Esempio n. 1
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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()
Esempio n. 2
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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()
Esempio n. 3
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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()
Esempio n. 5
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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()
Esempio n. 6
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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()
Esempio n. 7
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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()