KnowledgeExtraction folder moved in root

dream/KnowledgeExtraction/ConfidenceIntervals.py

+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+
+'''
+Created on 16 Nov 2013
+
+@author: Panos
+'''
+from rpy2 import robjects
+
+#The ConfidenceIntervals object
+class Intervals:
+    #Calculate the p (decimal number) confidence intervals for a sample of data
+    def ConfidIntervals(self,data,p): 
+  
+        data=robjects.FloatVector(data)  #The given list changes into float vector in order to be handled by RPy2
+        alpha=1-p
+        rsqrt=robjects.r['sqrt']     #Call square root function - R function 
+        rsd=robjects.r['sd']       #Call standard deviation function - R function
+        rmean=robjects.r['mean']   #Call mean function - R function
+        t=len(data)
+        n=rsqrt(t)
+        b=rsd(data)
+
+        rqt=robjects.r['qt']       #Call the cumulative probability distribution function for t distribution
+        q=rqt((1-(alpha/2)),t-1)
+        m=rmean(data)               #Calculate the sample average value
+
+        me=q[0]*(b[0]/n[0])        #Calculate the margin of error
+
+        #Calculate the lower and the upper bound 
+        lo=m[0]-me
+        up=m[0]+me
+        l=[lo,up]
+        return l
+
+
+
+
+

dream/KnowledgeExtraction/Plots.py

+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+
+'''
+Created on 16 Dec 2013
+
+@author: Panos
+'''
+from rpy2 import robjects
+
+#The Graphs object
+class Graphs:
+    #Graphs that can visualize the data samples 
+    
+    def Plots(self,data, fileName="plotChart.jpg"):
+        data=robjects.FloatVector(data)   #The given list changes into float vector in order to be handled by RPy2
+        rplot=robjects.r['plot']      #Call plot function - R function
+        rdev=robjects.r['dev.off']    #Call dev.off - R function
+        rjpeg=robjects.r['jpeg']      #Call jpeg - R function
+    
+        output=rjpeg(fileName)        #output the plot (jpeg file format) in the given directory
+        rplot(data, type="o", col="blue")     #Graph data sample and define color and type for the data points visualization
+        rdev
+        return output
+    
+    def ScatterPlot(self,data1,data2, fileName="scatterplot.jpg"):
+        #The given lists change into float vector in order to be handled by RPy2
+        data1=robjects.FloatVector(data1)
+        data2=robjects.FloatVector(data2)
+        
+        rplot=robjects.r['plot']
+        rdev=robjects.r['dev.off']
+        rjpeg=robjects.r['jpeg']
+    
+        output=rjpeg(fileName)            
+        rplot(data1,data2,main='Scatterplot',xlab="data1", ylab='data2',pch=19)    #Graph data samples and define type for the data points visualization
+        rdev
+        return output
+
+    def Histogram(self,data,fileName="histogram.jpg"):
+        data=robjects.FloatVector(data)   #The given list change into float vector in order to be handled by RPy2
+        
+        rhist=robjects.r['hist']          #Call hist function - R function
+        rdev=robjects.r['dev.off']
+        rjpeg=robjects.r['jpeg']
+        output=rjpeg(fileName)
+        
+        rhist(data, main='Histogram',col="lightblue")      #Create a histogram for the given data sample
+        rdev
+        return output
+    
+    def Barplot(self,data,fileName="barplot.jpg"):
+        data=robjects.FloatVector(data)     #The given list changes into float vector in order to be handled by RPy2
+        
+        rbarplot=robjects.r['barplot']      #Call barplot - R function
+        rdev=robjects.r['dev.off']
+        rjpeg=robjects.r['jpeg']
+        
+        output=rjpeg(fileName)
+        rbarplot(data, main='Barplot',border='blue')      #Create a bar plot for the given data sample
+        rdev
+        return output
+    
+    def TwoSetPlot(self, data1,data2, fileName="twosetplot.jpg"):
+        #The given lists change into float vector in order to be handled by RPy2
+        data1=robjects.FloatVector(data1)
+        data2=robjects.FloatVector(data2)
+        
+        rplot=robjects.r['plot']                          #Call plot - R function
+        rdev=robjects.r['dev.off']
+        rjpeg=robjects.r['jpeg']
+        rlines=robjects.r['lines']                        #Call lines function - R function
+        rbox=robjects.r['box']                            #Call box function - R function
+        rrange=robjects.r['range']                        #Call range - R function
+        
+        plot_colors= robjects.StrVector(["red", "forestgreen"])         #Define colors to be used in the plot
+        output=rjpeg(fileName)            
+    
+        rplot(data1, xlab="x", ylab="Total",ylim=rrange(data1,data2))   #Graph the first data sample and set axes' names 
+        rlines(data2)                                           #Graph the second data sample
+    
+        # Create box around plot
+        rbox()
+
+        # Graph data1 with thicker red dashed line
+        rlines(data1, type="l", lty=2, lwd=2, col=plot_colors[0])
+
+        # Graph data2 with thicker green dotted line
+        rlines(data2, type="l", lty=3, lwd=2, col=plot_colors[1])
+        rdev
+        return output
+    
+    def Pie(self, data1, fileName="pieChart.jpg"):
+        data1=robjects.FloatVector(data1)      #The given list changes into float vector in order to be handled by RPy2
+        rpaste=robjects.r['paste']          #Call paste - R function 
+        rround=robjects.r['round']          #Call round - R function 
+        rsum=robjects.r['sum']              #Call sum - R function
+        rpie=robjects.r['pie']              #Call pie - R function
+   
+        rdev=robjects.r['dev.off']
+
+        colors=robjects.StrVector(["white","grey70","grey90","grey50","grey60","black"])  #Define colors to be used for black&white print
+        s=rsum(data1)
+        d_labels=[0]*(len(data1))
+   
+        i=0
+        while i<len(data1):
+            d_labels[i]=((rround((data1[i]/s[0])*100,1)))      #Calculate the percentage for each data point, rounded to one decimal place
+            i+=1
+    
+        d_labels=rpaste(d_labels,"%",sep="")    #Concatenate a "%" car after each value           
+        rjpeg=robjects.r['jpeg']
+    
+        export=rjpeg(fileName)
+        rpie(data1,main="Data",col=colors,labels=d_labels,cex=0.8)   #Create a pie chart with defined heading and custom colors 
+        rdev
+        return export
+
+

dream/KnowledgeExtraction/StatisticalMeasures.py

+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+
+'''
+Created on 14 Nov 2013
+
+@author: Panos
+'''
+import rpy2.robjects as robjects
+from rpy2.robjects.packages import importr
+
+MASS= importr('MASS')
+
+#The BasicStatisticalMeasures object
+class BasicStatisticalMeasures:
+     # A variety of statistical measures are calculated in this object
+    def length(self, data):                      #Calculate the length of data sample
+        data=robjects.FloatVector(data)          ##The given list changes into float vector in order to be handled by RPy2
+        rlength = robjects.r['length']           #Call length function-R function
+        return rlength(data)[0]
+     
+    def summary(self, data):                    #Calculate the summary of data sample (output the results in a specific format used in R)
+        data=robjects.FloatVector(data)
+        rsummary = robjects.r['summary']        #Call summary - R function
+        return rsummary(data)
+        
+    def quantile(self,data):                   #Calculate the quantiles (0%,25%,50%,75%,100%) of the data sample
+        data=robjects.FloatVector(data)
+        rquantile = robjects.r['quantile']     #Call quantile - R function
+        return rquantile(data)
+    
+    def frequency(self,data):                  #Calculate the frequency of a data point in the sample
+        data=robjects.FloatVector(data)
+        rtable= robjects.r['table']            #Call table - R function
+        return rtable(data)
+        
+    def mean (self, data):                     #Calculate the mean value of a data sample 
+        data=robjects.FloatVector(data)
+        rmean = robjects.r['mean']             #Call mean - R function
+        return rmean(data)[0]
+    
+    def var (self, data):                      #Calculate the variance of a data sample
+        data=robjects.FloatVector(data)
+        rvar = robjects.r['var']               #Call variance function - R function
+        return rvar(data)[0]
+    
+    def sd (self, data):                       #Calculate the standard deviation of a data sample
+        data=robjects.FloatVector(data)
+        rsd = robjects.r['sd']                 #Call standard deviation function - R function
+        return rsd(data)[0]
+
+    def range (self, data):                    #Calculate the range of a data sample
+        data=robjects.FloatVector(data)
+        rrange = robjects.r['range']           #Call range function - R function
+        return rrange(data)[0]
+        
+    def IQR (self, data):                      #Calculate the Interquartile range (IQR) of a data sample
+        data=robjects.FloatVector(data)
+        rIQR = robjects.r['IQR']               #Call IQR function - R function
+        return rIQR(data)[0]
+    
+    def all(self, data):                       #Print the results of the above measures
+        data=robjects.FloatVector(data)
+        print 'The length of the data set is:', self.length(data)[0]
+        print 'The summary is:', self.summary(data)
+        print 'The quartiles and percentiles of the data set are:', self.quantile(data)
+        print 'The frequency of the datapoints are:', self.frequency(data)
+        print 'The mean value is:', self.mean(data)[0]
+        print 'The standard deviation is:', self.sd(data)[0]
+        print 'The variance is:', self.var(data)[0]
+        print 'The range is:', self.range(data)[0]
+        print 'The Interquartile Range is:', self.IQR(data)[0]
\ No newline at end of file

dream/KnowledgeExtraction/__init__.py

+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+# See http://peak.telecommunity.com/DevCenter/setuptools#namespace-packages
+try:
+    __import__('pkg_resources').declare_namespace(__name__)
+except ImportError:
+    from pkgutil import extend_path
+    __path__ = extend_path(__path__, __name__)
\ No newline at end of file