def elaz2radec_lst(el, az, lst, lat = 38.43312) :
"""DO NOT USE THIS ROUTINE FOR ANTHING THAT NEEDS TO BE RIGHT. IT DOES NOT
CORRECT FOR PRECESSION.
Calculates the Ra and Dec from elavation, aximuth, LST and Latitude.
This function is vectorized with numpy so should be fast. Standart numpy
broadcasting should also work.
All angles in degrees, lst in seconds. Latitude defaults to GBT.
"""
# Convert everything to radians.
el = sp.radians(el)
az = sp.radians(az)
lst = sp.array(lst, dtype = float)*2*sp.pi/86400
lat = sp.radians(lat)
# Calculate dec.
dec = sp.arcsin(sp.sin(el)*sp.sin(lat) +
sp.cos(el)*sp.cos(lat)*sp.cos(az))
# Calculate the hour angle
ha = sp.arccos((sp.sin(el) - sp.sin(lat)*sp.sin(dec)) /
(sp.cos(lat)*sp.cos(dec)))
ra = sp.degrees(lst - ha) % 360
return ra, sp.degrees(dec)
def elaz2radec_lst(el, az, lst, lat=38.43312):
"""DO NOT USE THIS ROUTINE FOR ANTHING THAT NEEDS TO BE RIGHT. IT DOES NOT
CORRECT FOR PRECESSION.
Calculates the Ra and Dec from elavation, aximuth, LST and Latitude.
This function is vectorized with numpy so should be fast. Standart numpy
broadcasting should also work.
All angles in degrees, lst in seconds. Latitude defaults to GBT.
"""
# Convert everything to radians.
el = sp.radians(el)
az = sp.radians(az)
lst = sp.array(lst, dtype=float) * 2 * sp.pi / 86400
lat = sp.radians(lat)
# Calculate dec.
dec = sp.arcsin(
sp.sin(el) * sp.sin(lat) + sp.cos(el) * sp.cos(lat) * sp.cos(az))
# Calculate the hour angle
ha = sp.arccos(
(sp.sin(el) - sp.sin(lat) * sp.sin(dec)) / (sp.cos(lat) * sp.cos(dec)))
ra = sp.degrees(lst - ha) % 360
return ra, sp.degrees(dec)
def transformPicture(self, accelData):
x, y, z = accelData[0], accelData[1], accelData[2]
offset = 512
# rotate
if self.wm.buttons["A"]:
self.c += 1
print(self.c)
if self.c % 2 == 0:
centeredZ = z - offset
centeredX = x - offset
rot_angle = int(
-(scipy.degrees(scipy.arctan2(centeredZ, centeredX)) - 90))
if rot_angle < 0:
rot_angle = 360 + rot_angle
self.image.setPixmap(
self.pixmap.transformed(
QtGui.QTransform().rotate(rot_angle),
1).scaledToHeight(400))
# zoom
if self.wm.buttons["Down"]:
centeredZ = z - offset
centeredY = y - offset
tilt_angle = scipy.degrees(scipy.arctan2(centeredZ,
centeredY)) - 90
scale_val = abs(tilt_angle / 100)
self.image.setPixmap(
self.pixmap.transformed(QtGui.QTransform().scale(
scale_val, scale_val)))
def main():
satstress_test_dir = os.path.dirname(os.path.abspath(__file__))
test_outfile = os.path.join(satstress_test_dir, "test_nsr_diurnal.pkl")
test_satellite = os.path.join("input", "Europa.satellite")
# Create a new satellite object, as defined by the input file:
the_sat = satstress.Satellite(open(test_satellite, 'r'))
# Create a StressCalc object, that calculates both the NSR and Diurnal
# stresses on the Satellite just instantiated:
the_stresses = satstress.StressCalc(
[satstress.Diurnal(the_sat),
satstress.NSR(the_sat)])
# do a series of calculations, varying all the inputs at each iteration
theta = 0.0
phi = 0.0
t = 0.0
# a list to store the calculation results in for later checking
Taulist = []
while the_stresses.stresses[1].Delta(
) > 0.01 and t < 2.0 * scipy.pi / the_sat.mean_motion():
print("""Tau(theta = %g, phi = %g, time = %g, Delta = %g) = """ %
(scipy.degrees(theta), scipy.degrees(phi), t,
the_stresses.stresses[1].Delta()))
Taulist.append(the_stresses.tensor(theta=theta, phi=phi, t=0))
print Taulist[-1], "\n"
theta = theta + scipy.pi / 23.0
phi = phi + scipy.pi / 11.0
t = t + (scipy.pi / 11.0) / the_sat.mean_motion()
# Re-construct the NSR stress (and the_stresses StressCalc object)
# changing the forcing period, so that we can have the NSR stresses
# vary with each iteration too.
the_sat.nsr_period = the_sat.nsr_period / 1.5
the_stresses = satstress.StressCalc(
[satstress.Diurnal(the_sat),
satstress.NSR(the_sat)])
# Now we want to compare this against reference output, just to make sure
# that we're getting the right numbers...
pickledTau = pickle.load(open(test_outfile, 'r'))
for (tau1, tau2) in zip(Taulist, pickledTau):
if tau1.all() != tau2.all():
print("\nTest failed. :(\n")
sys.exit(1)
print("\nTest passed! :)\n")
sys.exit()
def matrixToEuler(m,order='Aerospace',inDegrees=True):
if order == 'Aerospace' or order == 'ZYX':
sp = -m[2,0]
if sp < (1-EPS):
if sp > (-1+EPS):
p = arcsin(sp)
r = arctan2(m[2,1],m[2,2])
y = arctan2(m[1,0],m[0,0])
else:
p = -pi/2.
r = 0
y = pi-arctan2(-m[0,1],m[0,2])
else:
p = pi/2.
y = arctan2(-m[0,1],m[0,2])
r = 0
if inDegrees:
return degrees((y,p,r))
else:
return (y,p,r)
elif order == 'BVH' or order == 'ZXY':
sx = m[2,1]
if sx < (1-EPS):
if sx > (-1+EPS):
x = arcsin(sx)
z = arctan2(-m[0,1],m[1,1])
y = arctan2(-m[2,0],m[2,2])
else:
x = -pi/2
y = 0
z = -arctan2(m[0,2],m[0,0])
else:
x = pi/2
y = 0
z = arctan2(m[0,2],m[0,0])
if inDegrees:
return degrees((z,x,y))
else:
return (z,x,y)
elif order == "ZXZ":
x = arccos(m[2,2])
z2 = arctan2(m[2,0],m[2,1])
z1 = arctan2(m[0,2],-m[1,2])
if inDegrees:
return degrees((z1,x,z2))
else:
return (z1,x,z2)
def _update_strain(self):
self.e = (self.exy - self.eyx) / 2
self.k = (self.exx + self.eyy) * 1000000. / 2
self.strain = scipy.sqrt((self.exx - self.eyy) * (self.exx - self.eyy) + (self.exy + self.eyx) * (self.exy + self.eyx)) * 1000000.
self.k_max = self.k + self.strain / 2
self.k_min = self.k - self.strain / 2
self.az = scipy.degrees(2 * scipy.arctan2(self.exy + self.eyx, self.eyy - self.exx))
def apparent_elevation(theta_true, height):
"""
Calculate the apparent elevation of the target.
:param theta_true: The true elevation angle (degrees).
:param height: The height of the radar (km).
:return: The apparent elevation angle of the target (degrees).
"""
def integrand(z):
"""
Determine the integrand for calculating the apparent elevation angle.
:param z: The altitude (km).
:return: The integrand.
"""
# Effective radius of the Earth (km)
re = 6378.137
# Standard values
a = 0.000315
b = 0.1361
# Refractive index and derivative as a function of altitude
n_z = 1. + a * exp(-b * z)
np_z = -(a * b * exp(-b * z))
# Refractive index at the given height
n_h = 1. + a * exp(-b * height)
tan_phi = tan(
arccos(((re + height) * n_h) / ((re + z) * n_z) *
cos(radians(theta_true))))
return np_z / (n_z * tan_phi)
return theta_true - degrees(integrate.romberg(integrand, height, 1000))
def edgeStrength(self, phgeo, mask, kernelw):
edges = canny(phgeo, sigma=kernelw)
bounds = find_boundaries(edges, mode='thick')
slopedeg = degrees(self.slope(phgeo))
histv, _ = np.histogram(abs(slopedeg[(bounds & mask)]),
bins=self._slopeBins)
return histv
def _update_canvas(self):
"""
Update the figure when the user changes an input value.
:return:
"""
frequency = float(self.frequency.text())
cone_half_angle = radians(float(self.cone_half_angle.text()))
base_radius = float(self.base_radius.text())
# Set the incident angles
incident_angle = linspace(0, pi, 1801)
# Calculate the radar cross section
rcs = array([
radar_cross_section(frequency, cone_half_angle, base_radius, ia)
for ia in incident_angle
])
# Clear the axes for the updated plot
self.axes1.clear()
# Display the results
self.axes1.plot(degrees(incident_angle),
10 * log10(rcs[:, 0]),
'',
label='VV')
self.axes1.plot(degrees(incident_angle),
10 * log10(rcs[:, 1]),
'--',
label='HH')
# Set the plot title and labels
self.axes1.set_title('RCS vs Incident Angle', size=14)
self.axes1.set_ylabel('RCS (dBsm)', size=12)
self.axes1.set_xlabel('Incident Angle (deg)', size=12)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Set the legend
self.axes1.legend(loc='upper right', prop={'size': 10})
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Update the canvas
self.my_canvas.draw()
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
frequency = float(self.frequency.text())
number_of_elements = int(self.number_of_elements.text())
scan_angle = float(self.scan_angle.text())
element_spacing = float(self.element_spacing.text())
side_lobe_level = float(self.side_lobe_level.text())
# Get the selected antenna from the form
antenna_type = self.antenna_type.currentText()
# Set the angular span
theta = linspace(0.0, pi, 1000)
# Set up the args
kwargs = {
'number_of_elements': number_of_elements,
'scan_angle': radians(scan_angle),
'element_spacing': element_spacing,
'frequency': frequency,
'theta': theta,
'window_type': antenna_type,
'side_lobe_level': side_lobe_level
}
# Get the array factor
af = linear_array.array_factor(**kwargs)
# Clear the axes for the updated plot
self.axes1.clear()
# Create the line plot
self.axes1.plot(degrees(theta),
20.0 * log10(abs(af) + finfo(float).eps), '')
# Set the y axis limit
self.axes1.set_ylim(-80, 5)
# Set the x and y axis labels
self.axes1.set_xlabel("Theta (degrees)", size=12)
self.axes1.set_ylabel("Array Factor (dB)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the plot title and labels
self.axes1.set_title('Linear Array Antenna Pattern', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
def errorAngle(actual,estimate):
error = actual*estimate.conjugate()
error.normalise()
# Cast error to float so that we can deal with fixed point numbers
# throws an error otherwise
eAngle = degrees(2*arccos(float(error.w)))
eAngle = abs(eAngle - 360) if eAngle > 180 else eAngle
return eAngle
def collectData(self, accelData):
if self.count < 32:
self.count += 1
x, z = accelData[0], accelData[2]
rot_angle = int(-(scipy.degrees(scipy.arctan2(z - 512, x - 512)) -
90))
self.list.append(rot_angle)
else:
self.transformPicture(accelData)
self.count = 0
self.list = []
def _update_strain(self):
self.e = (self.exy - self.eyx) / 2
self.k = (self.exx + self.eyy) * 1000000. / 2
self.strain = scipy.sqrt((self.exx - self.eyy) *
(self.exx - self.eyy) +
(self.exy + self.eyx) *
(self.exy + self.eyx)) * 1000000.
self.k_max = self.k + self.strain / 2
self.k_min = self.k - self.strain / 2
self.az = scipy.degrees(
2 * scipy.arctan2(self.exy + self.eyx, self.eyy - self.exx))
def nichols_plot(sys_list, omega=None, grid=None):
"""Nichols plot for a system
Plots a Nichols plot for the system over a (optional) frequency range.
Parameters
----------
sys_list : list of LTI, or LTI
List of linear input/output systems (single system is OK)
omega : array_like
Range of frequencies (list or bounds) in rad/sec
grid : boolean, optional
True if the plot should include a Nichols-chart grid. Default is True.
Returns
-------
None
"""
# Get parameter values
grid = config._get_param('nichols', 'grid', grid, True)
# If argument was a singleton, turn it into a list
if not getattr(sys_list, '__iter__', False):
sys_list = (sys_list, )
# Select a default range if none is provided
if omega is None:
omega = default_frequency_range(sys_list)
for sys in sys_list:
# Get the magnitude and phase of the system
mag_tmp, phase_tmp, omega = sys.freqresp(omega)
mag = np.squeeze(mag_tmp)
phase = np.squeeze(phase_tmp)
# Convert to Nichols-plot format (phase in degrees,
# and magnitude in dB)
x = unwrap(sp.degrees(phase), 360)
y = 20 * sp.log10(mag)
# Generate the plot
plt.plot(x, y)
plt.xlabel('Phase (deg)')
plt.ylabel('Magnitude (dB)')
plt.title('Nichols Plot')
# Mark the -180 point
plt.plot([-180], [0], 'r+')
# Add grid
if grid:
nichols_grid()
def calSquareDegree_conver(sqdeg,dec0):
"""
input sqdeg: unit in deg.
dec0: dec center in decimal deg.
assume decw = sqrt(sqdeg)
return: raw
ref:http://en.wikipedia.org/wiki/Solid_angle
"""
sa = sqdeg/((180./S.pi)**2)
decw = S.sqrt(sqdeg)
raw = S.degrees(sa /(S.sin(S.radians(dec0+decw/2.) ) - S.sin(S.radians(dec0-decw/2.))))
return raw
def stopRecordingRot(self):
self.wm.accelerometer.unregister_callback(self.recordAccel)
for value in self.currentGestureData:
x, y, z = value[0], value[1], value[2]
rot_angle = int(-(sp.degrees(sp.arctan2(z-512, x-512)) - 90))
self.rotList.append(rot_angle)
print(self.rotList)
self.currentGestureData = []
direction = self.getDirection()
if direction == 1:
self.callback(2)
else:
self.callback(-2)
def calSquareDegree_conver(sqdeg,dec0):
"""
input sqdeg: unit in deg.
dec0: dec center in decimal deg.
assume decw = sqrt(sqdeg)
return: raw
ref:http://en.wikipedia.org/wiki/Solid_angle
"""
sa = sqdeg/((180./S.pi)**2)
decw = S.sqrt(sqdeg)
raw = S.degrees(sa /(S.sin(S.radians(dec0+decw/2.) ) - S.sin(S.radians(dec0-decw/2.))))
return raw
def nichols_plot(syslist, omega=None, grid=True):
"""Nichols plot for a system
Plots a Nichols plot for the system over a (optional) frequency range.
Parameters
----------
syslist : list of LTI, or LTI
List of linear input/output systems (single system is OK)
omega : array_like
Range of frequencies (list or bounds) in rad/sec
grid : boolean, optional
True if the plot should include a Nichols-chart grid. Default is True.
Returns
-------
None
"""
# If argument was a singleton, turn it into a list
if (not getattr(syslist, '__iter__', False)):
syslist = (syslist,)
# Select a default range if none is provided
if omega is None:
omega = default_frequency_range(syslist)
for sys in syslist:
# Get the magnitude and phase of the system
mag_tmp, phase_tmp, omega = sys.freqresp(omega)
mag = np.squeeze(mag_tmp)
phase = np.squeeze(phase_tmp)
# Convert to Nichols-plot format (phase in degrees,
# and magnitude in dB)
x = unwrap(sp.degrees(phase), 360)
y = 20*sp.log10(mag)
# Generate the plot
plt.plot(x, y)
plt.xlabel('Phase (deg)')
plt.ylabel('Magnitude (dB)')
plt.title('Nichols Plot')
# Mark the -180 point
plt.plot([-180], [0], 'r+')
# Add grid
if grid:
nichols_grid()
def transformPicture(self, accelData):
x, y, z = accelData[0], accelData[1], accelData[2]
offset = 512
# rotate
if self.wm.buttons["A"]:
self.c += 1
print(self.c)
if self.c % 2 == 0:
centeredZ = z - offset
centeredX = x - offset
rot_angle = int(-(scipy.degrees(scipy.arctan2(centeredZ, centeredX)) - 90))
if rot_angle < 0:
rot_angle = 360 + rot_angle
self.image.setPixmap(self.pixmap.transformed(
QtGui.QTransform().rotate(rot_angle), 1).scaledToHeight(400))
# zoom
if self.wm.buttons["Down"]:
centeredZ = z - offset
centeredY = y - offset
tilt_angle = scipy.degrees(scipy.arctan2(centeredZ, centeredY)) - 90
scale_val = abs(tilt_angle / 100)
self.image.setPixmap(self.pixmap.transformed(QtGui.QTransform().scale(scale_val, scale_val)))
def __init__(self, foilid, material, top_radius_cm, bottom_radius_cm, angle_rad): self.foilid = foilid self.material = material self.top_radius_cm = top_radius_cm self.bottom_radius_cm = bottom_radius_cm self.angle_rad = angle_rad self.angle_deg = scipy.degrees(self.angle_rad) self.geo_area_cm2 = self.getGeometricalArea() self.use_flag_mark = ' ' self.roughness_A = 0.0 self.n_spoke1 = 0 self.n_spoke2 = 0 self.spoke1_wid = 0.0 self.spoke2_wid = 0.0
def zoomPicture(self, accelData):
y, z = accelData[1], accelData[2]
offset = 512
centeredZ = z - offset
centeredY = y - offset
tilt_angle = scipy.degrees(scipy.arctan2(centeredZ, centeredY)) - 90
if tilt_angle <= -90:
tilt_angle = 360 + tilt_angle
scale_val = (tilt_angle / 100) + 1
if scale_val < 0:
scale_val = -scale_val
self.image.setPixmap(
self.pixmap.transformed(
QtGui.QTransform().scale(scale_val, scale_val),
QtCore.Qt.SmoothTransformation))
self.drawingPixmap = self.image.pixmap()
self.resetUndoRedoStack()
def main():
satstress_test_dir = os.path.dirname(os.path.abspath(__file__))
test_outfile = os.path.join(satstress_test_dir, "test_nsr_diurnal.pkl")
test_satellite = os.path.join("input", "Europa.satellite")
# Create a new satellite object, as defined by the input file:
the_sat = satstress.Satellite(open(test_satellite,'r'))
# Create a StressCalc object, that calculates both the NSR and Diurnal
# stresses on the Satellite just instantiated:
the_stresses = satstress.StressCalc([satstress.Diurnal(the_sat), satstress.NSR(the_sat)])
# do a series of calculations, varying all the inputs at each iteration
theta = 0.0
phi = 0.0
t = 0.0
# a list to store the calculation results in for later checking
Taulist = []
while the_stresses.stresses[1].Delta() > 0.01 and t < 2.0*scipy.pi/the_sat.mean_motion():
print("""Tau(theta = %g, phi = %g, time = %g, Delta = %g) = """ % (scipy.degrees(theta), scipy.degrees(phi), t, the_stresses.stresses[1].Delta()))
Taulist.append(the_stresses.tensor(theta=theta, phi=phi, t=0))
print Taulist[-1], "\n"
theta = theta + scipy.pi/23.0
phi = phi + scipy.pi/11.0
t = t + (scipy.pi/11.0)/the_sat.mean_motion()
# Re-construct the NSR stress (and the_stresses StressCalc object)
# changing the forcing period, so that we can have the NSR stresses
# vary with each iteration too.
the_sat.nsr_period = the_sat.nsr_period / 1.5
the_stresses = satstress.StressCalc([satstress.Diurnal(the_sat), satstress.NSR(the_sat)])
# Now we want to compare this against reference output, just to make sure
# that we're getting the right numbers...
pickledTau = pickle.load(open(test_outfile, 'r'))
for (tau1, tau2) in zip(Taulist, pickledTau):
if tau1.all() != tau2.all():
print("\nTest failed. :(\n")
sys.exit(1)
print("\nTest passed! :)\n")
sys.exit()
def angsep(x1,y1,x2,y2):
# """
# spherical angle separation, aka haversine formula
# input and output are in degrees
# """
dlat = sp.radians(y2 - y1)
dlon = sp.radians(x2 - x1)
y1 = sp.radians(y1)
y2 = sp.radians(y2)
a = sp.sin(dlat/2.)*sp.sin(dlat/2.) + sp.cos(y1)*sp.cos(y2)*sp.sin(dlon/2.)*sp.sin(dlon/2.)
#if a<0 or a>1.: print 'a:',a,x1,y1,x2,y2,dlat,dlon,'!!!!!'
try:
c = 2*sp.arctan2(sp.sqrt(a), sp.sqrt(1.-a))
except:
print '!!!:',a,x1,y1,x2,y2,dlat,dlon,'!!!!!'
c=0
pass
return sp.degrees(c)
def obsolete_calc_azimuth(lat1, lon1, lat2, lon2):
"""NOT USED
Calculate a trace azimuth given start and end positions.
lat1 latitude of start point
lon1 longitude of start point
lat2 latitude of end point
lon2 longitude of end point
Returns azimuth at start point in degrees, in range [0, 360).
"""
lat1 = radians(lat1)
lon1 = radians(lon1)
lat2 = radians(lat2)
lon2 = radians(lon2)
numerator = sin(lon1 - lon2) * cos(lat2)
denominator = sin(arccos((sin(lat2) * sin(lat1)) + (cos(lat1) * cos(lat2) *
cos(lon2 - lon1))))
azimuth = degrees(arcsin(numerator / denominator))
return azimuth
def obsolete_calc_azimuth(lat1, lon1, lat2, lon2):
"""NOT USED
Calculate a trace azimuth given start and end positions.
lat1 latitude of start point
lon1 longitude of start point
lat2 latitude of end point
lon2 longitude of end point
Returns azimuth at start point in degrees, in range [0, 360).
"""
lat1 = radians(lat1)
lon1 = radians(lon1)
lat2 = radians(lat2)
lon2 = radians(lon2)
numerator = sin(lon1 - lon2) * cos(lat2)
denominator = sin(
arccos((sin(lat2) * sin(lat1)) +
(cos(lat1) * cos(lat2) * cos(lon2 - lon1))))
azimuth = degrees(arcsin(numerator / denominator))
return azimuth
def obsolete_calc_azimuth3(lat1, lon1, lat2, lon2):
"""NOT USED
Calculate a trace azimuth given start and end positions.
lat1 latitude of start point
lon1 longitude of start point
lat2 latitude of end point
lon2 longitude of end point
Returns azimuth at start point in degrees, in range [0, 360).
"""
x = (lon1 - lon2) * (cos(lat1))
y = lat1 - lat2
if y == 0:
azimuth = 90
elif x == 0:
azimuth = 0
else:
azimuth = degrees(arctan2(x, y))
return azimuth
def obsolete_calc_azimuth3(lat1, lon1, lat2, lon2):
"""NOT USED
Calculate a trace azimuth given start and end positions.
lat1 latitude of start point
lon1 longitude of start point
lat2 latitude of end point
lon2 longitude of end point
Returns azimuth at start point in degrees, in range [0, 360).
"""
x = (lon1 - lon2) * (cos(lat1))
y = lat1 - lat2
if y == 0:
azimuth = 90
elif x == 0:
azimuth = 0
else:
azimuth = degrees(arctan2(x, y))
return azimuth
def plot_ellipse(ax, x, y, a, b, ang, **kwargs):
"""Plot ellipse(s) on the specified axis.
Parameters
----------
ax : axis
Axis to plot on.
x : float
X coordinate of center. All ellipses have same center.
y : float
Y coordinate of center. All ellipses have same center.
a : float or 1d array
Major axis of ellipse(s).
b : float or 1d array
Minor axis of ellipse(s).
ang : float
Angle of ellipse(s), in radian. All ellipses have same angle.
"""
for av, bv in zip(a, b):
ell = Ellipse([x, y], av, bv, angle=scipy.degrees(ang), **kwargs)
ax.add_artist(ell)
def _update_canvas(self):
"""
Update the figure when the user changes an input value.
:return:
"""
frequency = float(self.frequency.text())
nose_radius = float(self.nose_radius.text())
base_radius = float(self.base_radius.text())
length = float(self.length.text())
# Set the incident angles
incident_angle = linspace(0, pi, 1801)
# Calculate the radar cross section
rcs = array([
radar_cross_section(frequency, nose_radius, base_radius, length,
ia) for ia in incident_angle
])
# Clear the axes for the updated plot
self.axes1.clear()
# Display the results
self.axes1.plot(degrees(incident_angle), 10 * log10(rcs), '')
# Set the plot title and labels
self.axes1.set_title('RCS vs Incident Angle', size=14)
self.axes1.set_ylabel('RCS (dBsm)', size=12)
self.axes1.set_xlabel('Incident Angle (deg)', size=12)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Update the canvas
self.my_canvas.draw()
def populate_geo_coord_polygon(polygon, number_of_points, seed=None,
exclude=None, randoms=None):
"""Populate a polygon with uniformly distributed points.
Takes into account the curvature of the earth.
Input:
polygon - list of vertices of polygon, vertices in (lat, long)
number_of_points - (optional) number of points
seed - seed for random number generator (default=None)
exclude - list of polygons (inside main polygon) from where points
should be excluded
randoms - array of numbers between 0 and 1 to be used as the
random numbers for calculating the latitude of the
points. Obviously is this case the numbers are not
random. This should just be used for writing tests.
Output:
points - list of points inside polygon
Examples:
populate_polygon( [[0,0], [-40,0], [-40,1], [0,1]], 6,
randoms = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0))
will return six not so randomly selected points inside the unit square
"""
if seed is not None:
random.seed(seed)
if randoms is None:
randoms = random.random(number_of_points)
else:
# Ensure randoms is a numpy array
randoms = array(randoms)
# print polygon
points = []
# Find outer extent of polygon
max_x = min_x = polygon[0][1]
max_y = min_y = polygon[0][0]
for point in polygon[1:]:
x = point[1]
if x > max_x:
max_x = x
if x < min_x:
min_x = x
y = point[0]
if y > max_y:
max_y = y
if y < min_y:
min_y = y
max_y_rad = radians(max_y)
min_y_rad = radians(min_y)
# Do this until we have a points array of points inside the polygon that
# greater or equal the number of points requested (we'll slice at return)
while len(points) < number_of_points:
y_p_rad = arcsin(randoms * abs((sin(max_y_rad) - sin(min_y_rad))) +
sin(min_y_rad))
y_p = degrees(y_p_rad)
x_p = random.uniform(min_x, max_x, number_of_points)
points_p = array([y_p, x_p]).T
points_p = points_p[inside_polygon(points_p.tolist(), polygon)]
if exclude is not None and len(points_p) > 0:
for ex_poly in exclude:
points_p = points_p[
outside_polygon(points_p.tolist(), ex_poly)]
if len(points_p) > 0:
points.extend(points_p.tolist())
randoms = random.random(number_of_points)
return points[:number_of_points]
def setFoilDimension(self):
print('--- method: %s ---' % sys._getframe().f_code.co_name)
if not os.path.exists(self.yamlfile):
sys.stderr.write('input parameter_file does not exist.: %s\n' %
self.param['foil_dimension_file'])
quit()
self.foils = []
self.use_foilid_list = []
print(
"------------------------------------------------------------------------"
)
print("Foil Input Dimension")
print(
"Foil-ID : Material, Top radi, Bottom radi, Angle, Angle, GeoArea Use?"
)
print(
" (cm) (cm) (rad) (deg) (cm2) "
)
print(
"------------------------------------------------------------------------"
)
total_geometric_area = 0.0
total_geometric_area_obs1 = 0.0
total_geometric_area_obs2 = 0.0
for line in open(self.param['foil_dimension_file']):
cols = line.split()
if cols[0] == '#':
continue
foil_id = len(self.foils) + 1
foil_parab_top_radius = float(cols[0]) # mm
foil_parab_bot_radius = float(cols[1]) # mm
foil_parab_angle_rad = float(cols[2])
foil_parab_angle_deg = scipy.degrees(foil_parab_angle_rad)
foil_parab_bot_radius_cm = 0.1 * foil_parab_bot_radius
foil_parab_top_radius_cm = 0.1 * foil_parab_top_radius
if foil_parab_angle_deg <= self.param[
'maximum_incident_angle_deg'] and (
2.0 * foil_parab_top_radius_cm <=
self.param['maximum_diameter_cm']):
self.use_foilid_list.append(foil_id)
foil_cone_angle_rad = foil_parab_angle_rad # assumed for CubeSat
self.foils.append(
niresp.xrc.Foil(foil_id, self.material,
foil_parab_top_radius_cm,
foil_parab_bot_radius_cm, foil_cone_angle_rad))
self.foils[-1].roughness_A = self.param['roughness_A']
self.foils[-1].n_spoke1 = self.param['spoke']['n_spoke1']
self.foils[-1].n_spoke2 = self.param['spoke']['n_spoke2']
self.foils[-1].spoke1_wid = self.param['spoke']['spoke1_wid']
self.foils[-1].spoke2_wid = self.param['spoke']['spoke2_wid']
self.foils[-1].setUseFlag(self.use_foilid_list)
self.foils[-1].surface_model = self.param['surface_model']
self.foils[-1].showProperty()
foil_geometric_area = (foil_parab_top_radius_cm**2 -
foil_parab_bot_radius_cm**2) * np.pi
total_geometric_area += foil_geometric_area
total_geometric_area_obs1 += foil_geometric_area * self.foils[
-1].getSpokeObsculation()
subtraction = float(self.param['spoke']['n_spoke1']) * float(
self.param['spoke']['spoke1_wid']) * 10.0 * (
self.foils[-1].top_radius_cm -
self.foils[-1].bottom_radius_cm) * 10.0
subtraction += float(self.param['spoke']['n_spoke2']) * float(
self.param['spoke']['spoke2_wid']) * 10.0 * (
self.foils[-1].top_radius_cm -
self.foils[-1].bottom_radius_cm) * 10.0
value = foil_geometric_area - subtraction
total_geometric_area_obs2 += value
self.param['use_foilid_list'] = self.use_foilid_list
self.param['numof_use_foils'] = len(self.param['use_foilid_list'])
print("Total geometric area (cm2):", total_geometric_area)
print("after subtracting spoke obsculation: ",
total_geometric_area_obs2)
obst_1 = float(self.param['spoke']['n_spoke1']) * float(
self.param['spoke']['spoke1_wid']) * (
self.foils[-1].top_radius_cm - self.foils[0].bottom_radius_cm)
obst_2 = float(self.param['spoke']['n_spoke2']) * float(
self.param['spoke']['spoke2_wid']) * (
self.foils[-1].top_radius_cm - self.foils[0].bottom_radius_cm)
print(
"------------------------------------------------------------------------"
)
def _update_canvas(self):
"""
Update the figure when the user changes an input value.
:return:
"""
# Set up the incident angles
incident_angle = linspace(0., 0.5 * pi, 1000)
# Set up the keyword args
kwargs = {
'frequency': float(self.frequency.text()),
'incident_angle': incident_angle,
'relative_permittivity': self.relative_permittivity,
'relative_permeability': self.relative_permeability,
'conductivity': self.conductivity
}
# Calculate the reflection and transmission coefficients
reflection_coefficient_te, transmission_coefficient_te, reflection_coefficient_tm, \
transmission_coefficient_tm = plane_waves.reflection_transmission(**kwargs)
# Clear the axes for the updated plot
self.axes1.clear()
self.axes2.clear()
# Display the reflection coefficients
self.axes1.plot(degrees(incident_angle),
abs(reflection_coefficient_te),
'b',
label='|$\Gamma_{TE}$|')
self.axes1.plot(degrees(incident_angle),
abs(reflection_coefficient_tm),
'b--',
label='|$\Gamma_{TM}$|')
# Display the transmission coefficients
self.axes2.plot(degrees(incident_angle),
abs(transmission_coefficient_te),
'r',
label='|$T_{TE}$|')
self.axes2.plot(degrees(incident_angle),
abs(transmission_coefficient_tm),
'r--',
label='|$T_{TM}$|')
# Set the plot title and labels
self.axes1.set_title('Plane Wave Reflection and Transmission', size=14)
self.axes1.set_xlabel('Incident Angle (degrees)', size=12)
self.axes1.set_ylabel('|Reflection Coefficient|', size=12)
self.axes2.set_ylabel('|Transmission Coefficient|', size=12)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
self.axes2.tick_params(labelsize=12)
# Set the legend
self.axes1.legend(loc='upper right', prop={'size': 10})
self.axes2.legend(loc='upper left', prop={'size': 10})
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Update the canvas
self.my_canvas.draw()
def nichols_grid(cl_mags=None, cl_phases=None):
"""Nichols chart grid
Plots a Nichols chart grid on the current axis, or creates a new chart
if no plot already exists.
Parameters
----------
cl_mags : array-like (dB), optional
Array of closed-loop magnitudes defining the iso-gain lines on a
custom Nichols chart.
cl_phases : array-like (degrees), optional
Array of closed-loop phases defining the iso-phase lines on a custom
Nichols chart. Must be in the range -360 < cl_phases < 0
Returns
-------
None
"""
# Default chart size
ol_phase_min = -359.99
ol_phase_max = 0.0
ol_mag_min = -40.0
ol_mag_max = default_ol_mag_max = 50.0
# Find bounds of the current dataset, if there is one.
if plt.gcf().gca().has_data():
ol_phase_min, ol_phase_max, ol_mag_min, ol_mag_max = plt.axis()
# M-circle magnitudes.
if cl_mags is None:
# Default chart magnitudes
# The key set of magnitudes are always generated, since this
# guarantees a recognizable Nichols chart grid.
key_cl_mags = np.array([-40.0, -20.0, -12.0, -6.0, -3.0, -1.0, -0.5, 0.0,
0.25, 0.5, 1.0, 3.0, 6.0, 12.0])
# Extend the range of magnitudes if necessary. The extended arange
# will end up empty if no extension is required. Assumes that closed-loop
# magnitudes are approximately aligned with open-loop magnitudes beyond
# the value of np.min(key_cl_mags)
cl_mag_step = -20.0 # dB
extended_cl_mags = np.arange(np.min(key_cl_mags),
ol_mag_min + cl_mag_step, cl_mag_step)
cl_mags = np.concatenate((extended_cl_mags, key_cl_mags))
# N-circle phases (should be in the range -360 to 0)
if cl_phases is None:
# Choose a reasonable set of default phases (denser if the open-loop
# data is restricted to a relatively small range of phases).
key_cl_phases = np.array([-0.25, -45.0, -90.0, -180.0, -270.0, -325.0, -359.75])
if np.abs(ol_phase_max - ol_phase_min) < 90.0:
other_cl_phases = np.arange(-10.0, -360.0, -10.0)
else:
other_cl_phases = np.arange(-10.0, -360.0, -20.0)
cl_phases = np.concatenate((key_cl_phases, other_cl_phases))
else:
assert ((-360.0 < np.min(cl_phases)) and (np.max(cl_phases) < 0.0))
# Find the M-contours
m = m_circles(cl_mags, phase_min=np.min(cl_phases), phase_max=np.max(cl_phases))
m_mag = 20*sp.log10(np.abs(m))
m_phase = sp.mod(sp.degrees(sp.angle(m)), -360.0) # Unwrap
# Find the N-contours
n = n_circles(cl_phases, mag_min=np.min(cl_mags), mag_max=np.max(cl_mags))
n_mag = 20*sp.log10(np.abs(n))
n_phase = sp.mod(sp.degrees(sp.angle(n)), -360.0) # Unwrap
# Plot the contours behind other plot elements.
# The "phase offset" is used to produce copies of the chart that cover
# the entire range of the plotted data, starting from a base chart computed
# over the range -360 < phase < 0. Given the range
# the base chart is computed over, the phase offset should be 0
# for -360 < ol_phase_min < 0.
phase_offset_min = 360.0*np.ceil(ol_phase_min/360.0)
phase_offset_max = 360.0*np.ceil(ol_phase_max/360.0) + 360.0
phase_offsets = np.arange(phase_offset_min, phase_offset_max, 360.0)
for phase_offset in phase_offsets:
# Draw M and N contours
plt.plot(m_phase + phase_offset, m_mag, color='gray',
linestyle='dotted', zorder=0)
plt.plot(n_phase + phase_offset, n_mag, color='gray',
linestyle='dotted', zorder=0)
# Add magnitude labels
for x, y, m in zip(m_phase[:][-1] + phase_offset, m_mag[:][-1], cl_mags):
align = 'right' if m < 0.0 else 'left'
plt.text(x, y, str(m) + ' dB', size='small', ha=align, color='gray')
# Fit axes to generated chart
plt.axis([phase_offset_min - 360.0, phase_offset_max - 360.0,
np.min(cl_mags), np.max([ol_mag_max, default_ol_mag_max])])
def __init__(self, like, model, withErrors=False, param1=False, param2=False):
self.like = like
self.model = model
self.params = like.freeParameters()
self.vpars = [p.value for p in self.params]
self.sigma = sp.array([p.error for p in self.params])
bounds = [p.bounds for p in self.params]
print("Minimizing...", self.vpars, "with bounds", bounds)
self.res = minimize(self.negloglike, self.vpars, bounds=bounds, method='L-BFGS-B')
print(self.res, 'with noErrors =', withErrors)
if (withErrors):
hess = nd.Hessian(self.negloglike)(self.res.x)
eigvl, eigvc = la.eig(hess)
print ('Hessian', hess, eigvl,)
self.cov = la.inv(hess)
print('Covariance matrix \n', self.cov)
# set errors:
for i, pars in enumerate(self.params):
pars.setError(sp.sqrt(self.cov[i, i]))
# update with the final result
self.result(self.negloglike(self.res.x))
if param1 and param2:
for i, par in enumerate(self.like.freeParameters()):
if param1 == par.name: par1 = i
elif param2 == par.name: par2 = i
fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(111) #, aspect='equal')
val,vec = la.eig(self.cov[[par1,par2], :][:, [par1,par2]])
vec=vec.T
mn = self.res.x
vec[0]*=sp.sqrt(11.83*sp.real(val[0]))
vec[1]*=sp.sqrt(11.83*sp.real(val[1]))
plt.plot(mn[par1],mn[par2],'bo', label=self.model)
plt.plot([mn[par1]-vec[0][0],mn[par1]+vec[0][0]],
[mn[par2]-vec[0][1],mn[par2]+vec[0][1]],'r-')
plt.plot([mn[par1]-vec[1][0],mn[par1]+vec[1][0]],
[mn[par2]-vec[1][1],mn[par2]+vec[1][1]],'r-')
def eigsorted(cov):
vals, vecs = sp.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order], vecs[:,order]
vals, vecs = eigsorted(self.cov[[par1,par2], :][:, [par1,par2]])
theta = sp.degrees(np.arctan2(*vecs[:,0][::-1]))
sigmas = [2.3, 5.99, 11.83]
for sigs in sigmas:
w, h = 2 * sp.sqrt(vals) * sp.sqrt(sigs)
ell = Ellipse(xy=(mn[par1], mn[par2]), width = w, height = h, angle=theta, color='green', lw=4)
ell.set_facecolor('none')
ax.add_artist(ell)
plt.legend(loc='best')
plt.title('+'.join(self.like.compositeNames()), fontsize=10)
plt.show()
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
frequency = float(self.frequency.text())
number_of_elements = int(self.number_of_elements.text())
scan_angle_theta = float(self.scan_angle_theta.text())
scan_angle_phi = float(self.scan_angle_phi.text())
radius = float(self.radius.text())
# Set up the theta and phi arrays
n = 360
m = int(n / 8)
theta, phi = meshgrid(linspace(0.0 * pi, 0.5 * pi, n),
linspace(0.0, 2.0 * pi, n))
# Set up the args
kwargs = {
'number_of_elements': number_of_elements,
'scan_angle_theta': radians(scan_angle_theta),
'scan_angle_phi': radians(scan_angle_phi),
'radius': radius,
'frequency': frequency,
'theta': theta,
'phi': phi
}
# Get the array factor
af = circular_uniform.array_factor(**kwargs)
# Remove the color bar
try:
self.cbar.remove()
except:
# Initial plot
pass
# Clear the axes for the updated plot
self.axes1.clear()
# U-V coordinates for plotting the antenna pattern
uu = sin(theta) * cos(phi)
vv = sin(theta) * sin(phi)
if self.plot_type.currentIndex() == 0:
# Display the results
im = self.axes1.pcolor(uu, vv, abs(af), cmap="jet")
self.cbar = self.fig.colorbar(im,
ax=self.axes1,
orientation='vertical')
self.cbar.set_label("Normalized Electric Field (V/m)", size=10)
# Set the x- and y-axis labels
self.axes1.set_xlabel("U (sines)", size=12)
self.axes1.set_ylabel("V (sines)", size=12)
elif self.plot_type.currentIndex() == 1:
# Create the contour plot
self.axes1.contour(uu,
vv,
abs(af),
20,
cmap="jet",
vmin=-0.2,
vmax=1.0)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the x- and y-axis labels
self.axes1.set_xlabel("U (sines)", size=12)
self.axes1.set_ylabel("V (sines)", size=12)
else:
# Create the line plot
self.axes1.plot(degrees(theta[0]),
20.0 * log10(abs(af[m])),
'',
label='E Plane')
self.axes1.plot(degrees(theta[0]),
20.0 * log10(abs(af[0])),
'--',
label='H Plane')
# Set the y axis limit
self.axes1.set_ylim(-60, 5)
# Set the x and y axis labels
self.axes1.set_xlabel("Theta (degrees)", size=12)
self.axes1.set_ylabel("Array Factor (dB)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Place the legend
self.axes1.legend(loc='upper right', prop={'size': 10})
# Set the plot title and labels
self.axes1.set_title('Circular Array - Array Factor', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
frequency = float(self.frequency.text())
guide_width = float(self.guide_width.text())
guide_height = float(self.guide_height.text())
horn_width = float(self.horn_width.text())
horn_height = float(self.horn_height.text())
eplane_effective_length = float(self.eplane_effective_length.text())
hplane_effective_length = float(self.hplane_effective_length.text())
# Get the selected antenna from the form
antenna_type = self.antenna_type.currentIndex()
# Set the range and angular span
r = 1.0e9
# Set up the theta and phi arrays
n = 400
m = int(n / 4)
theta, phi = meshgrid(linspace(0.0, 0.5 * pi, n),
linspace(0.0, 2.0 * pi, n))
# Get the antenna parameters and antenna pattern for the selected antenna
if antenna_type == 0:
total_power_radiated = e_plane_sectoral.power_radiated(
guide_width, horn_height)
directivity = e_plane_sectoral.directivity(
guide_width, horn_height, eplane_effective_length, frequency)
_, et, ep, _, _, _ = e_plane_sectoral.far_fields(
guide_width, horn_height, eplane_effective_length, frequency,
r, theta, phi)
elif antenna_type == 1:
total_power_radiated = h_plane_sectoral.power_radiated(
guide_height, horn_width)
directivity = h_plane_sectoral.directivity(
guide_height, horn_width, hplane_effective_length, frequency)
_, et, ep, _, _, _ = h_plane_sectoral.far_fields(
guide_height, horn_width, hplane_effective_length, frequency,
r, theta, phi)
else:
total_power_radiated = pyramidal.power_radiated(
horn_width, horn_height)
directivity = pyramidal.directivity(horn_width, horn_height,
eplane_effective_length,
hplane_effective_length,
frequency)
_, et, ep, _, _, _ = pyramidal.far_fields(horn_width, horn_height,
eplane_effective_length,
hplane_effective_length,
frequency, r, theta, phi)
# Populate the form with the correct values
self.total_radiated_power.setText(
'{:.2e}'.format(total_power_radiated))
self.directivity.setText('{:.2f}'.format(directivity))
# Remove the color bar
try:
self.cbar.remove()
except:
# Initial plot
pass
# Clear the axes for the updated plot
self.axes1.clear()
# U-V coordinates for plotting the antenna pattern
uu = sin(theta) * cos(phi)
vv = sin(theta) * sin(phi)
# Normalized electric field magnitude for plotting
e_mag = sqrt(abs(et * et + ep * ep))
e_mag /= amax(e_mag)
if self.plot_type.currentIndex() == 0:
# Display the results
im = self.axes1.pcolor(uu, vv, e_mag, cmap="jet")
self.cbar = self.fig.colorbar(im,
ax=self.axes1,
orientation='vertical')
self.cbar.set_label("Normalized Electric Field(V/m)", size=10)
# Set the x- and y-axis labels
self.axes1.set_xlabel("U (sines)", size=12)
self.axes1.set_ylabel("V (sines)", size=12)
elif self.plot_type.currentIndex() == 1:
# Create the contour plot
self.axes1.contour(uu,
vv,
e_mag,
20,
cmap="jet",
vmin=-0.2,
vmax=1.0)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the x- and y-axis labels
self.axes1.set_xlabel("U (sines)", size=12)
self.axes1.set_ylabel("V (sines)", size=12)
else:
# Create the line plot
self.axes1.plot(degrees(theta[0]),
20.0 * log10(e_mag[m]),
'',
label='E Plane')
self.axes1.plot(degrees(theta[0]),
20.0 * log10(e_mag[0]),
'--',
label='H Plane')
# Set the y axis limit
self.axes1.set_ylim(-40, 5)
# Set the x and y axis labels
self.axes1.set_xlabel("Theta (degrees)", size=12)
self.axes1.set_ylabel("Normalized |E| (dB)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Place the legend
self.axes1.legend(loc='upper right', prop={'size': 10})
# Set the plot title and labels
self.axes1.set_title('Horn Antenna Pattern', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
def nichols_grid(cl_mags=None, cl_phases=None):
"""Nichols chart grid
Parameters
----------
cl_mags : array-like (dB), optional
Array of closed-loop magnitudes defining the iso-gain lines on a
custom Nichols chart.
cl_phases : array-like (degrees), optional
Array of closed-loop phases defining the iso-phase lines on a custom
Nichols chart. Must be in the range -360 < cl_phases < 0
Returns
-------
None
"""
# Default chart size
ol_phase_min = -359.99
ol_phase_max = 0.0
ol_mag_min = -40.0
ol_mag_max = default_ol_mag_max = 50.0
# M-circle magnitudes.
if cl_mags is None:
# Default chart magnitudes
key_cl_mags = np.array([
-40.0,
-20.0,
-12.0,
-6.0,
-3.0,
-1.0,
-0.5,
0.0,
0.25,
0.5,
1.0,
3.0,
6.0,
12.0,
])
# Extend the range of magnitudes if necessary.
cl_mag_step = -20.0 # dB
extended_cl_mags = np.arange(np.min(key_cl_mags),
ol_mag_min + cl_mag_step, cl_mag_step)
cl_mags = np.concatenate((extended_cl_mags, key_cl_mags))
else:
cl_mags = np.array(cl_mags)
# N-circle phases (should be in the range -360 to 0)
if cl_phases is None:
# Choose a reasonable set of default phases
key_cl_phases = np.array(
[-0.25, -45.0, -90.0, -180.0, -270.0, -325.0, -359.75])
if np.abs(ol_phase_max - ol_phase_min) < 90.0:
other_cl_phases = np.arange(-10.0, -360.0, -10.0)
else:
other_cl_phases = np.arange(-10.0, -360.0, -20.0)
cl_phases = np.concatenate((key_cl_phases, other_cl_phases))
else:
cl_phases = np.array(cl_phases)
assert (-360.0 < np.min(cl_phases)) and (np.max(cl_phases) < 0.0)
# Find the M-contours
m = m_circles(cl_mags,
phase_min=np.min(cl_phases),
phase_max=np.max(cl_phases))
m_mag = 20 * sp.log10(np.abs(m))
m_phase = sp.mod(sp.degrees(sp.angle(m)), -360.0) # Unwrap
# Find the N-contours
n = n_circles(cl_phases, mag_min=np.min(cl_mags), mag_max=np.max(cl_mags))
n_mag = 20 * sp.log10(np.abs(n))
n_phase = sp.mod(sp.degrees(sp.angle(n)), -360.0) # Unwrap
# Plot the contours behind other plot elements.
# The "phase offset" is used to produce copies of the chart
phase_offset_min = 360.0 * np.ceil(ol_phase_min / 360.0)
phase_offset_max = 360.0 * np.ceil(ol_phase_max / 360.0) + 360.0
phase_offsets = np.arange(phase_offset_min, phase_offset_max, 360.0)
data_m_mag = []
data_n_mag = []
for phase_offset in phase_offsets:
for indice in range(m_mag.shape[1]):
name = "{} dB".format(cl_mags[indice])
data_m_mag.append({
"x": m_phase[:, indice] + phase_offset,
"y": m_mag[:, indice],
"name": name,
})
for indice in range(n_mag.shape[1]):
name = "{} deg".format(cl_phases[indice])
data_n_mag.append({
"x": n_phase[:, indice] + phase_offset,
"y": n_mag[:, indice],
"name": name,
})
return data_m_mag, data_n_mag
def _update_canvas(self):
"""
Update the figure when the user changes an input value.
:return:
"""
frequency = float(self.frequency.text())
theta_inc = float(self.theta_inc.text())
phi_inc = float(self.phi_inc.text())
phi_obs = float(self.phi_obs.text())
# Set the scattering angles
theta = self.theta_obs.text().split(',')
theta_obs = linspace(radians(float(theta[0])), radians(float(theta[1])), 721)
# Monostatic or bistatic
mb = self.type.currentText()
rcs_theta = []
rcs_phi = []
kwargs = {'frequency': array([frequency]),
'vertices': self.vertices,
'faces': self.faces,
'phi_inc': radians(phi_inc),
'phi_obs': radians(phi_obs)}
b = scattering_matrix.ScatteringMatrix(**kwargs)
if mb == 'Monostatic':
for to in theta_obs:
b.theta_inc = to
b.theta_obs = to
sm = b.get_scattering_matrix()
rcs_theta.append(20.0 * log10(abs(sm[0]) + 1e-10))
rcs_phi.append(20.0 * log10(abs(sm[3]) + 1e-10))
else:
b.theta_inc = radians(theta_inc)
for to in theta_obs:
b.theta_obs = to
sm = b.get_scattering_matrix()
rcs_theta.append(20.0 * log10(abs(sm[0]) + 1e-10))
rcs_phi.append(20.0 * log10(abs(sm[3]) + 1e-10))
# Clear the axes for the updated plot
self.axes1.clear()
# Display the results
self.axes1.plot(degrees(theta_obs), rcs_phi, '', label='TE$^z$')
self.axes1.plot(degrees(theta_obs), rcs_theta, '--', label='TM$^z$')
# Set the plot title and labels
self.axes1.set_title('Physical Optics RCS vs Observation Angle', size=14)
self.axes1.set_ylabel('RCS (dBsm)', size=12)
self.axes1.set_xlabel('Observation Angle (deg)', size=12)
self.axes1.set_ylim(-40, max(rcs_theta) + 3)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Set the legend
self.axes1.legend(loc='best', prop={'size': 10})
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Update the canvas
self.my_canvas.draw()
def _update_canvas(self):
"""
Update the figure when the user changes an input value
:return:
"""
# Get the parameters from the form
frequency = float(self.frequency.text())
width = float(self.width.text())
height = float(self.height.text())
# Get the selected antenna from the form
antenna_type = self.antenna_type.currentIndex()
# Set the range and angular span
r = 1.0e9
# Set up the theta and phi arrays
n = 200
m = int(n / 4)
theta, phi = meshgrid(linspace(finfo(float).eps, 0.5 * pi, n),
linspace(finfo(float).eps, 2.0 * pi, n))
# Get the antenna parameters and antenna pattern for the selected antenna
if antenna_type == 0:
half_power_eplane, half_power_hplane, first_null_eplane, first_null_hplane = \
rectangular_uniform_ground_plane.beamwidth(width, height, frequency)
directivity = rectangular_uniform_ground_plane.directivity(
width, height, frequency)
sidelobe_level_eplane = rectangular_uniform_ground_plane.side_lobe_level(
)
sidelobe_level_hplane = sidelobe_level_eplane
_, et, ep, _, _, _ = rectangular_uniform_ground_plane.far_fields(
width, height, frequency, r, theta, phi)
elif antenna_type == 1:
half_power_eplane, half_power_hplane, first_null_eplane, first_null_hplane = \
rectangular_uniform_free_space.beamwidth(width, height, frequency)
directivity = rectangular_uniform_free_space.directivity(
width, height, frequency)
sidelobe_level_eplane = rectangular_uniform_free_space.side_lobe_level(
)
sidelobe_level_hplane = sidelobe_level_eplane
_, et, ep, _, _, _ = rectangular_uniform_free_space.far_fields(
width, height, frequency, r, theta, phi)
else:
half_power_eplane, half_power_hplane, first_null_eplane, first_null_hplane = \
rectangular_te10_ground_plane.beamwidth(width, height, frequency)
directivity = rectangular_te10_ground_plane.directivity(
width, height, frequency)
sidelobe_level_eplane, sidelobe_level_hplane = rectangular_te10_ground_plane.side_lobe_level(
)
_, et, ep, _, _, _ = rectangular_te10_ground_plane.far_fields(
width, height, frequency, r, theta, phi)
# The the text boxes on the form to the correct values
self.sll_eplane.setText('{:.2f}'.format(sidelobe_level_eplane))
self.sll_hplane.setText('{:.2f}'.format(sidelobe_level_hplane))
self.directivity.setText('{:.2f}'.format(directivity))
# Remove the color bar
try:
self.cbar.remove()
except:
# Initial plot
pass
# Clear the axes for the updated plot
self.axes1.clear()
# U-V coordinates for plotting the antenna pattern
uu = sin(theta) * cos(phi)
vv = sin(theta) * sin(phi)
# Normalized electric field magnitude for plotting
e_mag = sqrt(abs(et * et + ep * ep))
e_mag /= amax(e_mag)
if self.plot_type.currentIndex() == 0:
# Create the color plot
im = self.axes1.pcolor(uu, vv, e_mag, cmap="jet")
self.cbar = self.fig.colorbar(im,
ax=self.axes1,
orientation='vertical')
self.cbar.set_label("Normalized Electric Field(V/m)")
# Set the x- and y-axis labels
self.axes1.set_xlabel("U (sines)", size=12)
self.axes1.set_ylabel("V (sines)", size=12)
elif self.plot_type.currentIndex() == 1:
# Create the contour plot
self.axes1.contour(uu,
vv,
e_mag,
20,
cmap="jet",
vmin=-0.2,
vmax=1.0)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Set the x- and y-axis labels
self.axes1.set_xlabel("U (sines)", size=12)
self.axes1.set_ylabel("V (sines)", size=12)
else:
# Create the line plot
self.axes1.plot(degrees(theta[0]),
20.0 * log10(e_mag[m]),
'',
label='E Plane')
self.axes1.plot(degrees(theta[0]),
20.0 * log10(e_mag[0]),
'--',
label='H Plane')
# Set the y axis limit
self.axes1.set_ylim(-60, 5)
# Set the x and y axis labels
self.axes1.set_xlabel("Theta (degrees)", size=12)
self.axes1.set_ylabel("Normalized |E| (dB)", size=12)
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Place the legend
self.axes1.legend(loc='upper right', prop={'size': 10})
# Set the plot title and labels
self.axes1.set_title('Rectangular Aperture Antenna Pattern', size=14)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Update the canvas
self.my_canvas.draw()
def nichols_grid(cl_mags=None, cl_phases=None):
"""Nichols chart grid
Plots a Nichols chart grid on the current axis, or creates a new chart
if no plot already exists.
Parameters
----------
cl_mags : array-like (dB), optional
Array of closed-loop magnitudes defining the iso-gain lines on a
custom Nichols chart.
cl_phases : array-like (degrees), optional
Array of closed-loop phases defining the iso-phase lines on a custom
Nichols chart. Must be in the range -360 < cl_phases < 0
Returns
-------
None
"""
# Default chart size
ol_phase_min = -359.99
ol_phase_max = 0.0
ol_mag_min = -40.0
ol_mag_max = default_ol_mag_max = 50.0
# Find bounds of the current dataset, if there is one.
if plt.gcf().gca().has_data():
ol_phase_min, ol_phase_max, ol_mag_min, ol_mag_max = plt.axis()
# M-circle magnitudes.
if cl_mags is None:
# Default chart magnitudes
# The key set of magnitudes are always generated, since this
# guarantees a recognizable Nichols chart grid.
key_cl_mags = np.array([
-40.0, -20.0, -12.0, -6.0, -3.0, -1.0, -0.5, 0.0, 0.25, 0.5, 1.0,
3.0, 6.0, 12.0
])
# Extend the range of magnitudes if necessary. The extended arange
# will end up empty if no extension is required. Assumes that closed-loop
# magnitudes are approximately aligned with open-loop magnitudes beyond
# the value of np.min(key_cl_mags)
cl_mag_step = -20.0 # dB
extended_cl_mags = np.arange(np.min(key_cl_mags),
ol_mag_min + cl_mag_step, cl_mag_step)
cl_mags = np.concatenate((extended_cl_mags, key_cl_mags))
# N-circle phases (should be in the range -360 to 0)
if cl_phases is None:
# Choose a reasonable set of default phases (denser if the open-loop
# data is restricted to a relatively small range of phases).
key_cl_phases = np.array(
[-0.25, -45.0, -90.0, -180.0, -270.0, -325.0, -359.75])
if np.abs(ol_phase_max - ol_phase_min) < 90.0:
other_cl_phases = np.arange(-10.0, -360.0, -10.0)
else:
other_cl_phases = np.arange(-10.0, -360.0, -20.0)
cl_phases = np.concatenate((key_cl_phases, other_cl_phases))
else:
assert ((-360.0 < np.min(cl_phases)) and (np.max(cl_phases) < 0.0))
# Find the M-contours
m = m_circles(cl_mags,
phase_min=np.min(cl_phases),
phase_max=np.max(cl_phases))
m_mag = 20 * sp.log10(np.abs(m))
m_phase = sp.mod(sp.degrees(sp.angle(m)), -360.0) # Unwrap
# Find the N-contours
n = n_circles(cl_phases, mag_min=np.min(cl_mags), mag_max=np.max(cl_mags))
n_mag = 20 * sp.log10(np.abs(n))
n_phase = sp.mod(sp.degrees(sp.angle(n)), -360.0) # Unwrap
# Plot the contours behind other plot elements.
# The "phase offset" is used to produce copies of the chart that cover
# the entire range of the plotted data, starting from a base chart computed
# over the range -360 < phase < 0. Given the range
# the base chart is computed over, the phase offset should be 0
# for -360 < ol_phase_min < 0.
phase_offset_min = 360.0 * np.ceil(ol_phase_min / 360.0)
phase_offset_max = 360.0 * np.ceil(ol_phase_max / 360.0) + 360.0
phase_offsets = np.arange(phase_offset_min, phase_offset_max, 360.0)
for phase_offset in phase_offsets:
# Draw M and N contours
plt.plot(m_phase + phase_offset,
m_mag,
color='gray',
linestyle='dotted',
zorder=0)
plt.plot(n_phase + phase_offset,
n_mag,
color='gray',
linestyle='dotted',
zorder=0)
# Add magnitude labels
for x, y, m in zip(m_phase[:][-1] + phase_offset, m_mag[:][-1],
cl_mags):
align = 'right' if m < 0.0 else 'left'
plt.text(x,
y,
str(m) + ' dB',
size='small',
ha=align,
color='gray')
# Fit axes to generated chart
plt.axis([
phase_offset_min - 360.0, phase_offset_max - 360.0,
np.min(cl_mags),
np.max([ol_mag_max, default_ol_mag_max])
])
def nichols_plot(sys_list,
omega=None,
grid=True,
figure=None,
ax=None,
delay=False):
"""Nichols plot for a system
Plots a Nichols plot for the system over a (optional) frequency range.
Parameters
----------
sys_list : list of LTI, or LTI
List of linear input/output systems (single system is OK)
omega : array_like
Range of frequencies (list or bounds) in rad/sec
grid : boolean, optional
True if the plot should include a Nichols-chart grid. Default is True.
Returns
-------
None
"""
# If argument was a singleton, turn it into a list
if not getattr(sys_list, '__iter__', False):
sys_list = (sys_list, )
if omega is None:
omega = default_frequency_range(sys_list)
for sys in sys_list:
omega = np.asarray(omega)
if sys.isdtime(strict=True):
nyquistfrq = 2. * np.pi * 1. / sys.dt / 2.
omega = omega[omega < nyquistfrq]
# Get the magnitude and phase of the system
mag_tmp, phase_tmp, omega = sys.freqresp(omega)
mag = np.squeeze(mag_tmp)
phase = np.atleast_1d(np.squeeze(phase_tmp))
phase = unwrap(phase)
if np.any((phase * 180.0 / np.pi) >= 180):
phase = phase - 2 * np.pi
# Convert to Nichols-plot format (phase in degrees,
# and magnitude in dB)
x = unwrap(sp.degrees(phase), 360)
y = 20 * sp.log10(mag)
# Generate the plot
ax.plot(x, y)
ax.xaxis.set_major_formatter(mticker.FormatStrFormatter("%.1f °"))
ax.yaxis.set_major_formatter(mticker.FormatStrFormatter("%.1f dB"))
ax.set_xlabel('Fase (deg)')
ax.set_ylabel('Magnitud (dB)')
ax.set_title('Nichols Plot')
# Mark the -180 point
ax.plot([-180], [0], 'r+')
# Add grid
if grid:
nichols_grid(figure=figure, ax=ax, delay=delay)
return x, y, omega
def _update_canvas(self):
"""
Update the figure when the user changes an input value.
:return:
"""
frequency = float(self.frequency.text())
radius = self.radius.text().split(',')
mu_r = self.mu_r.text().split(',')
eps_r = self.eps_r.text().split(',')
number_of_modes = int(self.number_of_modes.text())
# To put the parameters in order
nr = len(radius)
mu = ones(nr + 1)
eps = ones(nr + 1)
ra = ones(nr)
# Set up the parameters in the correct order
i = 0
for r in radius:
ra[nr - 1 - i] = float(r)
i += 1
i = 0
for m, e in zip(mu_r, eps_r):
mu[nr - i] = float(m)
eps[nr - i] = float(e)
i += 1
# Flag for PEC core
pec = self.pec.currentText()
pec_b = False
if pec == 'True':
pec_b = True
# Set the observation angles
observation_angle = linspace(0, pi, 721)
An, Bn = coefficients(frequency, eps, mu, ra, number_of_modes, pec_b)
et = array([radar_cross_section(frequency, oa, 0, An, Bn) for oa in observation_angle])
ep = array([radar_cross_section(frequency, oa, 90, An, Bn) for oa in observation_angle])
# Clear the axes for the updated plot
self.axes1.clear()
# Display the results
self.axes1.plot(degrees(observation_angle), 20.0 * log10(abs(ep[:, 1])), '', label='TE')
self.axes1.plot(degrees(observation_angle), 20.0 * log10(abs(et[:, 0])), '--', label='TM')
# Set the plot title and labels
self.axes1.set_title('RCS vs Bistatic Angle', size=14)
self.axes1.set_ylabel('RCS (dBsm)', size=12)
self.axes1.set_xlabel('Observation Angle (deg)', size=12)
self.axes1.set_ylim(min(20.0 * log10(abs(et[:,0]))) - 3, max(20.0 * log10(abs(et[:,0]))) + 3)
# Set the tick label size
self.axes1.tick_params(labelsize=12)
# Set the legend
self.axes1.legend(loc='upper left', prop={'size': 10})
# Turn on the grid
self.axes1.grid(linestyle=':', linewidth=0.5)
# Update the canvas
self.my_canvas.draw()
def __get_theta_deg(self):
return scipy.degrees(self.theta)
