def plot_original_signal_from_intrinsic_mode_functions(sample_frequency,
imfs,
residue,
channel,
plotter=plt):
'''
:param sample_frequency:
:param imfs:
:param residue:
:param channel:
:param plotter:
:return:
'''
n_rows = len(imfs)
data_length = len(imfs[0])
final_signal = scipy.zeros(len(imfs[1]))
time_axis = scipy.linspace(start=0,
stop=data_length / sample_frequency,
num=data_length)
for i in range(n_rows):
final_signal = scipy.add(final_signal, imfs[i + 1])
final_signal = scipy.add(final_signal, residue)
f, axis = plotter.subplots(1, 1)
sup_title = "Channel " + channel
f.suptitle(sup_title, fontsize=18)
axis.plot(time_axis, final_signal)
axis.grid()
def __init__(self, fc, c_vel, alp_g, mu_los, mu_nlos, a, b, noise_var, hUAV, xUAV, yUAV, xUE, yUE):
dist = sp.sqrt( sp.add(sp.square(sp.subtract(yUAV, yUE)), sp.square(sp.subtract(xUAV, xUE))) )
R_dist = sp.sqrt( sp.add(sp.square(dist), sp.square(hUAV)) )
temp1 = sp.multiply(10, sp.log10(sp.power(fc*4*sp.pi*R_dist/c_vel, alp_g)))
temp2 = sp.multiply(sp.subtract(mu_los, mu_nlos), sp.divide(1, (1+a*sp.exp(-b*sp.arctan(hUAV/dist)-a))))
temp3 = sp.add(sp.add(temp1, temp2), mu_nlos)
self.pathloss = sp.divide(sp.real(sp.power(10, -sp.divide(temp3, 10))), noise_var)
def __init__(self, id, coverage_r, origin_x=0, origin_y=0, low_tx=0.5):
tx_d = sp.add(sp.multiply(sp.subtract(coverage_r, 200), sp.random.uniform(low=low_tx)), 50)
tx_angle = sp.multiply(2, sp.multiply(sp.pi, sp.random.rand(1, 1)))
self.tx_x = sp.add(origin_x, sp.multiply(tx_d, sp.cos(tx_angle)))
self.tx_y = sp.add(origin_y, sp.multiply(tx_d, sp.sin(tx_angle)))
plt.scatter(self.tx_x, self.tx_y, s=20, c='blue', marker='o')
plt.annotate(id, (self.tx_x + 10, self.tx_y + 10))
def __init__(self, id, coverage_r, origin_x=0, origin_y=0, low_tx=0.1):
tx_d = sp.multiply(coverage_r, sp.random.uniform(low=low_tx))
tx_angle = sp.multiply(0.5, sp.multiply(sp.pi, (sp.random.rand())))
self.tx_x = sp.add(origin_x, sp.multiply(tx_d, sp.sin(tx_angle)))
self.tx_y = sp.add(origin_y, sp.multiply(tx_d, sp.cos(tx_angle)))
plt.scatter(self.tx_x, self.tx_y, s=20, c='red')
plt.annotate(id, (self.tx_x + 10, self.tx_y + 10))
def __init__(self, id, coverage_r, origin_x=0, origin_y=0, low_tx=0.2):
# uav_d = sp.add(sp.multiply(sp.subtract(coverage_r, 100), sp.random.rand(1, 1)), 50)
uav_d = sp.add(sp.multiply(sp.subtract(coverage_r, 100), sp.random.uniform(low=low_tx)), 50)
uav_angle = sp.multiply(2, sp.multiply(sp.pi, sp.random.rand(1, 1)))
self.uav_x = sp.add(origin_x, sp.multiply(uav_d, sp.cos(uav_angle)))
self.uav_y = sp.add(origin_y, sp.multiply(uav_d, sp.sin(uav_angle)))
plt.scatter(self.uav_x, self.uav_y, s=40, c='red', marker='D')
plt.annotate(id, (self.uav_x + 10, self.uav_y + 10))
def european_option_rho(self): "Price of the call option" "the vectorized method can compute price of multiple options in array" numerator = sp.add( sp.log( sp.divide( self.spot_price, self.strike_price, ) ), sp.multiply( ( self.interest_rate - self.dividend_yield + 0.5*sp.power(self.sigma,2) ), self.time_to_maturity) ) d1 = sp.divide( numerator, sp.prod( [ self.sigma, sp.sqrt(self.time_to_maturity) ], axis=0, ) ) d2 = sp.add( d1, -sp.multiply( self.sigma, sp.sqrt(self.time_to_maturity) ) ) j = sp.product( [ self.spot_price, self.time_to_maturity, sp.exp( sp.multiply( -self.interest_rate, self.time_to_maturity ) ), ], axis=0 ) c_rho = j * self.bls_erf_value(d2) p_rho = -j * self.bls_erf_value(-d2) return c_rho, p_rho
def oht_alg(d2d_to_d2d_gains_diag, uav_to_d2d_gains, d2d_to_d2d_gains_diff, eta, power_UAV, power_cir_UAV):
theta_ini = Parameter(value=1 / 0.5)
iter = 0
epsilon = 1
theta_sol = 0
iter_phi = []
while epsilon >= 1e-2 and iter <= 20:
iter += 1
if iter == 1:
theta_ref = theta_ini.value
else:
theta_ref = theta_sol
term_x = sp.divide(1,
sp.multiply(sp.subtract(theta_ref, 1), sp.matmul(d2d_to_d2d_gains_diag, uav_to_d2d_gains)))
term_y = sp.add(
sp.multiply(sp.subtract(theta_ref, 1), sp.matmul(sp.transpose(d2d_to_d2d_gains_diff), uav_to_d2d_gains)),
sp.divide(1, eta * power_UAV))
a_1 = sp.add(sp.divide(sp.multiply(2, sp.log(sp.add(1, sp.divide(1, sp.multiply(term_x, term_y))))), theta_ref),
sp.divide(2, sp.multiply(theta_ref, sp.add(sp.multiply(term_x, term_y), 1))))
b_1 = sp.divide(1, sp.multiply(theta_ref, sp.multiply(term_x, sp.add(sp.multiply(term_x, term_y), 1))))
c_1 = sp.divide(1, sp.multiply(theta_ref, sp.multiply(term_y, sp.add(sp.multiply(term_x, term_y), 1))))
d_1 = sp.divide(sp.log(sp.add(1, sp.divide(1, sp.multiply(term_x, term_y)))), sp.square(theta_ref))
theta = NonNegative(1)
t_max = NonNegative(1)
obj_opt = Maximize(t_max)
constraints = [theta >= 1]
constraints.append(
t_max <= a_1 - sp.divide(b_1, sp.matmul(d2d_to_d2d_gains_diag, uav_to_d2d_gains)) * inv_pos(theta - 1)
- mul_elemwise(c_1,
sp.matmul(sp.transpose(d2d_to_d2d_gains_diff), uav_to_d2d_gains) * (theta - 1)
+ sp.divide(1, eta * power_UAV))
- d_1 * theta)
t1 = time.time()
prob = Problem(obj_opt, constraints)
prob.solve(solver=ECOS_BB)
theta_sol = theta.value
phi_n_sol = sp.multiply((theta_sol - 1) * eta * power_UAV, uav_to_d2d_gains)
x_rate = sp.matmul(d2d_to_d2d_gains_diag, phi_n_sol)
term_rate = sp.matmul(sp.transpose(d2d_to_d2d_gains_diff), phi_n_sol) + 1
rate_sol_ue = sp.divide(sp.log(sp.add(1, sp.divide(x_rate, term_rate))), theta_sol)
iter_maximin_rate = min(rate_sol_ue)
term_pow_iter = sp.subtract(1, sp.divide(1, theta_sol)) * eta * power_UAV * sp.add(1, sp.sum(
uav_to_d2d_gains)) + power_cir_UAV
iter_phi.append(t_max.value)
if iter >= 2:
epsilon = sp.divide(sp.absolute(sp.subtract(iter_phi[iter - 1], iter_phi[iter - 2])),
sp.absolute(iter_phi[iter - 2]))
iter_EE = sp.divide(sp.multiply(1e3, sp.divide(sp.sum(rate_sol_ue), term_pow_iter)), sp.log(2))
return iter_EE, theta_sol, iter_maximin_rate
def plot_original_signal_from_intrinsic_mode_functions(sample_frequency, imfs, residue, channel, plotter=plt):
n_rows = len(imfs)
data_length = len(imfs[0])
final_signal = scipy.zeros(len(imfs[1]))
time_axis = scipy.linspace(start=0, stop=data_length / sample_frequency, num=data_length)
for i in range(n_rows):
final_signal = scipy.add(final_signal, imfs[i + 1])
final_signal = scipy.add(final_signal, residue)
f, axis = plotter.subplots(1, 1)
sup_title = "Channel " + channel
f.suptitle(sup_title, fontsize=18)
axis.plot(time_axis, final_signal)
axis.grid()
def reflect1(v, u, c):
print("Reflect by vector math variant 1:")
c = 0
center_ = eT(center(len(v)))
print("center_:", center_)
print("v:", v)
v = scipy.subtract(v, center_)
print("v:", v)
print("u:", u)
print("c:", c)
v_dot_u = scipy.dot(v, u)
print("v_dot_u:", v_dot_u)
v_dot_u_minus_c = scipy.subtract(v_dot_u, c)
print("v_dot_u_minus_c:", v_dot_u_minus_c)
u_dot_u = scipy.dot(u, u)
print("u_dot_u:", u_dot_u)
quotient = scipy.divide(v_dot_u_minus_c, u_dot_u)
print("quotient:", quotient)
subtrahend = scipy.multiply((2 * quotient), u)
print("subtrahend:", subtrahend)
reflection = scipy.subtract(v, subtrahend)
print("reflection:", reflection)
reflection = scipy.add(reflection, center_)
print("reflection:", reflection)
return reflection
def european_option_delta(self): numerator = sp.add( sp.log( sp.divide( self.spot_price, self.strike_price ) ), sp.multiply( ( self.interest_rate - self.dividend_yield + 0.5*sp.power(self.sigma,2)), self.time_to_maturity ) ) d1 = sp.divide( numerator, sp.prod( [ self.sigma, sp.sqrt(self.time_to_maturity) ], axis=0, ) ) call_delta = self.bls_erf_value(d1) put_delta = call_delta - 1 return call_delta, put_delta
def __init__(self,
id,
coverage_r,
max_dist,
origin_x=0,
origin_y=0,
low_tx=0.2,
low_rx=0.5):
# def __init__(self, id, coverage_r, max_dist, origin_x=0, origin_y=0, low_tx=0.3, low_rx=0.2):
tx_d = sp.multiply(coverage_r, sp.random.uniform(low=low_tx))
tx_angle = sp.multiply(
2, sp.multiply(sp.pi, sp.subtract(sp.random.rand(), 1)))
self.tx_x = sp.add(origin_x, sp.multiply(tx_d, sp.sin(tx_angle)))
self.tx_y = sp.add(origin_y, sp.multiply(tx_d, sp.cos(tx_angle)))
plt.scatter(self.tx_x, self.tx_y, s=20, c='red')
plt.annotate(id, (self.tx_x + 10, self.tx_y + 10))
# plt.show()
def kValues(self,tn,yn,h):
#Initialise an empty vector k of the same length as b and init tnew
A = self.A
b = self.b
c = self.c
k = [ [0. * i *j for j in range(len(yn))] for i in range(len(b))]
tnew = 0
ynew = scipy.zeros(len(yn))
lincombinatie = ynew
for i in range(len(b)):
tnew = tn + c[i]*h
ynew = scipy.zeros(len(yn))
lincombinatie = scipy.zeros(len(yn))
for j in range(i):
prod = scipy.multiply(A[i][j]*h,k[j])
lincombinatie = scipy.add(lincombinatie,prod)
ynew = scipy.add(yn,lincombinatie)
k[i] = scipy.multiply(1,self.ode.f(tnew,ynew))
#k[i] = scipy.multiply(h,self.ode.f(tnew,ynew))
return k
def __init__(self,
id,
coverage_r,
max_dist,
origin_x=0,
origin_y=0,
low_tx=0.2,
low_rx=0.5):
# def __init__(self, id, coverage_r, max_dist, origin_x=0, origin_y=0, low_tx=0.3, low_rx=0.2):
tx_d = sp.multiply(coverage_r, sp.random.uniform(low=low_tx))
tx_angle = sp.multiply(
2, sp.multiply(sp.pi, sp.subtract(sp.random.rand(), 1)))
self.tx_x = sp.add(origin_x, sp.multiply(tx_d, sp.sin(tx_angle)))
self.tx_y = sp.add(origin_y, sp.multiply(tx_d, sp.cos(tx_angle)))
d2d_d = sp.multiply(max_dist, sp.random.uniform(low=low_rx))
d2d_angle = sp.multiply(
2, sp.multiply(sp.pi, sp.subtract(sp.random.rand(), 1)))
self.rx_x = sp.add(self.tx_x, sp.multiply(d2d_d, sp.sin(d2d_angle)))
self.rx_y = sp.add(self.tx_y, sp.multiply(d2d_d, sp.cos(d2d_angle)))
def loss_to_pair(self, pair, atg_a, atg_b, pl_exp=4, gamma=1e2):
dist = sp.sqrt(
sp.add(sp.square(pair.tx_x),
sp.add(sp.square(pair.tx_y), sp.square(self.h))))
phi = sp.multiply(sp.divide(180, sp.pi),
sp.arcsin(sp.divide(self.h, dist)))
pr_LOS = sp.divide(
1,
sp.add(
1,
sp.multiply(
atg_a, sp.exp(sp.multiply(-atg_b, sp.subtract(phi,
atg_a))))))
pr_NLOS = sp.subtract(1, pr_LOS)
total_loss = sp.add(
sp.multiply(pr_LOS, sp.power(dist, -pl_exp)),
sp.multiply(sp.multiply(pr_NLOS, gamma), sp.power(dist, -pl_exp)))
return total_loss
def loss_to_pair(self,
pair,
gain=1e-3,
exp_factor=sp.random.exponential(1),
pl_exp=3):
dist = sp.sqrt(
sp.add(sp.square(sp.subtract(self.tx_x, pair.rx_x)),
sp.square(sp.subtract(self.tx_y, pair.rx_y))))
loss = sp.multiply(
gain, sp.multiply(sp.square(exp_factor), sp.power(dist, -pl_exp)))
return loss
def step(self,tn,yn,h):
"""
takes a single time step using the runge-kutta method
y_(n+1) = y_n + sum(b_i*k_i)
Input:
tn -- current time
yn -- state at time tn
h - size of time step
Output:
y -- state at time t0+h
"""
k = self.kValues(tn,yn,h)
lincombinatie = scipy.zeros(len(yn))
for i in range(len(self.b)):
lincombinatie = scipy.add(scipy.multiply(k[i],self.b[i]*h), lincombinatie)
return yn + lincombinatie
def european_option_vega(self): numerator = sp.add( sp.log( sp.divide( self.spot_price, self.strike_price ) ), sp.multiply( ( self.interest_rate - self.dividend_yield + 0.5*sp.power(self.sigma,2)), self.time_to_maturity ) ) d1 = sp.divide( numerator, sp.prod( [ self.sigma, sp.sqrt(self.time_to_maturity) ], axis=0, ) ) val = sp.multiply( sp.multiply( self.spot_price, sp.exp( -sp.multiply( self.dividend_yield, self.time_to_maturity ) ) ), sp.exp(-sp.square(d1)*0.5) ) val = sp.multiply( val, sp.sqrt(self.time_to_maturity) ) vega = (1/sqrt(2*pi))*val return vega
def reflect_by_householder(v, u, c):
print("Reflect by Householder:")
print("chord:", v)
print("Unit normal vector:", u)
center_ = center(len(v))
tensor_ = scipy.outer(u, u)
print("tensor_:\n", tensor_)
product_ = scipy.multiply(tensor_, 2)
print("product_:", product_)
identity_ = scipy.eye(len(v))
print("identity_:\n", identity_)
householder = scipy.subtract(identity_, product_)
print("householder:\n", householder)
reflected_voices = scipy.matmul(householder, v)
print("reflected_voices:", reflected_voices)
translated_voices = scipy.subtract(v, center_)
print("moved to origin:", translated_voices)
reflected_translated_voices = scipy.matmul(householder, translated_voices)
print("reflected_translated_voices:", reflected_translated_voices)
reflection = scipy.add(reflected_translated_voices, center_)
print("moved from origin:", reflection)
print("reflection by householder:", reflection)
return reflection
def RMSLE(act, pred):
loss = sum((sp.log(sp.add(pred, 1)) - sp.log(sp.add(act, 1))) ** 2)
loss = sp.sqrt(loss * 1.0 / len(pred))
return loss
while epsilon >= 1e-2 and iter <= 20:
iter += 1
if iter == 1:
theta_ref = theta_ini.value
else:
theta_ref = theta_sol
term_x = sp.divide(
1,
sp.multiply(
sp.subtract(theta_ref, 1),
sp.matmul(d2d_to_d2d_gains_diag, uav_to_d2d_gains)))
term_y = sp.add(
sp.multiply(
sp.subtract(theta_ref, 1),
sp.matmul(sp.transpose(d2d_to_d2d_gains_diff),
uav_to_d2d_gains)),
sp.divide(1, eta * power_UAV))
a_1 = sp.add(
sp.divide(
sp.multiply(
2,
sp.log(
sp.add(
1, sp.divide(1,
sp.multiply(term_x,
term_y))))),
theta_ref),
sp.divide(
2,
def __init__(self, path, datatype, container=0, sizeTD1=0, process = False, lb = 0, phase = 0, ls = 0, zf = 0, ft_only = [], debug = False, hiper_skip_footer = 0, hiper_skip_header = 3, endianess = "<", maxLoad = 0):
""" This reads the data """
#plt.close()
if datatype == '':
print("No Datatype - Setting it to ntnmr")
datatype = "ntnmr"
self.carrier = 0
self.allFid = []
self.allFid.append([])
self.sizeTD1 = sizeTD1
self.title = ['no title']
self.vdList = []
self.files = [] #here we store open files so we can close them later
self.parDictionary = {}
self.debug = debug
if self.debug: print("The datatype is {0}".format(datatype))
if datatype == "Hiper":
"""Hiper EPR Data import for EPR experiments at St Andrews, UK.
Note you will have to adjust the skip_footer parameter,
depending on the length of the pulse programme that is appended to the data file."""
data = np.genfromtxt(path, skip_header = hiper_skip_header, skip_footer = hiper_skip_footer, delimiter = ",")
self.sizeTD1 = 1
timeList = data[:, 0]
iData = data[:, 1]
qData = data[:, 2]
self.sizeTD2 = len(qData)
if self.debug: print("sizeTD2: ", self.sizeTD2)
dwellTime = (timeList[1] - timeList[0])*1e-9
self.sweepWidthTD2 = int(1. /dwellTime)
if self.debug: print("SweepWidthTD2: ", self.sweepWidthTD2)
self.allFid[0].append(iData + 1j*qData)
self.fidTime = np.linspace(0, (self.sizeTD2-1)*dwellTime, num = self.sizeTD2)
if datatype == "Magritek":
#get sweep WidthTD2 based on dweelTime in acqu.par
if os.path.isfile(path + "/acqu.par"):
f_acqu = open(path + "/acqu.par", "r")
count = 0
while True:
count += 1
line = f_acqu.readline().strip()
if "=" in line:
line = line.split("=")
elif len(line) == 0 or count > 1000:
if self.debug: print("Ended dreading acqus file at line ", count)
break
else:
next
if len(line[0]) > 1:
self.parDictionary[line[0].strip()] = line[1].strip()
self.sweepWidthTD2 = int(1./(float(self.parDictionary["dwellTime"])*1e-6))
if self.debug: print("SweepWidthTD2: ", self.sweepWidthTD2)
if os.path.isfile(path + "/data.1d"):
f = open(path + "/data.1d", "rb")
f.seek(12)
print("Format: ", struct.unpack('<i', f.read(4))[0])
#get this information out of the acqu file.
f.seek(16)
self.sizeTD2 = struct.unpack('<i', f.read(4))[0]
print("Size TD2: ", self.sizeTD2)
f.seek(32)
dwellTime = 1./self.sweepWidthTD2
self.fidTime = np.linspace(0, (self.sizeTD2-1)*dwellTime, num = self.sizeTD2)
#t just contains floats with the time
t = struct.unpack('<' + 'f'*self.sizeTD2, f.read(4*self.sizeTD2))
data1 = struct.unpack('<' + 'f'*self.sizeTD2*2, f.read(4*self.sizeTD2*2))
realPart = np.array(data1[::2])
imagPart = np.array(data1[1::2])
self.allFid[0].append(realPart + 1j*imagPart)
self.frequency = np.linspace(-self.sweepWidthTD2/2,self.sweepWidthTD2/2, 2048)
elif os.path.isfile(path + "/data.2d"):
f = open(path + "/data.2d", "rb")
f.seek(12)
print("Format: ", struct.unpack('<i', f.read(4))[0])
#get this information out of the acqu file.
f.seek(16)
self.sizeTD2 = struct.unpack('<i', f.read(4))[0]
self.sizeTD1 = struct.unpack('<i', f.read(4))[0]
print("Size TD2: ", self.sizeTD2)
print("Size TD1: ", self.sizeTD1)
f.seek(32)
dwellTime = 1./self.sweepWidthTD2
self.data1 = struct.unpack('<' + 'f'*(self.sizeTD2*self.sizeTD1*2), f.read(4*self.sizeTD2*self.sizeTD1*2))
self.realStuff = np.array(self.data1[::2])
self.imagStuff = np.array(self.data1[1::2])
self.complexData = self.realStuff + 1j*self.imagStuff
self.allFid[0] = np.split(self.complexData, [self.sizeTD2*(i+1) for i in range(self.sizeTD1-1)])
self.fidTime = np.linspace(0, (self.sizeTD2-1)*dwellTime, num = self.sizeTD2)
else:
print("No 1D file found.")
if datatype == "varian":
# read the header file
if os.path.isfile(path + "/procpar"):
parFile = open(path + "/procpar", 'r')
rows = parFile.readlines()
lineCounter = 0;
for line in rows:
if line.find("np ") > -1:
nextLine = rows[lineCounter+1]
params = nextLine.split(" ")
totalComplexPoints = int(params[1]) #np =is R+I
self.sizeTD2 = totalComplexPoints/2
elif line.find("acqcycles") > -1:
nextLine = rows[lineCounter+1]
params = nextLine.split(" ")
self.sizeTD1 = int(params[1])
if self.sizeTD1 > 1:
self.is2D = True
else:
self.is2D = False
lineCounter = lineCounter+1
else:
print("No procpar file found.")
# read the binary data file
if os.path.isfile(path + "/fid"):
specpoints = self.sizeTD2
headerskip_init = 8
headerskip = 7
f = open(path + "/fid", 'rb');
data_array = np.fromfile(f, '>f', -1)
if not self.is2D:
self.allFid[0] = data_array[(headerskip_init + headerskip)::2] + 1j*data_array[(headerskip_init + headerskip + 1)::2]
else:
Nacq = (len(data_array) - headerskip_init) / (2 * specpoints + headerskip)
if Nacq != self.sizeTD1:
print("warning: inconsistent sizes")
for n in range(0, Nacq):
skipn = headerskip_init + (n+1)*headerskip + n*2*specpoints;
realPart = data_array[skipn+0:skipn+2*specpoints:2]
imagPart = 1j*data_array[skipn+1:skipn+2*specpoints:2]
self.allFid[0].append(sp.add(realPart, imagPart))
else:
print("No fid file found.")
if datatype == 'TopSpinOld':
self.f = open(path, mode='rb')
self.sizeTD2=1
self.sizeTD1=(int((os.stat(path)).st_size))/8
self.data = struct.unpack('>' + 'i'*(self.sizeTD2*2*self.sizeTD1), self.f.read(self.sizeTD2*2*self.sizeTD1*4))
for i in range(0, self.sizeTD1):
#print i
realPart = self.data[i*self.sizeTD2*2:(i+1)*self.sizeTD2*2:2]
imagPart = sp.multiply(self.data[i*self.sizeTD2*2+1:(i+1)*self.sizeTD2*2+1:2], 1j)
self.allFid[0].append(sp.add(realPart, imagPart))
if datatype == 'TopSpin':
if self.debug:
print("hi, this is self.debug for the TopSpin datatype")
#The acqus file containts the spectral width SW_h and 2*SizeTD2 as ##$TD
#The acqu2s file contains TD1 as ##$TD
directory = os.path.dirname(path)
acqusFile = open(directory + "/acqus", mode='r')
self.files.append(acqusFile)
if self.debug:
print("Importing TopSpin data")
#check if acqu2sfile exists, if yes, experiment is 2D!
if os.path.isfile(directory + "/acqu2s"):
acqu2sFile = open(directory + "/acqu2s", mode='r')
self.files.append(acqu2sFile)
acqu2File = open(directory + "/acqu2", mode='r')
self.files.append(acqu2File)
self.is2D = True
else:
self.is2D = False
self.sizeTD1 = 1
if self.debug:
print("2D: ", self.is2D)
#this could be crafted into a common routine which gives names of parameters
#parameters and works the same for e.g., spinsight and topspin
if self.debug:
print("reading acqus file")
count = 0
while True:
if self.debug:
print("count = ", count)
#try:
count += 1
line = acqusFile.readline().strip()
if self.debug:
print(line)
if "=" in line:
line = line.split("=")
elif len(line) > 0:
line = line.split(" ")
elif len(line) == 0 or count > 1000:
if self.debug: print("Ended reading acqus file at line ", count)
break
else:
next
#print line[0]
if line[0] == "##$SW_h":
#this line might be chopping the last digit off....
#self.sweepWidthTD2 = int(float(line[1][:-1]))
self.sweepWidthTD2 = int(float(line[1]))
if self.debug: print("SweepWidthTD2: ", self.sweepWidthTD2)
elif line[0] == "##$TD":
self.sizeTD2 = int(int(line[1])/2)
if self.debug: print("sizeTD2: ", self.sizeTD2)
elif line[0] == "##$SFO1":
self.carrier = float(line[1])*1e6
if self.debug: print("SFO1:", self.carrier)
elif len(line) == 0:
break
if len(line[0]) > 1:
if "@" in line[-1]:
#this line contains date, time, some unknown stuff and user, does not work with all bruker files, hence try only"
try:
self.parDictionary["date"] = line[1].strip()
self.parDictionary["time"] = line[2].strip()
except:
pass
elif line[0] == "##$D":
delays1 = acqusFile.readline().strip()
delays2 = acqusFile.readline().strip()
self.parDictionary["d"] = [float(d) for d in delays1.strip().split(" ")] + [float(d) for d in delays2.strip().split(" ")]
elif line[0] == "##$L":
loopCounters = acqusFile.readline().strip()
self.parDictionary["l"] = [float(l) for l in loopCounters.strip().split(" ")]
else:
if self.debug:
print("the catch all else")
if len(line) > 1:
self.parDictionary[line[0][2:].strip()] = line[1].strip()
else:
if self.debug:
print("skipped too short line")
if self.is2D == True:
if self.debug:
print("reading acqu2s file")
count = 0
while True:
if self.debug:
print("count = ", count)
#try:
count += 1
line = acqu2sFile.readline().strip()
if "=" in line:
line = line.split("=")
elif len(line) == 0 or count > 1000:
if self.debug: print("Ended reading acqu2s file at line ", count)
break
else:
next
#print line[0]
if line[0] == "##$TD" and self.sizeTD1 == 0:
self.sizeTD1 = int(line[1])
if self.debug: print("sizeTD1: ", self.sizeTD1)
elif len(line) == 0:
break
if os.path.isfile(directory + "/vdlist"):
if self.debug: print("VD File exists!")
self.vdList = np.loadtxt(directory + "/vdlist")
if self.debug:
print("TD2: ", self.sizeTD2)
print("TD1: ", self.sizeTD1)
print("Carrier:", self.carrier)
# if self.is2D:
# self.f = open(path + "/ser", mode='rb')
# else:
# self.f = open(path + "fid", mode='rb')#
# dataString = self.f.read(self.sizeTD2*2*self.sizeTD1*4)
# if self.debug: print "len(dataString): ", len(dataString)
# self.f.close()
#
# # here we read the FID data from fid/ser file
# # first convert the datasting into a list of numbers:
# if self.debug: print "Endianess: ", endianess
# self.data = struct.unpack(endianess + 'i'*(self.sizeTD2*2*self.sizeTD1), dataString)
if self.is2D:
self.f = open(path + "/ser", mode='rb')
else:
self.f = open(path + "fid", mode='rb')
self.files.append(self.f)
dataString = np.frombuffer(self.f.read(), dtype = endianess + "i4")
if self.debug: print("len(dataString) new: ", len(dataString))
self.data = dataString
#this is not how it should be done.
if self.sizeTD2 == 0:
self.sizeTD2 = int(len(self.data) / self.sizeTD1 / 2)
dwellTime = 1./self.sweepWidthTD2
self.fidTime = np.linspace(0, (self.sizeTD2-1)*dwellTime, num = self.sizeTD2)
# here we create one array of complex numbers for each of the FIDs
# i runs overa all fids in a ser file, in case of a fid file i = 0
# TD1 is number of FIDs, TD2 is number of datapoints in each FID
if maxLoad > 0:
self.sizeTD1 = maxLoad
for i in range(0, self.sizeTD1):
#print "sizeTD2: ", self.sizeTD2
#print i
realPart = self.data[i*self.sizeTD2*2:(i+1)*self.sizeTD2*2:2]
imagPart = sp.multiply(self.data[i*self.sizeTD2*2+1:(i+1)*self.sizeTD2*2+1:2], 1j)
self.allFid[0].append(sp.add(realPart, imagPart))
# here we read the experiment title (only the one stored in pdata/1):
# could be made to read titles from all pdata folders (if needed)
try:
pathToTitle = directory + '/pdata/1/title'
titleFile = open(pathToTitle, mode='r')
self.files.append(titleFile)
title = list(titleFile)
self.title = [line.strip() for line in title]
except:
if self.debug:
print("No title file.")
else:
pass
#close the files we opened:
for item in self.files:
item.close()
# delete all file handles so that nmrdata objects can be pickled.
self.files = []
del self.f
if datatype == 'spinsight':
chStr = "" # first read the channel, if succesful read the carrier frequency.
carrierStr = "carrier string not assigned"
self.is2D = False
dataFile = path + "/data"
print("dataFile is: ", dataFile)
self.f = open(dataFile, mode='rb')
self.f.seek(0)
acqFile = path + "/acq"
self.fACQ = open(acqFile)
count = 0
while True:
count += 1
try:
line = self.fACQ.readline().strip().split("=")
print(line)
if line[0] == "ch1":
chStr = line[1]
carrierStr = "sf" + chStr
elif line[0] == carrierStr:
self.carrier = float(line[1][:-1])*1e6
if line[0] == "dw":
print("Hellop: ", line[1])
self.sweepWidthTD2 = int(1/float(line[1][:-1]))
#print "sweepWidthTD2: ", self.sweepWidthTD2
elif line[0] == "array_num_values_pd" or line[0] == "array_num_values_tau" or line[0] == "array_num_values_pw90X" or line[0] == "array_num_values_tau1":
self.sizeTD1 = int(line[1])
self.is2D = True
elif len(line) < 2 and count > 10:
break
except:
break
#check if file named apnd exists
if self.is2D == True:
self.sizeTD2 = int((os.stat(dataFile)).st_size)/8/self.sizeTD1
else:
self.sizeTD2 = (int((os.stat(dataFile)).st_size))/8
print("sizeTD2 is: ", self.sizeTD2)
self.sizeTD1 = 1
print("sizeTD1 is: ", self.sizeTD1)
#print "Length 1: ", self.sizeTD1*self.sizeTD2*2
#print "Length 2: ", len(self.f.read(self.sizeTD1*self.sizeTD2*2*4))
self.f.seek(0)
self.data = struct.unpack('>' + 'i'*self.sizeTD1*self.sizeTD2*2, self.f.read(self.sizeTD1*self.sizeTD2*2*4))
print("Length of data: ", len(self.data))
for i in range(self.sizeTD1):
print(i)
realPart = np.array(self.data[i*self.sizeTD2:(i+1)*self.sizeTD2])
imagPart = np.array(self.data[self.sizeTD1*self.sizeTD2+i*self.sizeTD2:self.sizeTD1*self.sizeTD2+(i+1)*self.sizeTD2])
print("Length realPart: ", len(realPart))
print("Length imagPart: ", len(imagPart))
self.allFid[0].append(realPart + 1j*imagPart)
print("sizeTD1: ", self.sizeTD1)
self.fidTime = np.linspace(0, (self.sizeTD2-1)/float(self.sweepWidthTD2), self.sizeTD2)
if datatype == 'ntnmr':
""" File information taken from J. van Becks matNMR, thanks! """
self.f = open(path, mode='rb')
""" Length """
self.f.seek(16)
self.structureSize = struct.unpack('<I', self.f.read(4))[0]
#SizeTD2
self.f.seek(20)
self.sizeTD2 = struct.unpack('<I', self.f.read(4))[0]
#SizeTD1
if sizeTD1 > 0:
self.sizeTD1 = sizeTD1
else:
self.f.seek(24)
self.sizeTD1 = struct.unpack('<I', self.f.read(4))[0]
#SpectralFrequencyTD2/Anregungsfrequenz
self.f.seek(104)
self.spectralFrequencyTD2 = struct.unpack('<d', self.f.read(8))[0]*1e6
print('spec TD2 is', self.spectralFrequencyTD2)
#SpectralFrequencyTD1
self.f.seek(112)
self.spectralFrequencyTD1 = struct.unpack('<d', self.f.read(8))[0]
print("TD1 is", self.sizeTD1)
#SweepWidthTD2, SampleRate?
self.f.seek(260)
self.sweepWidthTD2 = struct.unpack('<d', self.f.read(8))[0]
#SweepWidthTD1
self.f.seek(268)
self.sweepWidthTD1 = struct.unpack('<d', self.f.read(8))[0]
#a, Daten im Float32, f und 4
self.f.seek(1056)
# self.sizeTD1 = 581
#print "The length is", len(self.f.read(self.sizeTD2*2*self.sizeTD1*4))
self.data = struct.unpack('<' + 'f'*(self.sizeTD2*2*self.sizeTD1), self.f.read(self.sizeTD2*2*self.sizeTD1*4))
self.f.close()
for i in range(0, self.sizeTD1):
#print i
realPart = self.data[i*self.sizeTD2*2:(i+1)*self.sizeTD2*2:2]
imagPart = sp.multiply(self.data[i*self.sizeTD2*2+1:(i+1)*self.sizeTD2*2+1:2], 1j)
self.allFid[0].append(sp.add(realPart, imagPart))
print("Data imported, Number of Experiments: ", self.sizeTD1)
if process == True:
self.process(ls = ls, zf = zf, lb = lb, phase = phase, ft_only = ft_only)
if datatype == 'TopSpin4':
if self.debug:
print("hi, this is self.debug for the TopSpin4 datatype")
#The acqus file containts the spectral width SW_h and 2*SizeTD2 as ##$TD
#The acqu2s file contains TD1 as ##$TD
directory = os.path.dirname(path)
acqusFile = open(directory + "/acqus", mode='r')
self.files.append(acqusFile)
if self.debug:
print("Importing TopSpin4 data")
#check if acqu2sfile exists, if yes, experiment is 2D!
if os.path.isfile(directory + "/acqu2s"):
acqu2sFile = open(directory + "/acqu2s", mode='r')
self.files.append(acqu2sFile)
acqu2File = open(directory + "/acqu2", mode='r')
self.files.append(acqu2File)
self.is2D = True
else:
self.is2D = False
self.sizeTD1 = 1
if self.debug:
print("2D: ", self.is2D)
#this could be crafted into a common routine which gives names of parameters
#parameters and works the same for e.g., spinsight and topspin
if self.debug:
print("reading acqus file")
count = 0
while True:
if self.debug:
print("count = ", count)
#try:
count += 1
line = acqusFile.readline().strip()
if self.debug:
print(line)
if "=" in line:
line = line.split("=")
elif len(line) > 0:
line = line.split(" ")
elif len(line) == 0 or count > 1000:
if self.debug: print("Ended reading acqus file at line ", count)
break
else:
next
#print line[0]
if line[0] == "##$SW_h":
#this line might be chopping the last digit off....
#self.sweepWidthTD2 = int(float(line[1][:-1]))
self.sweepWidthTD2 = int(float(line[1]))
if self.debug: print("SweepWidthTD2: ", self.sweepWidthTD2)
elif line[0] == "##$TD":
self.sizeTD2 = int(int(line[1])/2)
if self.debug: print("sizeTD2: ", self.sizeTD2)
elif line[0] == "##$SFO1":
self.carrier = float(line[1])*1e6
if self.debug: print("SFO1:", self.carrier)
elif len(line) == 0:
break
if len(line[0]) > 1:
if "@" in line[-1]:
#this line contains date, time, some unknown stuff and user, does not work with all bruker files, hence try only"
try:
self.parDictionary["date"] = line[1].strip()
self.parDictionary["time"] = line[2].strip()
except:
pass
elif line[0] == "##$D":
delays1 = acqusFile.readline().strip()
delays2 = acqusFile.readline().strip()
self.parDictionary["d"] = [float(d) for d in delays1.strip().split(" ")] + [float(d) for d in delays2.strip().split(" ")]
elif line[0] == "##$L":
loopCounters = acqusFile.readline().strip()
self.parDictionary["l"] = [float(l) for l in loopCounters.strip().split(" ")]
else:
if self.debug:
print("the catch all else")
if len(line) > 1:
self.parDictionary[line[0][2:].strip()] = line[1].strip()
else:
if self.debug:
print("skipped too short line")
if self.is2D == True:
if self.debug:
print("reading acqu2s file")
count = 0
while True:
if self.debug:
print("count = ", count)
#try:
count += 1
line = acqu2sFile.readline().strip()
if "=" in line:
line = line.split("=")
elif len(line) == 0 or count > 1000:
if self.debug: print("Ended reading acqu2s file at line ", count)
break
else:
next
#print line[0]
if line[0] == "##$TD" and self.sizeTD1 == 0:
self.sizeTD1 = int(line[1])
if self.debug: print("sizeTD1: ", self.sizeTD1)
elif len(line) == 0:
break
if os.path.isfile(directory + "/vdlist"):
if self.debug: print("VD File exists!")
self.vdList = np.loadtxt(directory + "/vdlist")
if self.debug:
print("TD2: ", self.sizeTD2)
print("TD1: ", self.sizeTD1)
print("Carrier:", self.carrier)
# if self.is2D:
# self.f = open(path + "/ser", mode='rb')
# else:
# self.f = open(path + "fid", mode='rb')#
# dataString = self.f.read(self.sizeTD2*2*self.sizeTD1*4)
# if self.debug: print "len(dataString): ", len(dataString)
# self.f.close()
#
# # here we read the FID data from fid/ser file
# # first convert the datasting into a list of numbers:
# if self.debug: print "Endianess: ", endianess
# self.data = struct.unpack(endianess + 'i'*(self.sizeTD2*2*self.sizeTD1), dataString)
if self.is2D:
self.f = open(path + "/ser", mode='rb')
else:
self.f = open(path + "fid", mode='rb')
self.files.append(self.f)
dataString = np.frombuffer(self.f.read(), dtype = endianess + "f8")
if self.debug: print("len(dataString) new: ", len(dataString))
self.data = dataString
#this is not how it should be done.
if self.sizeTD2 == 0:
self.sizeTD2 = int(len(self.data) / self.sizeTD1 / 2)
dwellTime = 1./self.sweepWidthTD2
self.fidTime = np.linspace(0, (self.sizeTD2-1)*dwellTime, num = self.sizeTD2)
# here we create one array of complex numbers for each of the FIDs
# i runs overa all fids in a ser file, in case of a fid file i = 0
# TD1 is number of FIDs, TD2 is number of datapoints in each FID
if maxLoad > 0:
self.sizeTD1 = maxLoad
for i in range(0, self.sizeTD1):
#print "sizeTD2: ", self.sizeTD2
#print i
realPart = self.data[i*self.sizeTD2*2:(i+1)*self.sizeTD2*2:2]
imagPart = sp.multiply(self.data[i*self.sizeTD2*2+1:(i+1)*self.sizeTD2*2+1:2], 1j)
self.allFid[0].append(sp.add(realPart, imagPart))
# here we read the experiment title (only the one stored in pdata/1):
# could be made to read titles from all pdata folders (if needed)
try:
pathToTitle = directory + '/pdata/1/title'
titleFile = open(pathToTitle, mode='r')
self.files.append(titleFile)
title = list(titleFile)
self.title = [line.strip() for line in title]
except:
if self.debug:
print("No title file.")
else:
pass
#close the files we opened:
for item in self.files:
item.close()
# delete all file handles so that nmrdata objects can be pickled.
self.files = []
del self.f
def get_sphere_vectors(features):
fshape = features.shape
features.shape = fshape[0], -1
npoints, ndims = features.shape
if npoints < MEAN_MAX_NPOINTS:
fmean = features.mean(0)
else:
# - try to optimize memory usage...
sel = features[:MEAN_MAX_NPOINTS]
fmean = sp.empty_like(sel[0,:])
sp.add.reduce(sel, axis=0, dtype="float32", out=fmean)
curr = sp.empty_like(fmean)
npoints_done = MEAN_MAX_NPOINTS
while npoints_done < npoints:
# check if can we overwrite (other process)
if path.exists(output_fname) and not overwrite:
warnings.warn("not allowed to overwrite %s" % output_fname)
return
sel = features[npoints_done:npoints_done+MEAN_MAX_NPOINTS]
sp.add.reduce(sel, axis=0, dtype="float32", out=curr)
sp.add(fmean, curr, fmean)
npoints_done += MEAN_MAX_NPOINTS
#fmean = features[:MEAN_MAX_NPOINTS].sum(0)
#npoints_done = MEAN_MAX_NPOINTS
#while npoints_done < npoints:
# fmean += features[npoints_done:npoints_done+MEAN_MAX_NPOINTS].sum(0)
# npoints_done += MEAN_MAX_NPOINTS
fmean /= npoints
if npoints < STD_MAX_NPOINTS:
fstd = features.std(0)
else:
# - try to optimize memory usage...
sel = features[:MEAN_MAX_NPOINTS]
mem = sp.empty_like(sel)
curr = sp.empty_like(mem[0,:])
seln = sel.shape[0]
sp.subtract(sel, fmean, mem[:seln])
sp.multiply(mem[:seln], mem[:seln], mem[:seln])
fstd = sp.add.reduce(mem[:seln], axis=0, dtype="float32")
npoints_done = MEAN_MAX_NPOINTS
while npoints_done < npoints:
# check if can we overwrite (other process)
if path.exists(output_fname) and not overwrite:
warnings.warn("not allowed to overwrite %s" % output_fname)
return
sel = features[npoints_done:npoints_done+MEAN_MAX_NPOINTS]
seln = sel.shape[0]
sp.subtract(sel, fmean, mem[:seln])
sp.multiply(mem[:seln], mem[:seln], mem[:seln])
sp.add.reduce(mem[:seln], axis=0, dtype="float32", out=curr)
sp.add(fstd, curr, fstd)
npoints_done += MEAN_MAX_NPOINTS
# slow version:
#fstd = ((features[:MEAN_MAX_NPOINTS]-fmean)**2.).sum(0)
#npoints_done = MEAN_MAX_NPOINTS
#while npoints_done < npoints:
# fstd += ((features[npoints_done:npoints_done+MEAN_MAX_NPOINTS]-fmean)**2.).sum(0)
# npoints_done += MEAN_MAX_NPOINTS
fstd = sp.sqrt(fstd/npoints)
fstd[fstd==0] = 1
sphere_vectors = (fmean, fstd)
features.shape = fshape
return sphere_vectors
#obtain genotype & phenotype for samples with complete phenotype
MY = SP.array(Y).transpose()
RMY = MY[MY[:, 0] > 0]
RY = RMY[:, 0]
RY = (RY - RY.mean()) / RY.std()
RX = RMY[:, 1:]
#train null model for these samples
COR = 1. / nf * SP.dot(RX, RX.transpose())
res = lmm_lasso.train_nullmodel(RY, COR)
delta = SP.exp(res[2])
print delta
#get fake phenotype
FX = MY[:, 1:]
FCOR = 1. / nf * SP.dot(FX, FX.transpose())
D = SP.diag(SP.array([delta] * len(Y[1])))
FY = SP.random.multivariate_normal(SP.array([0] * len(Y[1])),
SP.add(FCOR, D))
FY = (FY - FY.mean()) / FY.std()
FY = SP.array([FY])
with open("phenotype75.csv", "wb") as f:
writer = csv.writer(f)
writer.writerows(FY.transpose())
#validate fake phenotype, that it has similar delta as we start with
res = lmm_lasso.train_nullmodel(FY.transpose(), FCOR)
delta = SP.exp(res[2])
print delta
def __init__(self, path, endianess=">", debug=False, maxLoad=0):
"""This class only takes the following arguments:
- path: path to an NMR experiment
All other arguments are optional Keyword Arguments:
- endianess = ">"
- debug = False: set to True to output additional debuging information.
- maxLoad = 0: set to an integer value to limit loading and processing
to an NMR experiment, an optional argument for endianess"""
super().__init__(debug=debug)
if self.debug:
print("hi, this is self.debug for the RS2D datatype")
#The acqus file containts the spectral width SW_h and 2*SizeTD2 as ##$TD
#The acqu2s file contains TD1 as ##$TD
directory = os.path.dirname(path)
acqusFile = directory + "/header.xml"
if self.debug:
print("Importing RS2D data")
with open(acqusFile) as fp:
soup = BeautifulSoup(fp, 'lxml-xml')
allEntries = soup.find_all("entry")
for e in allEntries:
keyName = e.key.string
keyValue = e.value.value.string
res = e.find_all("value", {"xsi:type": "numberParam"})
if len(res) > 0:
r = float(e.value.value.string)
if r.is_integer():
self.parDictionary[e.key.string] = int(r)
else:
self.parDictionary[e.key.string] = r
self.sizeTD1 = self.parDictionary["ACQUISITION_MATRIX_DIMENSION_2D"]
self.sizeTD2 = self.parDictionary["ACQUISITION_MATRIX_DIMENSION_1D"]
self.sweepWidthTD2 = self.parDictionary["SPECTRAL_WIDTH"]
if self.sizeTD1 > 1:
self.is2D = True
else:
self.is2D = False
if self.debug: print("2D: ", self.is2D)
if self.debug:
print("TD2: ", self.sizeTD2)
print("TD1: ", self.sizeTD1)
print("Carrier:", self.carrier)
with open(path + "/data.dat", mode='rb') as f:
dataString = struct.unpack(
">" + "f" * (self.sizeTD2 * self.sizeTD1) * 2, f.read())
if self.debug: print("len(dataString) new: ", len(dataString))
self.data = dataString
if self.sizeTD2 == 0:
self.sizeTD2 = int(len(self.data) / self.sizeTD1 / 2)
self.dwellTime = 1. / self.sweepWidthTD2
self.fidTime = np.linspace(0, (self.sizeTD2 - 1) * self.dwellTime,
num=self.sizeTD2)
# here we create one array of complex numbers for each of the FIDs
# i runs over all fids in a ser file, in case of a fid file i = 0
# TD1 is number of FIDs, TD2 is number of datapoints in each FID
if maxLoad > 0:
self.sizeTD1 = maxLoad
for i in range(0, self.sizeTD1):
#print "sizeTD2: ", self.sizeTD2
#print i
realPart = self.data[i * self.sizeTD2 * 2:(i + 1) * self.sizeTD2 *
2:2]
imagPart = sp.multiply(
self.data[i * self.sizeTD2 * 2 + 1:(i + 1) * self.sizeTD2 * 2 +
1:2], 1j)
self.allFid[0].append(sp.add(realPart, imagPart))
# here we read the experiment title (only the one stored in pdata/1):
# could be made to read titles from all pdata folders (if needed)
try:
pathToTitle = directory + '/pdata/1/title'
titleFile = open(pathToTitle, mode='r')
title = list(titleFile)
self.title = [line.strip() for line in title]
except:
if self.debug:
print("No title file.")
else:
pass
self.shiftPoints = self.parDictionary["DIGITAL_FILTER_SHIFT"]
# This loop for channel realization - Monte Carlos
# ############################################################
for Mon in xrange(max_chan_realizaion):
d2d_pairs = []
uav = UAV(height)
for p in range(max_num_d2d_pairs):
# d2d_pairs.append(D2DPair(p, coverage_r, d2d_max, low_rx=0.8))
spread = False
while not spread:
pair = D2DPair(p, coverage_r, d2d_max, low_rx=0.0, low_tx=0.0)
if len(d2d_pairs) == 0:
break
for existing_pair in d2d_pairs:
d1 = sp.sqrt(
sp.add(
sp.square(sp.subtract(pair.tx_x, existing_pair.tx_x)),
sp.square(sp.subtract(pair.tx_y, existing_pair.tx_y))))
if d1 < 150:
spread = False
break
spread = True
d2d_pairs.append(pair)
for i in xrange(max_num_d2d_pairs):
for j in xrange(max_num_d2d_pairs):
max_d2d_to_d2d_gains[i, j, Mon] = sp.divide(
d2d_pairs[i].loss_to_pair(d2d_pairs[j]), noise_variance)
max_uav_to_d2d_gains[i, Mon] = sp.divide(
uav.loss_to_pair(d2d_pairs[i], atg_a, atg_b), noise_variance)
# print max_uav_to_d2d_gains
test_chan[0:num_d2d_pairs] = uav_to_d2d_gains
test_chan[num_d2d_pairs:dimen_input] = d2d_to_d2d_gains.ravel()
vec_chan_test = sp.array([test_chan])
X_test = vec_chan_test
test_tau_result = nn_model.predict(X_test, verbose=0)
test_theta_dnn = 1 / (1 - test_tau_result)
phi_n_sol = sp.multiply((test_theta_dnn - 1) * eta * power_UAV,
uav_to_d2d_gains)
x_rate = sp.matmul(d2d_to_d2d_gains_diag, sp.transpose(phi_n_sol))
term_rate = sp.matmul(sp.transpose(d2d_to_d2d_gains_diff),
sp.transpose(phi_n_sol)) + 1
rate_sol_ue = sp.divide(sp.log(sp.add(1, sp.divide(x_rate, term_rate))),
test_theta_dnn)
maximin_rate_test = min(rate_sol_ue)
term_pow_iter = sp.subtract(1, sp.divide(
1, test_theta_dnn)) * eta * power_UAV * sp.add(
1, sp.sum(uav_to_d2d_gains)) + power_cir_UAV
iter_EE_test = sp.divide(
sp.multiply(1e3, sp.divide(sp.sum(rate_sol_ue), term_pow_iter)),
sp.log(2))
EE_sol_test.append(iter_EE_test)
tau_sol_test.append(test_tau_result)
Maxmin_rate_test.append(maximin_rate_test)
# Calculate the total time of solving
time_sol = (time.time() - t0)
fmean = sp.empty_like(sel[0,:])
sp.add.reduce(sel, axis=0, dtype="float32", out=fmean)
curr = sp.empty_like(fmean)
npoints_done = MEAN_MAX_NPOINTS
while npoints_done < npoints:
# check if can we overwrite (other process)
if path.exists(output_fname) and not overwrite:
warnings.warn("not allowed to overwrite %s" % output_fname)
return
sel = train_features[npoints_done:npoints_done+MEAN_MAX_NPOINTS]
sp.add.reduce(sel, axis=0, dtype="float32", out=curr)
sp.add(fmean, curr, fmean)
npoints_done += MEAN_MAX_NPOINTS
#fmean = train_features[:MEAN_MAX_NPOINTS].sum(0)
#npoints_done = MEAN_MAX_NPOINTS
#while npoints_done < npoints:
# fmean += train_features[npoints_done:npoints_done+MEAN_MAX_NPOINTS].sum(0)
# npoints_done += MEAN_MAX_NPOINTS
fmean /= npoints
if npoints < STD_MAX_NPOINTS:
fstd = train_features.std(0)
else:
# - try to optimize memory usage...
def __init__(self, path, endianess="<", debug=False, maxLoad=0):
"""This class only takes the following arguments:
- path: path to an NMR experiment
All other arguments are optional Keyword Arguments:
- endianess = "<"
- debug = False: set to True to output additional debuging information.
- maxLoad = 0: set to an integer value to limit loading and processing
to an NMR experiment, an optional argument for endianess"""
super().__init__(debug=debug)
self.files = []
if self.debug:
print("hi, this is self.debug for the TopSpin datatype")
#The acqus file containts the spectral width SW_h and 2*SizeTD2 as ##$TD
#The acqu2s file contains TD1 as ##$TD
directory = os.path.dirname(path)
acqusFile = open(directory + "/acqus", mode='r')
self.files.append(acqusFile)
if self.debug:
print("Importing TopSpin data")
#check if acqu2sfile exists, if yes, experiment is 2D.
if os.path.isfile(directory + "/acqu2s"):
acqu2sFile = open(directory + "/acqu2s", mode='r')
self.files.append(acqu2sFile)
acqu2File = open(directory + "/acqu2", mode='r')
self.files.append(acqu2File)
self.is2D = True
else:
self.is2D = False
self.sizeTD1 = 1
if self.debug: print("2D: ", self.is2D)
#this could be crafted into a common routine which gives names of parameters
#parameters and works the same for e.g., spinsight and topspin
if self.debug: print("reading acqus file")
count = 0
# read the acqusFile
while True:
if self.debug: print("count = ", count)
count += 1
line = acqusFile.readline().strip()
if self.debug: print(line)
if "=" in line:
line = line.split("=")
elif len(line) > 0:
line = line.split(" ")
elif len(line) == 0 or count > 1000:
if self.debug:
print("Ended reading acqus file at line ", count)
break
else:
next
if line[0] == "##$SW_h":
#this line might be chopping the last digit off....
#self.sweepWidthTD2 = int(float(line[1][:-1]))
self.sweepWidthTD2 = int(float(line[1]))
if self.debug: print("SweepWidthTD2: ", self.sweepWidthTD2)
elif line[0] == "##$TD":
self.sizeTD2 = int(int(line[1]) / 2)
if self.debug: print("sizeTD2: ", self.sizeTD2)
elif line[0] == "##$SFO1":
self.carrier = float(line[1]) * 1e6
if self.debug: print("SFO1:", self.carrier)
elif len(line) == 0:
break
if len(line[0]) > 1:
if "@" in line[-1]:
#this line contains date, time, some unknown stuff and user, does not work with all bruker files, hence try only"
try:
self.parDictionary["date"] = line[1].strip()
self.parDictionary["time"] = line[2].strip()
except:
pass
elif line[0] == "##$D":
delays1 = acqusFile.readline().strip()
delays2 = acqusFile.readline().strip()
self.parDictionary["d"] = [
float(d) for d in delays1.strip().split(" ")
] + [float(d) for d in delays2.strip().split(" ")]
elif line[0] == "##$L":
loopCounters = acqusFile.readline().strip()
self.parDictionary["l"] = [
float(l) for l in loopCounters.strip().split(" ")
]
else:
if self.debug: print("the catch all else")
if len(line) > 1:
self.parDictionary[line[0]
[2:].strip()] = line[1].strip()
else:
if self.debug: print("skipped too short line")
if self.is2D == True:
if self.debug:
print("reading acqu2s file")
count = 0
while True:
if self.debug:
print("count = ", count)
#try:
count += 1
line = acqu2sFile.readline().strip()
if "=" in line:
line = line.split("=")
elif len(line) == 0 or count > 1000:
if self.debug:
print("Ended reading acqu2s file at line ", count)
break
else:
next
#print line[0]
if line[0] == "##$TD" and self.sizeTD1 == 0:
self.sizeTD1 = int(line[1])
if self.debug: print("sizeTD1: ", self.sizeTD1)
elif len(line) == 0:
break
if os.path.isfile(directory + "/vdlist"):
if self.debug: print("VD File exists!")
self.vdList = np.loadtxt(directory + "/vdlist")
if self.debug:
print("TD2: ", self.sizeTD2)
print("TD1: ", self.sizeTD1)
print("Carrier:", self.carrier)
if self.is2D:
self.f = open(path + "/ser", mode='rb')
else:
self.f = open(path + "fid", mode='rb')
self.files.append(self.f)
dataString = np.frombuffer(self.f.read(), dtype=endianess + "i4")
if self.debug: print("len(dataString) new: ", len(dataString))
self.data = dataString
if self.sizeTD2 == 0:
self.sizeTD2 = int(len(self.data) / self.sizeTD1 / 2)
dwellTime = 1. / self.sweepWidthTD2
self.fidTime = np.linspace(0, (self.sizeTD2 - 1) * dwellTime,
num=self.sizeTD2)
# here we create one array of complex numbers for each of the FIDs
# i runs overa all fids in a ser file, in case of a fid file i = 0
# TD1 is number of FIDs, TD2 is number of datapoints in each FID
if maxLoad > 0:
self.sizeTD1 = maxLoad
for i in range(0, self.sizeTD1):
#print "sizeTD2: ", self.sizeTD2
#print i
realPart = self.data[i * self.sizeTD2 * 2:(i + 1) * self.sizeTD2 *
2:2]
imagPart = sp.multiply(
self.data[i * self.sizeTD2 * 2 + 1:(i + 1) * self.sizeTD2 * 2 +
1:2], 1j)
self.allFid[0].append(sp.add(realPart, imagPart))
# here we read the experiment title (only the one stored in pdata/1):
# could be made to read titles from all pdata folders (if needed)
try:
pathToTitle = directory + '/pdata/1/title'
titleFile = open(pathToTitle, mode='r')
self.files.append(titleFile)
title = list(titleFile)
self.title = [line.strip() for line in title]
except:
if self.debug:
print("No title file.")
else:
pass
#close the files we opened:
for item in self.files:
item.close()
# delete all file handles so that nmrdata objects can be pickled.
self.files = []
del self.f
coor_ue.append(UE_loc(p, coverage_r, low_tx=0.2))
x_ue.append(UE_loc.coordinate_ue(coor_ue[p])[0])
y_ue.append(UE_loc.coordinate_ue(coor_ue[p])[1])
val_pl = []
chan_array = []
for j in xrange(num_uav):
pl_value = []
chan_val = []
for i in xrange(num_ue):
xUAV, yUAV, xUE, yUE = x_uav[j], y_uav[j], x_ue[i], y_ue[i]
val_pl_tem = Path_loss(fc, c_vel, alp_g, mu_los, mu_nlos, a, b,
noise_var, hUAV, xUAV, yUAV, xUE, yUE)
pl_value.append(Path_loss.pl_uav_ue(val_pl_tem))
chan_temp = sp.divide(sp.add(sp.randn(1), 1j * sp.randn(1)),
sp.sqrt(2))
chan_val.append(sp.multiply(Path_loss.pl_uav_ue(val_pl_tem),
chan_temp))
val_pl.append(pl_value)
chan_array.append(chan_val)
# ## the size of val_pl is (num_uav, num_ue)
# ## the size of chan_array is (num_uav, num_ue)
Pf = 200
Pr = 100
P_cm = 200
P_0m = 200
P_cir = 9000
Pow_cir = P_cir + num_uav * (P_cm + P_0m)
d2d_to_d2d_gains_diag = sp.subtract(d2d_to_d2d_gains,
d2d_to_d2d_gains_diff)
# ############################################################
# This code is used to find the initial point for EEmax algorithm
# ############################################################
theta_ini = Parameter(value=1 / (1 - 0.5))
phi_n_ini = sp.multiply((theta_ini.value - 1) * eta *
sp.divide(power_UAV, num_d2d_pairs),
uav_to_d2d_gains)
x_rate = sp.matmul(d2d_to_d2d_gains_diag, phi_n_ini)
term_rate = sp.matmul(sp.transpose(d2d_to_d2d_gains_diff),
phi_n_ini) + 1
rate_sol_ue = sp.divide(
sp.log(sp.add(1, sp.divide(x_rate, term_rate))),
theta_ini.value)
# print rate_sol_ue
rmin_ref = min(rate_sol_ue)
if rmin_ref <= 0.2 * sp.log(2):
rmin = rmin_ref
else:
rmin = 0.2 * sp.log(2)
pow_ = NonNegative(num_d2d_pairs)
objective = Minimize(sum_entries(pow_) / theta_ini)
constraints = []
c1 = d2d_to_d2d_gains_diag * pow_ >= (
exp(rmin * theta_ini) - 1) * (d2d_to_d2d_gains_diff * pow_ + 1)
c2 = 1 / theta_ini * pow_ <= (
def kernel_generate_fromcsv(input_csv_fname,
input_suffix,
output_fname,
corrcoef_type = DEFAULT_CORRCOEF_TYPE,
nowhiten = DEFAULT_NOWHITEN,
variable_name = DEFAULT_VARIABLE_NAME,
input_path = DEFAULT_INPUT_PATH,
overwrite = DEFAULT_OVERWRITE,
):
# add matlab's extension to the output filename if needed
if path.splitext(output_fname)[-1] != ".mat":
output_fname += ".mat"
# can we overwrite ?
if path.exists(output_fname) and not overwrite:
warnings.warn("not allowed to overwrite %s" % output_fname)
return
# --------------------------------------------------------------------------
# -- get training and testing filenames from csv
print "Processing %s ..." % input_csv_fname
csvr = csv.reader(open(input_csv_fname))
rows = [ row for row in csvr ]
ori_train_fnames = [ row[0] for row in rows if row[2] == "train" ][:LIMIT]
train_fnames = sp.array([ path.join(input_path, fname+input_suffix)
for fname in ori_train_fnames ][:LIMIT])
train_labels = sp.array([ row[1] for row in rows if row[2] == "train" ][:LIMIT])
ori_test_fnames = [ row[0] for row in rows if row[2] == "test" ][:LIMIT]
test_fnames = sp.array([ path.join(input_path, fname+input_suffix)
for fname in ori_test_fnames ][:LIMIT])
test_labels = sp.array([ row[1] for row in rows if row[2] == "test" ][:LIMIT])
ntrain = len(train_fnames)
ntest = len(test_fnames)
# --------------------------------------------------------------------------
# -- load features from train filenames
# set up progress bar
print "Loading training data ..."
pbar = ProgressBar(widgets=widgets, maxval=ntrain)
pbar.start()
fvector0 = io.loadmat(train_fnames[0])[variable_name].ravel()
featshape = fvector0.shape
featsize = fvector0.size
# go
train_features = sp.empty((ntrain,) + featshape, dtype='float32')
error = False
for i, fname in enumerate(train_fnames):
try:
fvector = io.loadmat(fname)[variable_name].ravel()
except TypeError:
print "[ERROR] couldn't open", fname, "deleting it!"
os.unlink(fname)
error = True
except:
print "[ERROR] unkwon with", fname
raise
assert(not sp.isnan(fvector).any())
assert(not sp.isinf(fvector).any())
train_features[i] = fvector.reshape(fvector0.shape)
pbar.update(i+1)
pbar.finish()
print "-"*80
if error:
raise RuntimeError("An error occured (load train). Exiting.")
# -- preprocess train
print "Preprocessing train features ..."
if nowhiten:
whiten_vectors = None
else:
fshape = train_features.shape
train_features.shape = fshape[0], -1
npoints, ndims = train_features.shape
if npoints < MEAN_MAX_NPOINTS:
fmean = train_features.mean(0)
else:
# - try to optimize memory usage...
sel = train_features[:MEAN_MAX_NPOINTS]
fmean = sp.empty_like(sel[0,:])
sp.add.reduce(sel, axis=0, dtype="float32", out=fmean)
curr = sp.empty_like(fmean)
npoints_done = MEAN_MAX_NPOINTS
while npoints_done < npoints:
sel = train_features[npoints_done:npoints_done+MEAN_MAX_NPOINTS]
sp.add.reduce(sel, axis=0, dtype="float32", out=curr)
sp.add(fmean, curr, fmean)
npoints_done += MEAN_MAX_NPOINTS
fmean /= npoints
if npoints < STD_MAX_NPOINTS:
fstd = train_features.std(0)
else:
# - try to optimize memory usage...
sel = train_features[:MEAN_MAX_NPOINTS]
mem = sp.empty_like(sel)
curr = sp.empty_like(mem[0,:])
seln = sel.shape[0]
sp.subtract(sel, fmean, mem[:seln])
sp.multiply(mem[:seln], mem[:seln], mem[:seln])
fstd = sp.add.reduce(mem[:seln], axis=0, dtype="float32")
npoints_done = MEAN_MAX_NPOINTS
while npoints_done < npoints:
sel = train_features[npoints_done:npoints_done+MEAN_MAX_NPOINTS]
seln = sel.shape[0]
sp.subtract(sel, fmean, mem[:seln])
sp.multiply(mem[:seln], mem[:seln], mem[:seln])
sp.add.reduce(mem[:seln], axis=0, dtype="float32", out=curr)
sp.add(fstd, curr, fstd)
npoints_done += MEAN_MAX_NPOINTS
fstd = sp.sqrt(fstd/npoints)
fstd[fstd==0] = 1
whiten_vectors = (fmean, fstd)
train_features.shape = fshape
train_features = preprocess_features(train_features,
whiten_vectors = whiten_vectors)
assert(not sp.isnan(sp.ravel(train_features)).any())
assert(not sp.isinf(sp.ravel(train_features)).any())
# -- train
categories = sp.unique(train_labels)
#if categories.size == 2:
# categories = [categories[0]]
#else:
# raise NotImplementedError("not sure if it works with ncats > 2")
corrcoef_kernels = {}
cat_index = {}
for icat, cat in enumerate(categories):
if corrcoef_type == 'pos_neg_mean':
#print train_features.shape
#print train_features[train_labels == cat].shape
#print train_features[train_labels != cat].shape
corrcoef_ker = train_features[train_labels == cat].sum(0) \
- train_features[train_labels != cat].sum(0)
corrcoef_ker /= ntrain
elif corrcoef_type == 'pos_mean_neg_mean':
corrcoef_ker = train_features[train_labels == cat].mean(0) \
- train_features[train_labels != cat].mean(0)
elif corrcoef_type == 'pos_mean':
corrcoef_ker = train_features[train_labels == cat].mean(0)
else:
raise ValueError("corrcoef_type '%s' not understood"
% corrcoef_type)
corrcoef_ker -= corrcoef_ker.mean()
corrcoef_ker_mag = sp.linalg.norm(corrcoef_ker)
assert corrcoef_ker_mag > 0
corrcoef_ker /= sp.linalg.norm(corrcoef_ker)
assert(not sp.isnan(corrcoef_ker).any())
assert(not sp.isinf(corrcoef_ker).any())
corrcoef_kernels[cat] = corrcoef_ker
cat_index[cat] = icat
# --------------------------------------------------------------------------
# -- load features from test filenames
# set up progress bar
print "Testing (on the fly) ..."
pbar = ProgressBar(widgets=widgets, maxval=ntest)
pbar.start()
# -- test
# XXX: code adapted from beta svm_ova_fromfilenames (to review!)
pred = sp.zeros((ntest))
distances = sp.zeros((ntest, len(categories)))
for itest, fname in enumerate(test_fnames):
try:
fvector = io.loadmat(fname)[variable_name].ravel()
except TypeError:
print "[ERROR] couldn't open", fname, "deleting it"
os.unlink(fname)
error = True
except:
print "[ERROR] unkwon with", fname
raise
assert(not sp.isnan(fvector).any())
assert(not sp.isinf(fvector).any())
# whiten if needed
if whiten_vectors is not None:
fmean, fstd = whiten_vectors
fvector -= fmean
assert((fstd!=0).all())
fvector /= fstd
assert(not sp.isnan(fvector).any())
assert(not sp.isinf(fvector).any())
# corrcoef
testv = fvector
testv -= testv.mean()
testv_mag = sp.linalg.norm(testv)
assert testv_mag > 0
testv /= testv_mag
for icat, cat in enumerate(categories):
corrcoef_ker = corrcoef_kernels[cat]
resp = sp.dot(testv, corrcoef_ker)
distances[itest, icat] = resp
pbar.update(itest+1)
pbar.finish()
print "-"*80
if error:
raise RuntimeError("An error occured (load test). Exiting.")
if len(categories) > 1:
pred = distances.argmax(1)
#print sp.array([cat_index[e] for e in test_labels]).astype('int')
gt = sp.array([cat_index[e] for e in test_labels]).astype("int")
perf = (pred == gt)
accuracy = 100.*perf.sum() / ntest
else:
pred = sp.sign(distances).ravel()
gt = sp.array(test_labels)
cat = categories[0]
gt[gt != cat] = -1
gt[gt == cat] = +1
gt = gt.astype("int")
perf = (pred == gt)
accuracy = 100.*perf.sum() / ntest
print distances.shape
print "Classification accuracy on test data (%):", accuracy
svm_labels = gt
# -- average precision
# XXX: redo this part to handle other labels than +1 / -1
ap = 0
if distances.shape[1] == 1:
distances = distances.ravel()
assert test_labels.ndim == 1
assert svm_labels.ndim == 1
# -- inverse predictions if needed
# (that is when svm was trained with flipped +1/-1 labels)
# -- convert test_labels to +1/-1 (int)
try:
test_labels = array([int(elt) for elt in test_labels])
if (test_labels != svm_labels).any():
distances = -distances
#if not ((test_labels==-1).any() and (test_labels==1).any()):
# test_labels[test_labels!=test_labels[0]] = +1
# test_labels[test_labels==test_labels[0]] = -1
#print test_labels
# -- convert test_labels to +1/-1 (int)
test_labels = array([int(elt) for elt in test_labels])
# -- get average precision
c = distances
#print c
si = sp.argsort(-c)
tp = sp.cumsum(sp.single(test_labels[si]>0))
fp = sp.cumsum(sp.single(test_labels[si]<0))
rec = tp/sp.sum(test_labels>0)
prec = tp/(fp+tp)
#print prec, rec
#from pylab import *
#plot(prec, rec)
#show()
ap = 0
rng = sp.arange(0,1.1,.1)
for th in rng:
p = prec[rec>=th].max()
if p == []:
p = 0
ap += p / rng.size
print "Average Precision:", ap
except ValueError:
ap = 0
# XXX: clean this
test_y = sp.array([svm_labels.ravel()==lab
for lab in sp.unique(svm_labels.ravel())]
)*2-1
test_y = test_y.T
print distances
# --------------------------------------------------------------------------
# -- write output file
if output_fname is not None:
print "Writing %s ..." % (output_fname)
# TODO: save more stuff (alphas, etc.)
data = {"accuracy": accuracy,
"average_precision":ap,
"test_distances": distances,
"test_labels": test_labels,
"test_y": test_y,
"svm_labels": svm_labels,
}
io.savemat(output_fname, data, format='4')
return accuracy
