Insert the ExcelOutput object that outputs the results from the calculation of...

Insert the ExcelOutput object that outputs the results from the calculation of statistical measures and distribution fitting statistical test in a given data sample

dream/KnowledgeExtraction/ExcelOutput.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 19 Feb 2014
+
+@author: Panos
+'''
+from xlwt import Workbook
+import rpy2.robjects as robjects
+from StatisticalMeasures import BasicStatisticalMeasures
+from DistributionFitting import DistFittest
+
+#=========================================== The ExcelOutput object =============================================================#
+#The ExcelOutput object export in Excel document both the calculated statistical measures and the distribution fitting test of a dataset 
+class Output(BasicStatisticalMeasures,DistFittest):
+    
+    def PrintStatisticalMeasures(self,data):
+        data=robjects.FloatVector(data)     #The given list changes into float vector in order to be handled by RPy2
+        
+        book = Workbook()                   
+        sheet1 = book.add_sheet('StatisticalMeasures', cell_overwrite_ok=True)  #Add one sheet in the excel document with the name StatisticalMeasures
+        
+        ###Give names to cells###
+        sheet1.write(0,0,('Dataset statistical measures'))
+        sheet1.write(3,0,('Length'))
+        
+        sheet1.write(4,0,('Summary'))
+        sheet1.write(4,1,('Min'))
+        sheet1.write(4,2,('1st Qu.'))
+        sheet1.write(4,3,('Median'))
+        sheet1.write(4,4,('Mean'))
+        sheet1.write(4,5,('3rd Qu.'))
+        sheet1.write(4,6,('Max'))
+        
+        sheet1.write(7,0,('Data points'))
+        
+        sheet1.write(10,0,('Quantile'))
+        sheet1.write(10,1,('0%'))
+        sheet1.write(10,2,('25%'))
+        sheet1.write(10,3,('50%'))
+        sheet1.write(10,4,('75%'))
+        sheet1.write(10,5,('100%'))
+        
+        sheet1.write(12,0,('Mean'))
+        sheet1.write(13,0,('Variance'))
+        sheet1.write(14,0,('Standard deviation'))
+        sheet1.write(16,0,('Range'))
+        sheet1.write(17,0,('Interquartile Range'))
+        
+        ###Length###
+        sheet1.write(3,1,(self.length(data)))
+        
+        ###Summary###
+        sheet1.write(5,1,(self.summary(data)[0]))
+        sheet1.write(5,2,(self.summary(data)[1]))
+        sheet1.write(5,3,(self.summary(data)[2])) 
+        sheet1.write(5,4,(self.summary(data)[3]))
+        sheet1.write(5,5,(self.summary(data)[4]))
+        sheet1.write(5,6,(self.summary(data)[5]))
+               
+        ###Dataset###
+        i=0
+        while (i<self.length(data)):
+            sheet1.write(7,i+1,((data[i])))
+            i=i+1
+               
+        ###Quantselfles###
+        sheet1.write(11,1,(self.quantile(data)[0]))
+        sheet1.write(11,2,(self.quantile(data)[1]))
+        sheet1.write(11,3,(self.quantile(data)[2]))
+        sheet1.write(11,4,(self.quantile(data)[3]))
+        sheet1.write(11,5,(self.quantile(data)[4]))
+        
+        ###Mean###
+        sheet1.write(12,1,(self.mean(data)))
+        
+        ###Varselfance####
+        sheet1.write(13,1,(self.var(data)))
+        
+        ###Standard devselfatselfon###
+        sheet1.write(14,1,(self.sd(data)))
+        
+        ###Range###
+        sheet1.write(16,1,(self.range(data)[0]))
+        sheet1.write(16,2,(self.range(data)[1]))
+        
+        ###selfnterquartselfle Range###
+        sheet1.write(17,1,(self.IQR(data)))
+        book.save('StatisticalMeasuresResults.xls')  #Save the excel document 
+        
+    
+    
+    def PrintDistributionFit(self,data):
+        data=robjects.FloatVector(data)
+        
+        book = Workbook()
+        sheet2 = book.add_sheet('Distribution Fitting', cell_overwrite_ok=True)
+        
+        ###Give names to cells###
+        sheet2.write(0,0,('DistributionFit'))
+        sheet2.write(1,0,('data points'))
+        
+        
+        sheet2.write(3,1,('Discrete distributions'))
+        sheet2.write(3,8,('Kolmogorov-Smirnov test'))
+        sheet2.write(12,14,('Best distribution fitting'))
+        
+        sheet2.write(8,1,('Poisson'))
+        sheet2.write(8,2,('lambda'))
+        sheet2.write(8,6,('D-statistic'))
+        sheet2.write(8,8,('p-value'))
+        
+        sheet2.write(11,1,('Geometric'))
+        sheet2.write(11,2,('probability'))
+        sheet2.write(11,6,('D-statistic'))
+        sheet2.write(11,8,('p-value'))
+        
+        sheet2.write(15,1,('Continuous distributions'))
+        
+        sheet2.write(17,1,('Normal'))
+        sheet2.write(17,2,('mean'))
+        sheet2.write(17,3,('standard deviation'))
+        sheet2.write(17,6,('D-statistic'))
+        sheet2.write(17,8,('p-value'))
+        
+        sheet2.write(21,1,('Exponential'))
+        sheet2.write(21,2,('rate'))
+        sheet2.write(21,6,('D-statistic'))
+        sheet2.write(21,8,('p-value'))
+        
+        sheet2.write(25,1,('Gamma'))
+        sheet2.write(25,2,('shape'))
+        sheet2.write(25,3,('rate'))
+        sheet2.write(25,6,('D-statistic'))
+        sheet2.write(25,8,('p-value'))
+        
+        sheet2.write(29,1,('Lognormal'))
+        sheet2.write(29,2,('log mean'))
+        sheet2.write(29,3,('log standard deviation'))
+        sheet2.write(29,6,('D-statistic'))
+        sheet2.write(29,8,('p-value'))
+        
+        sheet2.write(33,1,('Weibull'))
+        sheet2.write(33,2,('shape'))
+        sheet2.write(33,3,('scale'))
+        sheet2.write(33,6,('D-statistic'))
+        sheet2.write(33,8,('p-value'))
+        
+        sheet2.write(37,1,('Logistic'))
+        sheet2.write(37,2,('location'))
+        sheet2.write(37,3,('scale'))
+        sheet2.write(37,6,('D-statistic'))
+        sheet2.write(37,8,('p-value'))
+        
+        sheet2.write(41,1,('Cauchy'))
+        sheet2.write(41,2,('location'))
+        sheet2.write(41,3,('scale'))
+        sheet2.write(41,6,('D-statistic'))
+        sheet2.write(41,8,('p-value'))
+        
+        ###Dataset###
+        i=0
+        while (i<robjects.r['length'](data)[0]):
+            sheet2.write(1,i+1,((data[i])))
+            i=i+1
+
+        ###Poisson###
+        sheet2.write(9,2,(robjects.r['fitdistr'](data,'Poisson')[0][0]))
+        sheet2.write(9,6,(self.Pois_kstest(data)[0][0]))
+        sheet2.write(9,8,(self.Pois_kstest(data)[1][0]))
+        
+        ###Geometric###
+        sheet2.write(12,2,(robjects.r['fitdistr'](data,'Geometric')[0][0]))
+        sheet2.write(12,6,(self.Geom_kstest(data)[0][0]))
+        sheet2.write(12,8,(self.Geom_kstest(data)[1][0]))
+        
+        ###Normal###
+        sheet2.write(18,2,(robjects.r['fitdistr'](data,'Normal')[0][0]))
+        sheet2.write(18,3,(robjects.r['fitdistr'](data,'Normal')[0][1]))
+        sheet2.write(18,6,(self.Norm_kstest(data)[0][0]))
+        sheet2.write(18,8,(self.Norm_kstest(data)[1][0]))
+        
+        ###Exponential###
+        sheet2.write(22,2,(robjects.r['fitdistr'](data,'Exponential')[0][0]))
+        sheet2.write(22,6,(self.Exp_kstest(data)[0][0]))
+        sheet2.write(22,8,(self.Exp_kstest(data)[1][0]))
+        
+        ###Gamma###
+        sheet2.write(26,2,(robjects.r['fitdistr'](data,'Gamma')[0][0]))
+        sheet2.write(26,3,(robjects.r['fitdistr'](data,'Gamma')[0][1]))
+        sheet2.write(26,6,(self.Gam_kstest(data)[0][0]))
+        sheet2.write(26,8,(self.Gam_kstest(data)[1][0]))
+        
+        ###Lognormal###
+        sheet2.write(30,2,(robjects.r['fitdistr'](data,'Lognormal')[0][0]))
+        sheet2.write(30,3,(robjects.r['fitdistr'](data,'Lognormal')[0][1]))
+        sheet2.write(30,6,(self.Lognorm_kstest(data)[0][0]))
+        sheet2.write(30,8,(self.Lognorm_kstest(data)[1][0]))
+        
+        ###Weibull###
+        sheet2.write(34,2,(robjects.r['fitdistr'](data,'Weibull')[0][0]))
+        sheet2.write(34,3,(robjects.r['fitdistr'](data,'Weibull')[0][1]))
+        sheet2.write(34,6,(self.Weib_kstest(data)[0][0]))
+        sheet2.write(34,8,(self.Weib_kstest(data)[1][0]))
+        
+        ###Logistic###
+        sheet2.write(38,2,(robjects.r['fitdistr'](data,'Logistic')[0][0]))
+        sheet2.write(38,3,(robjects.r['fitdistr'](data,'Logistic')[0][1]))
+        sheet2.write(38,6,(self.Logis_kstest(data)[0][0]))
+        sheet2.write(38,8,(self.Logis_kstest(data)[1][0]))
+        
+        ###Cauchy###
+        sheet2.write(42,2,(robjects.r['fitdistr'](data,'Cauchy')[0][0]))
+        sheet2.write(42,3,(robjects.r['fitdistr'](data,'Cauchy')[0][1]))
+        sheet2.write(42,6,(self.Cauchy_kstest(data)[0][0]))
+        sheet2.write(42,8,(self.Cauchy_kstest(data)[1][0]))
+        
+        ###BestDistributionFit###
+        A=self.ks_test(data)
+        sheet2.write(14,14,(A.get('type')))
+        sheet2.write(14,15,(A.get('parameters')[0]))
+        sheet2.write(14,16,(A.get('parameters')[1]))
+        
+        #book.save('C:\Eclipse workspace\FirstPrototype\src\ExcelOutput_DistributionFitting.xls')    #Save the excel document in the given directory
+        book.save('DistributionFittingResults.xls')    #Save the excel document