смещения исходного контура
x = self.doubleSpinBox_x.value()
#y =Коэффициент уравнительного смещения
y = self.doubleSpinBox_y.value()
# n= Количество точек (точность построения)
#n=int(self.doubleSpinBox_n.value())
n=100
# заполня переменные
# Делительный диаметр
d = z * m
# Высота зуба h=
h = 2.25 * m
# Высота головки ha =
hav = m
# Высота ножки hf=
hf = 1.25 * m
#Диаметр вершин зубьев
#da = d + (2 * m)*(ha+x+y)
da = d + 2 * (ha + x - y) * m
#Диаметр впадин (справочно)
#df = d -(2 * hf)
df = d -2 * (ha + c - x) * m
#Окружной шаг зубьев или Шаг зацепления по дуге делительной окружности: Pt или p
pt = math.pi * m
#Окружная толщина зуба или Толщина зуба по дуге делительной окружности: St или S
#Суммарный коэффициент смещений: XΣ
X = 0.60 + 0.12
# St = 0.5 * pf
# St = 0.5 * pt
St = 0.5 * pt + 2 * x * m * math.tan(math.radians(a))
#inv a
inva=math.tan(math.radians(a))-math.radians(a)
#Угол зацепления invαw
invaw= (2 * X - math.tan(math.radians(a))) / (10+26) + inva
#Угол профиля
at = math.ceil(math.degrees(math.atan(math.tan(math.radians(a))
/math.cos( math.radians(b)))))
# Диаметр основной окружности
db = d * math.cos(math.radians(at))
#Диаметр начала выкружки зуба
D = 2 * m * ( ( z/( 2 * math.cos(math.radians(b)) )-(1-x)) ** 2 +
((1-x)/math.tan(math.radians(at)))**2)**0.5
#Промежуточные данные
yi = math.pi/2-math.radians(at)
hy = yi/(n-1)
x0 = math.pi/(4*math.cos(math.radians(b))
)+pf*math.cos(math.radians(at))+math.tan(math.radians(at))
y0 = 1-pf*math.sin(math.radians(at))-x
C = (math.pi/2+2*x*math.tan(math.radians(a))
)/z+math.tan(math.radians(at))-math.radians(at)
#Расчетный шаг точек эвольвенты
hdy = (da-D)/(n-1)
dyi = da
fi = 2*math.cos(math.radians(b))/z*(x0+y0*math.tan(yi))
#Заполняем текстовые поля в форме
# Делительный диаметр
# self.lineEdit_d.setText(str(d))
# Высота зуба h=
# self.lineEdit_h.setText(str(h))
# Высота головки ha =
# self.lineEdit_ha.setText(str(hav))
# Высота ножки hf=
# self.lineEdit_hf.setText(str(hf))
# Диаметр вершин зубьев
# self.lineEdit_da.setText(str(da))
# Диаметр впадин (справочно)
# self.lineEdit_df.setText(str(df))
# Окружной шаг зубьев Pt=
# self.lineEdit_Pt.setText(str(math.ceil(pt)))
# Окружная толщина зуба St=
# self.lineEdit_St.setText(str(math.ceil(St)))
# Угол профиля
# self.lineEdit_at.setText(str(at))
# Диаметр основной окружности
# self.lineEdit_db.setText(str(math.ceil(db)))
# Создаем списки
List_dyi=[]
List_Di=[]
List_Yei=[]
List_Xei=[]
List_Minus_Xei=[]
List_Xdai=[]
List_Ydai=[]
List_yi=[]
List_Ai=[]
List_Bi=[]
List_fi=[]
List_Ypki=[]
List_Xpki=[]
List_Minus_Xpki=[]
# Заполняем нуливой (первый )индекс списка значениями
List_dyi.append(dyi)
List_Di.append( math.acos( db/ List_dyi[0] ) - math.tan( math.acos( db / List_dyi[0] ) ) + C )
List_Yei.append(dyi / 2*math.cos( List_Di[0]))
List_Xei.append(List_Yei[0]*math.tan(List_Di[0]))
List_Minus_Xei.append(-List_Xei[0])
List_Xdai.append(-List_Xei[0])
List_Ydai.append(((da/2)**2-List_Xdai[0]**2)**0.5)
hda=(List_Xei[0]-List_Minus_Xei[0])/(n-1)
# Заполняем первый (второй по счету )индекс списка значениями
List_dyi.append(dyi-hdy)
List_Di.append( math.acos(db/List_dyi[1])-math.tan(math.acos(db/List_dyi[1]))+C)
List_Yei.append( List_dyi[1]/2*math.cos(List_Di[1]))
List_Xei.append( List_Yei[1]* math.tan(List_Di[1]))
List_Minus_Xei.append(-List_Xei[1])
List_Xdai.append(List_Xdai[0]+hda)
List_Ydai.append(((da/2)**2-List_Xdai[1]**2)**0.5)
Xdai=List_Xdai[1]
dyi=dyi-hdy
# Начинаем заполнять списки в цикле
i=0
while i < n-2:
i=i+1
dyi=dyi-hdy
List_Di.append(math.acos(db/dyi)-math.tan(math.acos(db/dyi))+C)
Di=math.acos(db/dyi)-math.tan(math.acos(db/dyi))+C
Yei=dyi/2*math.cos(Di)
Xei=Yei*math.tan(Di)
List_dyi.append(dyi)
List_Yei.append(dyi/2*math.cos(Di))
List_Xei.append(Yei*math.tan(Di))
List_Minus_Xei.append(-Xei)
Xdai=Xdai+hda
List_Xdai.append(Xdai)
List_Ydai.append(((da/2)**2-Xdai**2)**0.5)
#Заполняем последний индекс списка
List_dyi[n-1]=D
# Заполняем нуливой (первый )индекс списка значениями
List_yi.append(yi)
List_Ai.append(z/(2*math.cos(math.radians(b)))-y0-pf*math.cos(List_yi[0]) )
List_Bi.append(y0*math.tan(List_yi[0])+pf*math.sin(List_yi[0]))
List_fi.append(fi)
List_Ypki.append((List_Ai[0] * math.cos(fi)+List_Bi[0] * math.sin(fi)) * m)
List_Xpki.append((List_Ai[0] * math.sin(fi)-List_Bi[0] * math.cos(fi)) * m)
List_Minus_Xpki.append(-List_Xpki[0])
# Начинаем заполнять списки в цикле
i=0
while i < n-2:
i=i+1
yi=yi-hy
List_yi.append(yi)
Ai = z / (2 * math.cos(math.radians(b)))-y0-pf*math.cos(yi)
List_Ai.append( z / (2 * math.cos(math.radians(b)))-y0-pf*math.cos(yi))
Bi =y0*math.tan(yi)+pf*math.sin(yi)
List_Bi.append(y0*math.tan(yi)+pf*math.sin(yi))
fi = 2*math.cos(math.radians(b))/z*(x0+y0*math.tan(yi))
List_fi.append(2*math.cos(math.radians(b))/z*(x0+y0*math.tan(yi)))
List_Ypki.append((Ai*math.cos(fi)+Bi*math.sin(fi))*m)
Ypki=(Ai*math.cos(fi)+Bi*math.sin(fi))*m
Xpki=(Ai*math.sin(fi)-Bi*math.cos(fi))*m
List_Xpki.append((Ai*math.sin(fi)-Bi*math.cos(fi))*m)
List_Minus_Xpki.append(-Xpki)
#Заполняем последний индекс списка
List_yi.append(yi-yi)
List_Ai.append(z/(2*math.cos(math.radians(b)))-y0-pf*math.cos(List_yi[n-1]) )
List_Bi.append(y0*math.tan(List_yi[n-1])+pf*math.sin(List_yi[n-1]))
List_fi.append(2*math.cos(math.radians(b))/z*(x0+y0*math.tan(List_yi[n-1])))
List_Ypki.append((List_Ai[n-1] * math.cos(fi)+List_Bi[n-1] * math.sin(List_fi[n-1])) * m)
List_Xpki.append((List_Ai[n-1] * math.sin(fi)-List_Bi[n-1] * math.cos(List_fi[n-1])) * m)
List_Minus_Xpki.append(-List_Xpki[n-1])
# self.WiPfileZub(List_Yei,List_Xei,List_Minus_Xei,List_Ypki,List_Xpki,List_Minus_Xpki,List_Ydai,List_Xdai)
self.GragEvolvent(List_Minus_Xei+List_Minus_Xpki,List_Yei+List_Ypki,n)
DFreza = self.lineEditDFreza.text()
VisotaYAxis = self.lineEditVisota.text()
Diametr = self.lineEditDiametr.text()
if DFreza and VisotaYAxis:
E30 = (int(DFreza)/2) +float(VisotaYAxis)
else:
E30 = 0
angi_B=0
if ValkosZub:
angie_A:float = 90# угол А треугольника по высоте детали
angie_B:float = float(b) #угол B треугольника по высоте детали ( угол наклона зуба по чертежу)
angie_Y:float = 180 - (angie_A + angie_B) # трерий уго треугольника по высоте детали
side_c:float = float(E30)# высота детали первая сторона треугольника
side_a = side_c * math.sin(math.radians(angie_A)) / math.sin(math.radians(angie_Y))# вторая сторона треугольника
side_b = side_c * math.sin(math.radians(angie_B)) / math.sin(math.radians(angie_Y))# третия сторона треугольника ( ось Х)
sid_a:float = float(Diametr)/2 # радиус детали первая и вторая тророны треугольника по торцу
# sid_a:float = float(self.lineEdit_da.text())/2 # радиус детали первая и вторая тророны треугольника по торцу
sid_c:float = sid_a
if sid_c < 10 :
sid_a:float = 10
sid_c:float = 10
angi_B = float('{:.3f}'.format(math.degrees(math.acos((sid_a**2+sid_c**2-side_b**2)/(2*sid_a*sid_c)))))
QMessageBox.about (self, "Ошибка " , "Диаметр шестерни задан меньше 20 мм. \n Введите риальный диаметр шестерни " )
else:
angi_B = float('{:.3f}'.format(math.degrees(math.acos((sid_a**2+sid_c**2-side_b**2)/(2*sid_a*sid_c)))))# результат угол поворота стола
self.label_da.setText(str(round(da,1)))
self.label_d.setText(str(round(d,1)))
self.label_at.setText(str(round(at,1)))
self.label_db.setText(str(round(db,1)))
self.label_df.setText(str(round(df,1)))
self.label_St.setText(str(round(St,1)))
self.label_Pt.setText(str(round(pt,1)))
self.label_hf.setText(str(round(hf,1)))
self.label_ha.setText(str(round(ha,1)))
self.label_h.setText(str(round(h,1)))
self.lineEditDiametr.setText(str(da))
self.lineEditUgol.setText(str(angi_B))
"""
from numpy import arange, zeros, empty, math
import pylab as pl
import sys
PI = 3.1415927
A = 1
H = 0.02
V = 1
T = 0.2
U = zeros([51, 251])
Time = arange(0, 250, 1)
XXX = arange(0, 51 * H, H)
for i in range(51):
U[i, 0] = math.sin(i * PI * H)
U[i, 1] = math.sin(i * PI * H) + i * H * T * (1 - i * H)
for j in range(250):
U[0, j] = 0
U[50, j] = 0
for j in range(1, 250):
for i in range(1, 50):
U[i, j + 1] = U[i + 1, j] + U[i - 1, j] - U[i, j - 1]
fig = pl.figure(figsize=(10, 4))
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.plot(XXX, U[:, 0], 'r-', label='NT=0', linewidth=1.0)
y.append(temp)
# y.append(list(0))
spiral1 = not spiral1
# y.append(int(row[-1]))
# print("any decriptor ", x[0])
return x, y
x, y = read_dataset()
span_of_x = 4
if span_of_x == 4:
for i in x:
i.append(math.sin(i[0]))
i.append(math.sin(i[1]))
# labels = {0: 'spiral2', 1: 'spiral1'}
# Create the model
x_ = tf.placeholder(tf.float32, [None, span_of_x])
y_ = tf.placeholder(tf.float32, [None, 1])
nodesInH1 = 40
nodesInH2 = 40
# Create first layer weights
layer_0_weights = tf.Variable(tf.random_normal([span_of_x, nodesInH1]))
layer_0_bias = tf.Variable(tf.random_normal([nodesInH1]))
def cone_search(ra,
dec,
radius,
time_start,
time_end,
flag,
aws_profile,
aws_region,
s3_output_location,
local_output_location,
athena_database,
athena_workgroup,
query_life=10,
wait_time=0.1,
single_query=True):
def query_athena(query, query_args):
query = query.format(*query_args)
response = athena_client.start_query_execution(
QueryString=query,
QueryExecutionContext={'Database': athena_database},
ResultConfiguration={'OutputLocation': s3_output_location},
WorkGroup=athena_workgroup)
print('Query submitted:\n{}\n'.format(query))
rsp = athena_client.get_query_execution(
QueryExecutionId=response['QueryExecutionId'])
succeeded_query = True if rsp['QueryExecution']['Status'][
'State'] == 'SUCCEEDED' else False
num_sec_query_has_been_running = 0
# check to see if the query has succeeded
while not succeeded_query:
if num_sec_query_has_been_running >= query_life:
print(
'QUERY CANCELLED: Query {} has been running for ~{} seconds.'
.format(response['QueryExecutionId'],
num_sec_query_has_been_running))
_ = athena_client.stop_query_execution(
QueryExecutionId=response['QueryExecutionId'])
return None
if num_sec_query_has_been_running % 60 == 0 and num_sec_query_has_been_running:
duration = int(num_sec_query_has_been_running / 60)
word = 'minutes' if duration > 1 else 'minute'
print('...Query has been running for ~{} {}.'.format(
duration, word))
# wait until query has succeeded to start the next query
if num_sec_query_has_been_running + wait_time > query_life:
sleep_time = query_life - num_sec_query_has_been_running
else:
sleep_time = wait_time
time.sleep(sleep_time)
num_sec_query_has_been_running += sleep_time
rsp = athena_client.get_query_execution(
QueryExecutionId=response['QueryExecutionId'])
succeeded_query = True if rsp['QueryExecution']['Status'][
'State'] == 'SUCCEEDED' else False
return response['QueryExecutionId']
sess = boto3.Session(profile_name=aws_profile, region_name=aws_region)
athena_client = sess.client('athena')
s3_client = sess.client('s3')
s3_output_path = s3_output_location.replace('s3://', '').split('/')
bucket = s3_output_path[0]
additional_s3_path = s3_output_location.replace('s3://{}/'.format(bucket),
'')
queries = {
'single':
'''
SELECT *
FROM gPhoton_partitioned
WHERE zoneID BETWEEN {} AND {}
AND dec BETWEEN {} AND {}
AND ra BETWEEN {} AND {}
AND ({}*cx + {}*cy + {}*cz) > {}
AND time >= {} AND time < {}
AND flag = {};
''',
'multiple':
'''
SELECT *
FROM gPhoton_partitioned
WHERE zoneID = {}
AND dec BETWEEN {} AND {}
AND ra BETWEEN {} AND {}
AND ({}*cx + {}*cy + {}*cz) > {}
AND time >= {} AND time < {}
AND flag = {};
'''
}
cx = math.cos(math.radians(dec)) * math.cos(math.radians(ra))
cy = math.cos(math.radians(dec)) * math.sin(math.radians(ra))
cz = math.sin(math.radians(dec))
alpha = get_alpha(radius, dec)
if (ra - alpha) < 0:
ra = ra + 360
zoneHeight = 30.0 / 3600.0
min_zoneid = int(np.floor((dec - radius + 90.0) / zoneHeight))
max_zoneid = int(np.floor((dec + radius + 90.0) / zoneHeight))
query_args_collection = {
'non-conditional': [
dec - radius, dec + radius, ra - alpha, ra + alpha, cx, cy, cz,
math.cos(math.radians(radius)), time_start, time_end, flag
],
'conditional': [
dec - radius, dec + radius, 0, ra - 360 + alpha, cx, cy, cz,
math.cos(math.radians(radius)), time_start, time_end, flag
]
}
query_collection = ''
query_argument_collection = []
for zoneid in range(min_zoneid, max_zoneid + 1):
query = queries['single'] if single_query else queries['multiple']
zone_args = [min_zoneid, max_zoneid] if single_query else [zoneid]
query_args = zone_args + query_args_collection['non-conditional']
if (ra + alpha) > 360:
query = query.replace(';',
'') + '\n UNION ALL\n' + query
additional_args = [min_zoneid, max_zoneid
] if single_query else [zoneid]
query_args = query_args + additional_args + query_args_collection[
'conditional']
temp_query = query.replace(
';', ''
) + '\n UNION ALL\n' if not single_query and zoneid != max_zoneid else query
query_collection += temp_query
query_argument_collection.extend(query_args)
if single_query:
break
start_time = time.time()
execution_id = query_athena(query_collection, query_argument_collection)
print('Time taken to query: ~{:.4f} seconds'.format(time.time() -
start_time))
# get single CSV or accumulate the CSVs from the different SELECT statements
dfs = []
download_paths = []
if execution_id is not None:
path_to_csv = os.path.join(additional_s3_path, execution_id + '.csv')
download_path = os.path.join(local_output_location,
execution_id + '.csv')
start_time = time.time()
s3_client.download_file(bucket, path_to_csv, download_path)
print('Time taken to download: ~{:.4f} seconds'.format(time.time() -
start_time))
dfs.append(pd.read_csv(download_path, engine='python'))
download_paths.append(download_path)
if len(dfs):
df = pd.concat(dfs)
output_location = os.path.join(local_output_location,
str(uuid4()) + '.csv')
df.to_csv(output_location, index=False)
print('\nData written to {}\n'.format(output_location))
for download_path in download_paths:
os.remove(download_path)
print(df.head())
else:
print('No CSVs were found.')
