PEATDB.Ekin.Base.EkinProject.saveProject

All Samples(13) | Call(13) | Derive(0) | Import(0)

src/p/e/PEATDB-2.3/PEATDB/plugins/KineticsAnalysis.py PEATDB(Download)

                ferrs = E.estimateExpUncertainty(d, runs=10)
                E.addMeta(d, 'exp_errors', ferrs)
            E.saveProject(os.path.join(self.path, name.replace(' ','')+'_temp'))
 
        tempslst = ['room','60','65','70','75']

                E.insertDataset(ek, dup+'_'+'pHvsTm')
                E.fitDataset(dup+'_'+'pHvsTm', model='1 pKa 2 Chemical shifts', conv=1e-6, silent=True)
            E.saveProject()
        return

                Ekcats.addMeta(name+'_'+str(r), 'exp_errors', ferrs)
 
        Ekcats.saveProject(os.path.join(self.path, 'kcats'))
        Ekms.saveProject(os.path.join(self.path, 'kms'))
        Ekcatskms.saveProject(os.path.join(self.path, 'kcats_kms'))

src/p/e/PEATDB-2.3/PEATDB/Ekin/Titration.py PEATDB(Download)

                dc = cls.combineCurves(d1, d2)
                Ec.insertDataset(dc, d)
            Ec.saveProject(name[:5]+'_combined')
            for d in Ec.datasets:
                f, p = Ec.findBestModel(d, models=cls.models, strictchecking=True, alpha=0.05)
            Ec.saveProject(name[:5]+'_combined')

src/d/a/DataPipeline-1.2/DataPipeline/Base.py DataPipeline(Download)

                results[namelabel] = data
 
            Em.saveProject(fname)
            Em.exportDatasets(fname, append=True)
            if self.model1 != '':

            E,fits = self.processFits(rawdata=results, Em=Em)
            fname = os.path.join(self.workingdir, 'final')
            Em.saveProject(os.path.join(self.workingdir, fname))
            Em.exportDatasets(os.path.join(self.workingdir, fname))
            if self.model1 != '':

src/d/a/DataPipeline-1.2/DataPipeline/Testing.py DataPipeline(Download)

    print 'final fits', fits
    fname = os.path.join(p.workingdir,'results')
    Em.saveProject(fname)
    p.saveEkinPlotstoImages(Em, fname)
    print 'completed fit propagation test'