--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/src/breadcrumbs/src/UtilityMath.py	Thu Jun 03 18:13:32 2021 +0000
@@ -0,0 +1,156 @@
+"""
+Author: Timothy Tickle
+Description: Utility class for generic math functions.
+"""
+
+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#####################################################################################
+
+__author__ = "Timothy Tickle"
+__copyright__ = "Copyright 2012"
+__credits__ = ["Timothy Tickle"]
+__license__ = "MIT"
+__maintainer__ = "Timothy Tickle"
+__email__ = "ttickle@sph.harvard.edu"
+__status__ = "Development"
+
+#Import libaries
+import itertools
+import numpy as np
+import operator
+import random
+from ValidateData import ValidateData
+
+class UtilityMath():
+    """
+    Class to perform misc math methods.
+    """
+
+    ##
+    #Happy path test 2
+    @staticmethod
+    def funcConvertToBHQValue(ldPValues, iNumberOfTests=None):
+        """
+        Convert a list of p-value to a list of q-values.
+
+        :param	ldPValues:	List of doubles (p-values) to convert.
+        :type	List
+        :param	iNumberOfTests:	Number of (multiple) tests if different than the ldValue length. If not set the length of ldPValues is used.
+        :type	Integer
+        :return	List:	List of Q-values made with a BH modification.
+        """
+
+        #If the number of tests is not specified, use the number of pvalues
+        if(iNumberOfTests == None):
+            iNumberOfTests = len(ldPValues)
+        #Used to hold the pvalues as they are being manipulated
+        lsConvertToQValues = list()
+        #Is used to set the ordr of the pvalues as they are placed in the lsConvertToQValues
+        dOrder = 1
+        for dValue in ldPValues:
+            lsConvertToQValues.append([dValue,dOrder,None])
+            dOrder = dOrder + 1
+
+        #Sort by pvalue
+        lsConvertToQValues.sort(key=lambda x: x[0])
+
+        #Used to keep track of the current test number
+        iTest = 1
+        for dConvValue in lsConvertToQValues:
+            dConvValue[2] = dConvValue[0] * iNumberOfTests / iTest
+            iTest = iTest + 1
+
+        #Sort by original order
+        lsConvertToQValues.sort(key=lambda x: x[1])
+
+        #return just 1 dimension (the qvalue)
+        return [ldValues[2] for ldValues in lsConvertToQValues]
+
+    #Happy path tested 5
+    @staticmethod
+    def funcSampleWithReplacement(aData, iSelect):
+        """
+        Sample from a vector of data (aData) with replacement iSelect many objects.
+
+        :param	aData:	Data to sample from with replacement.
+        :type	List
+        :param	iSelect:	Amount of data to select from the original data population.
+        :type	Integer.
+        :return	List:	List of sampled data.
+                        Returns an empty list on error.
+        """
+
+        if iSelect and aData:
+            iDataSize = len(aData)
+            funcRandom, funcInt = random.random, int
+            lsSampling =  operator.itemgetter(*[funcInt(funcRandom() * iDataSize) for selected in itertools.repeat(None, iSelect)])(aData)
+            if isinstance(lsSampling, basestring):
+                lsSampling = [lsSampling]
+            return lsSampling
+        return []
+
+    #Happy Path Tested 2
+    @staticmethod
+    def funcSumRowsOfColumns(npaAbundance, lsSampleNames):
+        """
+        Takes the column names of a npArray and sums the rows into one column.
+
+        :param	npaAbundance:	Array of data to sum.
+        :type	Numpy Array
+        :param	lsSampleNames:	List of sample names.
+        :type	List	List of strings.
+        :return	List	List of data summed at each row.
+        """
+
+        #Compress by data name
+        npPooledSample = npaAbundance[lsSampleNames[0]]
+        for strSampleName in lsSampleNames[1:]:
+            #When combining, combine counts by summing
+            npPooledSample = npPooledSample + npaAbundance[strSampleName]
+        return list(npPooledSample)
+
+    #Testing Status: Light happy path testing 2
+    @staticmethod
+    def funcTransposeDataMatrix(npaMatrix, fRemoveAdornments=False):
+        """
+        Transposes a numpy array.
+
+        :param	npaMatrix:	Data matrix to transpose.
+        :type	Numpy Array	
+        :param	fRemoveAdornments:	Remove the first column before transposing.
+        :type	Boolean	True indicates removing the column.
+        :return	Boolean or Numpy Array:	Transposed array or a boolean indicating error.
+                                   Boolean	False is returned on error.
+        """
+
+        #Validate parameters
+        if(not ValidateData.funcIsValidNPArray(npaMatrix)):
+            print "".join(["Utility_Math:transposeDataMatrix::Error, transposeDataMatrix was an invalid structured array. Value =",str(npaMatrix)])
+            return False
+        if(not ValidateData.funcIsValidBoolean(fRemoveAdornments)):
+            print "".join(["Utility_Math:transposeDataMatrix::Error, fRemoveAdornments was an invalid boolean. Value =",str(fRemoveAdornments)])
+            return False
+
+        #Change to samples x taxa as is needed for the compute method below
+        #Also remove the first row which is taxa identification
+        conversionMatrix = [list(row)[fRemoveAdornments:] for row in npaMatrix]
+        return np.array(conversionMatrix).transpose()
+