def gint(self, axis=-1):
"""
Area integral of a field over a given axis.
"""
# If axis size is one, return itself
sh = self.data.shape
if sh[axis] == 1:
return self
r = 6380e3
X = sp.deg2rad(self.grid['lon'])
Y = sp.deg2rad(self.grid['lat'])
X *= sp.cos(Y)
x=[dates.date2num(self.time),self.grid['lev'],\
r*Y[sp.newaxis,sp.newaxis,:],r*X[sp.newaxis,sp.newaxis,:]]
y = self.data.view(sp.ma.MaskedArray)
# New dimensions
newsh = list(sh)
newsh[axis] = 1
# New grid and time
ind = [slice(None)] * 4
ind[axis] = slice(0, 1)
time = self.time[ind[0]]
g = self.grid.subset(kind=ind[1], jind=ind[2], iind=ind[3])
data = utl.gint(y, x[axis], axis)
data = data.reshape(newsh)
return Field(data, time, g, self.name)
def test_calc_kernel_f2_plot(self):
f2 = self.brdf.calc_kernel_f2(scipy.deg2rad(45), self.sensor_zenith, self.relative_azimuth)
pylab.plot(self.relative_azimuth, f2)
f2 = self.brdf.calc_kernel_f2(scipy.deg2rad(30), self.sensor_zenith, self.relative_azimuth)
pylab.plot(self.relative_azimuth, f2)
f2 = self.brdf.calc_kernel_f2(scipy.deg2rad(60), self.sensor_zenith, self.relative_azimuth)
pylab.plot(self.relative_azimuth, f2)
pylab.show()
def Hillshade_Smooth(RasterData, altitude, azimuth, z_factor):
"""Plots a Hillshade a la LSDRaster"""
zenith_rad = sp.deg2rad(altitude)
azimuth_rad = sp.deg2rad(azimuth)
def getangle(A, B):
"""
When A and B are two angles around the clock returns an angle of
the line that is connecting them.
"""
x = array([cos(deg2rad(A)), sin(deg2rad(A))])
y = array([cos(deg2rad(B)), sin(deg2rad(B))])
d = y - x
return rad2deg(math.atan2(d[1], d[0]))
def getangle(A, B):
"""
When A and B are two angles around the clock returns an angle of
the line that is connecting them.
"""
x = array([cos(deg2rad(A)), sin(deg2rad(A))])
y = array([cos(deg2rad(B)), sin(deg2rad(B))])
d = y - x
return rad2deg(math.atan2(d[1], d[0]))
def cylinders(shape: List[int],
radius: int,
nfibers: int,
phi_max: float = 0,
theta_max: float = 90):
r"""
Generates a binary image of overlapping cylinders. This is a good
approximation of a fibrous mat.
Parameters
----------
phi_max : scalar
A value between 0 and 90 that controls the amount that the fibers
lie out of the XY plane, with 0 meaning all fibers lie in the XY
plane, and 90 meaning that fibers are randomly oriented out of the
plane by as much as +/- 90 degrees.
theta_max : scalar
A value between 0 and 90 that controls the amount rotation in the
XY plane, with 0 meaning all fibers point in the X-direction, and
90 meaning they are randomly rotated about the Z axis by as much
as +/- 90 degrees.
Returns
-------
image : ND-array
A boolean array with ``True`` values denoting the pore space
"""
shape = sp.array(shape)
if sp.size(shape) == 1:
shape = sp.full((3, ), int(shape))
elif sp.size(shape) == 2:
raise Exception("2D fibers don't make sense")
im = sp.zeros(shape)
R = sp.sqrt(sp.sum(sp.square(shape)))
n = 0
while n < nfibers:
x = sp.rand(3) * shape
phi = sp.deg2rad(90 + 90 * (0.5 - sp.rand()) * phi_max / 90)
theta = sp.deg2rad(180 - 90 * (0.5 - sp.rand()) * 2 * theta_max / 90)
X0 = R * sp.array([
sp.sin(theta) * sp.cos(phi),
sp.sin(theta) * sp.sin(phi),
sp.cos(theta)
])
[X0, X1] = [X0 + x, -X0 + x]
crds = line_segment(X0, X1)
lower = ~sp.any(sp.vstack(crds).T < [0, 0, 0], axis=1)
upper = ~sp.any(sp.vstack(crds).T >= shape, axis=1)
valid = upper * lower
if sp.any(valid):
im[crds[0][valid], crds[1][valid], crds[2][valid]] = 1
n += 1
im = sp.array(im, dtype=bool)
dt = spim.distance_transform_edt(~im) < radius
return ~dt
def decodeMessageSensorUDP(self, msg):
""" This is used to decode message from sensorUDP application from the android market.
The orientation field was first used, but its conventions were unclear.
So now acceleration and magnetic vectors should be used"""
data = msg.split(', ')
if data[0]=='G':
# This is GPS message
time = decimalstr2float(data[2])
latitude_deg = decimalstr2float(data[3])
longitude_deg = decimalstr2float(data[4])
altitude = decimalstr2float(data[5])
hdop = decimalstr2float(data[7]) # Horizontal dilution of precision
vdop = decimalstr2float(data[8]) # Vertical dilution of precision
print time, latitude_deg, longitude_deg, altitude, hdop, vdop
if data[0]=='O':
# \note This is no more used as orientation convention were unclear
# 'O, 146, 1366575961732, 230,1182404, -075,2031250, 001,7968750'
[ u, u, # data not used \
heading_deg, # pointing direction of top of phone \
roll_deg, # around horizontal axis, positive clockwise [-180:180] \
pitch_deg] = decimalstr2float(data[1:]) # around vertical axis [_90:90]
elevation_deg = -sp.rad2deg(sp.arctan2( \
sp.cos(sp.deg2rad(pitch_deg))*sp.cos(sp.deg2rad(roll_deg)), \
sp.sqrt(1+sp.cos(sp.deg2rad(roll_deg))**2*(sp.sin(sp.deg2rad(pitch_deg))**2-1)))) #positive up
inclinaison_deg = pitch_deg #positive clockwise
print heading_deg, roll_deg, pitch_deg, elevation_deg, inclinaison_deg
if data[0] == 'A':
# Accelerometer data
# Index and sign are adjusted to obtain x through the screen, and z down
deltaT = decimalstr2float(data[2])/1000 - self.time_acceleration
if self.filterTimeConstant == 0.0:
alpha = 1
else:
alpha = 1-sp.exp(-deltaT/self.filterTimeConstant)
self.time_acceleration = decimalstr2float(data[2])/1000
self.acceleration_raw[0] = decimalstr2float(data[3])
self.acceleration_raw[1] = decimalstr2float(data[4])
self.acceleration_raw[2] = decimalstr2float(data[5])
# Filter the data
self.acceleration_filtered +=alpha*(sp.array(self.acceleration_raw)-self.acceleration_filtered)
if data[0] == 'M':
# Magnetometer data
# Index and sign are adjusted to obtain x through the screen, and z down
deltaT = decimalstr2float(data[2])/1000-self.time_magnetic
if self.filterTimeConstant == 0.0:
alpha = 1
else:
alpha = 1-sp.exp(-deltaT/self.filterTimeConstant)
self.time_magnetic = decimalstr2float(data[2])/1000
self.magnetic_raw[0] = decimalstr2float(data[3])
self.magnetic_raw[1] = decimalstr2float(data[4])
self.magnetic_raw[2] = -decimalstr2float(data[5])# Adapt to a bug in sensorUDP?
# Filter the data
self.magnetic_filtered += alpha*(sp.array(self.magnetic_raw)-self.magnetic_filtered)
def setUp(self):
self.test_file = '/home/marrabld/projects/DIMITRI_2.0/Input/Site_Libya4/MERIS/Proc_3rd_Reprocessing/MERIS_TOA_REF.dat'
tmp_dict = libdimitripy.ingest.DimitriFiles.read_dimitri_sav_file(self.test_file, 'MERIS')
# Test that we can use the dictionary to make a DimitriObject
self.test_object = libdimitripy.base.DimitriObject(tmp_dict)
self.test_band = 4
self.brdf = libdimitripy.brdf.RoujeanBRDF()
self.relative_azimuth = scipy.deg2rad(scipy.linspace(-90, 90, 180))
self.sensor_zenith = scipy.deg2rad(30)
def geoidheight(lat, lon):
"""
Calculate geoid height using the EGM96 Geopotential Model.
Parameters
----------
lat : array_like
Lateral coordinates [degrees]. Values must be -90 <= lat <= 90.
lon : array_like
Longitudinal coordinates [degrees]. Values must be 0 <= lon <= 360.
Returns
-------
out : array_like
Geoidheight [meters]
Examples
--------
>>> geoidheight(30, 20)
25.829999999999995
>>> geoidheight([30, 20],[40, 20])
[9.800000000000002, 14.43]
"""
global _EGM96
#convert the input value to array
itype, lat = to_ndarray(lat)
itype, lon = to_ndarray(lon)
if lat.shape != lon.shape:
raise Exception("Inputs must contain equal number of values.")
if (lat < -90).any() or (lat > 90).any() or not sp.isreal(lat).all():
raise Exception("Lateral coordinates must be real numbers" \
" between -90 and 90 degrees.")
if (lon < 0).any() or (lon > 360).any() or not sp.isreal(lon).all():
raise Exception("Longitudinal coordinates must be real numbers" \
" between 0 and 360 degrees.")
#if the model is not loaded, do so
if _EGM96 is None:
_EGM96 = _loadEGM96()
#shift lateral values to the right reference and flatten coordinates
lats = sp.deg2rad(-lat + 90).ravel()
lons = sp.deg2rad(lon).ravel()
#evaluate the spline and reshape the result
evl = _EGM96.ev(lats, lons).reshape(lat.shape)
return from_ndarray(itype, evl)
def geoidheight(lat, lon):
"""
Calculate geoid height using the EGM96 Geopotential Model.
Parameters
----------
lat : array_like
Lateral coordinates [degrees]. Values must be -90 <= lat <= 90.
lon : array_like
Longitudinal coordinates [degrees]. Values must be 0 <= lon <= 360.
Returns
-------
out : array_like
Geoidheight [meters]
Examples
--------
>>> geoidheight(30, 20)
25.829999999999995
>>> geoidheight([30, 20],[40, 20])
[9.800000000000002, 14.43]
"""
global _EGM96
# convert the input value to array
itype, lat = to_ndarray(lat)
itype, lon = to_ndarray(lon)
if lat.shape != lon.shape:
raise AerotbxValueError("Inputs must contain equal number of values.")
if (lat < -90).any() or (lat > 90).any() or not sp.isreal(lat).all():
raise AerotbxValueError("Lateral coordinates must be real numbers "
"between -90 and 90 degrees.")
if (lon < 0).any() or (lon > 360).any() or not sp.isreal(lon).all():
raise AerotbxValueError("Longitudinal coordinates must be real numbers "
"between 0 and 360 degrees.")
# if the model is not loaded, do so
if _EGM96 is None:
_EGM96 = _loadEGM96()
# shift lateral values to the right reference and flatten coordinates
lats = sp.deg2rad(-lat + 90).ravel()
lons = sp.deg2rad(lon).ravel()
# evaluate the spline and reshape the result
evl = _EGM96.ev(lats, lons).reshape(lat.shape)
return from_ndarray(itype, evl)
def geodetic2ecef(lat, lon, alt, degrees=True):
"""geodetic2ecef(lat, lon, alt)
[deg][deg][m]
Convert geodetic coordinates to ECEF."""
if degrees:
lat = deg2rad(lat)
lon = deg2rad(lon)
#lat, lon = radians(lat), radians(lon)
xi = sqrt(1 - esq * sin(lat))
x = (a / xi + alt) * cos(lat) * cos(lon)
y = (a / xi + alt) * cos(lat) * sin(lon)
z = (a / xi * (1 - esq) + alt) * sin(lat)
return x, y, z
def geodetic2ecef(lat, lon, alt, degrees=True):
"""geodetic2ecef(lat, lon, alt)
[deg][deg][m]
Convert geodetic coordinates to ECEF."""
if degrees:
lat=deg2rad(lat)
lon=deg2rad(lon)
#lat, lon = radians(lat), radians(lon)
xi = sqrt(1 - esq * sin(lat))
x = (a / xi + alt) * cos(lat) * cos(lon)
y = (a / xi + alt) * cos(lat) * sin(lon)
z = (a / xi * (1 - esq) + alt) * sin(lat)
return x, y, z
def callback(msg):
pcloud = PointCloud2()
pcloud.header.frame_id = '/radar'
cloud = []
for ii in range(0, 64):
x = msg.range[ii] * cos(deg2rad(msg.angle[ii]))
y = msg.range[ii] * sin(deg2rad(msg.angle[ii]))
z = 0.0
cloud.append([x, y, z])
pcloud = pc2.create_cloud_xyz32(pcloud.header, cloud)
pc_pub.publish(pcloud)
def callback(msg):
pcloud = PointCloud2()
pcloud.header.frame_id = '/radar'
cloud = []
for ii in range(0,64):
x = msg.range[ii] * cos(deg2rad(msg.angle[ii]))
y = msg.range[ii] * sin(deg2rad(msg.angle[ii]))
z = 0.0
cloud.append([x,y,z])
pcloud = pc2.create_cloud_xyz32(pcloud.header, cloud)
pc_pub.publish(pcloud)
def pca(self, keep=None, center=False, weight=True):
'''
Performss principal component analysis on data field, and stores
a PCA object. Please, remove climatology, detrend data etc before
calling this method.
If center=True, PCA object will center data using mean and standard deviation.
If weight=True, multiply data by area weights.
'''
nt, km, jm, im = self.data.shape
# multiply data by area factor, reshape, return matrix
if weight:
factor = sp.cos(sp.deg2rad(self.grid['lat']))
factor[factor < 0.] = 0.
factor = sp.sqrt(factor)
else:
factor = sp.ones(self.grid['lat'].shape)
mask = sp.ma.getmaskarray(self.data).copy()
self.data[mask] = 0.0
self.data *= factor[sp.newaxis, sp.newaxis]
X = self.data.reshape((nt, km * jm * im)).view(sp.ndarray)
self._pc = pca.PCA(X, center=center, keep=keep)
self.data /= factor[sp.newaxis, sp.newaxis]
self.data[mask] = self.data.fill_value
self.data.mask = mask
def gradient_patchset(patchset,**kwargs):
ax = patchset[0].axes
extremes = [ rect.get_extents().transformed(ax.transData.inverted()).extents for rect in patchset]
xmin_etr = min( i[0] for i in extremes )
xmax_etr = max( i[2] for i in extremes )
ymin_etr = min( i[1] for i in extremes )
ymax_etr = max( i[3] for i in extremes )
direction = multiget(kwargs,['direction','dir'],0)
try:
dir2rad = scipy.deg2rad(1.*direction)
xmean = (xmax_etr+xmin_etr)/2.
ymean = (ymax_etr+ymin_etr)/2.
xampl = (xmax_etr-xmin_etr)/2.
yampl = (ymax_etr-ymin_etr)/2.
dir_func = lambda x,y: ((x-xmean)/xampl)*np.cos(dir2rad) + ((y-ymean)/yampl)*np.sin(dir2rad)
except TypeError:
dir_func = direction
yy,xx = np.ogrid[ymin_etr:ymax_etr:101j,xmin_etr:xmax_etr:101j]
z = dir_func(xx,yy)
cmin,cmax = np.min(z),np.max(z)
kwargs.update(direction=dir_func,cmax=cmax,cmin=cmin)
imgs = []
for (xmin,ymin,xmax,ymax),rect in zip(extremes,patchset):
im = patch_gradient(rect,xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax,**kwargs)
imgs.append(im)
return imgs
def rotate(self, roll=0, tilt=0, yaw=0):
"""
Rotate the scatterer in space.
Parameters
----------
roll, tilt, yaw : float
Angles (in degrees) by which to rotate the scatterer. The
rotations are applied in order (roll, then tilt, then yaw).
"""
tilt, roll, yaw = sp.deg2rad(tilt), sp.deg2rad(roll), sp.deg2rad(yaw)
Rx = sp.matrix([[1, 0, 0], [0, sp.cos(roll), -sp.sin(roll)], [0, sp.sin(roll), sp.cos(roll)]])
Ry = sp.matrix([[sp.cos(tilt), 0, sp.sin(tilt)], [0, 1, 0], [-sp.sin(tilt), 0, sp.cos(tilt)]])
Rz = sp.matrix([[sp.cos(yaw), -sp.sin(yaw), 0], [sp.sin(yaw), sp.cos(yaw), 0], [0, 0, 1]])
R = Rz * Ry * Rx
self.cum_rotation = R * self.cum_rotation
for i in range(self.r.shape[1]):
self.r[:, i] = R * self.r[:, i]
def test_healpix_sphere(self):
# Sphere parameters.
R = 5
# Expected outputs of healpix_sphere() applied to inputs.
if RADIANS:
sigma_a = sqrt(3 - 3 * sin(a[1]))
else:
sigma_a = sqrt(3 - 3 * sin(deg2rad(a[1])))
ha = (pi / 4 * (1 - sigma_a), pi / 4 * (2 - sigma_a))
hb = (ha[0], -ha[1])
healpix_sphere_outputs = [(0, 0), (0, pi / 4),
(0, -pi / 4), (pi / 2, 0), (-pi / 2, 0),
(-pi, 0), (-3 * pi / 4, pi / 2),
(-3 * pi / 4, -pi / 2), ha, hb]
healpix_sphere_outputs = [
tuple(R * array(p)) for p in healpix_sphere_outputs
]
# Forward projection should be correct on test points.
f = Proj(proj='healpix', R=R)
given = inputs
get = [f(*p, radians=RADIANS) for p in given]
expect = healpix_sphere_outputs
# Fuzz to allow for rounding errors:
error = 1e-12
print()
print('=' * 80)
print('HEALPix forward projection, sphere with radius R = %s' % R)
print('input (radians) / expected output (meters) / received output')
print('=' * 80)
for i in range(len(get)):
print(given[i], expect[i], get[i])
self.assertTrue(rel_err(get[i], expect[i]) < error)
# Inverse of projection of a point p should yield p.
given = get
get = [f(*q, radians=RADIANS, inverse=True) for q in given]
expect = inputs
print('=' * 80)
print('HEALPix inverse projection, sphere with radius R = %s' % R)
print('input (meters) / expected output (radians) / received output')
print('=' * 80)
for i in range(len(get)):
print(given[i], expect[i], get[i])
self.assertTrue(rel_err(get[i], expect[i]) < error)
# Inverse projection of p below should return longitude of -pi.
# Previously, it was returning a number slightly less than pi
# because of a rounding error, which got magnified by
# wrap_longitude()
p = R * array((-7 * pi / 8, 3 * pi / 8))
get = f(*p, radians=RADIANS, inverse=True)
p1 = arcsin(1 - 1.0 / 12)
if not RADIANS:
p1 = rad2deg(p1)
expect = (-PI, p1)
self.assertTrue(rel_err(get, expect) < error)
def plot_angular_distribution():
"""
Plot the distribution of quasars in ra and dec for real data and
for simulated data
"""
ra_cen = 213.704
dec_cen = 53.083
# real and simulated data
real_file = helion_path + 'lya_forest/data/data_delta_transmission_RMplate.fits'
# real_file = helion_path + 'lya_forest/saclay/v4.4/v4.4.0_delta_transmission_RM.fits.gz'
simul_file = helion_path + 'lya_forest/london/v6.0/v6.0.0_delta_transmission_RM.fits.gz'
# simul_file = helion_path + 'lya_forest/saclay/v4.4/v4.4.0_delta_transmission_RM.fits.gz'
rd = tau_class.TauClass(real_file)
sd = tau_class.TauClass(simul_file)
tb = fitsio.read(helion_path + 'picca_run_delta/Catalogs/RM-qso.fits', 1)
fig, ax = plt.subplots(1, figsize=(7, 6.7))
stretch = sp.cos(sp.deg2rad(dec_cen))
ax.scatter((tb['RA'] - ra_cen) * stretch,
tb['DEC'] - dec_cen,
marker='+',
c='k',
alpha=0.6,
label=r'$\mathrm{All\ RM\ QSOs}$')
ax.scatter((rd.q_loc[:, 0] - ra_cen) * stretch,
rd.q_loc[:, 1] - dec_cen,
marker='o',
c='r',
alpha=0.6,
label=r'$\mathrm{QSOs\ used}$')
ax.scatter((sd.q_loc[:, 0] - ra_cen) * stretch,
sd.q_loc[:, 1] - dec_cen,
marker='o',
c='g',
alpha=0.6,
label=r'$\mathrm{Simulation\ 0}$')
circ = Circle([0, 0], 1.5, facecolor='none', edgecolor='k')
ax.add_patch(circ)
title_str = r'$\mathrm{RA}_{cen} = %.2f\ \mathrm{deg},' + \
r'\mathrm{DEC}_{cen} = %.2f\ \mathrm{deg}$'
plt.title(title_str % (ra_cen, dec_cen))
plt.xlabel(r'$\mathrm{RA\ offset\ [deg]}$')
plt.ylabel(r'$\mathrm{DEC\ offset\ [deg]}$')
plt.legend(loc='upper right')
plt.tight_layout()
# plt.savefig('ra_dec_dist.pdf')
plt.show()
def make_initial_catalogue(path):
from pywindow.catalogue import sky_to_cartesian
rng = scipy.random.RandomState(seed=42)
rmin, rmax = 2000., 3000.
size = 100000
catalogue = Catalogue()
distance = rng.uniform(2000., 3000., size=size)
ramin, ramax, decmin, decmax = 0., 30., -15, 15.
u1, u2 = rng.uniform(size=(2, size))
cmin = scipy.sin(scipy.deg2rad(decmin))
cmax = scipy.sin(scipy.deg2rad(decmax))
ra = ramin + u1 * (ramax - ramin)
dec = 90. - scipy.rad2deg(scipy.arccos(cmin + u2 * (cmax - cmin)))
catalogue['Position'] = sky_to_cartesian(distance, ra, dec)
catalogue['Weight'] = catalogue.ones()
decfrac = scipy.diff(scipy.sin(scipy.deg2rad([decmin, decmax])), axis=0)
rafrac = scipy.diff(scipy.deg2rad([ramin, ramax]), axis=0)
area = decfrac * rafrac
catalogue['NZ'] = catalogue['Weight'].sum() / (area *
(rmax - rmin)) / distance**2
catalogue.to_fits(path)
def get_angles(jsonresults):
"""
Extract the viewing angle information from the JSON object returned
from the database
:param jsonresults: JSON formatted string result from database query
:returns: sun zenith angle, sensor zenith angle, relative azimuth angle (in radians)
"""
angles = {}
for angle in ('SZA', 'SAA', 'VZA', 'VAA'):
angles[angle] = np.array([entry[angle] for entry in jsonresults])
sun_zenith = scipy.deg2rad(angles['SZA'])
sensor_zenith = scipy.deg2rad(angles['VZA'])
# Relative azimuth angle, should be in range 0-180
relative_azimuth = np.abs(scipy.deg2rad(angles['SAA']) - scipy.deg2rad(angles['VAA']))
fix = relative_azimuth > scipy.pi
relative_azimuth[fix] = 2*scipy.pi - relative_azimuth[fix]
return sun_zenith, sensor_zenith, relative_azimuth
def Rotate_Face(tilt):
"""
##########################################################
# Rotate_Face # #
################ #
# Inputs: N/A #
# #
# Outputs: R_GF (Trans. matrix from Geodetic to Face) #
# R_FG (Trans. matrix from Face to Geodetic) #
# #
# Summary: This helper function defines a transformation#
# matrix to convert x,y,z coordinates in face #
# relative coordinates to geodetic relative #
# x,y,z coordinates, or vice versa. #
# #
##########################################################
"""
AZ_ROT = scipy.deg2rad(tilt[1])
E_TILT = scipy.deg2rad(tilt[0])
#R_FG1 describes step 1 (transform xyz geodetic to a new xyz)
R_FG1 = scipy.array([[scipy.cos(AZ_ROT),
scipy.sin(AZ_ROT), 0],
[-scipy.sin(AZ_ROT),
scipy.cos(AZ_ROT), 0], [0, 0, 1]])
#R_FG2 describes step 2 (transform new xyz to xyz face relative)
R_FG2 = scipy.array([[scipy.cos(E_TILT), 0,
scipy.sin(E_TILT)], [0, 1, 0],
[-scipy.sin(E_TILT), 0,
scipy.cos(E_TILT)]])
#R_FG is the total transformation
R_FG = R_FG1.dot(R_FG2)
#R_FG is the inverse of the R_GF transformation and
# defines a face to geodetic transformation
R_GF = scipy.linalg.inv(R_FG)
return R_GF, R_FG
def rotation_transform_center(image, angle, center_xy=None):
"""
This function returns the transformation matrix for a rotation of a given image for a given angle
around a given center
The operation is implemented by the following steps to avoid unwanted translational side effects:
1.) Translate the image to the rotation center
2.) Rotating the image for the given angle
3.) Translate the image back to its original translatory position
image...ndarray
angle...angle in degree
center_xy...coordinates of rotation in image coordinates
"""
#If no rotation center is defined, set the center of the image as center
if center_xy is None:
cols, rows = image.shape[:2]
center_xy = sp.array((rows, cols)) / 2. - 0.5
#Calculate transformation matrices
tform1 = transform.AffineTransform(translation=-center_xy)
tform2 = transform.AffineTransform(rotation=sp.deg2rad(angle))
tform3 = transform.AffineTransform(translation=center_xy)
#Return transformation matrix
return tform1 + tform2 + tform3
def get_avg_sep():
real = tau_class.TauClass('../../data/delta-2.fits.gz')
real.get_data(skewers_perc=1., ylabel='DELTA')
avg_r = real.pixel_data[:, 2].mean()
print("Average distance is {}".format(avg_r))
ra, dec = real.q_loc[:, 0], real.q_loc[:, 1]
delta_ra = sp.abs(ra - ra[:, None])
delta_dec = sp.abs(dec - dec[:, None])
angle = 2 * sp.arcsin(
sp.sqrt(
sp.sin(delta_dec / 2.)**2 +
sp.cos(dec) * sp.cos(dec[:, None]) * sp.sin(delta_ra / 2.)**2))
# remove self-distances
sp.fill_diagonal(angle, sp.inf)
min_angle = angle.min(0) * 180 / sp.pi
avg_angle = min_angle.mean()
p_cmd = "Average distance is {} h^-1 Mpc \n".format(avg_r)
p_cmd += "Average angle is {} degree \n".format(avg_angle)
avg_sep = avg_angle * avg_r * sp.pi / 180
p_cmd += "Average transverse separation is {} h^-1 Mpc \n".format(avg_sep)
print(p_cmd)
# *************************************************************************
simul = tau_class.TauClass('../../data/simulations/'
'v6.0.1_delta_transmission_RMplate.fits')
simul.get_data(skewers_perc=1.)
avg_r = simul.pixel_data[:, 2].mean()
print("Average distance is {}".format(avg_r))
ra, dec = sp.deg2rad(simul.q_loc[:, 0]), sp.deg2rad(simul.q_loc[:, 1])
delta_ra = sp.abs(ra - ra[:, None])
delta_dec = sp.abs(dec - dec[:, None])
angle = 2 * sp.arcsin(
sp.sqrt(
sp.sin(delta_dec / 2.)**2 +
sp.cos(dec) * sp.cos(dec[:, None]) * sp.sin(delta_ra / 2.)**2))
# remove self-distances
sp.fill_diagonal(angle, sp.inf)
min_angle = angle.min(0) * 180 / sp.pi
avg_angle = min_angle.mean()
p_cmd = "Average distance is {} h^-1 Mpc \n".format(avg_r)
p_cmd += "Average angle is {} degree \n".format(avg_angle)
avg_sep = avg_angle * avg_r * sp.pi / 180
p_cmd += "Average transverse separation is {} h^-1 Mpc \n".format(avg_sep)
print(p_cmd)
def test_healpix_sphere(self):
# Sphere parameters.
R = 5
# Expected outputs of healpix_sphere() applied to inputs.
if RADIANS:
sigma_a = sqrt(3 - 3*sin(a[1]))
else:
sigma_a = sqrt(3 - 3*sin(deg2rad(a[1])))
ha = (pi/4*(1 - sigma_a), pi/4*(2 - sigma_a))
hb = (ha[0], -ha[1])
healpix_sphere_outputs = [
(0, 0),
(0, pi/4),
(0, -pi/4),
(pi/2, 0),
(-pi/2, 0),
(-pi, 0),
(-3*pi/4, pi/2),
(-3*pi/4, -pi/2),
ha,
hb
]
healpix_sphere_outputs = [tuple(R*array(p)) for p in
healpix_sphere_outputs]
# Forward projection should be correct on test points.
f = Proj(proj='healpix', R=R)
given = inputs
get = [f(*p, radians=RADIANS) for p in given]
expect = healpix_sphere_outputs
# Fuzz to allow for rounding errors:
error = 1e-12
print()
print('='*80)
print('HEALPix forward projection, sphere with radius R = %s' % R)
print('input (radians) / expected output (meters) / received output')
print('='*80)
for i in range(len(get)):
print(given[i], expect[i], get[i])
self.assertTrue(rel_err(get[i], expect[i]) < error)
# Inverse of projection of a point p should yield p.
given = get
get = [f(*q, radians=RADIANS, inverse=True) for q in given]
expect = inputs
print('='*80)
print('HEALPix inverse projection, sphere with radius R = %s' % R)
print('input (meters) / expected output (radians) / received output')
print('='*80)
for i in range(len(get)):
print(given[i], expect[i], get[i])
self.assertTrue(rel_err(get[i], expect[i]) < error)
# Inverse projection of p below should return longitude of -pi.
# Previously, it was returning a number slightly less than pi
# because of a rounding error, which got magnified by
# wrap_longitude()
p = R*array((-7*pi/8, 3*pi/8))
get = f(*p, radians=RADIANS, inverse=True)
p1 = arcsin(1 - 1.0/12)
if not RADIANS:
p1 = rad2deg(p1)
expect = (-PI, p1)
self.assertTrue(rel_err(get, expect) < error)
def flowprandtlmeyer(**flow):
"""
Prandtl-Meyer function for expansion waves.
This function accepts a given set of specific heat ratios and
an input of either Mach number, Mach angle or Prandtl-Meyer angle.
Inputs can be a single scalar or an array_like data structure.
Parameters
----------
gamma : array_like, optional
Specific heat ratio. Values must be greater than 1.
M : array_like
Mach number. Values must be greater than or equal to 1.
nu : array_like
Prandtl-Meyer angle [degrees]. Values must be
0 <= M <= 90*(sqrt((g+1)/(g-1))-1).
mu : array_like
Mach angle [degrees]. Values must be 0 <= M <= 90.
Returns
-------
out : (M, nu, mu)
Tuple of Mach number, Prandtl-Meyer angle, Mach angle.
Examples
--------
>>> flowprandtlmeyer(M=5)
(5.0, 76.920215508538789, 11.536959032815489)
"""
#parse the input
gamma, flow, mtype, itype = _flowinput(flow)
#calculate gamma-ratios for use in the equations
l = sp.sqrt((gamma-1)/(gamma+1))
#preshape mach array
M = sp.empty(flow.shape, sp.float64)
#use prandtl-meyer relation to solve for the mach number
if mtype in ["mach", "m"]:
if (flow < 1).any():
raise Exception("Mach number inputs must be real numbers greater" \
" than or equal to 1.")
M = flow
elif mtype in ["mu", "machangle"]:
if (flow < 0).any() or (flow > 90).any():
raise Exception("Mach angle inputs must be real numbers" \
" 0 <= M <= 90.")
M = 1 / sp.sin(sp.deg2rad(flow))
elif mtype in ["nu", "pm", "pmangle"]:
if (flow < 0).any() or (flow > 90*((1/l)-1)).any():
raise Exception("Prandtl-Meyer angle inputs must be real" \
" numbers 0 <= M <= 90*(sqrt((g+1)/(g-1))-1).")
M[:] = 2 #initial guess for the solution
for _ in xrange(_AETB_iternum):
b = sp.sqrt(M**2 - 1)
f = -sp.deg2rad(flow) + (1/l) * sp.arctan(l*b) - sp.arctan(b)
g = b*(1 - l**2) / (M*(1 + (l**2)*(b**2))) #derivative
M = M - (f / g) #Newton-Raphson
else:
raise Exception("Keyword input must be an acceptable string to" \
" select input parameter.")
#normal shock relations
b = sp.sqrt(M**2 - 1)
V = (1/l) * sp.arctan(l*b) - sp.arctan(b)
U = sp.arcsin(1 / M)
return from_ndarray(itype, M, sp.rad2deg(V), sp.rad2deg(U))
def test_calc_kernel_f1(self):
f1 = self.brdf.calc_kernel_f1(scipy.deg2rad(self.test_object.sun_zenith),
scipy.deg2rad(self.test_object.sensor_zenith),
scipy.deg2rad(self.test_object.relative_azimuth()))
self.assertIsInstance(f1, scipy.ndarray)
def cal_qx(self, tth, th, energy):
return 2.0 * sp.pi / self.cal_wave(energy) * (
sp.cos(sp.deg2rad((tth - th))) - sp.cos(sp.deg2rad(th)))
def patch_gradient(patch, direction = lambda x,y: x, **kwargs):
"""
take a patch and apply a gradient to it.
:patch: the patch to be decorated
:direction: if a number, indicates the direction of the linear gradient, otherway it should be a callable
the function take several optional arguments:
colormap: the colormap used for the gradient [any valid colormap, default cm.jet]
BUG: if the clipping patch is not a rectangle the alpha value get lost
"""
ax = plt.gca()
#loading of the default keywords
colormap = multiget(kwargs,['colormap','cmap','cm'],cm.jet)
colormap = plt.get_cmap(colormap)
resolution = multiget(kwargs,['resolution','res'],101j)
alpha = multiget(kwargs,['alpha'],1)
x_min = multiget(kwargs,['x_min','xmin'],-1)
x_max = multiget(kwargs,['x_max','xmax'],1)
y_min = multiget(kwargs,['y_min','ymin'],-1)
y_max = multiget(kwargs,['y_max','ymax'],1)
c_min = multiget(kwargs,['c_min','cmin'],None)
c_max = multiget(kwargs,['c_max','cmax'],None)
edgecolor = multiget(kwargs,['edgecolor','ec'],None)
linestyle = multiget(kwargs,['linestyle','ls'],None)
linewidth = multiget(kwargs,['linewidth','lw'],None)
#set the function of the gradient
try:
dir2rad = scipy.deg2rad(1.*direction)
xmean = (x_max+x_min)/2.
ymean = (y_max+y_min)/2.
xampl = (x_max-x_min)/2.
yampl = (y_max-y_min)/2.
dir_func = lambda x,y: ((x-xmean)/xampl)*np.cos(dir2rad) + ((y-ymean)/yampl)*np.sin(dir2rad)
except TypeError:
dir_func = direction
#get the extent of the patch
extent = patch.get_extents().transformed(ax.transData.inverted()).extents
extent[1],extent[2] = extent[2],extent[1]
#create the grid on which the function will be evaluated
yy,xx = np.ogrid[y_min:y_max:resolution,x_min:x_max:resolution]
data = dir_func(xx,yy)
#temporally disable the autoscale to avoid problem with the imshow
autoscale = ax.get_autoscale_on()
ax.set_autoscale_on(False)
#create the image on the patch
props = dict(extent=extent,origin='lower',cmap=colormap,alpha=alpha,aspect='auto')
if c_min is not None:
props.update(vmin=c_min)
if c_max is not None:
props.update(vmax=c_max)
im = ax.imshow(data,**props)
im.set_alpha(alpha)
#remove the foreground from the patch and set the line properties
patch.set_fc('none')
patch.set_alpha(alpha)
if edgecolor is not None: patch.set_edgecolor(edgecolor)
if linestyle is not None: patch.set_linestyle(linestyle)
if linewidth is not None: patch.set_linewidth(linewidth)
#apply the clipping and restore the original autoscale setting
im.set_clip_path(patch)
ax.set_autoscale_on(autoscale)
return im
operating_frequency = 2.477e9
wavelength = scipy.constants.c / operating_frequency
randarray = sp.loadtxt("arrays/randarray.dat") / wavelength
ourlinarray = sp.loadtxt("arrays/linarray.dat") / wavelength
ourcircarray = sp.loadtxt("arrays/circarray.dat") / wavelength
#Test Parameters
ants = randarray
nsamp = 2000
snr = -30
s1_aoa_deg = [80, 0]
s2_aoa_deg = [120, 0]
s1_aoa = (sp.deg2rad(s1_aoa_deg[0]), sp.deg2rad(s1_aoa_deg[1]))
#s2_aoa = (pi/2 + sp.randn()/2, sp.randn()/2)
s2_aoa = (sp.deg2rad(s2_aoa_deg[0]), sp.deg2rad(s2_aoa_deg[1]))
s1 = util.makesamples(ants, s1_aoa[0], s1_aoa[1], nsamp)
s2 = util.makesamples(ants, s2_aoa[0], s2_aoa[1], nsamp)
samples = s2 + s1
samples = util.awgn(samples, snr)
# add noise to s1 and s2
s1 = util.awgn(s1, snr)
s2 = util.awgn(s2, snr)
R = music.covar(samples)
est = music.Estimator(ants, R, nsignals=2)
def purcell_bi(physics, phase, network, r_toroid,
surface_tension='pore.surface_tension',
contact_angle='pore.contact_angle',
diameter='throat.diameter',
h_max='pore.diameter',
max_dist=True,
**kwargs):
r"""
Computes the throat capillary entry pressure assuming the throat is a
toroid. Pressure is a function of both pore and throat diameters so it
becomes a bi-dirctional calculation.
Parameters
----------
network : OpenPNM Network Object
The Network on which to apply the calculation
sigma : dict key (string)
The dictionary key containing the surface tension values to be used. If
a pore property is given, it is interpolated to a throat list.
theta : dict key (string)
The dictionary key containing the contact angle values to be used. If
a pore property is given, it is interpolated to a throat list.
throat_diameter : dict key (string)
The dictionary key containing the throat diameter values to be used.
r_toroid : float or array_like
The radius of the toroid surrounding the pore
Notes
-----
This approach accounts for the converging-diverging nature of many throat
types. Advancing the meniscus beyond the apex of the toroid requires an
increase in capillary pressure beyond that for a cylindical tube of the
same radius. The details of this equation are described by Mason and
Morrow [1]_, and explored by Gostick [2]_ in the context of a pore network
model.
"""
entity = diameter.split('.')[0]
if surface_tension.split('.')[0] == 'pore' and entity == 'throat':
sigma = phase[surface_tension]
sigma = phase.interpolate_data(data=sigma)
else:
sigma = phase[surface_tension]
if contact_angle.split('.')[0] == 'pore' and entity == 'throat':
theta = phase[contact_angle]
theta = phase.interpolate_data(data=theta)
else:
theta = phase[contact_angle]
# Mason and Morrow have the definitions switched
theta = 180 - theta
th = _sp.deg2rad(theta)
rt = network[diameter]/2
R = r_toroid
a_max = th - np.arcsin((np.sin(th))/(1+rt/R))
if max_dist and entity == 'throat':
# Perform analysis for entry into both pores
r_max = np.zeros([network.Nt, 2])
for j in range(2):
Pj = network['throat.conns'][:, j]
dj = network[h_max][Pj]
max_reached = np.zeros(network.Nt, dtype=bool)
alpha_reached = np.zeros(network.Nt)
# With increasing filling angle assess whether interface has passed
# the critical distance and record the critical angle
a_space = _sp.linspace(1e-3, _sp.pi, 181)
nudge = 0.001
for a_test in a_space:
nudgers = network.throats()[np.around(th-a_test, 0) == 90]
if len(nudgers) > 0:
th[nudgers] += nudge
r = R*(1+(rt/R)-_sp.cos(a_test))/_sp.cos(th-a_test)
# Vertical adjustment for centre of circle
y_off = R*np.sin(a_test)
# Angle between contact point - centre - vertical
zeta = (th-a_test-(np.pi/2))
c = y_off - r*np.cos(zeta)
y_max = c+r
ts = network.throats()[(y_max > (dj)) * (~max_reached)]
if len(ts) > 0:
max_reached[ts] = True
alpha_reached[ts] = a_test
if len(nudgers) > 0:
th[nudgers] -= nudge
# Any interfaces that never reach a wall are ok"
alpha_reached[~max_reached] = a_max[~max_reached]
temp = max_reached[np.abs(a_max) > np.abs(alpha_reached)]
temp_sum = np.around(100*np.sum(temp)/len(a_max), 2)
logger.info("Percentage max before BT " + str(temp_sum))
# Any interfaces that can expand to maximum curvature before
# hitting a pore wall are ok
mask = np.abs(a_max) < np.abs(alpha_reached)
alpha_reached[mask] = a_max[mask]
r_max[:, j] = (R*(1 + (rt/R) - _sp.cos(alpha_reached)) /
_sp.cos(th - alpha_reached))
value = (2*np.vstack((sigma, sigma)).T) / r_max
else:
r_max = R*(1+(rt/R)-_sp.cos(a_max))/_sp.cos(th-a_max)
value = 2*sigma/r_max
if entity == 'throat':
value = value[phase.throats(physics.name)]
else:
value = value[phase.pores(physics.name)]
return value
def toroidal(target,
mode='max',
r_toroid=5e-6,
target_Pc=None,
surface_tension='pore.surface_tension',
contact_angle='pore.contact_angle',
diameter='throat.diameter'):
r"""
Calculate the filling angle (alpha) for a given capillary pressure
Parameters
----------
target : OpenPNM Object
The object for which these values are being calculated. This
controls the length of the calculated array, and also provides
access to other necessary thermofluid properties.
mode : string (Default is 'max')
Determines what information to send back. Options are:
'max' : the maximum capillary pressure along the throat axis
'men' : return the meniscus info for a target pressure
r_toroid : float or array_like
The radius of the toroid surrounding the pore
target_Pc : float
The target capillary pressure
surface_tension : dict key (string)
The dictionary key containing the surface tension values to be used. If
a pore property is given, it is interpolated to a throat list.
contact_angle : dict key (string)
The dictionary key containing the contact angle values to be used. If
a pore property is given, it is interpolated to a throat list.
diameter : dict key (string)
The dictionary key containing the throat diameter values to be used.
Notes
-----
This approach accounts for the converging-diverging nature of many throat
types. Advancing the meniscus beyond the apex of the toroid requires an
increase in capillary pressure beyond that for a cylindical tube of the
same radius. The details of this equation are described by Mason and
Morrow [1]_, and explored by Gostick [2]_ in the context of a pore network
model.
"""
network = target.project.network
phase = target.project.find_phase(target)
element, sigma, theta = _get_key_props(phase=phase,
diameter=diameter,
surface_tension=surface_tension,
contact_angle=contact_angle)
# Mason and Morrow have the definitions switched
theta = _sp.deg2rad(theta)
rt = network[diameter]/2
Rf = r_toroid
a, r, R, s, t, p = syp.symbols('a, r, R, s, t, p')
rhs = -2*s/(R*(1+(r/R)-syp.cos(a))/syp.cos(t-a))
# lhs = p
# roots = syp.solve(lhs-rhs, a)
# _, r1, r2, _ = roots
eit = syp.exp(syp.I*t)
# There are 2 non-trivial solutions when rearranging the Purcell Pc eqn
# To solve for alpha (a), r1 and r2 - r2 is outside a_min and a_max,
# r1 is inside the range. It takes sympy to solve the equations so the
# root inside the range is provided below but can be verified by running
# the commented out lines above
r1 = -syp.I*syp.log((R*p*eit + p*r*eit -
syp.sqrt((2*R*p**2*r*eit +
2*R*p*s*syp.exp(2*syp.I*t) +
2*R*p*s + p**2*r**2*eit -
4*s**2*eit)*eit))/(R*p*eit - 2*s))
r2 = -syp.I*syp.log((R*p*eit + p*r*eit +
syp.sqrt((2*R*p**2*r*eit +
2*R*p*s*syp.exp(2*syp.I*t) +
2*R*p*s + p**2*r**2*eit -
4*s**2*eit)*eit))/(R*p*eit - 2*s))
a_min = t - syp.asin((syp.sin(t))/(1+r/R))
a_max = t - syp.pi + syp.asin((syp.sin(t))/(1+r/R))
# alpha at given Pc
fa_Pc = syp.lambdify((p, r, R, s, t), r1, 'numpy')
# Pc at given alpha
fPc = syp.lambdify((a, r, R, s, t), rhs, 'numpy')
# alphas where max and min Pc occurs
fa_max = syp.lambdify((r, R, t), a_max, 'numpy')
fa_min = syp.lambdify((r, R, t), a_min, 'numpy')
# Values at min and max
a_maxs = fa_max(rt, Rf, theta)
pc_max = fPc(a_maxs, rt, Rf, sigma, theta)
a_mins = fa_min(rt, Rf, theta)
pc_min = fPc(a_mins, rt, Rf, sigma, theta)
if mode == 'max':
return pc_max
elif target_Pc is None:
logger.exception(msg='Please supply a target capillary pressure' +
' when mode is not max')
if np.abs(target_Pc) < 1.0:
target_Pc = 1.0
# Masks to determine which throats to actually calculate alpha for
# Outside the valid range of pressures min or max values are used
over_range = target_Pc > pc_max
undr_range = target_Pc < pc_min
in_range = ~over_range * ~undr_range
alpha = np.zeros(len(rt))
if np.any(in_range):
alpha[in_range] = np.real(fa_Pc(target_Pc, rt[in_range], Rf,
sigma[in_range], theta[in_range]))
if np.any(over_range):
alpha[over_range] = a_maxs[over_range]
if np.any(undr_range):
alpha[undr_range] = a_mins[undr_range]
logger.info('Filling angles calculated for Pc: '+str(target_Pc))
men_data = {}
f = theta-alpha
# Handle potential divide by zero
f[np.abs(f) == np.pi/2] = f[np.abs(f) == np.pi/2]*(1-1e-12)
# Meniscus radius
r_men = Rf*(1 + rt/Rf - np.cos(alpha)) / np.cos((f))
# Vertical adjustment for centre of circle
y_off = Rf*np.sin(alpha)
# Angle between contact point - centre - vertical
zeta = (theta-alpha-np.pi/2)
# Distance that center of meniscus is below the plane of the throat
center = y_off - r_men*np.cos(zeta)
men_data['alpha'] = alpha
men_data['alpha_max'] = a_maxs
men_data['alpha_min'] = a_mins
men_data['radius'] = r_men
men_data['center'] = center
men_data['zeta'] = zeta
return men_data
def track(self):
print "Press right mouse button to pause or play"
print "Use left mouse button to select target"
print "Target color must be different from background"
print "Target must have width larger than height"
print "Target can be upside down"
#Parameters
isUDPConnection = False # Currently switched manually in the code
display = True
displayDebug = True
useBasemap = False
maxRelativeMotionPerFrame = 2 # How much the target can moved between two succesive frames
pixelPerRadians = 320
radius = pixelPerRadians
referenceImage = '../ObjectTracking/kite_detail.jpg'
scaleFactor = 0.5
isVirtualCamera = True
useHDF5 = False
# Open reference image: this is used at initlalisation
target_detail = Image(referenceImage)
# Get RGB color palette of target (was found to work better than using hue)
pal = target_detail.getPalette(bins = 2, hue = False)
# Open video to analyse or live stream
#cam = JpegStreamCamera('http://192.168.1.29:8080/videofeed')#640 * 480
if isVirtualCamera:
#cam = VirtualCamera('../../zenith-wind-power-read-only/KiteControl-Qt/videos/kiteFlying.avi','video')
#cam = VirtualCamera('/media/bat/DATA/Baptiste/Nautilab/kite_project/robokite/ObjectTracking/00095.MTS', 'video')
#cam = VirtualCamera('output.avi', 'video')
cam = VirtualCamera('../Recording/Videos/Flying kite images (for kite steering unit development)-YTMgX1bvrTo.flv','video')
virtualCameraFPS = 25
else:
cam = JpegStreamCamera('http://192.168.43.1:8080/videofeed')#640 * 480
#cam = Camera()
# Get a sample image to initialize the display at the same size
img = cam.getImage().scale(scaleFactor)
print img.width, img.height
# Create a pygame display
if display:
if img.width>img.height:
disp = Display((27*640/10,25*400/10))#(int(2*img.width/scaleFactor), int(2*img.height/scaleFactor)))
else:
disp = Display((810,1080))
#js = JpegStreamer()
# Initialize variables
previous_angle = 0 # target has to be upright when starting. Target width has to be larger than target heigth.
previous_coord_px = (0, 0) # Initialized to top left corner, which always exists
previous_dCoord = previous_coord_px
previous_dAngle = previous_angle
angles = []
coords_px = []
coord_px = [0, 0]
angle = 0
target_elevations = []
target_bearings = []
times = []
wasTargetFoundInPreviousFrame = False
i_frame = 0
isPaused = False
selectionInProgress = False
th = [100, 100, 100]
skycolor = Color.BLUE
timeLastTarget = 0
# Prepare recording
recordFilename = datetime.datetime.utcnow().strftime("%Y%m%d_%Hh%M_")+ 'simpleTrack'
if useHDF5:
try:
os.remove(recordFilename + '.hdf5')
except:
print('Creating file ' + recordFilename + '.hdf5')
""" The following line is used to silence the following error (according to http://stackoverflow.com/questions/15117128/h5py-in-memory-file-and-multiprocessing-error)
#000: ../../../src/H5F.c line 1526 in H5Fopen(): unable to open file
major: File accessability
minor: Unable to open file"""
h5py._errors.silence_errors()
recordFile = h5py.File(recordFilename + '.hdf5', 'a')
hdfSize = 0
dset = recordFile.create_dataset('kite', (2,2), maxshape=(None,7))
imset = recordFile.create_dataset('image', (2,img.width,img.height,3 ), maxshape=(None, img.width, img.height, 3))
else:
try:
os.remove(recordFilename + '.csv')
except:
print('Creating file ' + recordFilename + '.csv')
recordFile = file(recordFilename + '.csv', 'a')
csv_writer = csv.writer(recordFile)
csv_writer.writerow(['Time (s)', 'x (px)', 'y (px)', 'Orientation (rad)', 'Elevation (rad)', 'Bearing (rad)', 'ROT (rad/s)'])
# Launch a thread to get UDP message with orientation of the camera
mobile = mobileState.mobileState()
if isUDPConnection:
a = threading.Thread(None, mobileState.mobileState.checkUpdate, None, (mobile,))
a.start()
# Loop while not canceled by user
t0 = time.time()
previousTime = t0
while not(display) or disp.isNotDone():
t = time.time()
deltaT = (t-previousTime)
FPS = 1.0/deltaT
#print 'FPS =', FPS
if isVirtualCamera:
deltaT = 1.0/virtualCameraFPS
previousTime = t
i_frame = i_frame + 1
timestamp = datetime.datetime.utcnow()
# Receive orientation of the camera
if isUDPConnection:
mobile.computeRPY([2, 0, 1], [-1, 1, 1])
ctm = np.array([[sp.cos(mobile.roll), -sp.sin(mobile.roll)], \
[sp.sin(mobile.roll), sp.cos(mobile.roll)]]) # Coordinate transform matrix
if useBasemap:
# Warning this really slows down the computation
m = Basemap(width=img.width, height=img.height, projection='aeqd',
lat_0=sp.rad2deg(mobile.pitch), lon_0=sp.rad2deg(mobile.yaw), rsphere = radius)
# Get an image from camera
if not isPaused:
img = cam.getImage()
img = img.resize(int(scaleFactor*img.width), int(scaleFactor*img.height))
if display:
# Pause image when right button is pressed
dwn = disp.rightButtonDownPosition()
if dwn is not None:
isPaused = not(isPaused)
dwn = None
if display:
# Create a layer to enable user to make a selection of the target
selectionLayer = DrawingLayer((img.width, img.height))
if img:
if display:
# Create a new layer to host information retrieved from video
layer = DrawingLayer((img.width, img.height))
# Selection is a rectangle drawn while holding mouse left button down
if disp.leftButtonDown:
corner1 = (disp.mouseX, disp.mouseY)
selectionInProgress = True
if selectionInProgress:
corner2 = (disp.mouseX, disp.mouseY)
bb = disp.pointsToBoundingBox(corner1, corner2)# Display the temporary selection
if disp.leftButtonUp: # User has finished is selection
selectionInProgress = False
selection = img.crop(bb[0], bb[1], bb[2], bb[3])
if selection != None:
# The 3 main colors in the area selected are considered.
# Note that the selection should be included in the target and not contain background
try:
selection.save('../ObjectTracking/'+ 'kite_detail_tmp.jpg')
img0 = Image("kite_detail_tmp.jpg") # For unknown reason I have to reload the image...
pal = img0.getPalette(bins = 2, hue = False)
except: # getPalette is sometimes bugging and raising LinalgError because matrix not positive definite
pal = pal
wasTargetFoundInPreviousFrame = False
previous_coord_px = (bb[0] + bb[2]/2, bb[1] + bb[3]/2)
if corner1 != corner2:
selectionLayer.rectangle((bb[0], bb[1]), (bb[2], bb[3]), width = 5, color = Color.YELLOW)
# If the target was already found, we can save computation time by
# reducing the Region Of Interest around predicted position
if wasTargetFoundInPreviousFrame:
ROITopLeftCorner = (max(0, previous_coord_px[0]-maxRelativeMotionPerFrame/2*width), \
max(0, previous_coord_px[1] -height*maxRelativeMotionPerFrame/2))
ROI = img.crop(ROITopLeftCorner[0], ROITopLeftCorner[1], \
maxRelativeMotionPerFrame*width, maxRelativeMotionPerFrame*height, \
centered = False)
if display :
# Draw the rectangle corresponding to the ROI on the complete image
layer.rectangle((previous_coord_px[0]-maxRelativeMotionPerFrame/2*width, \
previous_coord_px[1]-maxRelativeMotionPerFrame/2*height), \
(maxRelativeMotionPerFrame*width, maxRelativeMotionPerFrame*height), \
color = Color.GREEN, width = 2)
else:
# Search on the whole image if no clue of where is the target
ROITopLeftCorner = (0, 0)
ROI = img
'''#Option 1
target_part0 = ROI.hueDistance(color=(142,50,65)).invert().threshold(150)
target_part1 = ROI.hueDistance(color=(93,16,28)).invert().threshold(150)
target_part2 = ROI.hueDistance(color=(223,135,170)).invert().threshold(150)
target_raw_img = target_part0+target_part1+target_part2
target_img = target_raw_img.erode(5).dilate(5)
#Option 2
target_img = ROI.hueDistance(imgModel.getPixel(10,10)).binarize().invert().erode(2).dilate(2)'''
# Find sky color
sky = (img-img.binarize()).findBlobs(minsize=10000)
if sky:
skycolor = sky[0].meanColor()
# Option 3
target_img = ROI-ROI # Black image
# Loop through palette of target colors
if display and displayDebug:
decomposition = []
i_col = 0
for col in pal:
c = tuple([int(col[i]) for i in range(0,3)])
# Search the target based on color
ROI.save('../ObjectTracking/'+ 'ROI_tmp.jpg')
img1 = Image('../ObjectTracking/'+ 'ROI_tmp.jpg')
filter_img = img1.colorDistance(color = c)
h = filter_img.histogram(numbins=256)
cs = np.cumsum(h)
thmax = np.argmin(abs(cs- 0.02*img.width*img.height)) # find the threshold to have 10% of the pixel in the expected color
thmin = np.argmin(abs(cs- 0.005*img.width*img.height)) # find the threshold to have 10% of the pixel in the expected color
if thmin==thmax:
newth = thmin
else:
newth = np.argmin(h[thmin:thmax]) + thmin
alpha = 0.5
th[i_col] = alpha*th[i_col]+(1-alpha)*newth
filter_img = filter_img.threshold(max(40,min(200,th[i_col]))).invert()
target_img = target_img + filter_img
#print th
i_col = i_col + 1
if display and displayDebug:
[R, G, B] = filter_img.splitChannels()
white = (R-R).invert()
r = R*1.0/255*c[0]
g = G*1.0/255*c[1]
b = B*1.0/255*c[2]
tmp = white.mergeChannels(r, g, b)
decomposition.append(tmp)
# Get a black background with with white target foreground
target_img = target_img.threshold(150)
target_img = target_img - ROI.colorDistance(color = skycolor).threshold(80).invert()
if display and displayDebug:
small_ini = target_img.resize(int(img.width/(len(pal)+1)), int(img.height/(len(pal)+1)))
for tmp in decomposition:
small_ini = small_ini.sideBySide(tmp.resize(int(img.width/(len(pal)+1)), int(img.height/(len(pal)+1))), side = 'bottom')
small_ini = small_ini.adaptiveScale((int(img.width), int(img.height)))
toDisplay = img.sideBySide(small_ini)
else:
toDisplay = img
#target_img = ROI.hueDistance(color = Color.RED).threshold(10).invert()
# Search for binary large objects representing potential target
target = target_img.findBlobs(minsize = 500)
if target: # If a target was found
if wasTargetFoundInPreviousFrame:
predictedTargetPosition = (width*maxRelativeMotionPerFrame/2, height*maxRelativeMotionPerFrame/2) # Target will most likely be close to the center of the ROI
else:
predictedTargetPosition = previous_coord_px
# If there are several targets in the image, take the one which is the closest of the predicted position
target = target.sortDistance(predictedTargetPosition)
# Get target coordinates according to minimal bounding rectangle or centroid.
coordMinRect = ROITopLeftCorner + np.array((target[0].minRectX(), target[0].minRectY()))
coord_px = ROITopLeftCorner + np.array(target[0].centroid())
# Rotate the coordinates of roll angle around the middle of the screen
rot_coord_px = np.dot(ctm, coord_px - np.array([img.width/2, img.height/2])) + np.array([img.width/2, img.height/2])
if useBasemap:
coord = sp.deg2rad(m(rot_coord_px[0], img.height-rot_coord_px[1], inverse = True))
else:
coord = localProjection(rot_coord_px[0]-img.width/2, img.height/2-rot_coord_px[1], radius, mobile.yaw, mobile.pitch, inverse = True)
target_bearing, target_elevation = coord
# Get minimum bounding rectangle for display purpose
minR = ROITopLeftCorner + np.array(target[0].minRect())
contours = target[0].contour()
contours = [ ROITopLeftCorner + np.array(contour) for contour in contours]
# Get target features
angle = sp.deg2rad(target[0].angle()) + mobile.roll
angle = sp.deg2rad(unwrap180(sp.rad2deg(angle), sp.rad2deg(previous_angle)))
width = target[0].width()
height = target[0].height()
# Check if the kite is upside down
# First rotate the kite
ctm2 = np.array([[sp.cos(-angle+mobile.roll), -sp.sin(-angle+mobile.roll)], \
[sp.sin(-angle+mobile.roll), sp.cos(-angle+mobile.roll)]]) # Coordinate transform matrix
rotated_contours = [np.dot(ctm2, contour-coordMinRect) for contour in contours]
y = [-tmp[1] for tmp in rotated_contours]
itop = np.argmax(y) # Then looks at the points at the top
ibottom = np.argmin(y) # and the point at the bottom
# The point the most excentered is at the bottom
if abs(rotated_contours[itop][0])>abs(rotated_contours[ibottom][0]):
isInverted = True
else:
isInverted = False
if isInverted:
angle = angle + sp.pi
# Filter the data
alpha = 1-sp.exp(-deltaT/self.filterTimeConstant)
if not(isPaused):
dCoord = np.array(previous_dCoord)*(1-alpha) + alpha*(np.array(coord_px) - previous_coord_px) # related to the speed only if cam is fixed
dAngle = np.array(previous_dAngle)*(1-alpha) + alpha*(np.array(angle) - previous_angle)
else :
dCoord = np.array([0, 0])
dAngle = np.array([0])
#print coord_px, angle, width, height, dCoord
# Record important data
times.append(timestamp)
coords_px.append(coord_px)
angles.append(angle)
target_elevations.append(target_elevation)
target_bearings.append(target_bearing)
# Export data to controller
self.elevation = target_elevation
self.bearing = target_bearing
self.orientation = angle
dt = time.time()-timeLastTarget
self.ROT = dAngle/dt
self.lastUpdateTime = t
# Save for initialisation of next step
previous_dCoord = dCoord
previous_angle = angle
previous_coord_px = (int(coord_px[0]), int(coord_px[1]))
wasTargetFoundInPreviousFrame = True
timeLastTarget = time.time()
else:
wasTargetFoundInPreviousFrame = False
if useHDF5:
hdfSize = hdfSize+1
dset.resize((hdfSize, 7))
imset.resize((hdfSize, img.width, img.height, 3))
dset[hdfSize-1,:] = [time.time(), coord_px[0], coord_px[1], angle, self.elevation, self.bearing, self.ROT]
imset[hdfSize-1,:,:,:] = img.getNumpy()
recordFile.flush()
else:
csv_writer.writerow([time.time(), coord_px[0], coord_px[1], angle, self.elevation, self.bearing, self.ROT])
if display :
if target:
# Add target features to layer
# Minimal rectange and its center in RED
layer.polygon(minR[(0, 1, 3, 2), :], color = Color.RED, width = 5)
layer.circle((int(coordMinRect[0]), int(coordMinRect[1])), 10, filled = True, color = Color.RED)
# Target contour and centroid in BLUE
layer.circle((int(coord_px[0]), int(coord_px[1])), 10, filled = True, color = Color.BLUE)
layer.polygon(contours, color = Color.BLUE, width = 5)
# Speed vector in BLACK
layer.line((int(coord_px[0]), int(coord_px[1])), (int(coord_px[0]+20*dCoord[0]), int(coord_px[1]+20*dCoord[1])), width = 3)
# Line giving angle
layer.line((int(coord_px[0]+200*sp.cos(angle)), int(coord_px[1]+200*sp.sin(angle))), (int(coord_px[0]-200*sp.cos(angle)), int(coord_px[1]-200*sp.sin(angle))), color = Color.RED)
# Line giving rate of turn
#layer.line((int(coord_px[0]+200*sp.cos(angle+dAngle*10)), int(coord_px[1]+200*sp.sin(angle+dAngle*10))), (int(coord_px[0]-200*sp.cos(angle + dAngle*10)), int(coord_px[1]-200*sp.sin(angle+dAngle*10))))
# Add the layer to the raw image
toDisplay.addDrawingLayer(layer)
toDisplay.addDrawingLayer(selectionLayer)
# Add time metadata
toDisplay.drawText(str(i_frame)+" "+ str(timestamp), x=0, y=0, fontsize=20)
# Add Line giving horizon
#layer.line((0, int(img.height/2 + mobile.pitch*pixelPerRadians)),(img.width, int(img.height/2 + mobile.pitch*pixelPerRadians)), width = 3, color = Color.RED)
# Plot parallels
for lat in range(-90, 90, 15):
r = range(0, 361, 10)
if useBasemap:
# \todo improve for high roll
l = m (r, [lat]*len(r))
pix = [np.array(l[0]), img.height-np.array(l[1])]
else:
l = localProjection(sp.deg2rad(r), \
sp.deg2rad([lat]*len(r)), \
radius, \
lon_0 = mobile.yaw, \
lat_0 = mobile.pitch, \
inverse = False)
l = np.dot(ctm, l)
pix = [np.array(l[0])+img.width/2, img.height/2-np.array(l[1])]
for i in range(len(r)-1):
if isPixelInImage((pix[0][i],pix[1][i]), img) or isPixelInImage((pix[0][i+1],pix[1][i+1]), img):
layer.line((pix[0][i],pix[1][i]), (pix[0][i+1], pix[1][i+1]), color=Color.WHITE, width = 2)
# Plot meridians
for lon in range(0, 360, 15):
r = range(-90, 91, 10)
if useBasemap:
# \todo improve for high roll
l = m ([lon]*len(r), r)
pix = [np.array(l[0]), img.height-np.array(l[1])]
else:
l= localProjection(sp.deg2rad([lon]*len(r)), \
sp.deg2rad(r), \
radius, \
lon_0 = mobile.yaw, \
lat_0 = mobile.pitch, \
inverse = False)
l = np.dot(ctm, l)
pix = [np.array(l[0])+img.width/2, img.height/2-np.array(l[1])]
for i in range(len(r)-1):
if isPixelInImage((pix[0][i],pix[1][i]), img) or isPixelInImage((pix[0][i+1],pix[1][i+1]), img):
layer.line((pix[0][i],pix[1][i]), (pix[0][i+1], pix[1][i+1]), color=Color.WHITE, width = 2)
# Text giving bearing
# \todo improve for high roll
for bearing_deg in range(0, 360, 30):
l = localProjection(sp.deg2rad(bearing_deg), sp.deg2rad(0), radius, lon_0 = mobile.yaw, lat_0 = mobile.pitch, inverse = False)
l = np.dot(ctm, l)
layer.text(str(bearing_deg), ( img.width/2+int(l[0]), img.height-20), color = Color.RED)
# Text giving elevation
# \todo improve for high roll
for elevation_deg in range(-60, 91, 30):
l = localProjection(0, sp.deg2rad(elevation_deg), radius, lon_0 = mobile.yaw, lat_0 = mobile.pitch, inverse = False)
l = np.dot(ctm, l)
layer.text(str(elevation_deg), ( img.width/2 ,img.height/2-int(l[1])), color = Color.RED)
#toDisplay.save(js)
toDisplay.save(disp)
if display :
toDisplay.removeDrawingLayer(1)
toDisplay.removeDrawingLayer(0)
recordFile.close()
def cal_qz(self, tth, th, energy):
return 2.0 * sp.pi / self.cal_wave(energy) * (
sp.sin(sp.deg2rad((tth - th))) + sp.sin(sp.deg2rad(th)))
def llz2xyz(lat, lon, depth):
"""
convert latitude, longitude, and altitude to earth-centered, earth-fixed (ECEF) cartesian
the output coordinates will be normalized to the radius of interest for the
displayed sphere as defined by the rad parameter above
code is based on:
http://www.mathworks.com/matlabcentral/fileexchange/7942-covert-lat--lon--alt-to-ecef-cartesian/content/lla2ecef.m
latitude, longitude, altitude to ECEF ("Earth-Centered, Earth-Fixed")
http://www.gmat.unsw.edu.au/snap/gps/clynch_pdfs/coordcvt.pdf
calculations based on:
http://rbrundritt.wordpress.com/2008/10/14/conversion-between-spherical-and-cartesian-coordinates-systems/
http://stackoverflow.com/questions/10473852/convert-latitude-and-longitude-to-point-in-3d-space
http://www.oosa.unvienna.org/pdf/icg/2012/template/WGS_84.pdf
computation verified using:
http://www.sysense.com/products/ecef_lla_converter/index.html
Parameters
----------
lat: float
latitude (deg)
lon: float
longitude (deg)
depth: float
depth (km)
Returns
-------
x: float
x-coordinate normalized to the radius of Earh
y: float
y-coordinate normalized to the radius of Earh
z: float
z-coordinate normalized to the radius of Earh
"""
import numpy as np
from scipy import deg2rad, rad2deg
alt = -1.0 * 1000.0 * depth # height above WGS84 ellipsoid (m)
lat = deg2rad(lat)
cosLat = np.cos(lat)
sinLat = np.sin(lat)
#
# World Geodetic System 1984
# WGS 84
#
erad = np.float64(
6378137.0) # Radius of the Earth in meters (equatorial radius, WGS84)
rad = 1 # sphere radius
e = np.float64(8.1819190842622e-2)
n = erad / np.sqrt(
1.0 - e * e * sinLat * sinLat) # prime vertical radius of curvature
lon = deg2rad(lon)
cosLon = np.cos(lon)
sinLon = np.sin(lon)
x = (n + alt) * cosLat * cosLon # meters
y = (n + alt) * cosLat * sinLon # meters
z = ((1 - e * e) * n + alt) * sinLat # meters
x = x * rad / erad # normalize to radius of rad
y = y * rad / erad # normalize to radius of rad
z = z * rad / erad # normalize to radius of rad
return x, y, z
def find_pattern_rotated(PF, pattern, image, rescale = 1.0, rotate=(-60,61,120),
roi_center=None, roi_size=(41,41), plot=False):
#Get current time to determine runtime of search
start_time = time.time()
#Initialize values needed later on
result = []
vmax = 0.0
vmin = sp.Inf
#Set region of interest
if roi_center is None:
roi_center = sp.array(im.shape[:2])/2.0 - 0.5
roi = center_roi_around(roi_center*rescale, roi_size)
#Give user some feedback on what is happening
print("Rescaling image and target by scale={rescale}.\n"
" image {0}x{1} px to {2:.2f}x{3:.02f} px."
.format(image.shape[0], image.shape[1],
image.shape[0]*rescale, image.shape[1]*rescale, rescale=rescale), flush=True)
print("ROI: center={0}, {1}, in unscaled image.\n"
" height={2}, width={3} in scaled image"
.format(roi_center[0], roi_center[1], roi_size[0], roi_size[1]))
if rotate[2]>1:
print("Now correlating rotations from {0}º to {1}º in {2} steps:"
.format(*rotate))
else:
print("Rotation is kept constant at {0}°".format(rotate[0]))
# Create rescaled copies of image and pattern, determine center coordinates of both
pattern_scaled = transform.rescale(pattern, rescale)
image_scaled = transform.rescale(image, rescale)
PF.set_image(image_scaled)
cols_scaled, rows_scaled = pattern_scaled.shape[:2]
pattern_scaled_center = sp.array((rows_scaled, cols_scaled))/2. - 0.5
cols, rows = pattern.shape[:2]
pattern_center = sp.array((rows, cols))/2. - 0.5
# Launch PatternFinder for all rotations defined in function input
rotations = sp.linspace(*rotate)
for r in rotations:
# Calculate transformation matrix for rotation around center of scaled pattern
rotation_matrix = rotation_transform_center(pattern_scaled,r,center_xy=pattern_scaled_center)
# Launch Patternfinder
out, min_coords, value = PF.find(transform.warp(pattern_scaled,rotation_matrix), image=None, roi=roi)
# Collect Min and Max values for plotting later on
outmax = out.max()
outmin = out.min()
if outmax > vmax:
vmax = outmax
if outmin < vmin:
vmin = outmin
# undo the rescale for the coordinates
min_coords = min_coords.astype(sp.float64) / rescale
# create a list of results for all rotations
result.append([r, min_coords, value, out])
# Progress bar... kind of :)
print(".",end="", flush=True)
print("")
print("took {0} seconds.".format(time.time()-start_time))
#Select the best result from the result list and extract its parameters
best_param_set = result[sp.argmin([r[2] for r in result])]
best_angle = best_param_set[0] # The rotation angle is the 0-th element in result
best_coord = best_param_set[1] # The coordinates are in the 2-nd element
best_value = best_param_set[2] # The actual value is the 3-rd element
# Calculate transformation to transform image onto pattern
move_to_center = transform.AffineTransform(translation=-(best_coord)[::-1])
move_back = transform.AffineTransform(translation=(best_coord[::-1]))
rotation = transform.AffineTransform(rotation=-sp.deg2rad(best_angle))
translation = transform.AffineTransform(translation=sp.asmatrix((best_coord-pattern_center)[::-1]))
T = translation + move_to_center + rotation + move_back
#Create a plot showing error over angle
if plot and rotate[2] > 1:
fig, ax = plt.subplots(1)
ax.plot([a[0] for a in result], [a[2] for a in result])
ax.set_xlabel('Angle (rotation)')
ax.set_ylabel('difference image-target')
plt.show()
#Create heat plot of where target is in image
if plot == 'all':
n_rows = int(sp.sqrt(len(result)))
n_cols = int(sp.ceil(len(result)/n_rows))
fig, ax = plt.subplots(n_rows, n_cols, squeeze=False, figsize = (2 * n_cols, 2 * n_rows))
fig.tight_layout(rect=[0, 0.03, 1, 0.97])
fig.suptitle("Correlation map of where target is in image\n", size=16)
n = 0
for i in range(n_rows):
for j in range(n_cols):
ax[i,j].axis("off")
if n < len(result):
ax[i,j].imshow(result[n][3], interpolation="nearest", cmap='cubehelix', vmin=vmin, vmax=vmax)
ax[i,j].annotate('Angle:{0:.2f}; Value:{1:.2f}'
.format(result[n][0],result[n][2]),[0,0])
n += 1
plt.show()
return T, best_value
def mason_model(network, phase, physics, f=0.6667, **kwargs):
Dt = network['throat.diameter']
theta = phase['throat.contact_angle']
sigma = phase['throat.surface_tension']
Pc = 4*sigma*sp.cos(f*sp.deg2rad(theta))/Dt
return Pc[network.throats(physics.name)]
def decodeMessageSensorUDP(self, msg):
""" This is used to decode message from sensorUDP application from the android market.
The orientation field was first used, but its conventions were unclear.
So now acceleration and magnetic vectors should be used"""
data = msg.split(', ')
if data[0] == 'G':
# This is GPS message
time = decimalstr2float(data[2])
latitude_deg = decimalstr2float(data[3])
longitude_deg = decimalstr2float(data[4])
altitude = decimalstr2float(data[5])
hdop = decimalstr2float(
data[7]) # Horizontal dilution of precision
vdop = decimalstr2float(data[8]) # Vertical dilution of precision
print time, latitude_deg, longitude_deg, altitude, hdop, vdop
if data[0] == 'O':
# \note This is no more used as orientation convention were unclear
# 'O, 146, 1366575961732, 230,1182404, -075,2031250, 001,7968750'
[
u,
u, # data not used \
heading_deg, # pointing direction of top of phone \
roll_deg, # around horizontal axis, positive clockwise [-180:180] \
pitch_deg
] = decimalstr2float(data[1:]) # around vertical axis [_90:90]
elevation_deg = -sp.rad2deg(sp.arctan2( \
sp.cos(sp.deg2rad(pitch_deg))*sp.cos(sp.deg2rad(roll_deg)), \
sp.sqrt(1+sp.cos(sp.deg2rad(roll_deg))**2*(sp.sin(sp.deg2rad(pitch_deg))**2-1)))) #positive up
inclinaison_deg = pitch_deg #positive clockwise
print heading_deg, roll_deg, pitch_deg, elevation_deg, inclinaison_deg
if data[0] == 'A':
# Accelerometer data
# Index and sign are adjusted to obtain x through the screen, and z down
deltaT = decimalstr2float(data[2]) / 1000 - self.time_acceleration
if self.filterTimeConstant == 0.0:
alpha = 1
else:
alpha = 1 - sp.exp(-deltaT / self.filterTimeConstant)
self.time_acceleration = decimalstr2float(data[2]) / 1000
self.acceleration_raw[0] = decimalstr2float(data[3])
self.acceleration_raw[1] = decimalstr2float(data[4])
self.acceleration_raw[2] = decimalstr2float(data[5])
# Filter the data
self.acceleration_filtered += alpha * (
sp.array(self.acceleration_raw) - self.acceleration_filtered)
if data[0] == 'M':
# Magnetometer data
# Index and sign are adjusted to obtain x through the screen, and z down
deltaT = decimalstr2float(data[2]) / 1000 - self.time_magnetic
if self.filterTimeConstant == 0.0:
alpha = 1
else:
alpha = 1 - sp.exp(-deltaT / self.filterTimeConstant)
self.time_magnetic = decimalstr2float(data[2]) / 1000
self.magnetic_raw[0] = decimalstr2float(data[3])
self.magnetic_raw[1] = decimalstr2float(data[4])
self.magnetic_raw[2] = -decimalstr2float(
data[5]) # Adapt to a bug in sensorUDP?
# Filter the data
self.magnetic_filtered += alpha * (sp.array(self.magnetic_raw) -
self.magnetic_filtered)
def purcell_filling_angle(physics, phase, network, r_toroid,
surface_tension='pore.surface_tension',
contact_angle='pore.contact_angle',
diameter='throat.diameter',
Pc=1e3,
**kwargs):
r"""
Calculate the filling angle (alpha) for a given capillary pressure
Parameters
----------
network : OpenPNM Network Object
The Network on which to apply the calculation
sigma : dict key (string)
The dictionary key containing the surface tension values to be used. If
a pore property is given, it is interpolated to a throat list.
theta : dict key (string)
The dictionary key containing the contact angle values to be used. If
a pore property is given, it is interpolated to a throat list.
throat_diameter : dict key (string)
The dictionary key containing the throat diameter values to be used.
r_toroid : float or array_like
The radius of the toroid surrounding the pore
Notes
-----
This approach accounts for the converging-diverging nature of many throat
types. Advancing the meniscus beyond the apex of the toroid requires an
increase in capillary pressure beyond that for a cylindical tube of the
same radius. The details of this equation are described by Mason and
Morrow [1]_, and explored by Gostick [2]_ in the context of a pore network
model.
!!! Takes mean contact angle and surface tension !!!
"""
from scipy import ndimage
entity = diameter.split('.')[0]
if surface_tension.split('.')[0] == 'pore' and entity == 'throat':
sigma = phase[surface_tension]
sigma = phase.interpolate_data(data=sigma)
else:
sigma = phase[surface_tension]
if contact_angle.split('.')[0] == 'pore' and entity == 'throat':
theta = phase[contact_angle]
theta = phase.interpolate_data(data=theta)
else:
theta = phase[contact_angle]
# Mason and Morrow have the definitions switched
theta = 180 - theta
theta = _sp.deg2rad(theta)
rt = network[diameter]/2
R = r_toroid
ratios = rt/R
a_max = theta - np.arcsin((np.sin(theta))/(1 + ratios))
def purcell_pressure(ratio, fill_angle, theta, sigma, R):
# Helper function
a_max = theta - np.arcsin((np.sin(theta))/(1+ratio))
fill_angle[fill_angle > a_max] = a_max
r_men = R*(1+(ratio)-_sp.cos(fill_angle))/_sp.cos(theta-fill_angle)
Pc = 2*sigma/r_men
return Pc
fill_angle = _sp.deg2rad(np.linspace(-30, 150, 1001))
alpha = np.zeros_like(ratios)
for T, ratio in enumerate(ratios):
mask = np.zeros_like(fill_angle, dtype=bool)
nudge = 100
all_Pc = purcell_pressure(ratio, fill_angle, theta[T], sigma[T], R)
if Pc > all_Pc.max():
# Target Pc out of range
lowest = fill_angle[np.argwhere(all_Pc == all_Pc.max())[0][0]]
else:
while np.sum(mask) == 0:
plus_mask = all_Pc < Pc + nudge
minus_mask = all_Pc > Pc - nudge
mask = np.logical_and(plus_mask, minus_mask)
if np.sum(mask) == 0:
nudge += 10
regions = ndimage.find_objects(ndimage.label(mask)[0])
rx = [np.mean(fill_angle[regions[r]]) for r in range(len(regions))]
root_x = np.asarray(rx)
lowest = np.min(root_x)
alpha[T] = lowest
logger.info('Filling angles calculated for Pc: '+str(Pc))
physics['throat.alpha_max'] = a_max
return _sp.rad2deg(alpha)
import scipy as sp
import matplotlib
from mpl_toolkits.mplot3d import axes3d
from scipy import interpolate
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib import cm
from scipy import linalg
import SU2_interp
from shutil import copyfile
import os
import sys
#Z = lambda x: sp.cos(x[0]-0.02) + sp.cos(x[1]+0.01)
Z = lambda x: (x[0]) + (x[1])
InternalAngle = sp.deg2rad(80);
quad_x = sp.array([10.0, 11.0, 11+sp.cos(InternalAngle), 10+sp.cos(InternalAngle) ])
quad_y = sp.array([10.0, 10.0, 10+sp.sin(InternalAngle), 10+sp.sin(InternalAngle) ])
quad_z = Z([quad_x, quad_y])
x_samples = sp.zeros((100,100))
y_samples = sp.zeros((100,100))
for i in range(100):
y_samples[:,i]= sp.linspace(10,10+sp.sin(InternalAngle),100)
x_lim = 10+(y_samples[i,0]-10)/sp.tan(InternalAngle)
x_samples[i,:] = sp.linspace(x_lim,1+x_lim,100)
#The correct values
z_samples = Z([x_samples, y_samples])
#The SciPy values
def track(self):
print "Press right mouse button to pause or play"
print "Use left mouse button to select target"
print "Target color must be different from background"
print "Target must have width larger than height"
print "Target can be upside down"
#Parameters
isUDPConnection = False # Currently switched manually in the code
display = True
displayDebug = True
useBasemap = False
maxRelativeMotionPerFrame = 2 # How much the target can moved between two succesive frames
pixelPerRadians = 320
radius = pixelPerRadians
referenceImage = '../ObjectTracking/kite_detail.jpg'
scaleFactor = 0.5
isVirtualCamera = True
useHDF5 = False
# Open reference image: this is used at initlalisation
target_detail = Image(referenceImage)
# Get RGB color palette of target (was found to work better than using hue)
pal = target_detail.getPalette(bins=2, hue=False)
# Open video to analyse or live stream
#cam = JpegStreamCamera('http://192.168.1.29:8080/videofeed')#640 * 480
if isVirtualCamera:
#cam = VirtualCamera('../../zenith-wind-power-read-only/KiteControl-Qt/videos/kiteFlying.avi','video')
#cam = VirtualCamera('/media/bat/DATA/Baptiste/Nautilab/kite_project/robokite/ObjectTracking/00095.MTS', 'video')
#cam = VirtualCamera('output.avi', 'video')
cam = VirtualCamera(
'../Recording/Videos/Flying kite images (for kite steering unit development)-YTMgX1bvrTo.mp4',
'video')
virtualCameraFPS = 25
else:
cam = JpegStreamCamera(
'http://192.168.43.1:8080/videofeed') #640 * 480
#cam = Camera()
# Get a sample image to initialize the display at the same size
img = cam.getImage().scale(scaleFactor)
print img.width, img.height
# Create a pygame display
if display:
if img.width > img.height:
disp = Display(
(27 * 640 / 10, 25 * 400 / 10)
) #(int(2*img.width/scaleFactor), int(2*img.height/scaleFactor)))
else:
disp = Display((810, 1080))
#js = JpegStreamer()
# Initialize variables
previous_angle = 0 # target has to be upright when starting. Target width has to be larger than target heigth.
previous_coord_px = (
0, 0) # Initialized to top left corner, which always exists
previous_dCoord = previous_coord_px
previous_dAngle = previous_angle
angles = []
coords_px = []
coord_px = [0, 0]
angle = 0
target_elevations = []
target_bearings = []
times = []
wasTargetFoundInPreviousFrame = False
i_frame = 0
isPaused = False
selectionInProgress = False
th = [100, 100, 100]
skycolor = Color.BLUE
timeLastTarget = 0
# Prepare recording
recordFilename = datetime.datetime.utcnow().strftime(
"%Y%m%d_%Hh%M_") + 'simpleTrack'
if useHDF5:
try:
os.remove(recordFilename + '.hdf5')
except:
print('Creating file ' + recordFilename + '.hdf5')
""" The following line is used to silence the following error (according to http://stackoverflow.com/questions/15117128/h5py-in-memory-file-and-multiprocessing-error)
#000: ../../../src/H5F.c line 1526 in H5Fopen(): unable to open file
major: File accessability
minor: Unable to open file"""
h5py._errors.silence_errors()
recordFile = h5py.File(
os.path.join(os.getcwd(), 'log', recordFilename + '.hdf5'),
'a')
hdfSize = 0
dset = recordFile.create_dataset('kite', (2, 2),
maxshape=(None, 7))
imset = recordFile.create_dataset('image',
(2, img.width, img.height, 3),
maxshape=(None, img.width,
img.height, 3))
else:
try:
os.remove(recordFilename + '.csv')
except:
print('Creating file ' + recordFilename + '.csv')
recordFile = file(
os.path.join(os.getcwd(), 'log', recordFilename + '.csv'), 'a')
csv_writer = csv.writer(recordFile)
csv_writer.writerow([
'Time (s)', 'x (px)', 'y (px)', 'Orientation (rad)',
'Elevation (rad)', 'Bearing (rad)', 'ROT (rad/s)'
])
# Launch a thread to get UDP message with orientation of the camera
mobile = mobileState.mobileState()
if isUDPConnection:
mobile.open()
# Loop while not canceled by user
t0 = time.time()
previousTime = t0
while not (display) or disp.isNotDone():
t = time.time()
deltaT = (t - previousTime)
FPS = 1.0 / deltaT
#print 'FPS =', FPS
if isVirtualCamera:
deltaT = 1.0 / virtualCameraFPS
previousTime = t
i_frame = i_frame + 1
timestamp = datetime.datetime.utcnow()
# Receive orientation of the camera
if isUDPConnection:
mobile.computeRPY([2, 0, 1], [-1, 1, 1])
ctm = np.array([[sp.cos(mobile.roll), -sp.sin(mobile.roll)], \
[sp.sin(mobile.roll), sp.cos(mobile.roll)]]) # Coordinate transform matrix
if useBasemap:
# Warning this really slows down the computation
m = Basemap(width=img.width,
height=img.height,
projection='aeqd',
lat_0=sp.rad2deg(mobile.pitch),
lon_0=sp.rad2deg(mobile.yaw),
rsphere=radius)
# Get an image from camera
if not isPaused:
img = cam.getImage()
img = img.resize(int(scaleFactor * img.width),
int(scaleFactor * img.height))
if display:
# Pause image when right button is pressed
dwn = disp.rightButtonDownPosition()
if dwn is not None:
isPaused = not (isPaused)
dwn = None
if display:
# Create a layer to enable user to make a selection of the target
selectionLayer = DrawingLayer((img.width, img.height))
if img:
if display:
# Create a new layer to host information retrieved from video
layer = DrawingLayer((img.width, img.height))
# Selection is a rectangle drawn while holding mouse left button down
if disp.leftButtonDown:
corner1 = (disp.mouseX, disp.mouseY)
selectionInProgress = True
if selectionInProgress:
corner2 = (disp.mouseX, disp.mouseY)
bb = disp.pointsToBoundingBox(
corner1,
corner2) # Display the temporary selection
if disp.leftButtonUp: # User has finished is selection
selectionInProgress = False
selection = img.crop(bb[0], bb[1], bb[2], bb[3])
if selection != None:
# The 3 main colors in the area selected are considered.
# Note that the selection should be included in the target and not contain background
try:
selection.save('../ObjectTracking/' +
'kite_detail_tmp.jpg')
img0 = Image(
"kite_detail_tmp.jpg"
) # For unknown reason I have to reload the image...
pal = img0.getPalette(bins=2, hue=False)
except: # getPalette is sometimes bugging and raising LinalgError because matrix not positive definite
pal = pal
wasTargetFoundInPreviousFrame = False
previous_coord_px = (bb[0] + bb[2] / 2,
bb[1] + bb[3] / 2)
if corner1 != corner2:
selectionLayer.rectangle((bb[0], bb[1]),
(bb[2], bb[3]),
width=5,
color=Color.YELLOW)
# If the target was already found, we can save computation time by
# reducing the Region Of Interest around predicted position
if wasTargetFoundInPreviousFrame:
ROITopLeftCorner = (max(0, previous_coord_px[0]-maxRelativeMotionPerFrame/2*width), \
max(0, previous_coord_px[1] -height*maxRelativeMotionPerFrame/2))
ROI = img.crop(ROITopLeftCorner[0], ROITopLeftCorner[1], \
maxRelativeMotionPerFrame*width, maxRelativeMotionPerFrame*height, \
centered = False)
if display:
# Draw the rectangle corresponding to the ROI on the complete image
layer.rectangle((previous_coord_px[0]-maxRelativeMotionPerFrame/2*width, \
previous_coord_px[1]-maxRelativeMotionPerFrame/2*height), \
(maxRelativeMotionPerFrame*width, maxRelativeMotionPerFrame*height), \
color = Color.GREEN, width = 2)
else:
# Search on the whole image if no clue of where is the target
ROITopLeftCorner = (0, 0)
ROI = img
'''#Option 1
target_part0 = ROI.hueDistance(color=(142,50,65)).invert().threshold(150)
target_part1 = ROI.hueDistance(color=(93,16,28)).invert().threshold(150)
target_part2 = ROI.hueDistance(color=(223,135,170)).invert().threshold(150)
target_raw_img = target_part0+target_part1+target_part2
target_img = target_raw_img.erode(5).dilate(5)
#Option 2
target_img = ROI.hueDistance(imgModel.getPixel(10,10)).binarize().invert().erode(2).dilate(2)'''
# Find sky color
sky = (img - img.binarize()).findBlobs(minsize=10000)
if sky:
skycolor = sky[0].meanColor()
# Option 3
target_img = ROI - ROI # Black image
# Loop through palette of target colors
if display and displayDebug:
decomposition = []
i_col = 0
for col in pal:
c = tuple([int(col[i]) for i in range(0, 3)])
# Search the target based on color
ROI.save('../ObjectTracking/' + 'ROI_tmp.jpg')
img1 = Image('../ObjectTracking/' + 'ROI_tmp.jpg')
filter_img = img1.colorDistance(color=c)
h = filter_img.histogram(numbins=256)
cs = np.cumsum(h)
thmax = np.argmin(
abs(cs - 0.02 * img.width * img.height)
) # find the threshold to have 10% of the pixel in the expected color
thmin = np.argmin(
abs(cs - 0.005 * img.width * img.height)
) # find the threshold to have 10% of the pixel in the expected color
if thmin == thmax:
newth = thmin
else:
newth = np.argmin(h[thmin:thmax]) + thmin
alpha = 0.5
th[i_col] = alpha * th[i_col] + (1 - alpha) * newth
filter_img = filter_img.threshold(
max(40, min(200, th[i_col]))).invert()
target_img = target_img + filter_img
#print th
i_col = i_col + 1
if display and displayDebug:
[R, G, B] = filter_img.splitChannels()
white = (R - R).invert()
r = R * 1.0 / 255 * c[0]
g = G * 1.0 / 255 * c[1]
b = B * 1.0 / 255 * c[2]
tmp = white.mergeChannels(r, g, b)
decomposition.append(tmp)
# Get a black background with with white target foreground
target_img = target_img.threshold(150)
target_img = target_img - ROI.colorDistance(
color=skycolor).threshold(80).invert()
if display and displayDebug:
small_ini = target_img.resize(
int(img.width / (len(pal) + 1)),
int(img.height / (len(pal) + 1)))
for tmp in decomposition:
small_ini = small_ini.sideBySide(tmp.resize(
int(img.width / (len(pal) + 1)),
int(img.height / (len(pal) + 1))),
side='bottom')
small_ini = small_ini.adaptiveScale(
(int(img.width), int(img.height)))
toDisplay = img.sideBySide(small_ini)
else:
toDisplay = img
#target_img = ROI.hueDistance(color = Color.RED).threshold(10).invert()
# Search for binary large objects representing potential target
target = target_img.findBlobs(minsize=500)
if target: # If a target was found
if wasTargetFoundInPreviousFrame:
predictedTargetPosition = (
width * maxRelativeMotionPerFrame / 2,
height * maxRelativeMotionPerFrame / 2
) # Target will most likely be close to the center of the ROI
else:
predictedTargetPosition = previous_coord_px
# If there are several targets in the image, take the one which is the closest of the predicted position
target = target.sortDistance(predictedTargetPosition)
# Get target coordinates according to minimal bounding rectangle or centroid.
coordMinRect = ROITopLeftCorner + np.array(
(target[0].minRectX(), target[0].minRectY()))
coord_px = ROITopLeftCorner + np.array(
target[0].centroid())
# Rotate the coordinates of roll angle around the middle of the screen
rot_coord_px = np.dot(
ctm, coord_px -
np.array([img.width / 2, img.height / 2])) + np.array(
[img.width / 2, img.height / 2])
if useBasemap:
coord = sp.deg2rad(
m(rot_coord_px[0],
img.height - rot_coord_px[1],
inverse=True))
else:
coord = localProjection(
rot_coord_px[0] - img.width / 2,
img.height / 2 - rot_coord_px[1],
radius,
mobile.yaw,
mobile.pitch,
inverse=True)
target_bearing, target_elevation = coord
# Get minimum bounding rectangle for display purpose
minR = ROITopLeftCorner + np.array(target[0].minRect())
contours = target[0].contour()
contours = [
ROITopLeftCorner + np.array(contour)
for contour in contours
]
# Get target features
angle = sp.deg2rad(target[0].angle()) + mobile.roll
angle = sp.deg2rad(
unwrap180(sp.rad2deg(angle),
sp.rad2deg(previous_angle)))
width = target[0].width()
height = target[0].height()
# Check if the kite is upside down
# First rotate the kite
ctm2 = np.array([[sp.cos(-angle+mobile.roll), -sp.sin(-angle+mobile.roll)], \
[sp.sin(-angle+mobile.roll), sp.cos(-angle+mobile.roll)]]) # Coordinate transform matrix
rotated_contours = [
np.dot(ctm2, contour - coordMinRect)
for contour in contours
]
y = [-tmp[1] for tmp in rotated_contours]
itop = np.argmax(y) # Then looks at the points at the top
ibottom = np.argmin(y) # and the point at the bottom
# The point the most excentered is at the bottom
if abs(rotated_contours[itop][0]) > abs(
rotated_contours[ibottom][0]):
isInverted = True
else:
isInverted = False
if isInverted:
angle = angle + sp.pi
# Filter the data
alpha = 1 - sp.exp(-deltaT / self.filterTimeConstant)
if not (isPaused):
dCoord = np.array(previous_dCoord) * (
1 - alpha) + alpha * (
np.array(coord_px) - previous_coord_px
) # related to the speed only if cam is fixed
dAngle = np.array(previous_dAngle) * (
1 - alpha) + alpha * (np.array(angle) -
previous_angle)
else:
dCoord = np.array([0, 0])
dAngle = np.array([0])
#print coord_px, angle, width, height, dCoord
# Record important data
times.append(timestamp)
coords_px.append(coord_px)
angles.append(angle)
target_elevations.append(target_elevation)
target_bearings.append(target_bearing)
# Export data to controller
self.elevation = target_elevation
self.bearing = target_bearing
self.orientation = angle
dt = time.time() - timeLastTarget
self.ROT = dAngle / dt
self.lastUpdateTime = t
# Save for initialisation of next step
previous_dCoord = dCoord
previous_angle = angle
previous_coord_px = (int(coord_px[0]), int(coord_px[1]))
wasTargetFoundInPreviousFrame = True
timeLastTarget = time.time()
else:
wasTargetFoundInPreviousFrame = False
if useHDF5:
hdfSize = hdfSize + 1
dset.resize((hdfSize, 7))
imset.resize((hdfSize, img.width, img.height, 3))
dset[hdfSize - 1, :] = [
time.time(), coord_px[0], coord_px[1], angle,
self.elevation, self.bearing, self.ROT
]
imset[hdfSize - 1, :, :, :] = img.getNumpy()
recordFile.flush()
else:
csv_writer.writerow([
time.time(), coord_px[0], coord_px[1], angle,
self.elevation, self.bearing, self.ROT
])
if display:
if target:
# Add target features to layer
# Minimal rectange and its center in RED
layer.polygon(minR[(0, 1, 3, 2), :],
color=Color.RED,
width=5)
layer.circle(
(int(coordMinRect[0]), int(coordMinRect[1])),
10,
filled=True,
color=Color.RED)
# Target contour and centroid in BLUE
layer.circle((int(coord_px[0]), int(coord_px[1])),
10,
filled=True,
color=Color.BLUE)
layer.polygon(contours, color=Color.BLUE, width=5)
# Speed vector in BLACK
layer.line((int(coord_px[0]), int(coord_px[1])),
(int(coord_px[0] + 20 * dCoord[0]),
int(coord_px[1] + 20 * dCoord[1])),
width=3)
# Line giving angle
layer.line((int(coord_px[0] + 200 * sp.cos(angle)),
int(coord_px[1] + 200 * sp.sin(angle))),
(int(coord_px[0] - 200 * sp.cos(angle)),
int(coord_px[1] - 200 * sp.sin(angle))),
color=Color.RED)
# Line giving rate of turn
#layer.line((int(coord_px[0]+200*sp.cos(angle+dAngle*10)), int(coord_px[1]+200*sp.sin(angle+dAngle*10))), (int(coord_px[0]-200*sp.cos(angle + dAngle*10)), int(coord_px[1]-200*sp.sin(angle+dAngle*10))))
# Add the layer to the raw image
toDisplay.addDrawingLayer(layer)
toDisplay.addDrawingLayer(selectionLayer)
# Add time metadata
toDisplay.drawText(str(i_frame) + " " + str(timestamp),
x=0,
y=0,
fontsize=20)
# Add Line giving horizon
#layer.line((0, int(img.height/2 + mobile.pitch*pixelPerRadians)),(img.width, int(img.height/2 + mobile.pitch*pixelPerRadians)), width = 3, color = Color.RED)
# Plot parallels
for lat in range(-90, 90, 15):
r = range(0, 361, 10)
if useBasemap:
# \todo improve for high roll
l = m(r, [lat] * len(r))
pix = [np.array(l[0]), img.height - np.array(l[1])]
else:
l = localProjection(sp.deg2rad(r), \
sp.deg2rad([lat]*len(r)), \
radius, \
lon_0 = mobile.yaw, \
lat_0 = mobile.pitch, \
inverse = False)
l = np.dot(ctm, l)
pix = [
np.array(l[0]) + img.width / 2,
img.height / 2 - np.array(l[1])
]
for i in range(len(r) - 1):
if isPixelInImage(
(pix[0][i], pix[1][i]), img) or isPixelInImage(
(pix[0][i + 1], pix[1][i + 1]), img):
layer.line((pix[0][i], pix[1][i]),
(pix[0][i + 1], pix[1][i + 1]),
color=Color.WHITE,
width=2)
# Plot meridians
for lon in range(0, 360, 15):
r = range(-90, 91, 10)
if useBasemap:
# \todo improve for high roll
l = m([lon] * len(r), r)
pix = [np.array(l[0]), img.height - np.array(l[1])]
else:
l= localProjection(sp.deg2rad([lon]*len(r)), \
sp.deg2rad(r), \
radius, \
lon_0 = mobile.yaw, \
lat_0 = mobile.pitch, \
inverse = False)
l = np.dot(ctm, l)
pix = [
np.array(l[0]) + img.width / 2,
img.height / 2 - np.array(l[1])
]
for i in range(len(r) - 1):
if isPixelInImage(
(pix[0][i], pix[1][i]), img) or isPixelInImage(
(pix[0][i + 1], pix[1][i + 1]), img):
layer.line((pix[0][i], pix[1][i]),
(pix[0][i + 1], pix[1][i + 1]),
color=Color.WHITE,
width=2)
# Text giving bearing
# \todo improve for high roll
for bearing_deg in range(0, 360, 30):
l = localProjection(sp.deg2rad(bearing_deg),
sp.deg2rad(0),
radius,
lon_0=mobile.yaw,
lat_0=mobile.pitch,
inverse=False)
l = np.dot(ctm, l)
layer.text(
str(bearing_deg),
(img.width / 2 + int(l[0]), img.height - 20),
color=Color.RED)
# Text giving elevation
# \todo improve for high roll
for elevation_deg in range(-60, 91, 30):
l = localProjection(0,
sp.deg2rad(elevation_deg),
radius,
lon_0=mobile.yaw,
lat_0=mobile.pitch,
inverse=False)
l = np.dot(ctm, l)
layer.text(str(elevation_deg),
(img.width / 2, img.height / 2 - int(l[1])),
color=Color.RED)
#toDisplay.save(js)
toDisplay.save(disp)
if display:
toDisplay.removeDrawingLayer(1)
toDisplay.removeDrawingLayer(0)
recordFile.close()
def flowprandtlmeyer(**flow):
"""
Prandtl-Meyer function for expansion waves.
This function accepts a given set of specific heat ratios and
an input of either Mach number, Mach angle or Prandtl-Meyer angle.
Inputs can be a single scalar or an array_like data structure.
Parameters
----------
gamma : array_like, optional
Specific heat ratio. Values must be greater than 1.
M : array_like
Mach number. Values must be greater than or equal to 1.
nu : array_like
Prandtl-Meyer angle [degrees]. Values must be
0 <= M <= 90*(sqrt((g+1)/(g-1))-1).
mu : array_like
Mach angle [degrees]. Values must be 0 <= M <= 90.
Returns
-------
out : (M, nu, mu)
Tuple of Mach number, Prandtl-Meyer angle, Mach angle.
Examples
--------
>>> flowprandtlmeyer(M=5)
(5.0, 76.920215508538789, 11.536959032815489)
"""
#parse the input
gamma, flow, mtype, itype = _flowinput(flow)
#calculate gamma-ratios for use in the equations
l = sp.sqrt((gamma - 1) / (gamma + 1))
#preshape mach array
M = sp.empty(flow.shape, sp.float64)
#use prandtl-meyer relation to solve for the mach number
if mtype in ["mach", "m"]:
if (flow < 1).any():
raise Exception("Mach number inputs must be real numbers greater" \
" than or equal to 1.")
M = flow
elif mtype in ["mu", "machangle"]:
if (flow < 0).any() or (flow > 90).any():
raise Exception("Mach angle inputs must be real numbers" \
" 0 <= M <= 90.")
M = 1 / sp.sin(sp.deg2rad(flow))
elif mtype in ["nu", "pm", "pmangle"]:
if (flow < 0).any() or (flow > 90 * ((1 / l) - 1)).any():
raise Exception("Prandtl-Meyer angle inputs must be real" \
" numbers 0 <= M <= 90*(sqrt((g+1)/(g-1))-1).")
M[:] = 2 #initial guess for the solution
for _ in xrange(_AETB_iternum):
b = sp.sqrt(M**2 - 1)
f = -sp.deg2rad(flow) + (1 / l) * sp.arctan(l * b) - sp.arctan(b)
g = b * (1 - l**2) / (M * (1 + (l**2) * (b**2))) #derivative
M = M - (f / g) #Newton-Raphson
else:
raise Exception("Keyword input must be an acceptable string to" \
" select input parameter.")
#normal shock relations
b = sp.sqrt(M**2 - 1)
V = (1 / l) * sp.arctan(l * b) - sp.arctan(b)
U = sp.arcsin(1 / M)
return from_ndarray(itype, M, sp.rad2deg(V), sp.rad2deg(U))
