latools/latools.py
def getstats(self, save=True, filename=None, samples=None, subset=None, ablation_time=False):
        """
        Return pandas dataframe of all sample statistics.
        """
        slst = []

        if samples is not None:
            subset = self.make_subset(samples)

        samples = self._get_samples(subset)

        for s in self.stats_calced:
            for nm in [n for n in samples if self.srm_identifier
                       not in n]:
                if self.stats[nm][s].ndim == 2:
                    # make multi - index
                    reps = np.arange(self.stats[nm][s].shape[-1])
                    ss = np.array([s] * reps.size)
                    nms = np.array([nm] * reps.size)
                    # make sub - dataframe
                    stdf = pd.DataFrame(self.stats[nm][s].T,
                                        columns=self.stats[nm]['analytes'],
                                        index=[ss, nms, reps])
                    stdf.index.set_names(['statistic', 'sample', 'rep'],
                                         inplace=True)
                else:
                    stdf = pd.DataFrame(self.stats[nm][s],
                                        index=self.stats[nm]['analytes'],
                                        columns=[[s], [nm]]).T

                    stdf.index.set_names(['statistic', 'sample'],
                                         inplace=True)
                slst.append(stdf)
        out = pd.concat(slst)

        if ablation_time:
            ats = self.ablation_times(samples=samples, subset=subset)
            ats['statistic'] = 'nanmean'
            ats.set_index('statistic', append=True, inplace=True)
            ats = ats.reorder_levels(['statistic', 'sample', 'rep'])

            out = out.join(ats)

        out.drop(self.internal_standard, 1, inplace=True)

        if save:
            if filename is None:
                filename = 'stat_export.csv'
            out.to_csv(self.export_dir + '/' + filename)

        self.stats_df = out

        return out
def getstats(self, save=True, filename=None, samples=None, subset=None, ablation_time=False):
        slst = []

        if samples is not None:
            subset = self.make_subset(samples)

        samples = self._get_samples(subset)

        for s in self.stats_calced:
            for nm in [n for n in samples if self.srm_identifier
                       not in n]:
                if self.stats[nm][s].ndim == 2:
                    # make multi - index
                    reps = np.arange(self.stats[nm][s].shape[-1])
                    ss = np.array([s] * reps.size)
                    nms = np.array([nm] * reps.size)
                    # make sub - dataframe
                    stdf = pd.DataFrame(self.stats[nm][s].T,
                                        columns=self.stats[nm]['analytes'],
                                        index=[ss, nms, reps])
                    stdf.index.set_names(['statistic', 'sample', 'rep'],
                                         inplace=True)
                else:
                    stdf = pd.DataFrame(self.stats[nm][s],
                                        index=self.stats[nm]['analytes'],
                                        columns=[[s], [nm]]).T

                    stdf.index.set_names(['statistic', 'sample'],
                                         inplace=True)
                slst.append(stdf)
        out = pd.concat(slst)

        if ablation_time:
            ats = self.ablation_times(samples=samples, subset=subset)
            ats['statistic'] = 'nanmean'
            ats.set_index('statistic', append=True, inplace=True)
            ats = ats.reorder_levels(['statistic', 'sample', 'rep'])

            out = out.join(ats)

        out.drop(self.internal_standard, 1, inplace=True)

        if save:
            if filename is None:
                filename = 'stat_export.csv'
            out.to_csv(self.export_dir + '/' + filename)

        self.stats_df = out

        return out
def getstats ( self , save = True , filename = None , samples = None , subset = None , ablation_time = False ) : slst = [ ] if samples is not None : subset = self . make_subset ( samples ) samples = self . _get_samples ( subset ) for s in self . stats_calced : for nm in [ n for n in samples if self . srm_identifier not in n ] : if self . stats [ nm ] [ s ] . ndim == 2 : # make multi - index reps = np . arange ( self . stats [ nm ] [ s ] . shape [ - 1 ] ) ss = np . array ( [ s ] * reps . size ) nms = np . array ( [ nm ] * reps . size ) # make sub - dataframe stdf = pd . DataFrame ( self . stats [ nm ] [ s ] . T , columns = self . stats [ nm ] [ 'analytes' ] , index = [ ss , nms , reps ] ) stdf . index . set_names ( [ 'statistic' , 'sample' , 'rep' ] , inplace = True ) else : stdf = pd . DataFrame ( self . stats [ nm ] [ s ] , index = self . stats [ nm ] [ 'analytes' ] , columns = [ [ s ] , [ nm ] ] ) . T stdf . index . set_names ( [ 'statistic' , 'sample' ] , inplace = True ) slst . append ( stdf ) out = pd . concat ( slst ) if ablation_time : ats = self . ablation_times ( samples = samples , subset = subset ) ats [ 'statistic' ] = 'nanmean' ats . set_index ( 'statistic' , append = True , inplace = True ) ats = ats . reorder_levels ( [ 'statistic' , 'sample' , 'rep' ] ) out = out . join ( ats ) out . drop ( self . internal_standard , 1 , inplace = True ) if save : if filename is None : filename = 'stat_export.csv' out . to_csv ( self . export_dir + '/' + filename ) self . stats_df = out return out
Return pandas dataframe of all sample statistics.
Return pandas dataframe of all sample statistics .