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(radical, algos, const, lambdas, occupancy, lock):
algoritmo = Algorithms()
nome = radical + str(occupancy)
data = panda.DataFrame(lambdas, columns=['lambdas'])
div = 10
opt = {'mi': .25, 'samples': 182}
constant = const[occupancy]
listN = np.split(constant['signalN'], div)
listT = np.split(constant['signalT'], div)
for algo in algos:
started = datetime.now()
print('Inicio %s ocupancia %d as %s' %
(algo, occupancy, started.strftime("%H:%M:%S %d/%m/%Y")))
res = []
constant['metodo'] = algo
for lamb in lambdas:
rms = []
opt['lambda'] = lamb
for i in range(div):
signalN = np.asarray(listN[i])
singalT = np.asarray(listT[i])
constant['signalN'] = signalN
constant['signalT'] = singalT
rms.append(algoritmo.getRMSfloat(constant, opt)['rms'])
std = np.asarray(rms, dtype=float)
res.append(std.std())
tmp = panda.DataFrame(res,
columns=[algo + ':' + str(occupancy) + ':std'])
data = panda.concat([data, tmp], axis=1, sort=False)
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.now()
print('Fim %s ocupancia %d as %s' %
(algo, occupancy, ended.strftime("%H:%M:%S %d/%m/%Y")))
def graphFDIP(self, occupancies, sO, eO, show=True, nome='', pattern='48b7e', path='./../tests/signals/'):
fig = plt.figure(1, figsize=[6, 4.5], dpi=160, facecolor='w', edgecolor='k')
colors = plt.cm.jet(np.linspace(0, 1, len(occupancies)))
ax = fig.add_subplot(1, 1, 1)
gerador = Signal()
algo = Algorithms()
minY, maxY = 10000, 0.000001
for occupancy in occupancies:
try:
signalT = np.genfromtxt(path + 'signalT_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
signalN = np.genfromtxt(path + 'signalN_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
signalTf = np.genfromtxt(path + 'fir/signalT_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
signalNf = np.genfromtxt(path + 'fir/signalN_' + pattern + '_' + str(occupancy) + '.csv', delimiter=',')
except:
print('Error get saved signals')
dados = []
ordens = []
for order in range(sO, eO, 2):
signalF = algo.FIR(order, signalNf, signalTf, signalN)
dados.append(gerador.rms(signalF - signalT))
ordens.append(order)
ax.plot(ordens, dados, label='Occupancy ' + str(occupancy), color=colors[occupancies.index(occupancy)])
minimum = min(dados)
maximum = max(dados)
minY = minimum if minY > minimum else minY
maxY = maximum if maxY < maximum else maxY
ax.plot(ordens[dados.index(minimum)], minimum, linestyle='none', marker='.', color='black')
#ax.set_title('RMS error by constant value')
# ax.set_xlabel('Constant value')
ax.set_xlabel('Order')
ax.set_ylabel('RMS Error distribution (ADC counts)')
ax.tick_params(axis='both', which='major', labelsize=8)
ax.tick_params(axis='both', which='minor', labelsize=0)
ax.tick_params(which='both', direction='out')
ax.grid(which='minor', alpha=0.3)
ax.grid(which='major', alpha=0.7)
minX, maxX = 2, 52
plt.subplots_adjust(left=0.15, right=0.98, top=0.99, bottom=0.1)
# ax.legend(loc='upper left', ncol=1, shadow=True, fancybox=True)
ax.set_xlim(minX - (maxX / 100), maxX + (maxX / 100))
ax.set_ylim(minY - (maxY / 100), maxY + (maxY / 100))
ax.set_xticks(np.arange(minX, maxX + (maxX / 100), maxX / 10))
ax.set_xticks(np.arange(minX, maxX + (maxX / 100), maxX / 50), minor=True)
ax.set_yticks(np.arange(minY, maxY + (maxY / 100), maxY / 10))
ax.set_yticks(np.arange(minY, maxY + (maxY / 100), maxY / 50), minor=True)
ax.xaxis.set_major_locator(MaxNLocator(nbins=15, integer=True))
if show:
plt.show()
plt.clf()
plt.cla()
plt.close()
else:
plt.savefig(nome + '.png')
fig.clear()
plt.close(fig)
def rodar(radical, algos, const, thresholdsAll, occupancy, lock):
gerador = Signal()
algoritmo = Algorithms()
nome = radical + str(occupancy)
data = panda.DataFrame([])
div = 10
opt = {'mi': .25, 'samples': 1820}
constant = const[occupancy]
listN = np.split(constant['signalN'], div)
listT = np.split(constant['signalT'], div)
for algo in algos:
started = datetime.now()
print('Inicio %s ocupancia %d as %s' %
(algo, occupancy, started.strftime("%H:%M:%S %d/%m/%Y")))
res = []
constant['metodo'] = algo
thresholds = thresholdsAll[algo + ':' + str(occupancy) + ':threshold']
for threshold in thresholds:
rms = []
for i in range(div):
if algo == 'FDIP':
concatN = np.asarray([])
concatT = np.asarray([])
for j in range(div):
if i != j:
concatN = np.concatenate(
(concatN, np.asarray(listN[j])))
concatT = np.concatenate(
(concatT, np.asarray(listT[j])))
constant['signalNf'] = concatN
constant['signalTf'] = concatT
signalN = np.asarray(listN[i])
singalT = np.asarray(listT[i])
constant['signalN'] = signalN
constant['signalT'] = singalT
rms.append(
gerador.roc(algoritmo.getRMSfloat(constant, opt)['signal'],
singalT,
threshold=threshold)['RMS'][0])
std = np.asarray(rms)
res.append(std.std())
tmp = panda.DataFrame(res,
columns=[algo + ':' + str(occupancy) + ':std'])
data = panda.concat([data, tmp], axis=1, sort=False)
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.now()
print('Fim %s ocupancia %d as %s' %
(algo, occupancy, ended.strftime("%H:%M:%S %d/%m/%Y")))
def rodarA(radical, algos, const, signalTrainT, signalTrainN, thresholdsAll,
occupancy, lock):
gerador = Signal()
algoritmo = Algorithms()
signalT = const[occupancy]['signalT']
nome = radical + '_' + str(occupancy)
data = panda.DataFrame([])
for algo in algos:
started = datetime.now()
print('Inicio %s ocupancia %d as %s' %
(algo, occupancy, started.strftime("%H:%M:%S %d/%m/%Y")))
res = []
const[occupancy]['metodo'] = algo
thresholds = thresholdsAll[algo + ':' + str(occupancy) + ':threshold']
for threshold in thresholds:
rms = []
for idx in range(10):
if algo == 'FDIP':
const[occupancy]['signalTf'] = signalTrainT[occupancy][idx]
const[occupancy]['signalNf'] = signalTrainN[occupancy][idx]
rms.append(
gerador.roc(algoritmo.getRMSfloat(
const[occupancy])['signal'],
signalT,
threshold=threshold)['RMS'][0])
else:
const[occupancy]['signalT'] = signalTrainT[occupancy][idx]
const[occupancy]['signalN'] = signalTrainN[occupancy][idx]
rms.append(
gerador.roc(algoritmo.getRMSfloat(
const[occupancy])['signal'],
signalTrainT[occupancy][idx],
threshold=threshold)['RMS'][0])
std = np.asarray(rms)
res.append(std.std())
tmp = panda.DataFrame(res,
columns=[algo + ':' + str(occupancy) + ':std'])
data = panda.concat([data, tmp], axis=1, sort=False)
with lock:
data.to_csv(nome + '.csv', index=False)
ended = datetime.now()
print('Fim %s ocupancia %d as %s' %
(algo, occupancy, ended.strftime("%H:%M:%S %d/%m/%Y")))
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)))
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 dataGenerate(self, occupancy, lock):
algorithm = Algorithms()
generator = Signal()
const = self.const
methods = const['methods']
iterations = const['iterations']
b = const['b']
e = const['e']
h = const['h']
H = const['H']
A = const['A']
B = const['B']
fill = const['fill']
fillAd, fillAe, fillCd, fillCe = fill
halfA = len(fillAd)
matrix = const['matrix']
window = const['window']
totalSamples = const['totalSamples']
signalT, signalN = generator.signalGenerator(totalSamples, b, fillAd, fillAe, matrix, exp_mean=occupancy)
signalTf, signalNf = generator.signalGenerator(totalSamples, b, fillAd, fillAe, matrix, exp_mean=occupancy)
nnzST = np.count_nonzero(signalT)
nzST = len(signalT) - nnzST
pike = int(np.max(signalN) + 1)
for method in methods:
if 'FIR' in method:
signalA = algorithm.FIR(26, signalNf, signalTf, signalN)
data = generator.roc(signalA, signalT)
data = data.rename(columns={s: 'FIR:' + str(occupancy) + ':' + s for s in list(data.columns.values)})
with lock:
self.addData(data)
elif 'MF' in method:
signalMf, data = algorithm.MatchedFw_roc(signalN, h, totalSamples, b, e, fill, signalT)
data = data.rename(columns={s: 'MF:' + str(occupancy) + ':' + s for s in list(data.columns.values)})
with lock:
self.addData(data)
elif 'MP' in method:
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 * totalSamples)
for i in range(totalSamples):
step = (i * window)
pace = step + window
if (e > 6):
pace = pace - (e - 6)
signalTw = np.concatenate((fillCd, signalT[step:pace], fillCe))
signalSw = np.concatenate((fillCd, signalN[step:pace], fillCe))
step += halfA
pace = step + b
if 'LS-OMP' in method:
x, fa, pd = algorithm.LS_OMP_roc(threshold, signalSw, signalTw, b, H)
elif 'OMP' in method:
x, fa, pd = algorithm.OMP_roc(threshold, signalSw, signalTw, b, H)
elif 'MP' in method:
x, fa, pd = algorithm.MP_roc(threshold, signalSw, signalTw, b, H)
signalA[step:pace] = x
faA += fa
pdA += pd
far = (faA / nzST)
pdr = (pdA / nnzST)
tmp = '%d,%.6f,%.6f,%.6f' % (threshold, generator.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(',')])
data = panda.DataFrame(res, columns=[method + ':' + str(occupancy) + ':threshold',
method + ':' + str(occupancy) + ':RMS',
method + ':' + str(occupancy) + ':FA',
method + ':' + str(occupancy) + ':DP'])
with lock:
self.addData(data)
else:
signalA = np.zeros(window * totalSamples)
mi = 0.25
lam = 0.0
for i in range(totalSamples):
step = (i * window)
pace = step + window
if (e > 6):
pace = pace - (e - 6)
signalS = np.concatenate((fillCd, signalN[step:pace], fillCe))
step += halfA
pace = step + b
xAll = signalS[3:b + 3]
Hs = H.T.dot(signalS)
if 'i' in method:
x = B.dot(signalS)
else:
x = xAll
for it in range(iterations):
if 'GD' in method:
x = algorithm.GD(x, Hs, A, mi)
elif 'SSF' in method:
x = algorithm.SSF(x, Hs, A, mi, lam)
elif 'PCD' in method:
x = algorithm.PCD(x, Hs, A, mi, lam)
elif 'TAS' in method:
x = algorithm.TAS(x, Hs, A, mi, lam)
signalA[step:pace] = x
data = generator.roc(signalA, signalT)
data = data.rename(columns={s: method + ':' + str(occupancy) + ':' + s for s in list(data.columns.values)})
with lock:
self.addData(data)
iteration,
muI,
lamb,
quantization,
gain,
constant,
path=path)
optV = {
'samples': 1820,
'mi': .25,
'gain': gain,
'bitsH': quantization
}
verilog.generate()
algorithm = Algorithms()
hard = XbYe()
algorithmv = hard.getAlgo([pattern])
try:
path = './../tests/signals/'
signalT = np.genfromtxt(path + 'signalT_' + pattern + '_30.csv',
delimiter=',')
signalN = np.genfromtxt(path + 'signalN_' + pattern + '_30.csv',
delimiter=',')
signalTf = np.genfromtxt(path + 'fir/signalT_' + pattern + '_30.csv',
delimiter=',')
signalNf = np.genfromtxt(path + 'fir/signalN_' + pattern + '_30.csv',
delimiter=',')
except:
print('Error ao carregar os sinais')
const = {
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)))
ax3.hist(signalC - signalT, orientation=u'horizontal')
# ax3.set_xlim(-1, 201)
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)
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 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)))