def makeMetrics(tmplFile,
train=None,
old=None,
params={},
trainOutFile='out.csv'):
""" Make the metrics """
maxTime = 60 # Number of time slice metrics
##################### BUILD A TemplateManager OBJECT ####################
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=old,
params=params)
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
for i in range(train.numH1):
P, freqs, bins = train.H1Sample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
hL.append([1, i] + out)
for i in range(train.numH0):
P, freqs, bins = train.H0Sample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
hL.append([0, i] + out)
hL = np.array(hL)
file = open(trainOutFile, 'w')
file.write("Truth,Index," + outHdr + "\n")
np.savetxt(file, hL, delimiter=',')
file.close()
def makeMetrics(tmplFile,train=None,old=None,params={},trainOutFile='out.csv'):
""" Make the metrics """
maxTime = 60 # Number of time slice metrics
##################### BUILD A TemplateManager OBJECT ####################
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=old, params=params)
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
for i in range(train.numH1):
P, freqs, bins = train.H1Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
hL.append([1, i] + out)
for i in range(train.numH0):
P, freqs, bins = train.H0Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
hL.append([0, i] + out)
hL = np.array(hL)
file = open(trainOutFile,'w')
file.write("Truth,Index,"+outHdr+"\n")
np.savetxt(file,hL,delimiter=',')
file.close()
def makeMetrics(tmplFile,
test=None,
old=None,
params={},
testOutFile='out.csv'):
""" Make the metrics """
maxTime = 60 # Number of time slice metrics
##################### BUILD A TemplateManager OBJECT ####################
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=old,
params=params)
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
hL = []
####################### LOOP THROUGH THE FILE ###########################
for i in range(test.nTest):
P, freqs, bins = test.TestSample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
hL.append(out)
hL = np.array(hL)
########################## WRITE TO FILE ################################
file = open(testOutFile, 'w')
file.write(outHdr + "\n")
np.savetxt(file, hL, delimiter=',')
file.close()
def main():
###################### WORKING DIRECTORY ########################
baseDir = globalConst.BASE_DIR
###################### SET OUTPUT FILE NAME HERE ########################
testOutFile = baseDir + 'workspace/testMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir + 'data/' # Data directory
templateDataDir = baseDir + 'template_data/' # Data directory
params = {
'NFFT': 256,
'Fs': 2000,
'noverlap': 192
} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TestData OBJECT #######################
train = fileio.TrainData(dataDir + 'train.csv', dataDir + 'train/')
templateTrain = fileio.TrainData(templateDataDir + 'train.csv',
templateDataDir + 'train/')
test = fileio.TestData(dataDir + 'test/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir + 'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=templateTrain,
params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12, 9), dtype='Float32')
bar1_ = np.zeros((12, 12), dtype='Float32')
bar2_ = np.zeros((12, 6), dtype='Float32')
bar_[:, 3:6] = 1.
bar1_[:, 4:8] = 1.
bar2_[:, 2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
hL = []
####################### LOOP THROUGH THE FILE ###########################
for i in range(test.nTest):
printStatus(i)
P, freqs, bins = test.TestSample(i + 1, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append(out)
hL = np.array(hL)
########################## WRITE TO FILE ################################
file = open(testOutFile, 'w')
file.write(outHdr + "\n")
np.savetxt(file, hL, delimiter=',')
file.close()
def main():
baseDir = '/home/nick/whale/' # Base directory
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir + 'workspace/trainMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir + 'data/' # Data directory
params = {
'NFFT': 256,
'Fs': 2000,
'noverlap': 192
} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData(dataDir + 'train.csv', dataDir + 'train/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir + 'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=train,
params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12, 9), dtype='Float32')
bar1_ = np.zeros((12, 12), dtype='Float32')
bar2_ = np.zeros((12, 6), dtype='Float32')
bar_[:, 3:6] = 1.
bar1_[:, 4:8] = 1.
bar2_[:, 2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
for i in range(train.numH1):
P, freqs, bins = train.H1Sample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append([1, i] + out)
for i in range(train.numH0):
P, freqs, bins = train.H0Sample(i, params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append([0, i] + out)
hL = np.array(hL)
file = open(trainOutFile, 'w')
file.write("Truth,Index," + outHdr + "\n")
np.savetxt(file, hL, delimiter=',')
file.close()
def main():
baseDir = '/home/nick/whale/' # Base directory
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir+'workspace/trainMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir+'data/' # Data directory
params = {'NFFT':256, 'Fs':2000, 'noverlap':192} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData(dataDir+'train.csv',dataDir+'train/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir+'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=train, params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12,9),dtype='Float32')
bar1_ = np.zeros((12,12),dtype='Float32')
bar2_ = np.zeros((12,6),dtype='Float32')
bar_[:,3:6] = 1.
bar1_[:,4:8] = 1.
bar2_[:,2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
for i in range(train.numH1):
P, freqs, bins = train.H1Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append([1, i] + out)
for i in range(train.numH0):
P, freqs, bins = train.H0Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
hL.append([0, i] + out)
hL = np.array(hL)
file = open(trainOutFile,'w')
file.write("Truth,Index,"+outHdr+"\n")
np.savetxt(file,hL,delimiter=',')
file.close()
def makeMetrics(tmplFile,test=None,old=None,params={},testOutFile='out.csv'):
""" Make the metrics """
maxTime = 60 # Number of time slice metrics
##################### BUILD A TemplateManager OBJECT ####################
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=old, params=params)
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
hL = []
####################### LOOP THROUGH THE FILE ###########################
for i in range(test.nTest):
P, freqs, bins = test.TestSample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
hL.append(out)
hL = np.array(hL)
########################## WRITE TO FILE ################################
file = open(testOutFile,'w')
file.write(outHdr+"\n")
np.savetxt(file,hL,delimiter=',')
file.close()
def main():
baseDir = globalConst.BASE_DIR
###################### SET OUTPUT FILE NAME HERE ########################
trainOutFile = baseDir+'workspace/trainMetrics.csv'
############################## PARAMETERS ###############################
dataDir = baseDir+'data/' # Data directory
templateDataDir = baseDir+'template_data/' # Data directory
params = {'NFFT':256, 'Fs':2000, 'noverlap':192} # Spectogram parameters
maxTime = 60 # Number of time slice metrics
######################## BUILD A TrainData OBJECT #######################
train = fileio.TrainData( dataDir+'train.csv',dataDir+'train/')
templateTrain = fileio.TrainData(templateDataDir+'train.csv',templateDataDir+'train/')
##################### BUILD A TemplateManager OBJECT ####################
tmplFile = baseDir+'moby/templateReduced.csv'
tmpl = templateManager.TemplateManager(fileName=tmplFile,
trainObj=templateTrain, params=params)
################## VERTICAL BARS FOR HIFREQ METRICS #####################
bar_ = np.zeros((12,9),dtype='Float32')
bar1_ = np.zeros((12,12),dtype='Float32')
bar2_ = np.zeros((12,6),dtype='Float32')
bar_[:,3:6] = 1.
bar1_[:,4:8] = 1.
bar2_[:,2:4] = 1.
########################### CREATE THE HEADER ###########################
outHdr = metrics.buildHeader(tmpl)
###################### LOOP THROUGH THE FILES ###########################
hL = []
print "\nprocessing whale clips"
for i in range(train.numH1):
printStatus(i)
clip_filename = train.h1[i]
fid = fileID(clip_filename)
P, freqs, bins = train.H1Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
#hL.append([1, i] + out)
hL.append( (fid, [1,i]+out) ) # NOTE added fid for sorting below
print "\nprocessing non-whale clips"
for i in range(train.numH0):
printStatus(i)
clip_filename = train.h0[i]
fid = fileID(clip_filename)
P, freqs, bins = train.H0Sample(i,params=params)
out = metrics.computeMetrics(P, tmpl, bins, maxTime)
out += metrics.highFreqTemplate(P, bar_)
out += metrics.highFreqTemplate(P, bar1_)
out += metrics.highFreqTemplate(P, bar2_)
#hL.append([0, i] + out)
hL.append( (fid, [0,i]+out) ) # NOTE added fid for sorting below
# NOTE Sort by fileID so metrics file lines are in
# sequential order (train1.aiff, train2.aiff....)
print "\nsorting metrics by fileID"
hL_sort = np.array( [metric_lst for fid, metric_lst in sorted(hL)] )
print "writing metrics to:", trainOutFile
file = open(trainOutFile,'w')
file.write("Truth,Index,"+outHdr+"\n")
np.savetxt(file,hL_sort,delimiter=',')
file.close()
