# -*- coding: utf-8 -*-
#bin/python
import tifffile as tiff
import numpy as np

import sys
import argparse

import funcReadLocations as readtxt

import sys

parser = argparse.ArgumentParser(description='MakeNormariztionFiles(mean std)')
parser.add_argument('--filelocationspath', default='resource')
parser.add_argument('--positivefilename', default='locationPositiveTrainData.txt')
parser.add_argument('--negativefilename', default='locationNegativeTrainData.txt')
parser.add_argument('--meanstdfilepath', default='meanstd')
args = parser.parse_args()

def mathdatameanstd(pfile,pindex,nfile,nindex,inputchanel):
	
	
	image=[]
	for channel in range(inputchanel):
		band=[]
		image.append(band)
	pNum= len(pindex)
	nNum= len(nindex)
	for NumData in pNum,nNum:
		if NumData is pNum:
			filelist = pfile
			index = pindex
		elif NumData is nNum:
			filelist = nfile
			index = nindex
		for item in range(NumData):
			arr = tiff.imread(filelist[index[item]])
			for channel in range(inputchanel):
				image[channel].append(np.float32(arr[channel]))
			
	mean=[]
	std=[]
	for channel in range(inputchanel):
		data = np.array(image[channel])
		mean.append(np.mean(data))
		std.append(np.std(data))

	return mean,std
	
if __name__ == "__main__":
	
	inputchanel=7
	
	pfilename = args.filelocationspath + "/"+args.positivefilename
	trainPosLocations = readtxt.ReadLocations(pfilename);
	nfilename = args.filelocationspath + "/"+args.negativefilename
	trainNegLocations = readtxt.ReadLocations(nfilename);
	
	negdatasize = len(trainNegLocations)
	posdatasize = len(trainPosLocations)
	
	mean,std = mathdatameanstd(trainPosLocations,range(posdatasize),trainNegLocations,range(negdatasize),inputchanel)
	
	file1 = open(str(args.meanstdfilepath)+".csv",'a')

	for channel in range(inputchanel):
		file1.write(str(mean[channel])+","+str(std[channel])+'\n')
	file1.close()
	
	

54c52ac23caa7563b6ba0aebc0a82b218fa9c541
<mask token>


def mathdatameanstd(pfile, pindex, nfile, nindex, inputchanel):
    image = []
    for channel in range(inputchanel):
        band = []
        image.append(band)
    pNum = len(pindex)
    nNum = len(nindex)
    for NumData in (pNum, nNum):
        if NumData is pNum:
            filelist = pfile
            index = pindex
        elif NumData is nNum:
            filelist = nfile
            index = nindex
        for item in range(NumData):
            arr = tiff.imread(filelist[index[item]])
            for channel in range(inputchanel):
                image[channel].append(np.float32(arr[channel]))
    mean = []
    std = []
    for channel in range(inputchanel):
        data = np.array(image[channel])
        mean.append(np.mean(data))
        std.append(np.std(data))
    return mean, std


<mask token>

<mask token>
parser.add_argument('--filelocationspath', default='resource')
parser.add_argument('--positivefilename', default=
    'locationPositiveTrainData.txt')
parser.add_argument('--negativefilename', default=
    'locationNegativeTrainData.txt')
parser.add_argument('--meanstdfilepath', default='meanstd')
<mask token>


def mathdatameanstd(pfile, pindex, nfile, nindex, inputchanel):
    image = []
    for channel in range(inputchanel):
        band = []
        image.append(band)
    pNum = len(pindex)
    nNum = len(nindex)
    for NumData in (pNum, nNum):
        if NumData is pNum:
            filelist = pfile
            index = pindex
        elif NumData is nNum:
            filelist = nfile
            index = nindex
        for item in range(NumData):
            arr = tiff.imread(filelist[index[item]])
            for channel in range(inputchanel):
                image[channel].append(np.float32(arr[channel]))
    mean = []
    std = []
    for channel in range(inputchanel):
        data = np.array(image[channel])
        mean.append(np.mean(data))
        std.append(np.std(data))
    return mean, std


if __name__ == '__main__':
    inputchanel = 7
    pfilename = args.filelocationspath + '/' + args.positivefilename
    trainPosLocations = readtxt.ReadLocations(pfilename)
    nfilename = args.filelocationspath + '/' + args.negativefilename
    trainNegLocations = readtxt.ReadLocations(nfilename)
    negdatasize = len(trainNegLocations)
    posdatasize = len(trainPosLocations)
    mean, std = mathdatameanstd(trainPosLocations, range(posdatasize),
        trainNegLocations, range(negdatasize), inputchanel)
    file1 = open(str(args.meanstdfilepath) + '.csv', 'a')
    for channel in range(inputchanel):
        file1.write(str(mean[channel]) + ',' + str(std[channel]) + '\n')
    file1.close()

<mask token>
parser = argparse.ArgumentParser(description='MakeNormariztionFiles(mean std)')
parser.add_argument('--filelocationspath', default='resource')
parser.add_argument('--positivefilename', default=
    'locationPositiveTrainData.txt')
parser.add_argument('--negativefilename', default=
    'locationNegativeTrainData.txt')
parser.add_argument('--meanstdfilepath', default='meanstd')
args = parser.parse_args()


def mathdatameanstd(pfile, pindex, nfile, nindex, inputchanel):
    image = []
    for channel in range(inputchanel):
        band = []
        image.append(band)
    pNum = len(pindex)
    nNum = len(nindex)
    for NumData in (pNum, nNum):
        if NumData is pNum:
            filelist = pfile
            index = pindex
        elif NumData is nNum:
            filelist = nfile
            index = nindex
        for item in range(NumData):
            arr = tiff.imread(filelist[index[item]])
            for channel in range(inputchanel):
                image[channel].append(np.float32(arr[channel]))
    mean = []
    std = []
    for channel in range(inputchanel):
        data = np.array(image[channel])
        mean.append(np.mean(data))
        std.append(np.std(data))
    return mean, std


if __name__ == '__main__':
    inputchanel = 7
    pfilename = args.filelocationspath + '/' + args.positivefilename
    trainPosLocations = readtxt.ReadLocations(pfilename)
    nfilename = args.filelocationspath + '/' + args.negativefilename
    trainNegLocations = readtxt.ReadLocations(nfilename)
    negdatasize = len(trainNegLocations)
    posdatasize = len(trainPosLocations)
    mean, std = mathdatameanstd(trainPosLocations, range(posdatasize),
        trainNegLocations, range(negdatasize), inputchanel)
    file1 = open(str(args.meanstdfilepath) + '.csv', 'a')
    for channel in range(inputchanel):
        file1.write(str(mean[channel]) + ',' + str(std[channel]) + '\n')
    file1.close()

import tifffile as tiff
import numpy as np
import sys
import argparse
import funcReadLocations as readtxt
import sys
parser = argparse.ArgumentParser(description='MakeNormariztionFiles(mean std)')
parser.add_argument('--filelocationspath', default='resource')
parser.add_argument('--positivefilename', default=
    'locationPositiveTrainData.txt')
parser.add_argument('--negativefilename', default=
    'locationNegativeTrainData.txt')
parser.add_argument('--meanstdfilepath', default='meanstd')
args = parser.parse_args()


def mathdatameanstd(pfile, pindex, nfile, nindex, inputchanel):
    image = []
    for channel in range(inputchanel):
        band = []
        image.append(band)
    pNum = len(pindex)
    nNum = len(nindex)
    for NumData in (pNum, nNum):
        if NumData is pNum:
            filelist = pfile
            index = pindex
        elif NumData is nNum:
            filelist = nfile
            index = nindex
        for item in range(NumData):
            arr = tiff.imread(filelist[index[item]])
            for channel in range(inputchanel):
                image[channel].append(np.float32(arr[channel]))
    mean = []
    std = []
    for channel in range(inputchanel):
        data = np.array(image[channel])
        mean.append(np.mean(data))
        std.append(np.std(data))
    return mean, std


if __name__ == '__main__':
    inputchanel = 7
    pfilename = args.filelocationspath + '/' + args.positivefilename
    trainPosLocations = readtxt.ReadLocations(pfilename)
    nfilename = args.filelocationspath + '/' + args.negativefilename
    trainNegLocations = readtxt.ReadLocations(nfilename)
    negdatasize = len(trainNegLocations)
    posdatasize = len(trainPosLocations)
    mean, std = mathdatameanstd(trainPosLocations, range(posdatasize),
        trainNegLocations, range(negdatasize), inputchanel)
    file1 = open(str(args.meanstdfilepath) + '.csv', 'a')
    for channel in range(inputchanel):
        file1.write(str(mean[channel]) + ',' + str(std[channel]) + '\n')
    file1.close()

# -*- coding: utf-8 -*-
#bin/python
import tifffile as tiff
import numpy as np

import sys
import argparse

import funcReadLocations as readtxt

import sys

parser = argparse.ArgumentParser(description='MakeNormariztionFiles(mean std)')
parser.add_argument('--filelocationspath', default='resource')
parser.add_argument('--positivefilename', default='locationPositiveTrainData.txt')
parser.add_argument('--negativefilename', default='locationNegativeTrainData.txt')
parser.add_argument('--meanstdfilepath', default='meanstd')
args = parser.parse_args()

def mathdatameanstd(pfile,pindex,nfile,nindex,inputchanel):
	
	
	image=[]
	for channel in range(inputchanel):
		band=[]
		image.append(band)
	pNum= len(pindex)
	nNum= len(nindex)
	for NumData in pNum,nNum:
		if NumData is pNum:
			filelist = pfile
			index = pindex
		elif NumData is nNum:
			filelist = nfile
			index = nindex
		for item in range(NumData):
			arr = tiff.imread(filelist[index[item]])
			for channel in range(inputchanel):
				image[channel].append(np.float32(arr[channel]))
			
	mean=[]
	std=[]
	for channel in range(inputchanel):
		data = np.array(image[channel])
		mean.append(np.mean(data))
		std.append(np.std(data))

	return mean,std
	
if __name__ == "__main__":
	
	inputchanel=7
	
	pfilename = args.filelocationspath + "/"+args.positivefilename
	trainPosLocations = readtxt.ReadLocations(pfilename);
	nfilename = args.filelocationspath + "/"+args.negativefilename
	trainNegLocations = readtxt.ReadLocations(nfilename);
	
	negdatasize = len(trainNegLocations)
	posdatasize = len(trainPosLocations)
	
	mean,std = mathdatameanstd(trainPosLocations,range(posdatasize),trainNegLocations,range(negdatasize),inputchanel)
	
	file1 = open(str(args.meanstdfilepath)+".csv",'a')

	for channel in range(inputchanel):
		file1.write(str(mean[channel])+","+str(std[channel])+'\n')
	file1.close()
	
	
