def main():
#==============================USER INPUTS======================================
#-------------------------------------------------------------------------------
# WHOEVER IS USING THIS PROGRAM SHOULD CHANGE THESE TO WHATEVER THEY LIKE
# SAX INPUTS
# JUST REPLACE THE NUMBERS, DONT TOUCH THE COMMENT AFTER ESPECIALLY THE #
word = 4 # word length for each channel
letter = 5 # alphabet size, has to be greater than 3. less than 20
# FILE INPUTS
# JUST ENTER THE STARTING NUMBER OF THE EXPERIMENT AND THE NUMBER OF
# FILES
starter = '2'
pstart = 2
num_files = 19
# Z-SCORE, NORMALIZING THE DATA
# TYPE 'False' IF YOU DONT WANT IT, 'True' IF YOU DO
zscore = False
# REGION OF ANALYSIS
roi = False
# LIST THE CONDITIONS NUMBERS WITHOUT ANY SPACES IN BETWEEN EACH ONE
conditions = "12" # Which conditions do we want to look at for each file
# FILE EXTENSION
# Change each time you run to reflect where you would like the results to
# be written to, MAKE SURE IT ALWAYS ENDS IN '.CSV
ext = '1v2_Rec.csv'
# ORANGE INPUTS
# K-NEAREST NEIGHBOR
num_k = 10 # how many neighbors
n_num = 100 # feature selection importance
#-------------------------------------------------------------------------------
# Running the single files
for i in range(pstart, num_files + 1):
heads = run(starter + '{0:0>3}'.format(str(i)), word, letter, num_k,
n_num, conditions, zscore, roi, ext)
# Running the single concatenated file
concatenate(num_files, starter, 'Sax_Output.tab', 3, 0, pstart)
concatenate(num_files, starter, 'Arff.arff', heads, 0, pstart)
# Writing ML Data for concatenated files
name = 'All_' + starter + '000_Data'
write_csv((name + '_ML_Data' + ext), orange(name, num_k, n_num))
# Running the Leaving a file out concatenated files
for i in range(pstart, num_files + 1):
concatenate(num_files, starter, 'Sax_Output.tab', 3, i, pstart)
concatenate(num_files, starter, 'Arff.arff', heads, i, pstart)
write_csv(
name + '_ML_Data_No' + starter + '{0:0>3}'.format(str(i)) + ext,
orange_two(starter, starter + '{0:0>3}'.format(str(i)), num_k,
n_num))
def main():
#==============================USER INPUTS======================================
#-------------------------------------------------------------------------------
# WHOEVER IS USING THIS PROGRAM SHOULD CHANGE THESE TO WHATEVER THEY LIKE
# SAX INPUTS
# JUST REPLACE THE NUMBERS, DONT TOUCH THE COMMENT AFTER ESPECIALLY THE #
word = 4 # word length for each channel
letter = 5 # alphabet size, has to be greater than 3. less than 20
# FILE INPUTS
# JUST ENTER THE STARTING NUMBER OF THE EXPERIMENT AND THE NUMBER OF
# FILES
starter = '2'
pstart = 2
num_files = 19
# Z-SCORE, NORMALIZING THE DATA
# TYPE 'False' IF YOU DONT WANT IT, 'True' IF YOU DO
zscore = False
# REGION OF ANALYSIS
roi = False
# LIST THE CONDITIONS NUMBERS WITHOUT ANY SPACES IN BETWEEN EACH ONE
conditions = "12" # Which conditions do we want to look at for each file
# FILE EXTENSION
# Change each time you run to reflect where you would like the results to
# be written to, MAKE SURE IT ALWAYS ENDS IN '.CSV
ext = '1v2_Rec.csv'
# ORANGE INPUTS
# K-NEAREST NEIGHBOR
num_k = 10 # how many neighbors
n_num = 100 # feature selection importance
#-------------------------------------------------------------------------------
# Running the single files
for i in range(pstart,num_files + 1):
heads=run(starter + '{0:0>3}'.format(str(i)),word,letter,num_k,n_num,
conditions,zscore,roi,ext)
# Running the single concatenated file
concatenate(num_files,starter,'Sax_Output.tab',3,0,pstart)
concatenate(num_files,starter,'Arff.arff',heads,0,pstart)
# Writing ML Data for concatenated files
name = 'All_'+starter+'000_Data'
write_csv((name+'_ML_Data'+ext),orange(name,num_k,n_num))
# Running the Leaving a file out concatenated files
for i in range(pstart,num_files+1):
concatenate(num_files,starter,'Sax_Output.tab',3,i,pstart)
concatenate(num_files,starter,'Arff.arff',heads,i,pstart)
write_csv(name+'_ML_Data_No'+starter+'{0:0>3}'.format(str(i))+ext,
orange_two(starter,starter+'{0:0>3}'.format(str(i)),num_k,
n_num))
def run(head, word, letter, num_k, n_num, conditions, zscore, roi, ext):
data_name = head + '_All_Data'
conds_name = 'conditions_' + head
#==========================INPUTTING DATA FILES=================================
# open files
data = open_csv(data_name + '.csv')
conds = open_csv(conds_name + '.csv')
num_chans = len(data[0]) / 2
# separates total data into separate oxy and deoxy files, to be used
# in the arff file generator and then Orange
data = data_format(data, num_chans)
conds = cond_format(conds)
num_conds = (len(conds) - 1) / 2
#===========================Z-SCORING THE DATA==================================
# NOTE: This section is optional, might not be used every time
if zscore:
for j in range(num_chans * 2):
zary = []
for i in range(len(data) - 2):
zary.append(data[i + 2][j])
zary = stats.zscore(zary)
for i in range(len(data) - 2):
data[i + 2][j] = zary[i]
#=======================REGION OF INTEREST ANALYSIS=============================
# NOTE: This section is optional, might not be used every time
if roi:
# Reading the ROI lines from a file
ROI_lines = []
#with open('ROI_'+head+'.txt','rb') as f:
with open('ROI_file_2000s.txt', 'rb') as f:
for line in f:
row = []
num = ""
for i in range(len(line)):
if line[i] != ',':
num += line[i]
else:
row.append(int(num))
num = ""
row.append(int(num[:len(num) - 1]))
ROI_lines.append(row)
num_chans = len(ROI_lines)
# Producing a new ROIized data table
firstl = ['Oxy'] + [''] * (num_chans - 1) + ['Deoxy'
] + [''] * (num_chans - 1)
secondl = []
for i in range(num_chans):
secondl += [('CH' + str(i + 1))]
new_data = []
for i in range(len(data) - 2):
oxy = []
deoxy = []
for line in ROI_lines:
aveo = 0
aved = 0
for j in range(len(line)):
aveo += data[i + 2][line[j] - 1]
aved += data[i + 2][line[j] + num_chans - 1]
oxy.append(float(aveo) / len(line))
deoxy.append(float(aved) / len(line))
new_data.append((oxy + deoxy))
data = [firstl, secondl * 2]
data += new_data
#========================CONVERTING TO MARKS FILE===============================
# Separate the oxy and deoxy data for the arff file generator
write_sep_data(data, num_chans, data_name)
marks = [['start', 'end', 'condition']]
for i in range(num_conds + 1):
if i == 0 or conditions.find(str(i)) == -1:
continue
for j in range(len(conds[i * 2]) - 2):
row = [
str(conds[i * 2 - 1][j + 2]),
str(conds[i * 2 - 1][j + 2] + conds[i * 2][j + 2]),
str(i)
]
marks.append(row)
# formats the conditions file into a [starts,ends, condition] file to
# be used in the arff file generator
write_csv(data_name + '_Marks.csv', marks)
#========================SAX/SUMMER '12 REPRESENTATIONS=========================
# Creating the Sax Representation (Writing it to a file)
sax_lines = []
sax = ""
for i in range(num_conds + 1):
# Checking which data we need by condition,
if i == 0 or conditions.find(str(i)) == -1:
continue
for j in range(len(conds[i * 2]) - 2):
start = conds[i * 2 - 1][j + 2]
run = conds[i * 2][j + 2]
for k in range(num_chans * 2): # getting every channel
ary = []
for m in range(run):
ary.append(data[start + m][k])
sax += sax_rep(word, letter, ary)[0]
newsax = ""
for n in range(len(sax)):
newsax += (str((ord(str(sax[n])) - 96)) + "\t")
# adding the header lines to the output file
if sax_lines == []:
first_line = ""
second_line = ""
for f in range(num_chans * 2):
for g in range(word):
first_line += ("Ch" + str((f % num_chans) + 1))
if f < num_chans:
first_line += "Oxy"
else:
first_line += "Deoxy"
first_line += ("Pos" + str(g + 1) + "\t")
second_line += "d\t"
first_line += "condition\n"
second_line += "d\n"
sax_lines.append(first_line)
sax_lines.append(second_line)
sax_lines.append((num_chans * word * 2) * "\t" + "class\n")
newsax += (str(i) + "\n")
sax_lines.append(newsax)
sax = ""
# Writing the SAX lines to a file
with open((data_name + '_Sax_Output.tab'), 'wb') as f:
for i in range(len(sax_lines)):
f.writelines(sax_lines[i])
# arff file generator passed in, pass 1 into subjects until further notice
arff_generate(data_name, num_conds, 1, num_chans, conditions)
#=============================PASSING IN ORANGE=================================
write_csv((head + '_ML_Data' + ext), orange(data_name, num_k, n_num))
return (66 * num_chans + 3)
def run(head,word,letter,num_k,n_num,conditions,zscore,roi,ext):
data_name = head + '_All_Data'
conds_name = 'conditions_' + head
#==========================INPUTTING DATA FILES=================================
# open files
data = open_csv(data_name+'.csv')
conds = open_csv(conds_name+'.csv')
num_chans = len(data[0])/2
# separates total data into separate oxy and deoxy files, to be used
# in the arff file generator and then Orange
data = data_format(data,num_chans)
conds = cond_format(conds)
num_conds = (len(conds)-1)/2
#===========================Z-SCORING THE DATA==================================
# NOTE: This section is optional, might not be used every time
if zscore:
for j in range(num_chans*2):
zary = []
for i in range(len(data)-2):
zary.append(data[i+2][j])
zary = stats.zscore(zary)
for i in range(len(data)-2):
data[i+2][j] = zary[i]
#=======================REGION OF INTEREST ANALYSIS=============================
# NOTE: This section is optional, might not be used every time
if roi:
# Reading the ROI lines from a file
ROI_lines = []
#with open('ROI_'+head+'.txt','rb') as f:
with open('ROI_file_2000s.txt','rb') as f:
for line in f:
row = []
num = ""
for i in range(len(line)):
if line[i] != ',':
num += line[i]
else:
row.append(int(num))
num = ""
row.append(int(num[:len(num)-1]))
ROI_lines.append(row)
num_chans = len(ROI_lines)
# Producing a new ROIized data table
firstl = ['Oxy'] + ['']*(num_chans-1)+['Deoxy']+['']*(num_chans-1)
secondl = []
for i in range(num_chans):
secondl += [('CH'+str(i+1))]
new_data = []
for i in range(len(data)-2):
oxy = []
deoxy = []
for line in ROI_lines:
aveo = 0
aved = 0
for j in range(len(line)):
aveo += data[i+2][line[j]-1]
aved += data[i+2][line[j]+num_chans-1]
oxy.append(float(aveo)/len(line))
deoxy.append(float(aved)/len(line))
new_data.append((oxy+deoxy))
data = [firstl,secondl*2]
data+=new_data
#========================CONVERTING TO MARKS FILE===============================
# Separate the oxy and deoxy data for the arff file generator
write_sep_data(data,num_chans,data_name)
marks = [['start','end','condition']]
for i in range(num_conds+1):
if i == 0 or conditions.find(str(i)) == -1:
continue
for j in range(len(conds[i*2])-2):
row = [str(conds[i*2-1][j+2]),
str(conds[i*2-1][j+2]+conds[i*2][j+2]),
str(i)]
marks.append(row)
# formats the conditions file into a [starts,ends, condition] file to
# be used in the arff file generator
write_csv(data_name+'_Marks.csv',marks)
#========================SAX/SUMMER '12 REPRESENTATIONS=========================
# Creating the Sax Representation (Writing it to a file)
sax_lines = []
sax = ""
for i in range(num_conds+1):
# Checking which data we need by condition,
if i == 0 or conditions.find(str(i)) == -1:
continue
for j in range(len(conds[i*2])-2):
start = conds[i*2-1][j+2]
run = conds[i*2][j+2]
for k in range(num_chans*2): # getting every channel
ary = []
for m in range(run):
ary.append(data[start+m][k])
sax += sax_rep(word,letter,ary)[0]
newsax = ""
for n in range(len(sax)):
newsax += (str((ord(str(sax[n]))-96))+"\t")
# adding the header lines to the output file
if sax_lines == []:
first_line = ""
second_line = ""
for f in range(num_chans*2):
for g in range(word):
first_line += ("Ch" + str((f%num_chans)+1))
if f < num_chans:
first_line += "Oxy"
else:
first_line += "Deoxy"
first_line += ("Pos"+str(g+1)+"\t")
second_line += "d\t"
first_line += "condition\n"
second_line += "d\n"
sax_lines.append(first_line)
sax_lines.append(second_line)
sax_lines.append((num_chans*word*2) * "\t" + "class\n")
newsax += (str(i)+"\n")
sax_lines.append(newsax)
sax = ""
# Writing the SAX lines to a file
with open((data_name+'_Sax_Output.tab'),'wb') as f:
for i in range(len(sax_lines)):
f.writelines(sax_lines[i])
# arff file generator passed in, pass 1 into subjects until further notice
arff_generate(data_name,num_conds,1,num_chans,conditions)
#=============================PASSING IN ORANGE=================================
write_csv((head+'_ML_Data'+ext),orange(data_name,num_k,n_num))
return (66*num_chans+3)
