def PCD(self,
x,
Hs,
A,
mud=math.inf,
lambd=0.0,
nu=math.inf,
returnMu=False):
B = Hs - A.dot(x)
if mud == math.inf:
try:
mu = B.T.dot(B) / B.T.dot(A).dot(B)
except:
mu = .5
else:
mu = mud
if nu == math.inf:
u = Utiliters()
nu = u.getPcdConst(A)
temp = (x + B) - (nu * lambd)
temp = np.where(temp < 0, 0, temp)
d = temp - x
x = x + (d.dot(mu))
if returnMu:
return x, mu
return x
def rodar(patterns, metodos, radical, sinais, occupancy, lock):
u = Utiliters()
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.datetime.now()
print('Started Nu Test with occupancy %d at %s' % (occupancy, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started Nu Test with occupancy %d' % occupancy)
algo = Algorithms()
matrizes = Matrizes()
matrix = matrizes.matrix()
samples = 1820
iterations = 331
for pattern in patterns:
nome = radical + pattern + '_' + str(occupancy)
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
halfA = e - int(math.ceil(e / 2))
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
try:
signalT = np.genfromtxt(sinais + 'signalT_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
signalN = np.genfromtxt(sinais + 'signalN_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(pattern, samples, b, fillAd, fillAe, matrix, sinais)
const = {'iterations': iterations, 'occupancy': occupancy, 'pattern': pattern, 'signalT': signalT,
'signalN': signalN}
itens = np.arange(0)
constantes = {}
for i in range(1, 101):
for j in range(1, 101):
if i != j:
aux = float('%.6g' % (i / j))
if not (aux in itens):
itens = np.append(itens, aux)
constantes.update({aux: str(i) + '#' + str(j)})
colunas = ['Nu', 'i', 'j']
for metodo in metodos:
colunas.append(metodo + ':' + str(occupancy) + ':RMS')
data = panda.DataFrame([], columns=colunas)
for nu in itens:
opt = {'nu': nu, 'mi': .25}
row = [nu]
row.extend(constantes.get(nu).split('#'))
for metodo in metodos:
const['metodo'] = metodo
row.append(algo.getRMSfloat(const, opt)['rms'])
l = len(data)
data.loc[l] = row
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.datetime.now()
print('Ended Nu Test with occupancy %d at %s after %s' % (
occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"), u.totalTime(startedI)))
info.info('Ended Nu Test with occupancy %d after %s' % (occupancy, u.totalTime(startedI)))
def rodar(patterns, metodos, conf, radical, sinais, occupancy, lock):
u = Utiliters()
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.datetime.now()
print('Started Lambda Test with occupancy %d at %s' %
(occupancy, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started Lambda Test with occupancy %d' % occupancy)
algo = Algorithms()
matrizes = Matrizes()
matrix = matrizes.matrix()
samples = 1820
iterations = conf['iterations']
for pattern in patterns:
nome = radical + pattern + '_' + str(occupancy)
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
halfA = e - int(math.ceil(e / 2))
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
try:
signalT = np.genfromtxt(sinais + 'signalT_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalN = np.genfromtxt(sinais + 'signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(
pattern, samples, b, fillAd, fillAe, matrix, sinais)
const = {
'iterations': iterations,
'occupancy': occupancy,
'pattern': pattern,
'signalT': signalT,
'signalN': signalN
}
colunas = ['Lambda']
for metodo in metodos:
for ite in range(1, iterations):
colunas.append(metodo + ':' + str(occupancy) + ':RMS:' +
str(ite))
data = panda.DataFrame([], columns=colunas)
for lam in range(conf['sL'], conf['eL']):
if lam != 0:
lamb = lam / 10
else:
lamb = 0
opt = {'lambda': lamb}
row = [lamb]
for metodo in metodos:
const['metodo'] = metodo
for ite in range(1, iterations):
const['iterations'] = ite
row.append(algo.getRMSfloat(const, opt)['rms'])
l = len(data)
data.loc[l] = row
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.datetime.now()
print('Ended Lambda Test with occupancy %d at %s after %s' %
(occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(startedI)))
info.info('Ended Lambda Test with occupancy %d after %s' %
(occupancy, u.totalTime(startedI)))
class Simulations:
def multiProcessSimulation(self, metodos, conf, radical, sinais):
m = Manager()
loock = m.Lock()
pool = ProcessPoolExecutor()
futures = [
pool.submit(rodar, patterns, metodos, conf, radical, sinais,
occupancy, loock) for occupancy in occupancies
]
for future in futures:
future.result()
if __name__ == '__main__':
startedAll = datetime.datetime.now()
u = Utiliters()
occupancies = [1, 5, 10, 20, 30, 40, 50, 60, 90]
# occupancies = [1, 10]
# metodos = ['SSF', 'SSFi', 'SSFls', 'SSFlsi', 'SSFlsc', 'SSFlsci', 'PCD', 'PCDi']
metodos = ['SSF']
iterations = 331
# conf = {'sL': -20, 'eL': 21, 'iterations': iterations}
conf = {'sL': -2, 'eL': 2, 'iterations': iterations}
print('Start Simulations at ' + startedAll.strftime("%H:%M:%S %d/%m/%Y"))
patterns = ['48b7e']
#patterns = ['48b7e', '8b4e']
sinais = './../tests/signals/'
simulations = Simulations()
timestr = time.strftime("%Y%m%d-%H%M%S")
radical = './../results/lambda_result_' + timestr + '_'
def rodar(patterns, radical, sinais, occupancy, lock, metodos, load=None):
u = Utiliters()
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.now()
print('Started ROC generate for occupancy %d at %s' %
(occupancy, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started ROC generate for occupancy %d' % (occupancy))
algo = Algorithms()
gerador = Signal()
matrizes = Matrizes()
matrix = matrizes.matrix()
samples = 1820
for pattern in patterns:
if load is None:
data = panda.DataFrame([])
nome = radical + pattern + '_' + str(occupancy) + '.csv'
else:
nome = load + pattern + '_' + str(occupancy) + '.csv'
data = panda.read_csv(nome)
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
if halfCd > 0:
fillCd = np.zeros(halfCd)
else:
fillCd = np.arange(0)
if halfCe > 0:
fillCe = np.zeros(halfCe)
else:
fillCe = np.arange(0)
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
fill = [fillAd, fillAe, fillCd, fillCe]
H, A, B = matrizes.generate(b)
h = matrix[0:7, 5]
try:
signalT = np.genfromtxt(sinais + 'signalT_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalN = np.genfromtxt(sinais + 'signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalTf = np.genfromtxt(sinais + 'fir/signalT_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalNf = np.genfromtxt(sinais + 'fir/signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(
pattern, samples, b, fillAd, fillAe, matrix, sinais)
const = {
'iterations': 331,
'occupancy': occupancy,
'pattern': pattern,
'signalT': signalT,
'signalN': signalN
}
opt = {'samples': samples}
nnzST = np.count_nonzero(signalT)
nzST = len(signalT) - nnzST
pike = int(np.max(signalN) + 1)
if ('FDIP' in metodos) or ('MF' in metodos):
started = datetime.now()
print('Started ROC generate for old with occupancy %d at %s' %
(occupancy, started.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started ROC generate for old with occupancy %d' %
(occupancy))
metodo = 'FDIP'
if metodo in metodos:
const['metodo'] = metodo
constf = const
constf['signalNf'] = signalNf
constf['signalTf'] = signalTf
roc = gerador.roc(algo.getRMSfloat(constf)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'MF'
if metodo in metodos:
signalMf, roc = algo.MatchedFw_roc(signalN, h, samples, b, e,
fill, signalT)
roc = roc.rename(
columns={
s: 'MF:' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
ended = datetime.now()
print(
'Ended ROC generate for old with occupancy %d at %s after %s'
% (occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(started)))
info.info('Ended ROC generate for old with occupancy %d after %s' %
(occupancy, u.totalTime(started)))
if (' MF ' in metodos) or ('OMP ' in metodos) or ('LS-OMP' in metodos):
started = datetime.now()
print('Started ROC generate for Greedy with occupancy %d at %s' %
(occupancy, started.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started ROC generate for Greedy with occupancy %d' %
(occupancy))
metodo = ' MP '
if metodo in metodos:
threshold, pdr, far = 0, 5, 5
res = []
while (float('{:.2f}'.format(pdr)) > 0.01) or (float(
'{:.2f}'.format(far)) > 0.01):
faA, pdA = 0, 0
signalA = np.zeros(window * samples)
for i in range(samples):
step = (i * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalTw = np.concatenate(
(fillCd, signalT[step:paso], fillCe))
signalSw = np.concatenate(
(fillCd, signalN[step:paso], fillCe))
step += halfA
paso = step + b
x, fa, pd = algo.MP_roc(threshold, signalSw, signalTw,
b, H)
signalA[step:paso] = x
faA += fa
pdA += pd
far = (faA / nzST)
pdr = (pdA / nnzST)
tmp = '%d,%.6f,%.6f,%.6f' % (
threshold, gerador.rms(signalA - signalT), far, pdr)
if threshold < pike:
threshold += 1
else:
if threshold == pike:
threshold = math.ceil(pike / 100) * 100
else:
threshold += 100
res.append([float(s) for s in tmp.split(',')])
roc = panda.DataFrame(
res,
columns=[
'MP:' + str(occupancy) + ':threshold',
'MP:' + str(occupancy) + ':RMS',
'MP:' + str(occupancy) + ':FA',
'MP:' + str(occupancy) + ':DP'
])
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'OMP '
if metodo in metodos:
threshold, pdr, far = 0, 5, 5
res = []
while (float('{:.2f}'.format(pdr)) > 0.01) or (float(
'{:.2f}'.format(far)) > 0.01):
faA, pdA = 0, 0
signalA = np.zeros(window * samples)
for i in range(samples):
step = (i * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalTw = np.concatenate(
(fillCd, signalT[step:paso], fillCe))
signalSw = np.concatenate(
(fillCd, signalN[step:paso], fillCe))
step += halfA
paso = step + b
x, fa, pd = algo.OMP_roc(threshold, signalSw, signalTw,
b, H)
signalA[step:paso] = x
faA += fa
pdA += pd
far = (faA / nzST)
pdr = (pdA / nnzST)
tmp = '%d,%.6f,%.6f,%.6f' % (
threshold, gerador.rms(signalA - signalT), far, pdr)
if (threshold < pike):
threshold += 1
else:
if threshold == pike:
threshold = math.ceil(pike / 100) * 100
else:
threshold += 100
res.append([float(s) for s in tmp.split(',')])
roc = panda.DataFrame(
res,
columns=[
'OMP:' + str(occupancy) + ':threshold',
'OMP:' + str(occupancy) + ':RMS',
'OMP:' + str(occupancy) + ':FA',
'OMP:' + str(occupancy) + ':DP'
])
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'LS-OMP'
if metodo in metodos:
threshold, pdr, far = 0, 5, 5
res = []
while (float('{:.2f}'.format(pdr)) > 0.01) or (float(
'{:.2f}'.format(far)) > 0.01):
faA, pdA = 0, 0
signalA = np.zeros(window * samples)
for i in range(samples):
step = (i * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalTw = np.concatenate(
(fillCd, signalT[step:paso], fillCe))
signalSw = np.concatenate(
(fillCd, signalN[step:paso], fillCe))
step += halfA
paso = step + b
x, fa, pd = algo.LS_OMP_roc(threshold, signalSw,
signalTw, b, H)
signalA[step:paso] = x
faA += fa
pdA += pd
far = (faA / nzST)
pdr = (pdA / nnzST)
tmp = '%d,%.6f,%.6f,%.6f' % (
threshold, gerador.rms(signalA - signalT), far, pdr)
if (threshold < pike):
threshold += 1
else:
if threshold == pike:
threshold = math.ceil(pike / 100) * 100
else:
threshold += 100
res.append([float(s) for s in tmp.split(',')])
roc = panda.DataFrame(
res,
columns=[
'LS-OMP:' + str(occupancy) + ':threshold',
'LS-OMP:' + str(occupancy) + ':RMS',
'LS-OMP:' + str(occupancy) + ':FA',
'LS-OMP:' + str(occupancy) + ':DP'
])
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
ended = datetime.now()
print(
'Ended ROC generate for Greedy with occupancy %d at %s after %s'
% (occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(started)))
info.info(
'Ended ROC generate for Greedy with occupancy %d after %s' %
(occupancy, u.totalTime(started)))
started = datetime.now()
print('Started ROC generate for Shrinkage with occupancy %d at %s' %
(occupancy, started.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started ROC generate for Shrinkage with occupancy %d' %
(occupancy))
metodo = 'DS'
if metodo in metodos:
const['metodo'] = metodo
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'GD '
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'GDi'
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'SSF '
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = .25
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'SSFi'
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = .25
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'SSFls '
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = .25
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'SSFlsi'
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = .25
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'SSFlsc '
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = .25
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'SSFlsci'
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = .25
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'PCD '
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = math.inf
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
metodo = 'PCDi'
if metodo in metodos:
metodo = metodo.strip()
const['metodo'] = metodo
opt['mi'] = math.inf
roc = gerador.roc(algo.getRMSfloat(const, opt)['signal'], signalT)
roc = roc.rename(
columns={
s: metodo + ':' + str(occupancy) + ':' + s
for s in list(roc.columns.values)
})
data = panda.concat([data, roc], axis=1, sort=False)
with lock:
data.to_csv(nome, index=False)
ended = datetime.now()
print(
'Ended ROC generate for Shrinkage with occupancy %d at %s after %s'
% (occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(started)))
info.info(
'Ended ROC generate for Shrinkage with occupancy %d after %s' %
(occupancy, u.totalTime(started)))
ended = datetime.now()
print('Ended ROC generate for occupancy %d at %s after %s' %
(occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(startedI)))
info.info('Ended ROC generate for occupancy %d after %s' %
(occupancy, u.totalTime(startedI)))
def withoutInitial(const, radical, path, quantization, lock):
u = Utiliters()
occupancy = 30
samples = 1820
pattern = '48b7e'
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.datetime.now()
print('Started Quantization Test, for quant %d at %s' % (quantization, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started Quantization Test, for quant %d' % quantization)
gerador = Signal()
matrizes = Matrizes()
verilog = XbYe()
algov = verilog.getAlgo([pattern])
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
u = Utiliters()
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
if halfCd > 0:
fillCd = np.zeros(halfCd)
else:
fillCd = np.arange(0)
if halfCe > 0:
fillCe = np.zeros(halfCe)
else:
fillCe = np.arange(0)
matrix = matrizes.matrix()
nome = radical + pattern + '_' + str(occupancy)
bitsH = quantization
bitsA = bitsH + 5
align = bitsA
mh, ma, mb = matrizes.generateFix(bitsH, bitsA)
try:
signalT = np.genfromtxt(path + 'signalT_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
signalN = np.genfromtxt(path + 'signalN_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(pattern, samples, b, fillAd, fillAe, matrix, path)
for block in range(const['sB'], const['eB']):
iterations = block * window
for gain in range(const['sG'], const['eG']):
bits = gain + 10
signalA = np.zeros(window * samples)
for ite in range(samples):
step = (ite * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalS = np.concatenate((fillCd, signalN[step:paso], fillCe))
step += halfA
paso = step + b
Hs, x = algov.sipo(signalS, gain, mh)
x = algov.SSF([x, Hs, ma, iterations, bits, align, .25, 0])
x = np.where(x < 0, 0, x)
signalA[step:paso] = np.fix(np.divide(x, pow(2, gain)))
rms = gerador.rms(signalA - signalT)
with lock:
with open(nome + '.csv', 'a') as file:
file.write(str(iterations) + u.s(quantization) + u.s(gain) + u.s(rms) + '\n')
ended = datetime.datetime.now()
print('Ended Quantization Test, for pseudoGain %d at %s after %s' % (
quantization, ended.strftime("%H:%M:%S %d/%m/%Y"), u.totalTime(startedI)))
info.info('Ended Quantization Test, for pseudoGain %d after %s' % (quantization, u.totalTime(startedI)))
class Simulations:
def multiProcessSimulation(self, const, radical, path, initial=True):
m = Manager()
loock = m.Lock()
pool = ProcessPoolExecutor()
if initial:
futures = [pool.submit(withInitial, const, radical, path, pseudoGain, loock) for pseudoGain in pseudoGains]
else:
futures = [pool.submit(withoutInitial, const, radical, path, quant, loock) for quant in range(const['sQ'], const['eQ'])]
for future in futures:
future.result()
if __name__ == '__main__':
startedAll = datetime.datetime.now()
u = Utiliters()
print('Start Simulations at ' + startedAll.strftime("%H:%M:%S %d/%m/%Y"))
pseudoGains = np.arange(8) + 1
# pseudoGains = np.arange(1) + 1
ssf = True
ssfi = True
occupancy = 30
const = {'sG': 5, 'eG': 16, 'sQ': 5, 'eQ': 21, 'sB': 1, 'eB': 7}
# const = {'sG': 5, 'eG': 7, 'sQ': 5, 'eQ': 6, 'sB': 1, 'eB': 2}
pattern = '48b7e'
path = './../tests/signals/'
simulations = Simulations()
timestr = time.strftime("%Y%m%d-%H%M%S")
radical = './../results/testVerilogQuantization_' + timestr + '_'
open(radical + 'info.log', 'w').close()
def graphViewer(self, methods, occupancies, kind):
if 'ROC' in kind:
self.graphROC(methods, occupancies, self.data)
elif 'RMS' in kind:
self.graphRMS(methods, occupancies, self.data)
def test(const, occupancy, lock):
testSystem = TestSystem(const)
testSystem.dataGenerate(occupancy, lock)
return testSystem.data
if __name__ == '__main__':
matrizes = Matrizes()
util = Utiliters()
pattern = '8b4e'
totalSamples = 20
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
if halfCd > 0:
fillCd = np.zeros(halfCd)
from src.utiliters.mathLaboratory import Signal
def load_cfg(path):
data = []
with open(path, 'r') as f:
for row in csv.DictReader(f,
delimiter=';',
quotechar='|',
quoting=csv.QUOTE_MINIMAL):
data.append(row)
return data
if __name__ == '__main__':
u = Utiliters()
# path to work with
path = '../results/'
#path = os.getcwd().replace('\\', '/') + '/../../../Verilog/Implementation/'
#Algorithm;Pattern;Input;Samples;Iterations;Quantization;Gain;Mu;Lambda;Const
# config = load_cfg('configuration.cfg')
# for i in range(len(config)):
# # Algorithm to be used in this simulation
# algo = str(config[i].get('Algorithm'))
# # LHC collision pattern
# pattern = config[i].get('Pattern')
# # minimum iteration required, the real value is dependent of the pattern adopted
# iteration = int(str(config[i].get('Iterations')))
# # if quantization still zero as above the precision above will be used
# quantization = int(config[i].get('Quantization'))
# # gain desirable to the simulation
def rodar(patterns, metodos, radical, sinais, occupancy, lock):
u = Utiliters()
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.datetime.now()
print('Started Mi full Test with occupancy %d at %s' %
(occupancy, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started Mi full Test with occupancy %d' % occupancy)
algo = Algorithms()
matrizes = Matrizes()
samples = 1820
iterations = 331
for pattern in patterns:
nome = radical + pattern + '_' + str(occupancy)
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
u = Utiliters()
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
if halfCd > 0:
fillCd = np.zeros(halfCd)
else:
fillCd = np.arange(0)
if halfCe > 0:
fillCe = np.zeros(halfCe)
else:
fillCe = np.arange(0)
H, A, B = matrizes.generate(b)
matrix = matrizes.matrix()
try:
signalN = np.genfromtxt(sinais + 'signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(
pattern, samples, b, fillAd, fillAe, matrix, sinais)
colunas = ['Window']
for metodo in metodos:
for ite in range(1, iterations):
colunas.append(metodo + ':' + str(occupancy) + ':mu:' +
str(ite))
data = panda.DataFrame([], columns=colunas)
for its in range(samples):
step = (its * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalS = np.concatenate((fillCd, signalN[step:paso], fillCe))
step += halfA
Hs = H.T.dot(signalS)
row = [its]
for metodo in metodos:
musL = []
for ite in range(1, iterations):
if 'i' in metodo:
x = B.dot(signalS)
else:
x = signalS[3:b + 3]
muA, mu = 0.0, 0.0
for i in range(ite):
if 'GDP' in metodo:
x, mu = algo.GDP(x, Hs, A, returnMu=True)
elif 'GD' in metodo:
x, mu = algo.GD(x, Hs, A, returnMu=True)
elif 'SSFlsc' in metodo:
x, mu = algo.SSFlsc(x, Hs, A, returnMu=True)
elif 'SSFls' in metodo:
x, mu = algo.SSFls(x, Hs, A, returnMu=True)
elif 'SSF' in metodo:
x, mu = algo.SSF(x, Hs, A, returnMu=True)
elif 'PCD' in metodo:
x, mu = algo.PCD(x, Hs, A, returnMu=True)
muA += mu
musL.append('%.6f' % (muA / ite))
row.extend(musL)
l = len(data)
data.loc[l] = row
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.datetime.now()
print('Ended Mi full Test with occupancy %d at %s after %s' %
(occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(startedI)))
info.info('Ended Mi full Test with occupancy %d after %s' %
(occupancy, u.totalTime(startedI)))
def getRMSfloat(self, const, opt=None):
if opt is None:
opt = {}
gerador = Signal()
util = Utiliters()
matrizes = Matrizes()
iterations = const['iterations']
occupancy = const['occupancy']
pattern = const['pattern']
signalT = const['signalT']
signalN = const['signalN']
metodo = const['metodo']
if 'FDIP' in metodo:
try:
if 'order' in opt:
order = opt['order']
else:
order = 26
signalA = self.FDIP(order, const['signalNf'],
const['signalTf'], signalN)
result = {
'rms': gerador.rms(signalA - signalT),
'signal': signalA
}
return result
except:
return None
if 'lambda' in opt:
lamb = opt['lambda']
else:
lamb = 0
if 'mi' in opt:
mi = opt['mi']
else:
mi = math.inf
if 'samples' in opt:
samples = opt['samples']
else:
samples = 1820
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
if halfCd > 0:
fillCd = np.zeros(halfCd)
else:
fillCd = np.arange(0)
if halfCe > 0:
fillCe = np.zeros(halfCe)
else:
fillCe = np.arange(0)
if 'maxB' in opt:
maxB = opt['maxB']
else:
maxB = None
H, A, B = matrizes.generate(b, maxB)
if 'DS' in metodo:
AA = np.vstack((np.hstack(
(A, np.negative(A))), np.hstack((np.negative(A), A))))
uns = np.ones(b)
if 'MF' in metodo:
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
fill = [fillAd, fillAe, fillCd, fillCe]
matrix = matrizes.matrix()
h = matrix[0:7, 5]
if 'threshold' in opt:
threshold = opt['threshold']
else:
threshold = util.getBestThreshold(metodo, occupancy)
signalA = self.MatchedFw(signalN, h, threshold, samples, b, e,
fill)
result = {'rms': gerador.rms(signalA - signalT), 'signal': signalA}
return result
if 'constPCD' in opt:
constPCD = opt['constPCD']
else:
constPCD = util.getPcdConst(A)
if 'nu' in opt:
nu = opt['nu']
else:
nu = util.getNuConst(occupancy)
signalA = np.zeros(window * samples)
for ite in range(samples):
step = (ite * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalS = np.concatenate((fillCd, signalN[step:paso], fillCe))
step += halfA
paso = step + b
if 'DS' in metodo:
signalA[step:paso] = self.DantzigSelec(signalS, b, H, AA, uns)
continue
if 'LS-OMP' in metodo:
if 'threshold' in opt:
threshold = opt['threshold']
else:
threshold = util.getBestThreshold(metodo, occupancy)
signalA[step:paso] = self.LS_OMP(threshold, signalS, b, H)
continue
if 'OMP' in metodo:
if 'threshold' in opt:
threshold = opt['threshold']
else:
threshold = util.getBestThreshold(metodo, occupancy)
signalA[step:paso] = self.OMP(threshold, signalS, b, H)
continue
if 'MP' in metodo:
if 'threshold' in opt:
threshold = opt['threshold']
else:
threshold = util.getBestThreshold(metodo, occupancy)
signalA[step:paso] = self.MP(threshold, signalS, b, H)
continue
Hs = H.T.dot(signalS)
if 'i' in metodo:
x = B.dot(signalS)
else:
x = signalS[3:b + 3]
for i in range(iterations):
if 'GDP' in metodo:
x = self.GDP(x, Hs, A, mi)
elif 'GD' in metodo:
x = self.GD(x, Hs, A, mi)
elif 'SSFlsc' in metodo:
x = self.SSFlsc(x, Hs, A, mi, lamb, nu)
elif 'SSFls' in metodo:
x = self.SSFls(x, Hs, A, mi, lamb, constPCD)
elif 'SSF' in metodo:
x = self.SSF(x, Hs, A, mi, lamb)
elif 'PCD' in metodo:
x = self.PCD(x, Hs, A, mi, lamb, constPCD)
x = np.where(x < 0, 0, x)
signalA[step:paso] = x
result = {
'rms': '%.6g' % gerador.rms(signalA - signalT),
'signal': signalA
}
return result
def FDIP(self, Order, signalNf, signalTf, signalN, verilog=False):
G = np.zeros([len(signalNf), Order])
Gtruth = np.zeros([len(signalTf), Order])
for i in range(Order):
G[:, i] = np.roll(signalNf, i)
Gtruth[:, i] = np.roll(signalTf, i)
y = Gtruth[:, math.ceil(Order / 2) - 1]
tmp = np.linalg.inv(G.T.dot(G))
tetha = tmp.dot(G.T).dot(y)
tetha = tetha.flat
filtro = []
for s in range(len(signalN[:20])):
soma = 0
for t in range(len(tetha)):
#print(int(tetha[t]*math.pow(2,32)))
soma += signalN[s] * tetha[t]
# exit()
filtro.append(soma)
util = Utiliters()
#util.printM(filtro)
if verilog:
arq = './../results/main.v'
ltetha = len(tetha)
line = ['// Filter Finite Impulse Response - FIR\n\n']
line.append(
'module main\n(\n\tinput\t\t\t\t clk,\n\tinput signed\t[10:0] x_adc,\n\toutput signed\t[10:0] y_dac\n);\n\n'
)
line.append('reg signed [10:0] r [' + str(ltetha - 1) + ':0];\n')
line.append('always @ (posedge clk)\n')
line.append('begin\n')
# tmp = int(round(math.pow(2, 32) * tetha[-1]))
tmp = int(round(math.pow(2, 32) * tetha[0]))
# aux = ''
aux = ' * 34\'d' + str(tmp) if tmp > 0 else ' * -34\'d' + str(
abs(tmp))
line.append('\tr[ 0] <= (x_adc ' + aux + ');\n')
# for cont in range(1, ltetha-1):
for cont in range(1, ltetha):
# tmp = int(round(math.pow(2, 32) * tetha[ltetha - cont -1]))
tmp = int(round(math.pow(2, 32) * tetha[cont]))
# aux = ''
aux = ' * 34\'d' + str(tmp) if tmp > 0 else ' * -34\'d' + str(
abs(tmp))
line.append('\tr[' + util.sstr(cont) + '] <= (r[' +
util.sstr(cont - 1) + '] ' + aux + ');\n')
line.append('end\n\n')
line.append('wire signed [10:0] tmp;\n')
# tmp = int(round(math.pow(2, 32) * tetha[-1]))
# tmp = int(round(math.pow(2, 32) * tetha[0]))
aux = ''
# aux = '(x_adc * 34\'d' + str(tmp) + ')' if tmp > 0 else '- (x_adc * 34\'d' + str(abs(tmp)) + ')'
line.append('assign tmp = (' + aux)
aux, aux1 = '', ''
for cont in range(1, ltetha):
# tmp = int(round(math.pow(2, 32) * tetha[ltetha - cont - 1]))
# tmp = int(round(math.pow(2, 32) * tetha[cont]))
aux += 'r[' + str(ltetha - cont) + '] + '
# aux += ' + (r[' + str(ltetha - cont - 1) + '] * 34\'d' + str(tmp) + ')' if tmp > 0 else ' - (r[' + str(
# ltetha - cont - 1) + '] * 34\'d' + str(abs(tmp)) + ')'
#line.append(aux + ') >>> 22;\nendmodule\n\n')
# line.append(aux + ') >>> 30;\nassign y_dac = tmp[10] == 1 ? 0 : tmp;\n\nendmodule\n')
line.append(
aux[:-3] +
') >>> 30;\nassign y_dac = tmp[10] == 1 ? 0 : tmp;\n\nendmodule\n'
)
file = open(arq, 'w')
for linha in line:
file.write(linha)
file.close()
mlt = int(len(tetha) / 2)
# FILTRO FIR
signalF = np.convolve(tetha, signalN)
#util.printM(signalF[mlt - 1:mlt +19])
# exit()
signalF = signalF[mlt - 1:-mlt]
return np.where(signalF < 0, 0, signalF)
ax3.set_ylim(minY2 - (maxY2 / 100), maxY2 + (maxY2 / 100))
ax3.tick_params(axis='both', which='major', labelsize=8)
ax3.set_yticklabels([])
ax3.set_xlabel('Histogram')
# plt.show()
plt.savefig('../graphics/results/' + label + '.png')
fig.clear()
plt.close(fig)
if __name__ == '__main__':
matrizes = Matrizes()
algo = Algorithms()
gerador = Signal()
util = Utiliters()
pattern = '8b4e'
totalSamples = 20
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
if halfCd > 0:
fillCd = np.zeros(halfCd)
def rodar(patterns, metodos, radical, sinais, occupancy, lock):
u = Utiliters()
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.datetime.now()
print('Started B Fator Test with occupancy %d at %s' %
(occupancy, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started B Fator Test with occupancy %d' % occupancy)
algo = Algorithms()
matrizes = Matrizes()
matrix = matrizes.matrix()
samples = 1820
iterations = 331
for pattern in patterns:
nome = radical + pattern + '_' + str(occupancy)
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
halfA = e - int(math.ceil(e / 2))
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
try:
signalT = np.genfromtxt(sinais + 'signalT_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalN = np.genfromtxt(sinais + 'signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(
pattern, samples, b, fillAd, fillAe, matrix, sinais)
const = {
'iterations': iterations,
'occupancy': occupancy,
'pattern': pattern,
'signalT': signalT,
'signalN': signalN
}
# H, A, B = matrizes.generate(b, 0)
# s, e = int(round(max(B.min(), B.max(), key=abs)*1000)), int(round(min(B.min(), B.max(), key=abs)*1000)-1)
fatores = [
2.38, 2.34, 2.3, 2.22, 2.06, 1.62, 1.46, 1.42, 1.4, 1.3, 1.22,
1.14, 1.02, 0.9, 0.86, 0.74, 0.7, 0.66, 0.62, 0.58, 0.54, 0.5,
0.46, 0.38, 0.34, 0.3, 0.26, 0.22, 0.18, 0.14, 0.1, 0.06, 0.02,
0.006, 0.005, 0.004, 0.003, 0.002, 0.001, 0
]
elementos = {
2.38: 0,
2.34: 38,
2.3: 40,
2.22: 42,
2.06: 44,
1.62: 46,
1.46: 119,
1.42: 125,
1.4: 127,
1.3: 130,
1.22: 132,
1.14: 134,
1.02: 136,
0.9: 140,
0.86: 180,
0.74: 187,
0.7: 221,
0.66: 227,
0.62: 230,
0.58: 233,
0.54: 235,
0.5: 275,
0.46: 278,
0.38: 310,
0.34: 320,
0.3: 363,
0.26: 369,
0.22: 409,
0.18: 452,
0.14: 494,
0.1: 551,
0.06: 638,
0.02: 816,
0.006: 986,
0.005: 1050,
0.004: 1062,
0.003: 1128,
0.002: 1148,
0.001: 1280,
0: 2592
}
# te = [np.count_nonzero(B)]
# for i in range(s, e, -1):
# aux = i / 1000
# aaux = abs(aux)
# H, A, B = matrizes.generate(b, aaux)
# tmp = np.count_nonzero(B)
# if not tmp in te:
# fatores.append(aaux)
# elementos[aaux] = tmp
# te.append(tmp)
fatores = sorted(np.abs(fatores))
colunas = ['Fator', 'Elementos']
for metodo in metodos:
for ite in range(1, iterations):
colunas.append(metodo + ':' + str(occupancy) + ':RMS:' +
str(ite))
data = panda.DataFrame([], columns=colunas)
for fator in fatores:
opt = {'maxB': fator, 'mi': .25}
row = ['%.3f' % fator]
row.append(elementos[fator])
for metodo in metodos:
const['metodo'] = metodo
for ite in range(1, iterations):
const['iterations'] = ite
row.append(algo.getRMSfloat(const, opt)['rms'])
l = len(data)
data.loc[l] = row
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.datetime.now()
print('Ended B Fator Test with occupancy %d at %s after %s' %
(occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(startedI)))
info.info('Ended B Fator Test with occupancy %d after %s' %
(occupancy, u.totalTime(startedI)))
def getRMSfix(self, const, opt):
if opt is None:
opt = {}
gerador = Signal()
matrizes = Matrizes()
util = Utiliters()
iterations = const['iterations']
occupancy = const['occupancy']
pattern = const['pattern']
signalT = const['signalT']
signalN = const['signalN']
metodo = const['metodo']
if 'lambda' in opt:
lamb = opt['lambda']
else:
lamb = 0
if 'mi' in opt:
mi = opt['mi']
else:
mi = math.inf
if 'samples' in opt:
samples = opt['samples']
else:
samples = 1820
if 'gain' in opt:
gain = opt['gain']
else:
gain = 0
if 'bitsH' in opt:
bitsH = opt['bitsH']
else:
bitsH = 5
if 'bitsB' in opt:
bitsB = opt['bitsB']
else:
bitsB = None
bits = gain + 10
bitsA = bitsH + 5
align = bitsA
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
bwindow = b + 6
window = b + e
halfA = e - int(math.ceil(e / 2))
halfCd = int(math.ceil((bwindow - window) / 2))
halfCe = int(bwindow - window - halfCd)
if halfCd > 0:
fillCd = np.zeros(halfCd)
else:
fillCd = np.arange(0)
if halfCe > 0:
fillCe = np.zeros(halfCe)
else:
fillCe = np.arange(0)
mh, ma, mb = matrizes.generateFix(bitsH, bitsA, bitsB)
H, A, B = matrizes.generate(b)
if 'constPCD' in opt:
constPCDv = opt['constPCD']
else:
constPCD = util.getPcdConst(A)
constPCDv = int(np.round(constPCD * math.pow(2, gain)))
if 'nu' in opt:
nuV = opt['nu']
else:
nu = util.getNuConst(occupancy)
nuV = int(np.round(nu * math.pow(2, gain)))
signalA = np.zeros(window * samples)
for ite in range(samples):
step = (ite * window)
paso = step + window
if (e > 6):
paso = paso - (e - 6)
signalS = np.concatenate((fillCd, signalN[step:paso], fillCe))
step += halfA
paso = step + b
Hs, x = self.sipo(signalS, gain, mh, mb, bitsB)
if 'GDP' in metodo:
x = self.GDP([x, Hs, ma, iterations, bits, align, mi])
elif 'GD' in metodo:
x = self.GD([x, Hs, ma, iterations, bits, align, mi])
elif 'SSFlsc' in metodo:
x = self.TAS([x, Hs, ma, iterations, bits, align, mi, lamb, gain, nuV])
elif 'SSFls' in metodo:
x = self.TAS([x, Hs, ma, iterations, bits, align, mi, lamb, gain, nuV])
elif 'SSF' in metodo:
x = self.SSF([x, Hs, ma, iterations, bits, align, mi, lamb])
elif 'PCD' in metodo:
x = self.PCD([x, Hs, ma, iterations, bits, align, mi, lamb, gain, constPCDv])
x = np.where(x < 0, 0, x)
signalA[step:paso] = np.fix(np.divide(x, pow(2, gain)))
result = {'rms': '%.6g' % gerador.rms(signalA - signalT), 'signal': signalA}
return result
def rodar(patterns, metodos, conf, radical, sinais, occupancy, lock):
u = Utiliters()
info = u.setup_logger('information', radical + 'info.log')
startedI = datetime.datetime.now()
print('Started Mi Test with occupancy %d at %s' %
(occupancy, startedI.strftime("%H:%M:%S %d/%m/%Y")))
info.info('Started Mi Test with occupancy %d' % occupancy)
algo = Algorithms()
matrizes = Matrizes()
matrix = matrizes.matrix()
samples = 1820
for pattern in patterns:
nome = radical + pattern + '_' + str(occupancy)
bunch = pattern.rsplit('b', 1)
empty = bunch[1].rsplit('e', 1)
b = int(bunch[0])
e = int(empty[0])
halfA = e - int(math.ceil(e / 2))
fillAd = np.zeros(halfA)
fillAe = np.zeros(e - halfA)
try:
signalT = np.genfromtxt(sinais + 'signalT_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalN = np.genfromtxt(sinais + 'signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalTf = np.genfromtxt(sinais + 'fir/signalT_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
signalNf = np.genfromtxt(sinais + 'fir/signalN_' + pattern + '_' +
str(occupancy) + '.csv',
delimiter=',')
except:
print('Error get saved signals')
signalT, signalN, signalTf, signalNf = u.sgen(
pattern, samples, b, fillAd, fillAe, matrix, sinais)
const = {
'iterations': conf['iterations'],
'occupancy': occupancy,
'pattern': pattern,
'signalT': signalT,
'signalN': signalN
}
const['metodo'] = 'FDIP'
constf = const
constf['signalNf'] = signalNf
constf['signalTf'] = signalTf
rms = algo.getRMSfloat(constf)['rms']
colunas = ['Iterations', 'mu']
for metodo in metodos:
colunas.append(metodo + ':' + str(occupancy) + ':RMS')
colunas.append('FDIP:' + str(occupancy) + ':RMS')
data = panda.DataFrame([[np.inf] * (len(colunas) - 1) + [rms]],
columns=colunas)
for it in range(1, conf['iterations']):
const['iterations'] = it
for muI in range(conf['sM'], conf['eM']):
muF = 1 / math.pow(2, muI)
if muF == 1.0:
mi = math.inf
else:
mi = muF
opt = {'mi': mi}
row = [it, muF]
for metodo in metodos:
const['metodo'] = metodo
row.append(algo.getRMSfloat(const, opt)['rms'])
row.append(np.inf)
l = len(data)
data.loc[l] = row
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.datetime.now()
print('Ended Mi Test with occupancy %d at %s after %s' %
(occupancy, ended.strftime("%H:%M:%S %d/%m/%Y"),
u.totalTime(startedI)))
info.info('Ended Mi Test with occupancy %d after %s' %
(occupancy, u.totalTime(startedI)))