def test_qerfi():
"""
Test the qerfi function which is the inverse of qerf - the solution for x to q = Q(x).
The approximation is due to Hastings, Jr. (1995). Both the area prediction and
point to point prediction modes are tested.
"""
#test p2p
q = [
0.0100000000000000, 0.100000000000000, 0.500000000000000,
0.900000000000000, 0.990000000000000
]
actual_answer = qerfi(q)
expected_answer = [2.3268, 1.2817, 0.0000, -1.2817, -2.3268]
assert actual_answer == expected_answer
#test area prediction mode
q = [0.5000]
actual_answer = qerfi(q)
expected_answer = [0]
assert actual_answer == expected_answer
q = [0.5, 0.9, 0.1]
actual_answer = qerfi(q)
expected_answer = [0.0, -1.2817, 1.2817]
assert actual_answer == expected_answer
def itmlogic_p2p(main_user_defined_parameters, surface_profile_m):
"""
Run itmlogic in point to point (p2p) prediction mode.
Parameters
----------
main_user_defined_parameters : dict
User defined parameters.
surface_profile_m : list
Contains surface profile measurements in meters.
Returns
-------
output : list of dicts
Contains model output results.
"""
prop = main_user_defined_parameters
#DEFINE ENVIRONMENTAL PARAMETERS
# Terrain relative permittivity
prop['eps'] = 15
# Terrain conductivity (S/m)
prop['sgm'] = 0.005
# Climate selection (1=equatorial,
# 2=continental subtropical, 3=maritime subtropical,
# 4=desert, 5=continental temperate,
# 6=maritime temperate overland,
# 7=maritime temperate, oversea (5 is the default)
prop['klim'] = 5
# Surface refractivity (N-units): also controls effective Earth radius
prop['ens0'] = 314
#DEFINE STATISTICAL PARAMETERS
# Confidence levels for predictions
qc = [50, 90, 10]
# Reliability levels for predictions
qr = [1, 10, 50, 90, 99]
# Number of points describing profile -1
pfl = []
pfl.append(len(surface_profile_m) - 1)
pfl.append(0)
for profile in surface_profile_m:
pfl.append(profile)
# Refractivity scaling ens=ens0*exp(-zsys/9460.)
# (Average system elev above sea level)
zsys = 0
# Note also defaults to a continental temperate climate
# Setup some intermediate quantities
# Initial values for AVAR control parameter: LVAR=0 for quantile change,
# 1 for dist change, 2 for HE change, 3 for WN change, 4 for MDVAR change,
# 5 for KLIM change
prop['lvar'] = 5
# Inverse Earth radius
prop['gma'] = 157E-9
# Conversion factor to db
db = 8.685890
#Number of confidence intervals requested
nc = len(qc)
#Number of reliability intervals requested
nr = len(qr)
#Length of profile in km
dkm = prop['d']
#Profile range step, select option here to define range step from profile
#length and # of points
xkm = 0
#If DKM set <=0, find DKM by mutiplying the profile step by number of
#points (not used here)
if dkm <= 0:
dkm = xkm * pfl[0]
#If XKM is <=0, define range step by taking the profile length/number
#of points in profile
if xkm <= 0:
xkm = dkm // pfl[0]
#Range step in meters stored in PFL(2)
pfl[1] = dkm * 1000 / pfl[0]
#Store profile in prop variable
prop['pfl'] = pfl
#Zero out error flag
prop['kwx'] = 0
#Initialize omega_n quantity
prop['wn'] = prop['fmhz'] / 47.7
#Initialize refractive index properties
prop['ens'] = prop['ens0']
#Scale this appropriately if zsys set by user
if zsys != 0:
prop['ens'] = prop['ens'] * math.exp(-zsys / 9460)
#Include refraction in the effective Earth curvature parameter
prop['gme'] = prop['gma'] * (1 - 0.04665 * math.exp(prop['ens'] / 179.3))
#Set surface impedance Zq parameter
zq = complex(prop['eps'], 376.62 * prop['sgm'] / prop['wn'])
#Set Z parameter (h pol)
prop['zgnd'] = np.sqrt(zq - 1)
#Set Z parameter (v pol)
if prop['ipol'] != 0:
prop['zgnd'] = prop['zgnd'] / zq
#Flag to tell qlrpfl to set prop.klim=prop.klimx and set lvar to initialize avar routine
prop['klimx'] = 0
#Flag to tell qlrpfl to use prop.mdvar=prop.mdvarx and set lvar to initialize avar routine
prop['mdvarx'] = 11
#Convert requested reliability levels into arguments of standard normal distribution
zr = qerfi([x / 100 for x in qr])
#Convert requested confidence levels into arguments of standard normal distribution
zc = qerfi([x / 100 for x in qc])
#Initialization routine for point-to-point mode that sets additional parameters
#of prop structure
prop = qlrpfl(prop)
## Here HE = effective antenna heights, DL = horizon distances,
## THE = horizon elevation angles
## MDVAR = mode of variability calculation: 0=single message mode,
## 1=accidental mode, 2=mobile mode, 3 =broadcast mode, +10 =point-to-point,
## +20=interference
#Free space loss in db
fs = db * np.log(2 * prop['wn'] * prop['dist'])
#Used to classify path based on comparison of current distance to computed
#line-of-site distance
q = prop['dist'] - prop['dlsa']
#Scaling used for this classification
q = max(q - 0.5 * pfl[1], 0) - max(-q - 0.5 * pfl[1], 0)
#Report dominant propagation type predicted by model according to parameters
#obtained from qlrpfl
if q < 0:
print('Line of sight path')
elif q == 0:
print('Single horizon path')
else:
print('Double-horizon path')
if prop['dist'] <= prop['dlsa']:
print('Diffraction is the dominant mode')
elif prop['dist'] > prop['dx']:
print('Tropospheric scatter is the dominant mode')
print('Estimated quantiles of basic transmission loss (db)')
print('Free space value {} db'.format(str(fs)))
print('Confidence levels {}, {}, {}'.format(str(qc[0]), str(qc[1]),
str(qc[2])))
# Confidence levels for predictions
qc = [50, 90, 10]
# Reliability levels for predictions
qr = [1, 10, 50, 90, 99]
output = []
for jr in range(0, (nr)):
for jc in range(0, nc):
#Compute corrections to free space loss based on requested confidence
#and reliability quantities
avar1, prop = avar(zr[jr], 0, zc[jc], prop)
output.append({
'distance_km': prop['d'],
'reliability_level_%': qr[jr],
'confidence_level_%': qc[jc],
'propagation_loss_dB':
fs + avar1 #Add free space loss and correction
})
return output
def test_itmlogic_area():
"""
Test the model in area prediction mode.
"""
prop = {}
#Antenna height 1 (m), Antenna height 2 (m)
prop['hg'] = [3.3, 1.3]
#Frequency (MHz)
prop['fmhz'] = 20
#Terrain irregularity parameter dh (m)
prop['dh'] = 102
#Surface refractivity (N-units)
prop['ens0'] = 301
#Relative permittivity of ground
prop['eps'] = 15
#Conductivity (S/m) of ground
prop['sgm'] = 0.001
#Climate selection (1=equatorial, 2=continental subtropical,
#3=maritime subtropical, 4=desert, 5=continental temperate,
#6=maritime temperate overland, 7=maritime temperate,
#oversea (5 is the default)
prop['klimx'] = 5
#0 = horizontal polarization, 1 = vertical
prop['ipol'] = 1
#Mode of variability: Single Message=0, Accidental=1,
#Mobile=2, Broadcast=3
prop['mdvarx'] = 3
#Percent of time requested for computation
QT = [50]
#Percent of locations requested for computation
QL = [50]
#Confidence levels of computation
QC = [50, 90, 10]
#Initial distance (km) for loop over range
D0 = 10
#Max distance 1 (km) for loop over range
D1 = 150
#Increment 1 (km) for loop over range
DS1 = 10
#Max distance 2 (km) for coarser loop over range (starts beyond D1)
D2 = 500
#Increment 2 (km) for coarser loop over range
DS2 = 50
#Siting criterion for antenna 1, 0=random, 1= careful, 2= very careful
KST = [2, 2]
#Refractivity scaling ens=ens0*exp(-zsys/9460.);
#(Average system elev above sea level)
zsys = 0
#Rescale requested percentages into their corresponding normal distribution arguments
ZT = qerfi([x / 100 for x in QT])[0]
ZL = qerfi([x / 100 for x in QL])[0]
ZC = qerfi([x / 100 for x in QC])
#The number of confidence intervals requested
NC = len(QC)
#Don't allow negative distances
if (D0 <= 0):
D0 = DS1
#Set initial distance to 2 km if D0<=0
if (D0 <= 0):
D0 = 2
#If final distance less than initial, only do one distance
if (D1 <= D0) or (DS1 <= 0):
ND = 1
D1 = D0
#Otherwise compute the number of distance points in the loop
#and recompute the final distance using this grid
else:
DS = DS1
ND = math.floor((D1 - D0) / DS + 1.75)
D1 = D0 + (ND - 1) * DS
#Repeat these corrections for the "coarse" grid in range that follows the fine grid
if (D2 <= D1) or (DS2 <= 0):
NDC = 0 #If input parameters are wrong, don't do a coarse grid
#Otherwise set up appropriate coarse grid
else:
NDC = ND
ND = math.floor((D2 - D1) / DS2 + 0.75)
D2 = D1 + ND * DS2
ND = NDC + ND
#Standard Earth curvature parameter
prop['gma'] = 157E-9
#Scale factor for converting Np/km to dB/km
DB = 8.685890
#Scale factor to convert km to m
AKM = 1000
#Initialize error flag to 0
prop['kwx'] = 0
#Initialize the omega_n parameter
prop['wn'] = prop['fmhz'] / 47.7
#Initialize the surface refractivity
prop['ens'] = prop['ens0']
#Adjust surface refractivity parameter if zsys set by user
if (zsys != 0):
prop['ens'] = prop['ens'] * math.exp(-zsys / 9460)
#Implement refractive effects on Earth curvature
prop['gme'] = prop['gma'] * (1 - 0.04665 * math.exp(prop['ens'] / 179.3))
#Initialize lvar parameter (this is used when updating AVAR with new input parameters)
prop['lvar'] = 0
#Compute ground effective impedance zq parameter
zq = complex(prop['eps'], 376.62 * prop['sgm'] / prop['wn'])
#Compute ground effective impedance z parameter (horizontal pol)
prop['zgnd'] = math.sqrt(zq.real - 1)
#Compute ground effective impedance z parameter (vertical pol)
if (prop['ipol'] != 0):
prop['zgnd'] = prop['zgnd'] / zq
#Qlra initializes all the required parameters for area-prediction mode given
#siting type and other params already set in "prop"
prop = qlra(KST, prop)
#Starting distances for loop over range
D = D0
#First range step is that of the fine grid
DT = DS
FS = []
DD = []
output = []
for JD in range(0, ND): #0-22
#Ensure that AVAR routines adjust only for distance (not quantiles which are set)
prop['lvar'] = max(1, prop['lvar'])
#Compute baseline propagation loss at current range
prop = lrprop(D * AKM, prop)
#Compute and store baseline loss
FS.append(DB * np.log(2 * prop['wn'] * prop['dist']))
#Store distance at this increment
DD.append(D)
#Loop over confidence intervals requested by user
for JC in range(0, NC): #0-3
#Get confidence interval
confidence_level = QC[JC]
#Compute adjustment for specified confidence levels
avar1, prop = avar(ZT, ZL, ZC[JC], prop)
#Store results
output.append({
'distance_km': D,
'confidence_level_%': confidence_level,
'propagation_loss_dB':
FS[JD] + avar1 #Add in the adjustment for this level
})
#Switch to the coarse grid increment in range when we get there
if JD + 1 == NDC:
DT = DS2
#Increment range
D = D + DT
for result in output:
if result['distance_km'] == 10 and result['confidence_level_%'] == 50:
assert result['propagation_loss_dB'] == 111.69200844812511
if result['distance_km'] == 10 and result['confidence_level_%'] == 90:
assert result['propagation_loss_dB'] == 121.59437954264777
if result['distance_km'] == 10 and result['confidence_level_%'] == 10:
assert result['propagation_loss_dB'] == 101.78963735360244
if result['distance_km'] == 500 and result['confidence_level_%'] == 50:
assert result['propagation_loss_dB'] == 215.27421197676375
if result['distance_km'] == 500 and result['confidence_level_%'] == 90:
assert result['propagation_loss_dB'] == 221.71014370503855
if result['distance_km'] == 500 and result['confidence_level_%'] == 10:
assert result['propagation_loss_dB'] == 208.83828024848896
def run_itmlogic(surface_profile_m, distance_km):
"""
Run itmlogic in point to point (p2p) prediction mode.
Parameters
----------
surface_profile_m : list
Contains surface profile measurements in meters.
distance_km : float
Distance between the transmitter and receiver.
Returns
-------
output : list of dicts
Contains model output results.
"""
prop = {}
#Frequencies used in the campaign (GHz)
frequencies = [2.488]
#Polarization selection (0=horizontal, 1=vertical)
prop['ipol'] = 1
#Receiver heights (m)
rxht = [2.56, 2.2]
#Transmitter heights (m) (UAV)
txht = list(range(10, (130+1), 1))
#Terrain relative permittivity
prop['eps'] = 15
#Terrain conductivity (S/m)
prop['sgm'] = 0.005
#Surface refractivity (N-units): also controls effective Earth radius -> unknown
prop['ens0'] = 314
# # Climate selection (1=equatorial,
# # 2=continental subtropical, 3=maritime subtropical,
# # 4=desert, 5=continental temperate,
# # 6=maritime temperate overland,
# # 7=maritime temperate, oversea (5 is the default)
prop['klim'] = 5
#Refractivity scaling; (Average system elev above sea level)
zsys = 0
#Confidence levels for predictions
qc = [50]
#Reliability levels for predictions
qr = [50]
#Preliminary calcs
#Conversion factor to dB
DB = 8.685890
NC = len(qc)
NR = len(qr)
ZR = qerfi(qr) #ZR = qerfi(qr / 100)
ZC = qerfi(qc) #ZC = qerfi(qc / 100)
# Inverse Earth radius
prop['gma'] = 157E-9
prop['ens'] = prop['ens0']
if zsys != 0:
prop['ens'] = prop['ens'] * math.exp(-zsys / 9460)
prop['gme'] = prop['gma'] * (1 - 0.04665 * math.exp(prop['ens'] / 179.3))
PFL = []
PFL.append(len(distance_km)-1)
PFL.append(distance_km[1] - distance_km[0])
PFL = PFL + terrain_height_no_tree
#Length of profile (km)
prop['d'] = distance_km[-1]/1000
prop['pfl'] = PFL
output = []
for frequency in range(0, len(frequencies)):
prop['fmhz'] = frequencies[frequency] * 1000
prop['wn'] = prop['fmhz'] / 47.7
zq = complex(prop['eps'], (376.62 * prop['sgm'] / prop['wn']))
prop['zgnd'] = np.sqrt(zq - 1)
if prop['ipol'] != 0:
prop['zgnd'] = prop['zgnd'] / zq
if frequency == 0:
#Rx height varies with frequency
prop['hg'] = [0, 0]
prop['hg'][0] = rxht[0]
else:
prop['hg'] = [0, 0]
prop['hg'][0] = rxht[1]
#Tx ht (UAV)
for iht in range(0, len(txht)):
hg = txht[iht]
# Antenna 2 height (m)
prop['hg'][1] = hg
#Setup some intermediate quantities
#Initial values for AVAR control parameter:
#LVAR=0 for quantile change, 1 for dist change,
#2 for HE change, 3 for WN change,
# 4 for MDVAR change, 5 for KLIM change
prop['lvar'] = 5
#Zero out error flag
prop['kwx'] = 0
prop['klimx'] = 0
prop['mdvarx'] = 11
prop = qlrpfl(prop)
#Here HE = effective antenna heights
#DL = horizon distances
#THE = horizon elevation angles
#MDVAR = mode of variability calculation: 0=single message mode,
#1=accidental mode, 2=mobile mode, 3=broadcast mode,
#+10 =point-to-point, +20=interference
# Free space loss in dB
FS = DB * np.log(2 * prop['wn'] * prop['dist'])
for jr in range(0, NR):
xlb = []
for jc in range(0, NC):
avar1, prop = avar(ZR[jr], 0, ZC[jc], prop)
xlb.append(FS + avar1)
output.append((prop['fmhz'], hg, qr[jr], xlb[0]))
return output