--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Matrix_Multiply.py	Tue Nov 27 14:20:40 2018 -0500
@@ -0,0 +1,173 @@
+'''
+Created on March 6, 2018
+
+@author: Bob Brown based on John Weinstein's algorithm
+'''
+
+import os
+import re
+import shutil
+import traceback
+import sys, traceback, argparse
+import numpy as np
+import warnings
+#import scipy.stats as ss
+from Matrix_Validate_import import reader, Labeler, MatchLabels
+import math
+warnings.filterwarnings('error')
+
+# John Weinsteins algorithm  by bob brown   https://discover.nci.nih.gov/CorrelateMatrices/help.do
+#http://www.blog.pythonlibrary.org/2014/04/30/reading-excel-spreadsheets-with-python-and-xlrd/
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('input_file1', help='text file input matrix(include .txt in name)')
+    parser.add_argument('transpose', type=str, help='transpose matrix 1?')
+    parser.add_argument('input_file2', help='text file input matrix(include .txt in name)')
+    parser.add_argument('choice', type=str, help='Choose Normalization Method: 1 = Z-score, 2 = Mean Centered, 3 = log2, 4= rank')
+#    parser.add_argument('scaleValue', help='optional scaling factor for matrix)')
+    parser.add_argument('out_fileName', help='text file output matrix(include .txt in name)')
+    args = parser.parse_args()
+    if args.transpose == "": args.transpose = 'n'
+    return args
+
+
+def Matrix_Multiply(matrix1, matrix2):
+
+    try: 
+#TODO handle NANs
+
+        matrixOut= np.dot(matrix1, matrix2)
+    
+
+    except Exception as err:
+        traceback.print_exc()
+        sys.exit(-5)
+
+    return(matrixOut)
+
+
+#CorrelateMatrices  correlation acorss 2 martices   https://discover.nci.nih.gov/CorrelateMatrices/home.do
+def Correlate_Matrices(matrix1, matrix2):
+
+    #try:    
+    # Leave both matrices as size axn and bxn and treat a is column and b as row
+    #matrix1T = Transpose(matrix1) 
+    
+#TODO handle NANs
+    numRows1,numColumns1= np.shape(matrix1)
+    
+    numRows2,numColumns2= np.shape(matrix2)
+    matrixOut= []
+
+    if numColumns1 != numRows2:
+        print("ERROR number columns Matrix 1 ", str(numColumns1), " not equal number rows for Matrix 2 ",str(numRows2))
+        sys.exit(-1)
+#TODO need to look for NANs??
+
+    for i in range(numRows1):
+        vectorM1 = matrix1[i][:]
+        meanVec1 = np.nanmean(vectorM1)
+        varStdDev1  = np.nanstd(vectorM1, ddof=1)
+        lowStdDev1  = False
+         #if equals zero
+        if abs(varStdDev1) < .000001:
+            print("ERROR Variance value almost zero", str(varStdDev1), " for Matrix 1 Row ",str(i+1))
+            lowStdDev1= True
+        correlationRow= []
+        
+        for j in range(numColumns2):
+            vectorM2 = []
+            for t in range(numRows2):
+                vectorM2.append(matrix2[t][j])
+            meanVec2 = np.nanmean(vectorM2)
+            varStdDev2  = np.nanstd(vectorM2, ddof=1)
+            lowStdDev2= False
+            #if equals zero
+            if abs(varStdDev2) < .000001:
+                print("ERROR Variance value almost zero", str(varStdDev2), " for Matrix 2 Column ",str(j+1))
+                lowStdDev2= True
+
+            covarStdDev12= 0
+            
+            if not lowStdDev1 and not lowStdDev2:
+                #try:
+                for pos in range(len(vectorM1)):
+                    covarStdDev12 += ((vectorM1[pos]-meanVec1)/varStdDev1)*((vectorM2[pos]-meanVec2)/varStdDev2)
+#                bottom= (numColumns1 -1)*(varStdDev1*varStdDev2)   
+#                correlationRow.append( covarStdDev12/bottom)
+                correlationRow.append( covarStdDev12/(numColumns1 -1))
+                #except:  bad value because of NAN or other
+            else:
+                correlationRow.append("divide by 0")   # cannot calculate correlation  var too small
+                    
+        matrixOut.append(correlationRow)
+            
+#     except Exception as err:
+#         traceback.print_exc()
+#         sys.exit(-6)
+
+    return(matrixOut)
+
+#----------------------------------------------------------------------
+def Transpose(in_mat):
+    out_mat     = []
+    numRows,numColumns= np.shape(in_mat)
+    
+    for i in range(numColumns):
+        temp= []
+        for j in range(numRows):
+            temp.append(in_mat[j][i])
+        out_mat.append(temp)
+    #print( str(out_mat))
+    return out_mat
+
+
+#----------------------------------------------------------------------
+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")
+            print("Matrices must have dimensions  AxB and BxC.  A can equal C (square matrices)")
+            sys.exit(-1)
+    
+        if selection == "MatrixMultiply":
+            matrixOut= Matrix_Multiply(matrix1, matrix2 )
+        
+        elif selection == "Corr2Matrices" or selection == "Corr1Matrix":
+            matrixOut = Correlate_Matrices(matrix1, matrix2)
+
+        Labeler(matrixOut,column_labels2,row_labels1,args.out_fileName)
+        
+        print("Matrix Multiply  "+str(len(row_labels1))+" by "+str(len(column_labels1))+" Matrix 1 by "+str(len(row_labels2))+" by "+str(len(column_labels2))+" matrix 2")
+        print("Output Matrix dimensions are "+str(len(row_labels1))+" by "+str(len(column_labels2))+"\n")
+            
+    except Exception as err:
+        traceback.print_exc()
+        sys.exit(-3)
+    
+    sys.exit(0)
\ No newline at end of file