def plotRTEMSpectra(resultsList):
xData = np.array([result.source.wavelength for result in resultsList])
RTEData = np.array(
[np.sq(np.abs(result.rTE)) for result in resultsList])
RTMData = np.array(
[np.sq(np.abs(result.rTM)) for result in resultsList])
fig, ax = plt.subplots(2, 1)
ax[0].plot(xData, RTEData)
ax[1].plot(xData, RTMData)
ax[0].set_title('RTE and RTM vs wavelength')
ax[0].set_ylabel('RTE')
ax[1].set_title('RTM vs wavelength')
ax[1].set_xlabel('wavelength (um)')
ax[1].set_ylabel('RTM')
fig.suptitle('RTE / TM vs wavelength', fontsize=16)
def calculate_beta_squircle(self, a, ss, rr, identifier):
"""takes all points of the workspace, parameter s and radious r of squircle for
every point and returns the value of beta, eventually all this wont be required.
a: points
ss: value of paramter s
rr: radius of the sqauircle
identifier: 0 if object is an obstacle and 1 if its workspace
"""
beta = []
for i in range(len(a)):
x = a[:, 0][i]
y = a[:, 1][i]
z = a[:, 2][i]
temp1 = (2 - 4 * ss**2) * (x * y)**2
temp2 = (x**2 + y**2 + np.sqrt(x**4 + y**4 + temp1)) / 2
beta_temp = (temp2 + z**2) / 2 + sq(temp2**2 + z**4 +
(2 - 4 * ss**2) * temp2 *
z**2) / 2 - rr**2
if identifier == 1 and beta_temp >= 0:
beta_temp = 0
elif identifier == 0 and beta_temp <= 0:
beta_temp = 0
if identifier == 1:
beta.append(-beta_temp)
else:
beta.append(beta_temp)
#if identifier == 1:
# beta = [-i for i in beta]
return beta
def calculate_beta_squircle (self, a, ss, rr, identifier):
"""takes all points of the workspace, parameter s and radious r of squircle for
every point and returns the value of beta, eventually all this wont be required.
a: points
ss: value of paramter s
rr: radius of the sqauircle
identifier: 0 if object is an obstacle and 1 if its workspace
"""
#x = a[:,0]; y = a[:,1]; z = a[:,2]
beta = []#; beta_has_nan = False
#print ss, rr
for i in range(len(a)):
x = a[:,0][i]; y = a[:,1][i]; z = a[:,2][i]
temp1 = (2 - 4 * ss**2) * (x * y)**2
temp2 = (x**2 + y**2 + np.sqrt(x**4 + y**4 + temp1)) / 2
beta_temp = (temp2 + z**2)/2 + sq(temp2**2 + z**4 + (2-4*ss**2)*temp2*z**2)/2 - rr**2
if identifier == 1 and beta_temp >= 0:
beta.append(-beta_temp)
else:
beta.append(beta_temp)
"""TODO: cross-check if reverting sign of beta for boundary is needed"""
#print beta_has_nan
if identifier == 0:
return beta
else:
beta = [-i for i in beta]
return beta
def nav_func(self, q):
Beta, b0, b1, b2, b3, b4 = self.get_beta(q)
dBeta_dq = -2*(q-self.q0)*b1*b2*b3*b4 + \
2*b0*((q-self.q1)*b2*b3*b4 + (q-self.q2)*b3*b4*b1 + \
(q-self.q3)*b4*b1*b2 + (q-self.q4)*b1*b2*b3)
a = nrm(q - self.qd)**2
b = nrm(q - self.qd)**(2 * self.k) + Beta
c = pwr(b, 1.0 / self.k)
phi0 = a / c
dphi_dq = 2 * (q - self.qd) / c - (a / (self.k * b * c)) * (
2 * self.k * pwr(sq(a), 2 * self.k - 2) * (q - self.qd) + dBeta_dq)
return phi0, dphi_dq
def calculate_beta(self, a, ss, rr, identifier):
"""takes all points of the workspace, parameter s and radious r of squircle for
every point and returns the value of beta, eventually all this wont be required.
a: points
ss: value of paramter s
rr: radius of the sqauircle
identifier: 0 if object is an obstacle and 1 if its workspace
"""
x = a[:, 0]
y = a[:, 1]
z = a[:, 2]
temp1 = (2 - 4 * ss**2) * (x * y)**2
temp2 = (x**2 + y**2 + np.sqrt(x**4 + y**4 + temp1)) / 2.0
beta_temp = (temp2 +
z**2) / 2 + sq(temp2**2 + z**4 +
(2 - 4 * ss**2) * temp2 * z**2) / 2 - rr**2
if identifier == 1:
beta_temp = [i if i < 0 else 0 for i in beta_temp]
beta = [-1.0 * j for j in beta_temp]
else:
beta_temp = [i if i > 0 else 0 for i in beta_temp]
beta = [j for j in beta_temp]
return np.asarray(beta)
def constraintEquation(qi, i):
id = l2i(i, "t0")
t0, t1, t2, t3 = qi[id], qi[id + 1], qi[id + 2], qi[id + 3]
constraintVector = sq(t0) + sq(t1) + sq(t2) + sq(t3) - 1
return constraintVector
def __init__(self,path=None,root='./',format='nodc',verbose=True):
self.pr=[]
pr = profile()
if path is not None:
if type(path) is list:
flist=[];prof_dict={}
for file in path:
if format == 'nodc':
try:
f=netCDF4.Dataset(root+file['path'])
except:
if verbose:
print('Unable to open '+root+file['path'])
continue
date_string = file['date_string']
pr.data={}
pr.data['DataType']=f.variables['data_type'][:].tostring()
pr.data['ref_dateTime']=f.variables['reference_date_time'][:].tostring()
pr.data['time']=f.variables['juld'][:][0]
reft = pr.data['ref_dateTime']
reft = 'days since '+reft[0:4]+'-'+reft[4:6]+'-'+reft[6:9]
pr.data['ncDateTime_indx']=parser.parse(date_string)
pr.data['ncDateTime']=netCDF4.num2date(pr.data['time'],reft)
pr.data['wmoid']=f.variables['platform_number'][:].tostring()
pr.data['cycle_number']=f.variables['cycle_number'][:][0]
pr.data['direction']=f.variables['direction'][:].tostring()
pr.data['latitude']=f.variables['latitude'][:][0]
pr.data['longitude']=f.variables['longitude'][:][0]
pr.data['pressure']=sq(f.variables['pres'][:] )
try:
pr.data['pressure_adj']=sq(f.variables['pres_adjusted'][:])
except:
pr.data['pressure_adj']=None
pr.data['temp']=sq(f.variables['temp'][:])
try:
pr.data['temp_adj']=sq(f.variables['temp_adjusted'][:])
except:
pr.data['temp_adj']=None
try:
pr.data['salt']=sq(f.variables['psal'][:])
except:
pr.data['salt']=None
try:
pr.data['salt_adj']=sq(f.variables['psal_adjusted'][:])
except:
pr.data['salt_adj']=None
try:
pr.data['positioning_system']=string.join(f.variables['positioning_system'][:])
except:
pr.data['positioning_system']=None
try:
pr.data['position_qc']=string.join(f.variables['position_qc'][:])
except:
pr.data['position_qc']=None
# Avoid confusion with direction attribute
try:
dp = numpy.roll(pr.data['pressure'],shift=-1)-pr.data['pressure']
except:
dp = [0]
if numpy.isscalar(dp):
dp=[dp]
if len(dp) > 4:
dp = dp[:-2]
if len(dp) > 4:
if numpy.all(dp>0):
pr.data['direction']='A'
pr.data['min_dp']=numpy.min(dp)
elif numpy.all(dp<0):
pr.data['direction']='D'
pr.data['min_dp']=numpy.min(-dp)
else:
continue
else:
continue
pr_new = copy.copy(pr)
self.pr.append(pr_new)
elif format == 'pangea':
f=open(file['path'])
pr.data={}
pr.data['DataType']=None
pr.data['ref_dateTime']=None
pr.data['ncDateTime']=file['ncDateTime']
#%% import numpy #%% numpy.sqrt(5) #%% import numpy as np #%% np.sqrt(5) #%% from numpy import sqrt as sq, cbrt as cu #%% sq(5) #%% cu(8) #%% a = np.array([1,2,3]) print(a) type(a) #%% a.shape #%% b = np.array([[1,2],[3,4],[5,6]])
def build(fle, R, parameters):
dforward = parameters[13]
dstarboard = parameters[14]
twtcorr = parameters[15]
raycorr = parameters[16]
# Fancy characters (degree sign, lower-case sigma, plus-minus)
deg = u'\N{DEGREE SIGN}'
sig = u'\u03C3'
pm = u'\u00b1'
# Anonymous functions and formatting strings to make things more tractable.
dst = lambda rx, ry, rz, x, y, z: sq((M(rx) - x)**2 + (M(ry) - y)**2 +
(M(rz) - z)**2)
a = '{:>11} {: >11.5f} {}{: .5f}\n'
fmt1 = lambda s1, f1, s2, f2: a.format(s1, f1, s2, f2)
b = '{:>3} {: >11.5f} {: <11.5f} {: <11.5f} {: >8.5f} {: >8.5f} {: >8.5f}\n'
fmt2 = lambda f1, f2, f3, f4, f5, f6, f7: b.format(f1, f2, f3, f4, f5, f6,
f7)
c = '{:>14} {:<11} {:<11} {:<10} {:<9} {}'
# .txt header and main results.
fle.write('Bootstrap Inversion Results (' + pm + ' 2' + sig +
' uncertainty)\n')
fle.write('\n')
fle.write('{:>11} {} \n'.format('Station:', R['sta']))
fle.write(
fmt1('Lat:', M(R['lat_sta']), deg + ' ' + pm, 2 * S(R['lat_sta'])))
fle.write(
fmt1('Lon:', M(R['lon_sta']), deg + ' ' + pm, 2 * S(R['lon_sta'])))
fle.write(fmt1('X:', M(R['x_sta']), 'm ' + pm, 2 * S(R['x_sta'])))
fle.write(fmt1('Y:', M(R['y_sta']), 'm ' + pm, 2 * S(R['y_sta'])))
fle.write(fmt1('Depth:', M(R['z_sta']), 'm ' + pm, 2 * S(R['z_sta'])))
fle.write(fmt1('Water Vel.:', M(R['vpws']), 'm ' + pm, 2 * S(R['vpws'])))
fle.write(fmt1('Drift:', M(R['drifts']), 'm ' + pm, 2 * S(R['drifts'])))
fle.write(fmt1('Drift Az:', M(R['azs']), 'm ' + pm, 2 * S(R['azs'])))
fle.write(fmt1('dz:', M(R['dzs']), 'm ' + pm, 2 * S(R['dzs'])))
fle.write('\n')
fle.write(
fmt1('RMS:', M(R['E_rms'] * 1000), 'ms ' + pm,
2 * S(R['E_rms']) * 1000))
fle.write('\n')
fle.write('{} {} \n'.format('Bad Pings Removed:', R['Nbad']))
fle.write('{} {} \n'.format('Doppler Correction:',
('YES' if twtcorr else 'NO')))
fle.write('{} {} \n'.format('Ray Bending Correction:',
('YES' if raycorr else 'NO')))
fle.write('{} {} {} \n'.format('GPS-Transponder Offset: dforward = ',
dforward, ' m'))
fle.write('{} {} {} \n'.format(' dstarboard = ',
dstarboard, ' m'))
fle.write('{:=<80} \n'.format(''))
# .txt survey points.
fle.write(
c.format('Lat (' + deg + ')', 'Lon (' + deg + ')', 'Range (m)',
'Resid (s)', 'Vr (m/s)', 'TWT corr. (ms)\n'))
for i in range(len(R['svy_lats'])):
fle.write(
fmt2(
i + 1, R['svy_lats'][i], R['svy_lons'][i],
dst(R['x_sta'][i], R['y_sta'][i], R['z_sta'][i],
R['svy_xs'][i], R['svy_ys'][i], R['svy_zs'][i]),
R['dtwts'][i] * 1000, R['vrs'][i], R['corrs'][i] * 1000))
fle.close()
def angleDiffGoodness(theta1, psi1, theta2, psi2, sigma): if (theta1 == theta2 and psi1 == psi2): return 1 temp = np.sq(np.sin((theta1-theta2)/2)) dist = 2*np.arcsin(np.sqrt( temp + np.cos(theta1)*np.cos(theta2)*np.sq(np.sin(psi1-psi2/2)))) return np.exp(-np.sq(dist)/np.sq(sigma))
def evaluate(vars): r = vars[0][2] goodness = np.exp(-np.sq(r - self.mean)/np.sq(self.sigma)) return -goodness*self.scale #the cost
def __init__(self, path=None, root='./', format='nodc', verbose=True):
self.pr = []
pr = profile()
if path is not None:
if type(path) is list:
flist = []
prof_dict = {}
for file in path:
if format == 'nodc':
try:
f = netCDF4.Dataset(root + file['path'])
except:
if verbose:
print('Unable to open ' + root + file['path'])
continue
date_string = file['date_string']
pr.data = {}
pr.data['DataType'] = f.variables[
'data_type'][:].tostring()
pr.data['ref_dateTime'] = f.variables[
'reference_date_time'][:].tostring()
pr.data['time'] = f.variables['juld'][:][0]
reft = pr.data['ref_dateTime']
reft = 'days since ' + reft[0:4] + '-' + reft[
4:6] + '-' + reft[6:9]
pr.data['ncDateTime_indx'] = parser.parse(date_string)
pr.data['ncDateTime'] = netCDF4.num2date(
pr.data['time'], reft)
pr.data['wmoid'] = f.variables[
'platform_number'][:].tostring()
pr.data['cycle_number'] = f.variables[
'cycle_number'][:][0]
pr.data['direction'] = f.variables[
'direction'][:].tostring()
pr.data['latitude'] = f.variables['latitude'][:][0]
pr.data['longitude'] = f.variables['longitude'][:][0]
pr.data['pressure'] = sq(f.variables['pres'][:])
try:
pr.data['pressure_adj'] = sq(
f.variables['pres_adjusted'][:])
except:
pr.data['pressure_adj'] = None
pr.data['temp'] = sq(f.variables['temp'][:])
try:
pr.data['temp_adj'] = sq(
f.variables['temp_adjusted'][:])
except:
pr.data['temp_adj'] = None
try:
pr.data['salt'] = sq(f.variables['psal'][:])
except:
pr.data['salt'] = None
try:
pr.data['salt_adj'] = sq(
f.variables['psal_adjusted'][:])
except:
pr.data['salt_adj'] = None
try:
pr.data['positioning_system'] = string.join(
f.variables['positioning_system'][:])
except:
pr.data['positioning_system'] = None
try:
pr.data['position_qc'] = string.join(
f.variables['position_qc'][:])
except:
pr.data['position_qc'] = None
# Avoid confusion with direction attribute
try:
dp = numpy.roll(pr.data['pressure'],
shift=-1) - pr.data['pressure']
except:
dp = [0]
if numpy.isscalar(dp):
dp = [dp]
if len(dp) > 4:
dp = dp[:-2]
if len(dp) > 4:
if numpy.all(dp > 0):
pr.data['direction'] = 'A'
pr.data['min_dp'] = numpy.min(dp)
elif numpy.all(dp < 0):
pr.data['direction'] = 'D'
pr.data['min_dp'] = numpy.min(-dp)
else:
continue
else:
continue
pr_new = copy.copy(pr)
self.pr.append(pr_new)
elif format == 'pangea':
f = open(file['path'])
pr.data = {}
pr.data['DataType'] = None
pr.data['ref_dateTime'] = None
pr.data['ncDateTime'] = file['ncDateTime']
