def main():
try:
args = get_args()
#sys.stdout.write(str(args)+"\n")
nanList= ["NAN", "NA", "N/A", "-","?","nan", "na", "n/a"]
matrix, og_cols,og_rows = reader(args.input_file_txt)
#old_reader matrix, og_rows, og_cols = reader(args.input_file_txt)
# if float(args.thresh) < 0.000001:
# print('Invalid negative threshold chosen = '+str(args.thresh)+" choose positive value")
# sys.exit(-4)
if args.choice == "Histogram":
Histo(matrix)
elif args.choice == "CreateFiles":
CreateFiles(args.output_file_info)
elif args.choice == "Variance":
if args.axes == "Row":
matrix, filter_rows, filter_cols,delCnt,minVal,maxVal = Variance_Percent_Filter_row(matrix,1,og_rows,og_cols,True)
labeler(matrix,filter_rows,filter_cols,args.output_file_txt)
# if delCnt < 1:
# print('\nNO Filtering occurred for rows using variance < '+str(args.thresh)+ ' by row. Matrix row minimum variance= %.2f' % minVal+' and maximum variance= %.2f' % maxVal)
# sys.stderr.write('\nFiltering out rows using variance < '+str(args.thresh)+ ' removed '+str(delCnt)+' rows')
# sys.exit(-1)
# else:
# print('\nFiltering out rows using variance < '+str(args.thresh)+ ' removed '+str(delCnt)+' rows')
elif args.axes == "Column":
matrix, filter_rows, filter_cols,delCnt,minVal,maxVal = Variance_Percent_Filter_col(matrix,1,og_rows,og_cols,True)
labeler(matrix,filter_rows,filter_cols,args.output_file_txt)
# if delCnt < 1:
# print('\nNO Filtering occurred for columns using variance < '+str(args.thresh)+ ' by columns. Matrix columns minimum variance= %.2f' % minVal+' and maximum variance= %.2f' % maxVal)
# sys.stderr.write('\nFiltering out rows using variance < '+str(args.thresh)+ ' removed '+str(delCnt)+' rows')
# sys.exit(-1)
# else:
# print('\nFiltering out columns using variance < '+str(args.thresh)+ ' removed '+str(delCnt)+' columns')
else:
print('Invalid Axes = '+str(args.axes))
sys.exit(-1)
else:
print("Invalid Filter Choice = "+str(args.choice))
sys.exit(-2)
except Exception as err:
traceback.print_exc()
sys.exit(-3)
def main():
try:
args = get_args()
scaleValue = float(args.scalevalue)
offsetValue = float(args.offsetvalue)
#print(args)
#sys.stdout.write(str(args)+"\n")
matrix, og_cols, og_rows = reader(args.input_file_txt)
if args.choice == "z_score_normalization":
if args.axes == "Row":
matrix = Zscore_row(matrix)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("zcore, row")
elif args.axes == "Column":
matrix = Zscore_col(matrix)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("zscore, column")
else:
print("zscore, invalid axis")
elif args.choice == "mean_center_normalization":
if args.axes == "Row":
matrix = MeanMedianCenter_row(matrix, "mean")
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("mean-center by row")
elif args.axes == "Column":
matrix = MeanMedianCenter_col(matrix, "mean")
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("mean-center by column")
else:
print("meancenter, invalid axis")
elif args.choice == "median_center_normalization":
if args.axes == "Row":
matrix = MeanMedianCenter_row(matrix, "median")
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("median-center by row")
elif args.axes == "Column":
matrix = MeanMedianCenter_col(matrix, "median")
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("median-center by column")
else:
print("meancenter, invalid axis")
elif args.choice == "add_offset":
if args.axes == "Row":
#offset = -100 #!!!! TODO REMOVE AND ADD WHEN clause to xml to get value
matrix = ScaleOffset_row(matrix, 1.0, offsetValue)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("offset of " + str(offsetValue) + " by row")
elif args.axes == "Column":
matrix = ScaleOffset_col(matrix, 1.0, offsetValue)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("offset of " + str(offsetValue) + " by column")
else:
print("offset" + str(offsetValue) +
" invalid axis -not row or column")
elif args.choice == "scale":
if args.axes == "Row":
#scaleValue = 1000 #!!!! TODO REMOVE AND ADD WHEN clause to xml to get value
matrix = ScaleOffset_row(matrix, scaleValue, 0.0)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("scaling " + str(scaleValue) + " by row")
elif args.axes == "Column":
matrix = ScaleOffset_col(matrix, scaleValue, 0.0)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("scaling " + str(scaleValue) + " by column")
else:
print("scaling " + str(scaleValue) + " invalid axis")
elif args.choice == "transpose":
matrix = Transpose(matrix) #issue using same matrix?
labeler(matrix, og_rows, og_cols,
args.output_file_txt) #swapped row&col labels
print("transpose mxn matrix to nxm size")
elif args.choice == "ln_normalization":
matrix = Convert2Logs(matrix, "log2", offsetValue)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("log2 plus " + str(offsetValue) +
" normalization for all values")
elif args.choice == "log_normalization":
matrix = Convert2Logs(matrix, "log10", offsetValue)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("log10 normalization for all values")
elif args.choice == "rank":
if args.axes == "Row":
matrix = Rankdata_ByRow(matrix)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("performed rank normalization by row")
elif args.axes == "Column":
matrix = Rankdata_ByColumn(matrix)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("performed rank normalization by column")
else:
print("rank, invalid axis")
elif args.choice == "divide_by_sum":
if args.axes == "Row":
matrix = Divide_By_Sum_row(matrix)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("performed divide row N values by row N's sum")
elif args.axes == "Column":
matrix = Divide_By_Sum_col(matrix)
labeler(matrix, og_cols, og_rows, args.output_file_txt)
print("performed divide column N values by column N's sum")
else:
print("divide_by_sum, invalid axis")
else:
print("Invalid normalization Choice")
except Exception as err:
traceback.print_exc()
sys.exit(1)
def main():
args = get_args()
#print(args)
#sys.stdout.write(str(args))
#sys.stdout.write( '\nValid NAN identifiers are "NA","N/A","-", and "?"')
matrix, og_cols, og_rows = reader(args.input_file_txt)
# if nonNumCnt > 0:
# print('\nERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '+str(nonNumCnt)+ ', %.2f' % (100.0*nonNumCnt/(1.0*len(og_cols)*len(og_rows)))+'%' )
# #sys.stderr.write('\nERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '+str(nonNumCnt)+ ', %.2f' % (100.0*nonNumCnt/(1.0*len(og_cols)*len(og_rows)))+'%' )
# if nanCnt > 0:
# print('\nWARNING Matrix has '+str(nanCnt)+' that is %.2f' % (100.0*nanCnt/(1.0*len(og_cols)*len(og_rows)))+'% known NAN identifiers')
# sys.exit(-1)
# else:
# if nanCnt > 0:
# print('\nWARNING Matrix has NO unknown non-numbers in matrix, but contains '+str(nanCnt)+' that is %.2f' % (100.0*nanCnt/(1.0*len(og_cols)*len(og_rows)))+'% known NAN identifiers')
# else:
# print('Matrix is Good-to-Go -- all numbers in data area. ')
#with open(args.output_file_txt,'w') as f:
# f.write("Use original input file for further processing\n")
#f.close()
#sys.exit(0)
# TODO !!!!! Below if MDA decides to use it TURNED OFF FOR NOW
# TODO !!!!! Below if MDA decides to use it TURNED OFF FOR NOW
if args.replacement == "Mean":
if args.axes == "Row":
matrix, nonNumCnt, nanCnt = nan_replacer_mean_rows(matrix)
Labeler(matrix, og_cols, og_rows, args.output_file_txt)
#OLD_labeler(matrix, og_cols, og_rows, args.output_file_txt)
#print('Mean,Row')
if nonNumCnt > 0:
print(
'ERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '
+ str(nonNumCnt) + ', %.2f' %
(100.0 * nonNumCnt /
(1.0 * len(og_cols) * len(og_rows))) + '%')
sys.stderr.write(
'ERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '
+ str(nonNumCnt) + ', %.2f' %
(100.0 * nonNumCnt /
(1.0 * len(og_cols) * len(og_rows))) + '%')
if nanCnt > 0:
print('WARNING Matrix has ' + str(nanCnt) +
' that is %.2f' %
(100.0 * nanCnt /
(1.0 * len(og_cols) * len(og_rows))) +
'% known NAN identifiers')
sys.exit(-1)
else:
if nanCnt > 0:
print('\nWARNING Matrix has ' + str(nanCnt) +
' that is %.2f' %
(100.0 * nanCnt /
(1.0 * len(og_cols) * len(og_rows))) +
'% known NAN identifiers')
else:
print('\nMatrix is Good-to-Go -- all numbers in matrix. ')
sys.exit(0)
elif args.axes == "Column":
matrix, nonNumCnt, nanCnt = nan_replacer_mean_columns(matrix)
Labeler(matrix, og_cols, og_rows, args.output_file_txt)
#OLD_labeler(matrix, og_cols, og_rows, args.output_file_txt)
#print('Mean,Column')
if nonNumCnt > 0:
print(
'\nERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '
+ str(nonNumCnt) + ', %.2f' %
(100.0 * nonNumCnt /
(1.0 * len(og_cols) * len(og_rows))) + '%')
sys.stderr.write(
'\nERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '
+ str(nonNumCnt) + ', %.2f' %
(100.0 * nonNumCnt /
(1.0 * len(og_cols) * len(og_rows))) + '%')
if nanCnt > 0:
print('\nWARNING Matrix has ' + str(nanCnt) +
' that is %.2f' %
(100.0 * nanCnt /
(1.0 * len(og_cols) * len(og_rows))) +
'% known NAN identifiers')
sys.exit(-1)
else:
if nanCnt > 0:
print('\nWARNING Matrix has ' + str(nanCnt) +
' that is %.2f' %
(100.0 * nanCnt /
(1.0 * len(og_cols) * len(og_rows))) +
'% known NAN identifiers')
else:
print('\nMatrix is Good-to-Go -- all numbers in matrix. ')
sys.exit(0)
else:
print('Mean, but given Invalid Axis= ' + str(args.axes))
sys.stderr.write('Mean, but given Invalid Axis= ' + str(args.axes))
elif args.replacement == "Zero":
matrix, nonNumCnt, nanCnt = nan_replacer_zero(matrix)
Labeler(matrix, og_cols, og_rows, args.output_file_txt)
#OLD_labeler(matrix, og_cols, og_rows, args.output_file_txt)
if nonNumCnt > 0:
print(
'\nERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '
+ str(nonNumCnt) + ', %.2f' %
(100.0 * nonNumCnt /
(1.0 * len(og_cols) * len(og_rows))) + '%')
sys.stderr.write(
'\nERROR Matrix has non-numbers that are non-NAN identifiers in matrix. Total and percent unknown strings found = '
+ str(nonNumCnt) + ', %.2f' %
(100.0 * nonNumCnt /
(1.0 * len(og_cols) * len(og_rows))) + '%')
if nanCnt > 0:
print('\nWARNING Matrix has ' + str(nanCnt) +
' that is %.2f' %
(100.0 * nanCnt / (1.0 * len(og_cols) * len(og_rows))) +
'% known NAN identifiers')
sys.exit(-1)
else:
if nanCnt > 0:
print('\nWARNING Matrix has ' + str(nanCnt) +
' that is %.2f' %
(100.0 * nanCnt / (1.0 * len(og_cols) * len(og_rows))) +
'% known NAN identifiers')
else:
print('\nMatrix is Good-to-Go -- all numbers in matrix. ')
sys.exit(0)
else:
print('zero, but given Invalid Axis= ' + str(args.axes))
sys.stderr.write('zero, but given Invalid Axis= ' + str(args.axes))
sys.exit(-2)
#----------------------------------------------------------------------
if __name__ == "__main__":
# input_file1 = "/Users/bobbrown/Desktop/Gene-by-var.txt"
# input_file2 = "/Users/bobbrown/Desktop/var-by-sample.txt"
# out_fileName = "/Users/bobbrown/Desktop/MatixMult-1-2-Out.txt"
# selection = "MatrixMultiply"
#TODO address NANs ???
try:
args = get_args()
selection= args.choice
matrix1,column_labels1,row_labels1 = reader(args.input_file1) # to be transposed later
matrix2,column_labels2,row_labels2 = reader(args.input_file2)
if args.transpose == 'y' or args.input_file1 == args.input_file2:
matrix1 = Transpose(matrix1)
print("\n>>>NOTICE Transposed first matrix so matrix 1 columns = Matrix 2 number rows ")
temp = row_labels1 #swap labels for output matrix
row_labels1 = column_labels1 #swap labels for output matrix
column_labels1= temp #swap labels for output matrix
MatchLabels(column_labels1,row_labels2) # verfiy labels and their order match
if len(column_labels1) != len(row_labels2):
print("\n>>> ERROR attempting to multiple Matrices of incompatible dimensions ")
print("First Matrix is "+str(len(row_labels1))+" by "+str(len(column_labels1))+" where second Matrix is "+str(len(og_row2))+" by "+str(len(column_labels2))+"\n")
def main():
try:
args = get_args()
#sys.stdout.write(str(args)+"\n")
# <option value="LowerLimit">Minimum Absolute(Cell) Values to remove row/column</option>
# <option value="UpperLimit">Maximum Absolute(Cell) Values to remove row/column</option>
# <option value="NANnumber">NAN Number Cells Limit to remove row/column</option>
# <option value="NANpercent">NAN Percent Cells Limit to remove row/column</option>
nanList = ["NAN", "NA", "N/A", "-", "?", "nan", "na", "n/a"]
matrix, column_header_list, row_header_list = reader(
args.input_file_txt)
#old_reader matrix, row_header_list, column_header_list = reader(args.input_file_txt)
threshold = float(args.thresh)
if threshold < 0.000001:
print('Invalid negative or near-zero threshold chosen = ' +
str(args.thresh) + " choose positive value")
sys.exit(-4)
#VariancePercent
if args.choice == "VariancePercent" or args.choice == "VarianceCount": # > percent variance
if args.axes == "Row":
if args.choice == "VarianceCount":
threshold = (1 - threshold / len(row_header_list)) * 100.0
matrix, filter_rows, filter_cols, delCnt, minVal, maxVal = Variance_Percent_Filter_row(
matrix, threshold, row_header_list, column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for rows using variance percentile < '
+ str(args.thresh) +
' by row. Matrix row minimum variance= %.2f' %
minVal + ' and maximum variance= %.2f' % maxVal)
sys.stderr.write(
'\nFiltering out rows using variance percentile < ' +
str(args.thresh) + ' removed ' + str(delCnt) + ' rows')
sys.exit(-1)
else:
print('\nFiltering out rows using variance percentile < ' +
str(args.thresh) + ' removed ' + str(delCnt) +
' rows')
elif args.axes == "Column":
if args.choice == "VarianceCount":
threshold = (1 -
threshold / len(column_header_list)) * 100.0
matrix, filter_rows, filter_cols, delCnt, minVal, maxVal = Variance_Percent_Filter_col(
matrix, threshold, row_header_list, column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for columns using variance percentile < '
+ str(args.thresh) +
' by columns. Matrix columns minimum variance= %.2f' %
minVal + ' and maximum variance= %.2f' % maxVal)
sys.stderr.write(
'\nNO Filtering out rows using variance percentile < '
+ str(args.thresh) + ' removed ' + str(delCnt) +
' rows')
sys.exit(-1)
else:
print(
'\nFiltering out columns using variance percentile < '
+ str(args.thresh) + ' removed ' + str(delCnt) +
' columns')
else:
print('Invalid Axes =' + str(args.thresh))
sys.exit(-1)
#LowerLimit
elif args.choice == "LowerLimit": #!! todo is NOT lower or upper limit but range of values
if args.axes == "Row":
matrix, filter_rows, filter_cols, delCnt, minVal, maxVal = UpperLowerLimit_Filter_Row(
'lower', matrix, threshold, row_header_list,
column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for rows using LowerLimit < '
+ str(args.thresh) +
' by row. Matrix row minimum range= %.2f' % minVal +
' and maximum range= %.2f' % maxVal)
sys.stderr.write(
'\nNO Filtering out rows using LowerLimit < ' +
str(args.thresh) + ' removed ' + str(delCnt) + ' rows')
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' rows with Lower Limit < ' + str(args.thresh))
elif args.axes == "Column":
matrix, filter_rows, filter_cols, delCnt, minVal, maxVal = UpperLowerLimit_Filter_Col(
'lower', matrix, threshold, row_header_list,
column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for columns using Lower Limit < '
+ str(args.thresh) +
' by columns. Matrix columns minimum range= %.2f' %
minVal + ' and maximum range= %.2f' % maxVal)
sys.stderr.write(
'\nNO Filtering out rows using Lower Limit < ' +
str(args.thresh) + ' removed ' + str(delCnt) + ' rows')
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' columns with Lower Limit < ' + str(args.thresh))
#UpperLimit
elif args.choice == "UpperLimit": #!! todo is NOT lower or upper limit but range of values
if args.axes == "Row":
matrix, filter_rows, filter_cols, delCnt, minVal, maxVal = UpperLowerLimit_Filter_Row(
'upper', matrix, threshold, row_header_list,
column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for rows using Upper Limit < '
+ str(args.thresh) +
' by row. Matrix row minimum range= %.2f' % minVal +
' and maximum range= %.2f' % maxVal)
sys.stderr.write(
'\nNO Filtering out rows using Upper Limit < ' +
str(args.thresh) +
' by row. Matrix row minimum range= %.2f' % minVal +
' and maximum range= %.2f' % maxVal)
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' rows with UpperLimit < ' + str(args.thresh))
elif args.axes == "Column":
matrix, filter_rows, filter_cols, delCnt, minVal, maxVal = UpperLowerLimit_Filter_Col(
'upper', matrix, threshold, row_header_list,
column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for columns using UpperLimit < '
+ str(args.thresh) +
' by columns. Matrix columns minimum range= %.2f' %
minVal + ' and maximum range= %.2f' % maxVal)
sys.stderr.write(
'\nFiltering out rows using UpperLimit < ' +
str(args.thresh) +
' by columns. Matrix columns minimum range= %.2f' %
minVal + ' and maximum range= %.2f' % maxVal)
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' columns with UpperLimit < ' + str(args.thresh))
#MADlimit
elif args.choice == "MADcount" or args.choice == "MADpercent": #!! is lowerlimit of median absolute deviation medians
threshold = threshold
if args.axes == "Row":
if args.choice == "MADpercent":
threshold = len(row_header_list) * threshold / 100.0
matrix, filter_rows, filter_cols, delCnt, maxVal = Row_Value_MAD(
matrix, threshold, row_header_list, column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print('\nNO Filtering occurred for rows using MAD < ' +
str(threshold) +
' by row. Matrix row MAD maximum value= %.2f' %
maxVal)
sys.stderr.write(
'\nFiltering out rows using MAD < ' + str(threshold) +
' by row. Matrix row MAD maximum value= %.2f' %
maxVal)
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' rows using MAD maximum value > ' + str(threshold))
elif args.axes == "Column":
if args.choice == "MADpercent":
threshold = len(column_header_list) * threshold / 100.0
matrix, filter_rows, filter_cols, delCnt, maxVal = Col_Value_MAD(
matrix, threshold, row_header_list, column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for columns using MAD < ' +
str(threshold) +
' by columns. Matrix columns MAD maximum value= %.2f'
% maxVal)
sys.stderr.write(
'\nFiltering out columns using MAD < ' +
str(threshold) +
' by columns. Matrix columns MAD maximum value= %.2f'
% maxVal)
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' columns using MAD maximum value > ' +
str(threshold))
#NANlimit
elif args.choice == "NANlimit" or args.choice == "NANpercent":
maxNANs = int(args.thresh)
val = ' '
if args.choice == "NANpercent":
n, m = np.shape(matrix)
maxNANs = int(int(args.thresh) * n / 100)
val = '%'
if args.axes == "Row":
matrix, filter_rows, filter_cols, delCnt, maxFoundNANs = NAN_Filter_Row(
matrix, nanList, maxNANs, row_header_list,
column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for rows using NAN limit = or > '
+ str(args.thresh) + val +
' by row. Matrix row max NAN count is =' +
str(maxFoundNANs))
sys.stderr.write(
'\nNO Filtering out rows using NAN limit = or > ' +
str(args.thresh) + val +
' by row. Matrix row max NAN count is =' +
str(maxFoundNANs))
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' rows using NAN limit = or > ' + str(args.thresh) +
val)
elif args.axes == "Column":
matrix, filter_rows, filter_cols, delCnt, maxFoundNANs = NAN_Filter_Column(
matrix, nanList, maxNANs, row_header_list,
column_header_list)
Labeler(matrix, filter_cols, filter_rows, args.output_file_txt)
if delCnt < 1:
print(
'\nNO Filtering occurred for columns using NAN limit = or > '
+ str(args.thresh) + val +
' by columns. Matrix columns max NAN count is = ' +
str(maxFoundNANs))
sys.stderr.write(
'\nNO Filtering out columns using NAN limit = or > ' +
str(args.thresh) + val +
' by columns. Matrix columns max NAN count is = ' +
str(maxFoundNANs))
sys.exit(-1)
else:
print('\nFiltered out ' + str(delCnt) +
' columns using NAN limit = or > ' +
str(args.thresh) + val)
# elif args.choice == "covariance":
# if args.axes == "Row":
# matrix, filter_rows, filter_cols = CoVariance_Percent_Filter_row(matrix,args.thresh,row_header_list,column_header_list)
# Labeler(matrix,filter_rows,filter_cols,args.output_file_txt)
# print('Covariance_Filter on row')
# elif args.axes == "Column":
# matrix, filter_rows, filter_cols = CoVariance_Percent_Filter_col(matrix,args.thresh,row_header_list,column_header_list)
# Labeler(matrix,filter_rows,filter_cols,args.output_file_txt)
# print('Covariance_Filter on column')
else:
print('Invalid Axes = ' + str(args.axes))
sys.exit(-1)
else:
print("Invalid Filter Choice = " + str(args.choice))
sys.exit(-2)
except Exception as err:
traceback.print_exc()
sys.exit(-3)