def parallel_cumulative_blame(self, branch='master', limit=None, skip=None, num_datapoints=None, committer=True,
                                  workers=1, ignore_globs=None, include_globs=None):
        """
        Returns the blame at every revision of interest. Index is a datetime, column per committer, with number of lines
        blamed to each committer at each timestamp as data.

        :param branch: (optional, default 'master') the branch to work in
        :param limit: (optional, default None), the maximum number of revisions to return, None for no limit
        :param skip: (optional, default None), the number of revisions to skip. Ex: skip=2 returns every other revision, None for no skipping.
        :param num_datapoints: (optional, default=None) if limit and skip are none, and this isn't, then num_datapoints evenly spaced revs will be used
        :param committer: (optional, defualt=True) true if committer should be reported, false if author
        :param ignore_globs: (optional, default=None) a list of globs to ignore, default none excludes nothing
        :param include_globs: (optinal, default=None) a list of globs to include, default of None includes everything.
        :param workers: (optional, default=1) integer, the number of workers to use in the threadpool, -1 for one per core.
        :return: DataFrame

        """

        if not _has_joblib:
            raise ImportError('''Must have joblib installed to use parallel_cumulative_blame(), please use
            cumulative_blame() instead.''')

        revs = self.revs(branch=branch, limit=limit, skip=skip, num_datapoints=num_datapoints)

        if self.verbose:
            print('Beginning processing for cumulative blame:')

        revisions = json.loads(revs.to_json(orient='index'))
        revisions = [revisions[key] for key in revisions]

        ds = Parallel(n_jobs=workers, backend='threading', verbose=5)(
            delayed(_parallel_cumulative_blame_func)
            (self, x, committer, ignore_globs, include_globs) for x in revisions
        )

        revs = DataFrame(ds)
        del revs['rev']

        revs['date'] = to_datetime(revs['date'].map(datetime.datetime.fromtimestamp))
        revs.set_index(keys=['date'], drop=True, inplace=True)
        revs = revs.fillna(0.0)

        # drop 0 cols
        for col in revs.columns.values:
            if col != 'col':
                if revs[col].sum() == 0:
                    del revs[col]

        # drop 0 rows
        keep_idx = []
        committers = [x for x in revs.columns.values if x != 'date']
        for idx, row in revs.iterrows():
            if sum([row[x] for x in committers]) > 0:
                keep_idx.append(idx)

        revs = revs.ix[keep_idx]
        revs.sort_index(ascending=False, inplace=True)

        return revs
def parallel_cumulative_blame ( self , branch = 'master' , limit = None , skip = None , num_datapoints = None , committer = True , workers = 1 , ignore_globs = None , include_globs = None ) : if not _has_joblib : raise ImportError ( '''Must have joblib installed to use parallel_cumulative_blame(), please use
            cumulative_blame() instead.''' ) revs = self . revs ( branch = branch , limit = limit , skip = skip , num_datapoints = num_datapoints ) if self . verbose : print ( 'Beginning processing for cumulative blame:' ) revisions = json . loads ( revs . to_json ( orient = 'index' ) ) revisions = [ revisions [ key ] for key in revisions ] ds = Parallel ( n_jobs = workers , backend = 'threading' , verbose = 5 ) ( delayed ( _parallel_cumulative_blame_func ) ( self , x , committer , ignore_globs , include_globs ) for x in revisions ) revs = DataFrame ( ds ) del revs [ 'rev' ] revs [ 'date' ] = to_datetime ( revs [ 'date' ] . map ( datetime . datetime . fromtimestamp ) ) revs . set_index ( keys = [ 'date' ] , drop = True , inplace = True ) revs = revs . fillna ( 0.0 ) for col in revs . columns . values : if col != 'col' : if revs [ col ] . sum ( ) == 0 : del revs [ col ] keep_idx = [ ] committers = [ x for x in revs . columns . values if x != 'date' ] for idx , row in revs . iterrows ( ) : if sum ( [ row [ x ] for x in committers ] ) > 0 : keep_idx . append ( idx ) revs = revs . ix [ keep_idx ] revs . sort_index ( ascending = False , inplace = True ) return revs
Returns the blame at every revision of interest. Index is a datetime, column per committer, with number of lines
        blamed to each committer at each timestamp as data.

        :param branch: (optional, default 'master') the branch to work in
        :param limit: (optional, default None), the maximum number of revisions to return, None for no limit
        :param skip: (optional, default None), the number of revisions to skip. Ex: skip=2 returns every other revision, None for no skipping.
        :param num_datapoints: (optional, default=None) if limit and skip are none, and this isn't, then num_datapoints evenly spaced revs will be used
        :param committer: (optional, defualt=True) true if committer should be reported, false if author
        :param ignore_globs: (optional, default=None) a list of globs to ignore, default none excludes nothing
        :param include_globs: (optinal, default=None) a list of globs to include, default of None includes everything.
        :param workers: (optional, default=1) integer, the number of workers to use in the threadpool, -1 for one per core.
        :return: DataFrame